From 41423a1dbc42a31c4852181842e2c880780c08c1 Mon Sep 17 00:00:00 2001
From: Decoupes Remy <remy.decoupes@gmail.com>
Date: Wed, 28 Aug 2024 16:50:54 +0200
Subject: [PATCH] run the script with ERGO updates (zenodo) and try service
 datasets

---
 params/datasets.csv                           | 2790 +++++++++++++----
 xml_generated/.xml                            |    4 +-
 .../1990_2015_GrossDomesticProduct.xml        |   36 +-
 ..._MiddleInfraRed_FourierProcessed_1k_ER.xml |  105 +-
 ..._2019_MODIS_EVI_FourierProcessed_1k_ER.xml |  109 +-
 ...eDayTemperature_FourierProcessed_1k_ER.xml |  110 +-
 ...ightTemperature_FourierProcessed_1k_ER.xml |  109 +-
 ...019_MODIS_NDVI_Fourier_Processed_1k_ER.xml |  108 +-
 ..._MiddleInfraRed_FourierProcessed_1k_ER.xml |   34 +-
 ...021__MODIS__EVI_FourierProcessed_1k_ER.xml |   31 +-
 ...eDayTemperature_FourierProcessed_1k_ER.xml |   34 +-
 ...ightTemperature_FourierProcessed_1k_ER.xml |   32 +-
 ...21__MODIS__NDVI_FourierProcessed_1k_ER.xml |   32 +-
 .../2001_2022_ERA5_SoilMoisture_ER_5k.xml     |   40 +-
 ...22_MODIS_EnhancedVegetationIndex_5k_ER.xml |   43 +-
 ...2010_2022_ERA5_MonthlyPrecipitation_5k.xml |   47 +-
 ...022_MODIS_LandSurfaceTemperature_5k_ER.xml |   53 +-
 ..._MiddleInfraRed_FourierProcessed_1k_ER.xml |   59 +-
 ...020__VIIRS__EVI_FourierProcessed_1k_ER.xml |   63 +-
 ...eDayTemperature_FourierProcessed_1k_ER.xml |   64 +-
 ...ightTemperature_FourierProcessed_1k_ER.xml |   63 +-
 ..._2020__VIIRS__NDVI_FourierProcessed_ER.xml |   63 +-
 xml_generated/2013_FarmLabour_ER.xml          |   17 +-
 ...ODIS_RelativeHumidity_FourierProcessed.xml |   69 +-
 xml_generated/2017_RoadDensity_OSM_ER.xml     |   30 +-
 .../2019_Herbaceous_Wetlands_Copernicus.xml   |   43 +-
 xml_generated/2019_SurfaceWater_PROBA_1k.xml  |   50 +-
 ...OOD_StandardMappingandAnalysispolygons.xml |   28 +-
 ...020_WorldpopHumanPopulation_Age_Gender.xml |   40 +-
 ...1_2022_MonthlyDistribution_WaterBodies.xml |   48 +-
 xml_generated/2021_FloodRisk_JRC.xml          |   45 +-
 ...erinary_Hospital_FacilityDistributions.xml |   62 +-
 ...2021_WNV_AvianHost_DistributionIndices.xml |   47 +-
 ...22_2023_SpatialModelVector_Ixodesticks.xml |   43 +-
 .../2022_2023_SpatialModel_TBEHosts.xml       |   52 +-
 ...alModelHyalomma_marginatum_lusitanicum.xml |   35 +-
 ..._WeightedMammal_HostDistributionModels.xml |   57 +-
 ...2023_SpatialModels_Mosquitovectors_WNV.xml |   48 +-
 ...mp_IxodesRicinus_Difference_2020to2022.xml |   55 +-
 .../2024_VIIRS_CumTemp_IxodesRicinus.xml      |   52 +-
 ...mp_IxodesRicinus_Difference_2020to2022.xml |   55 +-
 ...m_two_event-based_surveillance_systems.xml |    4 +-
 ...ine_Fever,_and_West-Nile_Virus_Disease.xml |    4 +-
 ...uidelines_and_manually_labelled_corpus.xml |    4 +-
 ...an_Footprint_dataset_from_1982_to_2000.xml |    8 +-
 ...a_events_affecting_mammals_from_ProMED.xml |   21 +-
 ..._differents_digital_surveillance_tools.xml |    4 +-
 xml_generated/COVID-19-line-list.xml          |    4 +-
 xml_generated/COVID-19_Tweet_dataset.xml      |    4 +-
 xml_generated/COVID-19_contact_tracing.xml    |    4 +-
 xml_generated/COVID-19_influenza.xml          |    4 +-
 ...ID-19_international_cases_as_of_Feb_13.xml |    4 +-
 xml_generated/COVID-19_vaccination.xml        |    4 +-
 ...ng_and_the_shape_of_COVID-19_epidemics.xml |    4 +-
 ...ciated_changes_in_COVID-19_severity_a.xml" |    4 +-
 ...rivers_of_TBEV_at_the_Palearctic_scale.xml |    4 +-
 .../Consensus_LandUse_Earthenv_1k_ER.xml      |   36 +-
 ...om_literature:_Tick-borne_encephalitis.xml |    4 +-
 ...acted_from_literature:_West_Nile_Virus.xml |    4 +-
 ...navirus_disease_2019_outbreak_in_China.xml |    4 +-
 ...ality:_Classification_of_news_articles.xml |    4 +-
 ...s,_Influenza_A_and_Chikungunya_dataset.xml |    4 +-
 ...from_the_literature:_Tularemia_dataset.xml |    4 +-
 ...ase_study_of_Avian_Influenza_-_dataset.xml |    4 +-
 xml_generated/EPIcx-bal_COVID-19_schools.xml  |    4 +-
 ...COVID-19_Adherence_and_sustainability_.xml |    4 +-
 ...-lab_COVID-19_Impact_school_reopening_.xml |    4 +-
 ...cx-lab_COVID-19_Underdetection_France_.xml |    4 +-
 xml_generated/EPIcx-lab_COVID-19_attitude.xml |    4 +-
 .../EPIcx-lab_COVID-19_curfew_B.1.1.7.xml     |    4 +-
 ...PIcx-lab_COVID-19_importation_lockdown.xml |    4 +-
 ...x-lab_COVID-19_importation_risk_Africa.xml |    4 +-
 ...x-lab_COVID-19_importation_risk_Europe.xml |    4 +-
 ...COVID-19_importation_risk_summer_Delta.xml |    4 +-
 .../EPIcx-lab_COVID-19_mobility_effect.xml    |    4 +-
 .../EPIcx-lab_COVID-19_socio_economic.xml     |    4 +-
 xml_generated/EpiDCA.xml                      |    4 +-
 xml_generated/EpiNorm.xml                     |    4 +-
 xml_generated/EpidBioBERT.xml                 |    4 +-
 xml_generated/EpidBioELECTRA.xml              |    4 +-
 ...ied_by_PADI-web,_media_monitoring_tool.xml |    4 +-
 xml_generated/Gazetteer_Access_Tool.xml       |    4 +-
 xml_generated/GeoNLPlify.xml                  |    4 +-
 xml_generated/GeospaCy.xml                    |    4 +-
 ...ic_episodes_occurring_from_2020_onward.xml |    4 +-
 ...r_PADI-web_implemented_in_Ocean_Indian.xml |    4 +-
 ...ata_Related_to_Avian_Influenza_Disease.xml |    4 +-
 ...ng_disease_data_from_different_sources.xml |    4 +-
 ..._____________________________________.xml" |    6 +-
 ...OD_-_News_AMR_dataset_-_Hackathon_2022.xml |    4 +-
 xml_generated/MOOD_Press_Tweets_Collector.xml |    4 +-
 ...007-2021,_CHELSA_Climate_and_WorldClim.xml |    6 +-
 .../Novel-SARS-CoV-2-P1-Lineage-in-Brazi.xml  |    4 +-
 xml_generated/PADI-web.xml                    |    6 +-
 ...ews_articles_sources_manually_labelled.xml |    4 +-
 ...orpus:_news_articles_manually_labelled.xml |    4 +-
 ...s_used_for_the_EpidBioELECTRA_approach.xml |    4 +-
 .../PADI-web_diseases_corpus_of_events.xml    |    4 +-
 xml_generated/PhyCovA.xml                     |    4 +-
 ...taset_for_Relative_spatial_information.xml |    4 +-
 ..._Avian_Influenza:_past_and_the_future..xml |    4 +-
 ..._heavy_rainfall_on_dengue_transmission.xml |    4 +-
 xml_generated/RESAPATH.xml                    |    4 +-
 ...nalysis_covering_2014\342\200\2232021.xml" |    4 +-
 .../SARS-CoV-2_EUR_PHYLOGEOGRAPHY.xml         |    4 +-
 ...parameters_as_10km_resolution_GeoTIFFs.xml |    6 +-
 xml_generated/SNEToolkit.xml                  |    4 +-
 ...tial_Opinion_Mining_of_COVID-19_Tweets.xml |    4 +-
 ...ematical_models_of_infectious_diseases.xml |    4 +-
 ...ematical_models_of_infectious_diseases.xml |    4 +-
 ...tion_on_European_COVID-19_trajectories.xml |    4 +-
 xml_generated/arbocartoR_app.xml              |    4 +-
 xml_generated/arbocartoR_package.xml          |    4 +-
 xml_generated/covid19_spell.xml               |    4 +-
 ...s_of_the_SARS-CoV-2_epidemic_in_the_UK.xml |    4 +-
 xml_generated/epiCurve.xml                    |    4 +-
 ..._epidemiological_hypothesis_testing\".xml" |    4 +-
 xml_generated/new_york_variants.xml           |    4 +-
 xml_generated/resistancebank.org.xml          |    4 +-
 xml_generated/sars_cov_2_pipeline.xml         |    4 +-
 120 files changed, 3959 insertions(+), 1323 deletions(-)

diff --git a/params/datasets.csv b/params/datasets.csv
index 6f971f0..fedc6f8 100644
--- a/params/datasets.csv
+++ b/params/datasets.csv
@@ -1,143 +1,443 @@
 EC portal,Title/Identifier (if no DOI available),Description of the dataset/Abstract,Database Key words,Producer,Full name of corresponding author (with email contact),Year,Accessible y/n,Give the DOI (or URL) to access the dataset in the data repository,"Is this dataset reusable?
 Yes/No","If the dataset is linked to a publication, specify the DOI of the publication",Licence,Code available (link),Link (html) to an image/logo/figure representing the database,geographical extent,No,Usefull for which diseases ,Concept ID (NCBI) ,Dataset or Software,Dataset type,Dashboard,Data produced by MOOD (Prod.) or only used (Used) within the project,Accessibility (Public or MPo -MOOD partners only),uuid,n
-NO,RESAPATH,"Data set of antimicrobial resistance in bacterial pathogens isolated from diseased animals (all species) in France, aggregated at departement level and per month","AMR, Diseased animals",ANSES,Géraldine Cazeau,2006-2022,No,,YES, ,,,https://shiny-public.anses.fr/ENresapath2/,France – data available at the geographical department level,,AMR,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120144,144
-YES,Annotation of epidemiological information in animal disease-related news articles: guidelines and manually labelled corpus,"This dataset contains two files: (i) An annotated corpus (""epi_info_corpus‧xlsx"") containing 486 manually annotated sentences extracted from 32 animal disease-related news articles. These news articles were obtained from the database of an event-based biosurveillance system dedicated to animal health surveillance, PADI-web (https://padi-web.cirad.fr/en/). The first sheet (‘article_metadata’) provides metadata about the news articles : (1) id_article, the unique id of a news article, (2) title, the title of the news article, (3) source, the name of the news article website, (3) publication_date, the publication date of the news article (mm-dd-yyyy) and (4) URL, the web URL of the news article. The second sheet (‘annot_sentences’) contains the annotated sentences: each row corresponds to a sentence from a news article. Each sentence has two distinct labels, Event type and Information type. The set of columns is : (1) id_article, the id of the news article to which the sentence belongs, (2) id_sentence, the unique id of the sentence, indicating its position in the news content (integer ranging from 1 to n, n being the total number of sentences in the news article), (3) sentence_text, the sentence textual content, (4) event_type, the Event type label and (5) information_type, the Information type label. Event type labels indicate the relation between the sentence and the epidemiological context, i‧e. current event (CE), risk event (RE), old event (OE), general (G) and irrelevant (IR). Information type labels indicate the type of epidemiological information, i‧e descriptive epidemiology (DE), distribution (DI), preventive and control measures (PCM), economic and political consequences (EPC), transmission pathway (TP), concern and risk factors (CRF), general epidemiology (GE) and irrelevant (IR). (ii) The annotation guidelines (""epi_info_guidelines‧doc"") providing a detailed description of each category.",,CIRAD,,2022,Dataverse,https://doi.org/10.18167/DVN1/YGAKNB,YES,https://doi.org/10.1038/s41597-022-01743-2,CC BY 4.0 ,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120044,44
-NO,Avian Influenza events affecting mammals from ProMED,This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day extracted from EBS Padi-web and normalized to the ESA platform standard. Weekly updated. ,,CIRAD,,2023,,,NO,,,,https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120048,48
-YES,"Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset","These datasets contain a set of news articles in English, French and Spanish extracted from Medisys (i.e. advanced search) according the following criteria:
-(1) Keywords (at least): COVID-19, ncov2019, cov2019, coronavirus;
-(2) Keywords (all words): masque (French), mask (English), máscara (Spanish)
-(3) Periods: March 2020, May 2020, July 2020;
-(4) Countries: UK (English), Spain (Spanish), France (French). ",,CIRAD,,2020,Dataverse,https://dataverse.cirad.fr/dataset.xhtml?persistentId=doi:10.18167/DVN1/ZUA8MF,YES,https://doi.org/10.1016/j.dib.2020.106356,CC4.0,N/A,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120058,58
-YES,,"This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article ""Dissemination of information in event-based surveillance, a case study of Avian Influenza"".
-
-The reports were published between 1 July 2018 and 30st June 2019 and described one or several avian influenza outbreaks.  We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official.
-
-In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter SE and receptor sources SR. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized).
-
-The script for data analyses is available at https://github.com/SarahVal/EBS-network.",,CIRAD,,2022,Zenodo,https://doi.org/10.5281/zenodo.7324144,YES,N/A,CC40,,Not on the publication page,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120068,68
-YES,"European West Nile virus outbreak event data identified by PADI-web, media monitoring tool","This repository contains 3 datasets related to human and animal West Nile virus geo-referenced outbreak locations in Europe, as well as climate and vector covariates captured by PADI-web media monitoring tool in online news articles, between 2010 and 2022. The data is available both as raw and curated datasets, composed of free text news articles describing West Nile virus outbreaks and extracted information on outbreaks. The data can be useful for spatial risk assessment of West Nile virus emergence in Europe, as well as improving information extraction (i.e., outbreak locations and dates, hosts, vectors, climate factors etc.) from outbreak related news articles. (2022-07-25)","Text-mining, information extraction, manual curation, West Nile, outbreaks, epidemic intelligence, risk mapping",CIRAD,"Elena Arsevska, elena.arsevska@cirad.fr",2022,Dataverse,https://doi.org/10.18167/DVN1/ZNVEPK,YES,"Not yer, undergping","CC0 - ""Public Domain Dedication""",No,Not on the publication page,,,WNV,C0043124,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120093,93
-YES,Keywords for PADI-web implemented in Ocean Indian,"Keywords for 3 diseases (Leptospirosis, Dengue, Influenza) and syndromic surveillance in 4 languages",,CIRAD,,2023,Dataverse,https://doi.org/10.18167/DVN1/E7WMAO,YES,,,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120098,98
-YES,Labeled Entities from Social Media Data Related to Avian Influenza Disease,"This dataset is composed of spatial (ie. location) and thematic entities (ie. disease, symptoms, virus) concerning avian influenza in social media textual data, in English. It was created from three corpora: - The first one is composed of 10 transcriptions of YouTube videos and 70 tweets annotated manually by an annotator. - The second corpus is composed of the same textual data as corpus 1 but annotated automatically with Named Entity Recognition (NER) tools. These two corpora are create to do an evaluation of the NER tools and apply them to a larger corpus. - The third corpus is composed of 100 transcriptions of YouTube videos automatically annoted with NER tools. The aim of the annotation task was to recognize spatial information, as the name of cities and epidemiological information, as the name of diseases. An annotation guideline was created in order to have an unified annotation and help the annotators. This dataset can be used to train or evaluate natural language processing approaches such as specialized entity recognition.",,CIRAD,,2021,Dataverse,https://doi.org/10.15454/GR5EFS,YES,https://doi.org/10.1016/j.dib.2022.108317,CC0,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png?size=140,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120099,99
-NO,PADI-web,"PADI-web (Platform for Automated extraction of Disease Information from the web) is an automated biosurveillance system dedicated to the monitoring of online news sources for the detection of animal health infectious events. PADI-web automatically collects news with customised queries, classifies them and extracts epidemiological information (diseases, dates, symptoms, hosts and locations). In order to identify relevant news with PADI-web, specific models using machine learning approaches and labeled data have been integrated for monitoring plant diseases (e.g. Xylella fastidiosa). A limited version is available with free access for research or web monitoring. We warmly invite you to cite the project (cf. Publications). To get access to advanced functionalities and the extended data, please do not hesitate to contact us at padi-web@cirad.fr.","Epidemiology, health",CIRAD,Mathieu.Roche@cirad.fr,,yes,https://plant.padi-web.cirad.fr/,YES,https://doi.org/10.1016/j.onehlt.2021.100357,https://padi-web.cirad.fr/en/privacy-policy/,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,"HPAI, TBE, Leptospirosis, AMR, WNE, Lyme, Unknown disease",,Software,,,,,c4702d92-80b3-11ee-b962-0242ac120112,112
-YES,PADI-web corpus used for the EpidBioELECTRA approach,"This dataset contains a set of news articles in English related to animal disease outbreaks, that have been used to train and evaluate EpidBioELECTRA epidemiological classifier and explainer. It is composed of 70,707 articles in csv format found in several folders (relevant folder contains 34,015 news articles labelled relevant, while irrelevant folder contains 36,692 irrelevant articles), with information about the article itself (publication date, title, content, url, etc.). Thematic feature folder contains relevant and irrelevant labelled thematic features (disease, host, location, cases, etc) as contained in relevant and irrelevant documents by sentence id organized in year and month of the article. These labels were machine generated by PADIWeb classifier. ",,CIRAD,,2023,Dataverse,https://doi.org/10.18167/DVN1/WD1UC2,YES,,CC BY 4.0 ,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120113,113
-YES,PADI-web COVID-19 corpus: news articles manually labelled,"This dataset contains two files: a corpus of 275 manually labelled COVID-2019-news articles retrieved by PADI-web from Dec. 31, 2019, to Jan. 26, 2020 (padiweb_covid19.xlsx), and the multi-term expressions extracted from the news articles content using a text-mining tool, BIOTEX (biotex_covid19.xlsx). ",,CIRAD,,2020,Dataverse,https://doi.org/10.18167/DVN1/MSLEFC,YES,https://doi.org/10.1016/j.compag.2019.105163,,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120114,114
-NO,COVID-19 Tweet dataset,"COVID-19 Tweets for the period of January 2020 for UK, Italy and France",,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2021,Github,https://github.com/mehtab-alam/spatial_opinion_mining/tree/master/datasets,YES,"https://doi.org/10.5220/0010887800003123
-
-https://doi.org/10.1016/j.ijid.2021.12.065",,https://github.com/mehtab-alam/spatial_opinion_mining/tree/master,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,"UK, Italy, France",,COVID-19,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120125,125
-NO,PADI-web AI corpus: news articles/sources manually labelled,Avian Influenza relevant articles and irrelevant articles +  manually labelled relevant and irrelevant sources,,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2023,Github,https://github.com/mehtab-alam/data_quality/tree/master/datasets,YES,"https://doi.org/10.1007/978-3-031-04447-2_18 
-
-https://doi.org/10.1109/C-CODE58145.2023.10139883",,https://github.com/mehtab-alam/data_quality/tree/master,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,HPAI,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120126,126
-NO,PADI-web diseases corpus of events,"Events related articles of AI, TBE, COVID-19, Lyme, AMR",,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2022,Github,https://github.com/mehtab-alam/RSI_Disease_Dataset/tree/master,YES,"https://doi.org/10.1080/15230406.2023.2264753
-
-https://doi.org/10.5194/agile-giss-3-16-2022",,https://github.com/mehtab-alam/GeospaCy,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,"HPAI, TBE, COVID-19, Lyme, AMR",,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120127,127
-NO,Polygons dataset for Relative spatial information,Polygons geojson of relative spatial information for various cities across Europe and UK,,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2022,Github,https://github.com/mehtab-alam/RSI-Tagger/tree/master/geojson,YES,"https://doi.org/10.1080/15230406.2023.2264753
-
-https://doi.org/10.5194/agile-giss-3-16-2022",,https://github.com/mehtab-alam/GeospaCy,https://www.tandfonline.com/cms/asset/e0162320-3e8c-48d0-a81b-a8e48d943828/tcag_a_2264753_f0003_c.jpg,,,,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120128,128
-NO,Spatial Opinion Mining of COVID-19 Tweets,Spatial Opinion Mining of COVID-19 Tweets through H-TFIDF and other features,"NLP, text-mining",CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2021,Github,,,,,https://github.com/mehtab-alam/spatial_opinion_mining/tree/master,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120133,133
-NO,Data quality: Classification of news articles,Avian Influenza relevant articles and irrelevant articles classification,"NLP, text-mining",CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2023,Github,,,,,https://github.com/mehtab-alam/data_quality/tree/master,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120134,134
-NO,GeospaCy,Relative spatial information extraction and its geographical referencing,"NLP, text-mining",CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2023,Github,,,,,https://github.com/mehtab-alam/GeospaCy,https://www.tandfonline.com/cms/asset/e0162320-3e8c-48d0-a81b-a8e48d943828/tcag_a_2264753_f0003_c.jpg,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120135,135
-NO,arbocartoR app,Modeling the risk of emergence of aedes-borne diseases - Shiny interface,"simulation, aedes, arboviroses",Cirad,Hammami Pachka (pachka.hammami@cirad;fr),2023,Github,,YES,,GNU General Public License,https://forgemia.inra.fr/sk8/sk8-apps/sa/astre/arbocarto-r-app https://arbocarto-r-app.sk8.inrae.fr/,https://collaboratif.cirad.fr/share/proxy/alfresco-noauth/api/internal/shared/node/dAlwJZfsRn6IYbW7iAhNtQ/content/thumbnails/imgpreview?c=force&lastModified=imgpreview%3A1696497093303,,,"Dengue, Chikungunya, Zika",,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120139,139
-NO,arbocartoR package,Modeling the risk of emergence of aedes-borne diseases - R package,"simulation, aedes, arboviroses",Cirad,Hammami Pachka (pachka.hammami@cirad;fr),2023,Github,,YES,,Creative Commons Attribution 4.0 International Public License,https://forgemia.inra.fr/umr-astre/arbocartoR,https://collaboratif.cirad.fr/share/proxy/alfresco-noauth/api/internal/shared/node/dAlwJZfsRn6IYbW7iAhNtQ/content/thumbnails/imgpreview?c=force&lastModified=imgpreview%3A1696497093303,,,"Dengue, Chikungunya, Zika",,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120140,140
-YES,Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021,"Covariates for the UK, code of the model","ticks, companion animals, pets",CIRAD/ OGH,Elena Arsevska,2014-2021,Yes,https://doi.org/10.5281/zenodo.7625174,YES,https://doi.org/10.1186/s13071-023-06094-4,,,,UK,,"Tick borne diseases, ticks",,Dataset,Covariate,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120145,145
-YES,MOOD Maps of Google community mobility change during the COVID-19 outbreak,"The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The data and the PDF files  were originally posted  at https://www.google.com/covid19/mobility/.
-
-Abstract: The original Google data has been processed so that the data for each period is mappable using global standardised shapefiles  for  Europe and its neighbours.  The daily values from the original datasets have been converted to 3 day moving avergages  and  were produced twice a week from early Febrary 2020 until October 2022.  The data are exressed as a percentage of a baseline value defined  as the value caluculated in January 2020.  The data are available for a number of location categories:  Workplaces,  residence, parks and natural areas,  retail and recreation, gricery and pharmacy and transit stations.  ","Google, human mobility, Covid19",ERGO,William Wint (william.wint@gmail.com),2020-2022,Figshare,https://doi.org/10.6084/m9.figshare.12130980.v155,YES,"Unknown but the data have beed downlaoded 94000 times,  so probably","CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,Not on the publication page,,,COVID-19,C5203670,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120108,109
-NO,1990_2015_GrossDomesticProduct,"GDP 2015 (purchasing power parity, full description in document kummuetal2018sdata20184.pdf. 
-
-Abstract: a gridded data set of Gross domestic product (purchasing power parity) produced by Kummu, M. et al. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Sci. Data 5:180004 doi: 10.1038/sdata.2018.4 (2018) has been extracted for the MOOD extent","GDP, Gridded","ERGO
+NO,1990_2015_GrossDomesticProduct,"Abstract:
+ 
+ A gridded data set of Gross domestic product (purchasing power parity) produced by Kummu, M. et al. Gridded global dataset for Gross Domestic Product and Human Development Index over 1990–2015. Sci. Data 5:180004 doi: 10.1038/sdata.2018.4 (2018). ERGO has extracted the MOOD extent for this dataset. 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ full description in document kummuetal2018sdata20184.pdf. 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Source: 
+ 
+ Data obtained from GDP 2015 (purchasing power parity, full description in document kummuetal2018sdata20184.pdf). 
+ 
+ Software used:
+ ArcMap 10.8
+ 
+ License: CC-BY-SA 4.0
+ 
+ Processed by:
+ ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project","GDP, Gridded","ERGO
 ","William Wint (william.wint@gmail.com)
-",1990-2015,Yes,https://tinyurl.com/GDP9015,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ergdp.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120001,1
-NO,2001_2019_MODIS_Channel3_MiddleInfraRed_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MIR) derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",1990-2015,Yes,https://doi.org/10.5281/zenodo.13221047,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ergdp.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120001,1
+NO,2001_2019_MODIS_Channel3_MiddleInfraRed_FourierProcessed_1k_ER,"Overview:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ MIR: Middle Infra-Red
+  
+ Abstract:
  
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
  
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100""","MODIS,MIR,Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","MODIS,MIR,Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01191k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_wg_0119_03_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120002,2
-NO,2001_2019_MODIS_EVI_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Enhanced Vegetation Index (EVI) derived from the MOD13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of EVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.13134567,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_wg_0119_03_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120002,2
+NO,2001_2019_MODIS_EVI_FourierProcessed_1k_ER,"Overview:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ EVI: Enhanced Vegetation Index
  
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
  
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100""","MODIS, EVI, Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","MODIS, EVI, Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01191k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1915a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120003,3
-NO,2001_2019_MODIS_LandSurfaceDayTemperature_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data 07 - DLST: Day-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.13134567,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1915a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120003,3
+NO,2001_2019_MODIS_LandSurfaceDayTemperature_FourierProcessed_1k_ER,"Overview:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ 
+ DLST: Day-time Land Surface Temperature
+ 
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
+ 
+ Parameter Fourier Variable Image values are
+ MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
+ LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
+ NDVI (14) and EVI (15) VR Value * 10000
+ ALL D1,D2,D3,Da Percentages
+ ALL E1,E2,E3 Percentages
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","MODIS, Seasonal daytime Land Temperature, Fourier Processing","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.13134567,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1907a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120004,4
+NO,2001_2019_MODIS_LandSurfaceNightTemperature_FourierProcessed_1k_ER,"Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ 
+ NLST: Night-time Land Surface Temperature
+ 
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
+ 
+ Parameter Fourier Variable Image values are
+ MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
+ LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
+ NDVI (14) and EVI (15) VR Value * 10000
+ ALL D1,D2,D3,Da Percentages
+ ALL E1,E2,E3 Percentages
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","MODIS, Seasonal night time Land Temperature, Fourier Processing","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.13134567,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1908a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120005,5
+NO,2001_2019_MODIS_NDVI_Fourier Processed_1k_ER,"Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ 
+ NDVI: Normalised Difference Vegetation Index
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
+ 
+ Parameter Fourier Variable Image values are
+ MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
+ LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
+ NDVI (14) and EVI (15) VR Value * 10000
+ ALL D1,D2,D3,Da Percentages
+ ALL E1,E2,E3 Percentages
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","MODIS, NVDI, Fourier Processed","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.13134567,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1914a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120006,6
+NO,2001_2021_ MODIS _Channel3_MiddleInfraRed_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+  
+ MIR: Middle Infra-Red
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -165,22 +465,31 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, min Max Index Value * 10000
- 
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","MODIS, Seasonal daytime Land Temperature, Fourier Processing","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)","MODIS,MIR,Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01191k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1907a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120004,4
-NO,2001_2019_MODIS_LandSurfaceNightTemperature_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data 08 - NLST: Night-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.10078149,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_03_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120007,7
+NO,2001_2021_ MODIS _EVI_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+ EVI: Enhanced Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -208,21 +517,32 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100","MODIS, Seasonal night time Land Temperature, Fourier Processing","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)","MODIS, EVI, Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01191k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1908a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120005,5
-NO,2001_2019_MODIS_NDVI_Fourier Processed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Normalised Difference Vegetation Index (NDVI) derived from the MOD13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of NDVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.10078149,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_15_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120008,8
+NO,2001_2021_ MODIS _LandSurfaceDayTemperature_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+ 
+ DLST: Day-time Land Surface Temperature
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -250,20 +570,32 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100""","MODIS, NVDI, Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)","MODIS, Seasonal daytime Land Temperature, Fourier Processing","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01191k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er1914a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120006,6
-NO,2001_2021_ MODIS _Channel3_MiddleInfraRed_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MIR) derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.10078149,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_07_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120009,9
+NO,2001_2021_ MODIS _LandSurfaceNightTemperature_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+ 
+ NLST: Night-time Land Surface Temperature
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -291,21 +623,32 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100""","MODIS,MIR,Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)","MODIS, Seasonal night time Land Temperature, Fourier Processing","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01211k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_03_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120007,7
-NO,2001_2021_ MODIS _EVI_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Enhanced Vegetation Index (EVI) derived from the MOD13A2 Product from USGS for the period 2001 -2021 and is an update of hthe previous 2011 - 2019 series. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of EVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.10078149,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_08_a1(1).png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120010,10
+NO,2001_2021_ MODIS _NDVI_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+ 
+ NDVI: Normalised Difference Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -338,110 +681,93 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100""","MODIS, EVI, Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)","MODISS, NVDI, Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01211k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_15_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120008,8
-NO,2001_2021_ MODIS _LandSurfaceDayTemperature_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data 07 - DLST: Day-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2021, as an update of the 2001-2019 series,, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.10078149,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_14_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120011,11
+NO,2001_2022_ERA5_SoilMoisture_ER_5k,"Abstract:
+ 
+ Soil moisture data have been downloaded from the ECWMF ERA5 reanalysis dataset and then windowed to provide 5km raster datasets for the MOOD extent for the years 2001- 2022 (Oct 2022)
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
  
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+ File naming scheme: 
  
- Parameter Fourier Variable Image values are
- MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
- LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
- NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
- NDVI (14) and EVI (15) VR Value * 10000
- ALL D1,D2,D3,Da Percentages
- ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","MODIS, Seasonal daytime Land Temperature, Fourier Processing","ERGO
+ era5corsoilmoist+ Year+ Month +.tif
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 69.0000000000000000,82.0000000000000000
+ Spatial resolution:
+ 0.25 (5000m)
+ Temporal resolution:
+ Monthly from 2001 to 2022
+ 
+ 
+ Pixel values
+ 
+ Soil Moisture Precentage
+ 
+ Source: 
+  ECWMF ERA5 Soil Moisture
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8","era5, soil moisture, 5km","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01211k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_07_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120009,9
-NO,2001_2021_ MODIS _LandSurfaceNightTemperature_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data 08 - NLST: Night-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2021, as an update of the 2001-2019 series,, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2001-2022,Yes,https://doi.org/10.5281/zenodo.13123014,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_era5soilmoist2020oct.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120012,12
+NO,2006_2022_MODIS_EnhancedVegetationIndex_5k_ER,"Abstract:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline from 2001-2021.
+ This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modeled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.
  
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
+  
  
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+ File naming scheme: 
  
- Parameter Fourier Variable Image values are
- MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
- LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
- NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
- NDVI (14) and EVI (15) VR Value * 10000
- ALL D1,D2,D3,Da Percentages
- ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100","MODIS, Seasonal night time Land Temperature, Fourier Processing","ERGO
+ There are two zip files. One includes data from 2006 to 2021, and the second is for 2022. 
+ 
+ the files name are: moeve5km16day + year + day (out of 365) : moeve5km16day2022049.tif
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 5k
+ Temporal resolution:
+ 16-day from 2006 to 2022
+ 
+ 
+ Pixel values:
+ 
+ Vegetation Index
+ 
+ Source: 
+ MODIS NASA : MOD13C1
+ 
+ 
+ Software used:
+  
+ The software used for map production is ESRI ArcMap 10.8","MODIS, 5km, Enhanced Vegegation Index","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01211k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_08_a1(1).png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120010,10
-NO,2001_2021_ MODIS _NDVI_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Normalised Difference Vegetation Index (NDVI) derived from the MOD13A2 Product from USGS. the period 2001 2021 and is an update of the previous 2011 - 2019 series The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of NDVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2021, as an update of the 2001-2019 series, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.13122989,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/overview_moodeviApr_22_5k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120013,13
+NO,2015_MODIS_RelativeHumidity_FourierProcessed,"This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS Relative Humidity data:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline from 2001-2021.
+ Abstract :
  
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
+ This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MID) derived from the MOD11A2 Product from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ MODIS is a sensor on two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture.
+ 
+  
+ 
+ The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+  
+ 
+ File naming scheme: 
  
  The last two characters of each file name denote the output from Fourier processing:
  a0 - mean
@@ -459,50 +785,144 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+  
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  Decadal 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
- MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
- LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
- NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
- NDVI (14) and EVI (15) VR Value * 10000
+ RH values A0, A1, A2, A3, Min, Max, Vr Reflectance values 
+ 
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100""","MODISS, NVDI, Fourier Processed","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/tfamodis01211k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_er_0121_14_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120011,11
-NO,2001_2008-2022_ERA5_SoilMoisture_ER_5k,"soil moisture for WNV. 
-
-Abstract: Soil moisture data have been downloaded from ECWMF ERA5 reanalysis dataset then windowed to provide 5km ratser datasets for the MOOD extent for the years 2001, 2008- 2022","era5, soil moisture, 5km","ERGO
-","William Wint (william.wint@gmail.com)
-","2001 , 2008-2022",Yes,https://tinyurl.com/era5soilmoisture01225k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_era5soilmoist2020oct.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120012,12
-NO,2006_2022_MODIS_EnhancedVegetationIndex_5k_ER,"MODIS 16 day Enhanced Vegetation Index, 5km, 2001-2021. 
-
-Abstract: This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. Itis designed for use with administraytive level analysis which need to used covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.","MODIS, 5km, Enhanced Vegegation Index","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/modis0119,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/overview_moodeviApr_22_5k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120013,13
-NO,2015_MODIS_RelativeHumidity_FourierProcessed,"This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MID) derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture.","MODIS, Yearly,RelativeHumidity, Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ Middle Infra Red (MID) derived from the MOD11A2(MODIS NASA) Product from USGS
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","MODIS, Yearly,RelativeHumidity, Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2015,Yes,https://tinyurl.com/tfarhmodis1021,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_82094a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120014,14
-NO,2010_2022_ERA5_MonthlyPrecipitation_5k,"Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2010 - 2022. 
-
-Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2022 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.","ERA5, Precipitation, 5km","ERGO
+",2015,Yes,https://doi.org/10.5281/zenodo.13122979,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_82094a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120014,14
+NO,2010_2022_ERA5_MonthlyPrecipitation_5k,"Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2010 - 2022. 
+ 
+ Abstract:
+ 
+ Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting, for 2010 - 2022. The original data is at 0.25-degree resolution and was downscaled by ERA extraction algorithms. The daily data have been aggregated into decadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Monthly Precipitation: 2022 moeraprecmmmonthly2022.zip ; 2010 to 2021 moeraprecmmmonthly20102021.zip
+ 
+ Daily, decadal, and annual precipitation can be found here. 
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.25 (5000m) 
+ Temporal resolution:
+ Monthly from 2010 to 2022
+ 
+ Pixel values
+ 
+ Precipitation in meter
+ 
+ 
+ Source: 
+ 
+ ERA5 Precipitation by the European Centre for Medium-Range Weather Forecasting
+ 
+ 
+ Software used:
+  
+ The software used for map production is ESRI ArcMap 10.8","ERA5, Precipitation, 5km","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/Era5Preciptation10215km,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_era__pr__mon22_5k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120015,15
-NO,2010_2022_MODIS_LandSurfaceTemperature_5k_ER,"MODIS daily, decadal monthly and annual land surface Temperature, 5km, 2010-2020. 
-
-Abstract: this is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. Itis designed for use with administraytive level analysis which need to used covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.","MODIS ,5k, Land Surface Temperature","ERGO
+",2022,Yes,https://doi.org/10.5281/zenodo.13122970,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_era__pr__mon22_5k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120015,15
+NO,2010_2022_MODIS_LandSurfaceTemperature_5k_ER,"MODIS daily, decadal, and monthly land surface Temperature, 5km, 2010-2022. 
+ 
+ Abstract:
+ 
+ This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Monthly Day LST : 2022 MOODMonthlyDLST2022.zip ; 2010 to 2021 MOODMonthlyDLST20102021.zip
+ 
+ Monthly Night LST 2022 MOODMonthlyNLST2022.zip ; 2010 to 2021 MOODMonthlyNLST20102021.zip
+ 
+ Decadal Day LST: 2022 MOODMOD11c2Dekadaldlst2022.zip ; 2010 to 2021 MOODMOD11C2DEKADALDLST20102021.zip
+ 
+ Decadal Night LST: 2022 MOODMODC11dekadalnlst2022.zip ; 2010 to 2021 MOODMOD11C2DEKADALNLST20102021.zip
+ 
+ Daily Day LST: 2022 MOODMOD11C1DAILYDLST2022.zip ; 2010 to 2021 MOODMOD11C1DAILYDLST20102021.zip
+ 
+ Daily Night LST: 2022 MOODMOD11C1DAILYNLST2022.zip ; 2010 to 2021 MOODMOD11C1DAILYNLST20102021.zip
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 5k
+ Temporal resolution:
+ Daily, Decadal, and monthly from 2010 to 2022
+ 
+ Pixel values
+ 
+ Temperature Degree
+ 
+ 
+ Source: 
+ MODIS NASA : MOD11A1
+ 
+ 
+ Software used:
+  
+ The software used for map production is ESRI ArcMap 10.8","MODIS ,5k, Land Surface Temperature","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/modis0119,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/overview_moodlstApr_22_5k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120016,16
-NO,2012_2020_ VIIRS _Channel3_MiddleInfraRed_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global VIIRS data for Middle Infra Red (MIR) derived from the VNP13A2 Product from USGS. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP13A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.13122959,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/overview_moodlstApr_22_5k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120016,16
+NO,2012_2020_ VIIRS _Channel3_MiddleInfraRed_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ MIR: Middle Infra-Red
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
  
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -525,6 +945,22 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -533,19 +969,40 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","VIIRS,MIR,Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","VIIRS,MIR,Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/tfaviirs12201k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2003a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120017,17
-NO,2012_2020_ VIIRS _EVI_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global VIIRS data for Enhanced Vegetation Index (EVI) derived from the VNP13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. TheVNP13A2 product contains an 16-day average of EVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2020,Yes,https://doi.org/10.5281/zenodo.12914013,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2003a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120017,17
+NO,2012_2020_ VIIRS _EVI_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ EVI: Enhanced Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
  
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -568,6 +1025,22 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -576,19 +1049,41 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","VIIRS, NVDI, Fourier Processed","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, NVDI, Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/tfaviirs12201k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2015a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120018,18
-NO,2012_2020_ VIIRS _LandSurfaceDayTemperature_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global VIIRS data 07 - DLST: Day-time Land Surface Temperature derived from the VNP21A2 Product from USGS. The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP21A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2020,Yes,https://doi.org/10.5281/zenodo.12914013,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2015a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120018,18
+NO,2012_2020_ VIIRS _LandSurfaceDayTemperature_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ DLST: Day-time Land Surface Temperature
+ 
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
  
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -611,6 +1106,22 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -619,19 +1130,40 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","VIIRS, Seasonal daytime Land Temperature, Fourier Processing","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, Seasonal daytime Land Temperature, Fourier Processing","ERGO
 ","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/tfaviirs12201k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2003a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120019,19
-NO,2012_2020_ VIIRS _LandSurfaceNightTemperature_FourierProcessed_1k_ER,"This is a set of images produced by temporal Fourier analysis of global VIIRS data 08 - NLST: Night-time Land Surface Temperature derived from the VNP21A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP21A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2020,Yes,https://doi.org/10.5281/zenodo.12914013,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2003a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120019,19
+NO,2012_2020_ VIIRS _LandSurfaceNightTemperature_FourierProcessed_1k_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ NLST: Night-time Land Surface Temperature
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
  
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -654,6 +1186,22 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -662,19 +1210,40 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","VIIRS, Seasonal night time Land Temperature, Fourier Processing","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, Seasonal night time Land Temperature, Fourier Processing","ERGO
 ","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/tfaviirs12201k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2008a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120020,20
-NO,2012_2020_ VIIRS _NDVI_FourierProcessed_ER,"This is a set of images produced by temporal Fourier analysis of global VIIRS data for Normalised Difference Vegetation Index (NDVI) derived from the VNP13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP13A2 product contains an 16-day average of NDVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2020,Yes,https://doi.org/10.5281/zenodo.12914013,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2008a0.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120020,20
+NO,2012_2020_ VIIRS _NDVI_FourierProcessed_ER,"Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ NDVI: Normalised Difference Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
  
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -697,6 +1266,22 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -705,215 +1290,866 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)","VIIRS, EVI, Fourier Processed","ERGO
-","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/tfaviirs12201k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2014a0.png,,,"Lyme, TBE, WNV, USUTU",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120021,21
-NO,2013_FarmLabour_ER,"Farm Labour. 
-
-Abstract: Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by country. xlsx files.","Farm Labour, Eurosat, Europe","ERGO
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, EVI, Fourier Processed","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/farmlabourer13,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/erfarmlabour1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120022,22
-NO,2017_RoadDensity_OSM_ER,"Road density for unclassified, minor, major and autoroutes 2017. 
-
-Abstract: This dataset was produced by extracting all rood data from national level Open Steet Map archuves for 2017. The length of each type of road was calcluated for a series of 5 square km grids covering the MOOD study area.",road density,"ERGO
+",2020,Yes,https://doi.org/10.5281/zenodo.12914013,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/er2014a0.png,,,"Lyme, TBE, WNV, USUTU",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120021,21
+NO,2013_FarmLabour_ER,"Abstract:
+ 
+ Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by country. xlsx files.
+ 
+ Source: 
+ Eurostat farm Labour by NUTS 
+ Software used:
+ Excel","Farm Labour, Eurosat, Europe","ERGO
 ","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/RoadDensityerOSM17,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overvieow_roadDensity_OSM5k.png,,,"Lyme, Mosquito borne Flaviviruses, Covid",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120023,23
-NO,2019_2020_ERA5_MonthlyPrecipitation_FourierProcessed,"This is a set of images produced by temporal Fourier analysis of Monthly precipiation proivided by the ERA5 dataset for the period 2019-2020n from the European Centre for Medium Range Weather forecasting. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. 
-
-Abstract: Monthly precipitation values were extracted from ERA5 files for the years 2019 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+",2022,Yes,https://doi.org/10.5281/zenodo.12913705,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/erfarmlabour1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120022,22
+NO,2017_RoadDensity_OSM_ER,"Abstract:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection.
+ This dataset was produced by extracting all road data from national-level Open Steet Map archives for 2017. The length of each type of road was calculated for a series of 5 square km grids covering the MOOD study area.
  
- The next two characters identify the channel:
- 19 - precipitation and 20 refers to the year timeline of 2019-2020.
+  
  
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -24.5705863176307275,23.6115332000000535 : 44.5127470157025726,73.0115332000000308
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ The year 2017
+ Pixel values:
+  Km of road per square km 
+ Source: 
+ Open Street Map
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8",road density,"ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12913627,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overvieow_roadDensity_OSM5k.png,,,"Lyme, Mosquito borne Flaviviruses, Covid",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120023,23
+NO,2001_2019_ERA5_MonthlyPrecipitation_FourierProcessed,"This is a set of images produced by temporal Fourier analysis of Monthly precipitation provided by the ERA5 dataset for 2001-2019 from the European Centre for Medium-Range Weather Forecasting. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. 
+ 
+ Abstract:
+ 
+ Monthly precipitation values were extracted from ERA5 files for the years 2019 through 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ File naming scheme:
+  The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection.
+  
+  The next two characters identify the channel:
+  20 for precipitation and 19 refers to the year timeline of 2001-2019.
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+  
+  
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 2001-2019
+ Pixel values:
+ 
+ Parameter Fourier Variable Image values are
+  A0, A1, A2, A3, Index Value * 10
+  ALL D1,D2,D3,Da Percentages
+  ALL E1,E2,E3 Percentages
+  ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+  
+ 
+ 
+ Source: 
+  Monthly Precipitation for ERA5 from the European Centre for Medium-Range Weather Forecasting (ECMWF)
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8","Precipitaion, ERA5 ECMWF, Fourier Processed","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/ 10.5281/zenodo.12913559,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_19_20_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120024,24
+NO,2019_Herbaceous_Wetlands_Copernicus,"Copernicus global land cover, herbaceous wetlands, 100m, and proportion herbaceous wetland (1km) in 2019. 
+ 
+ Abstract:
+ 
+ Landuse/landcover datasets are provided through the Copernicus climate data service, (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963).
+ 
+ This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)
+ 
+  
+ 
+ File naming scheme: 
+ 
+ This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 100-meter and 1000-meter
+ 
+ Temporal resolution:
+ The year 2019
+ 
+ Pixel values:
+  The proportion of each pixel that is assigned as herbaceous wetland
+ 
+ Source: 
+ The Copernicus climate data service
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8","wetlands, coperncius, Proba","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12913360,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/cglopsherbwet.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120025,25
+NO,2019_SurfaceWater_PROBA_1k,"PROBA V 2019 Permanent and Seasonal water 1km. 
+ 
+ Abstract:
+ 
+ These layers were extracted for the MOOD extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, & Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050). 
+ 
+ Several kilometre resolution derivatives have been produced from the original 100m resolution dataset for seasonal and permanent water categories: 
+ 
+  
+ 
+ File naming scheme: 
+ Distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif); 
+ 
+ Percentage of seasonal water (erprobavLLC2019pcseaswat1km.tif);
+ 
+ Percentage of permanent water (erprobavLLC2019pcpermwat1km.tif)
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ 
+ Temporal resolution:
+ seasonal and permament 
+ 
+ 
+ Pixel values:
+ Meter and Percentage
+ 
+ 
+ Source: 
+ The Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8","Proba, permnent, seasonal, water","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12819570,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_surfacewater_Proba_1k_2019.png,,,"Avian Flu, Tularaemia, Leptospirosis, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120026,26
+NO,2020_2023_VIIRS_CumTemp_IxodesRicinus,"Abstract:
+ 
+ This dataset presents cumulative temperature masks that identify areas that are warm enough to stimulate tick questing activity, using a temperature threshold of 6 C. A series of scripts download and process VIIRS EO imagery of Land Surface Temperature to create temperature masks every 8 days using a combination of decadal (8-day) and daily satellite data. This dataset includes 2020, 2021, 2022, and 2023.
+ 
+ These result in Boolean masks where suitable areas according to temperature limits on I. ricinus are identified as 1 and unsuitable areas as 0. This mask can then be applied to the existing seasonal Tick model to make a more timely prediction of tick activity based on recent temperatures.
+ 
+ Image acquisition:
+ 
+ Two different products are downloaded VIIRS Land Surface Temperature/Emissivity 8-day L3 Global 1 km SIN grid (VNP21A2, version 6) and VIIRS Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN grid (VNP21A1D, version 6). 
+ 
+ Processing:
+ The cumulative temperature mask is processed in two steps through two separate scripts:
+ 
+ a. Acquisition of 1km VIIRS Land Surface Temperature imagery from NASA's data repository.
+ b. Importing of the imagery into a suitable format from which regularly updated masks are calculated.
+ 
+ File naming schema:
+ 
+  ER+ year + 8-day number (46 in total) for example: ER2333C68.tif 23 refers to the year 2023 and 33 (decadal number) in this example refers to the 22nd of September.
+ 
+ ERCumTemp + year + month+ day: ERCumTemp230109.jpg, 230109 refers to the 8-day starting 9th of January.
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day for years 2020 to 2023
+ 
+ 
+ Pixel values:
+ 
+ Suitable areas as 1 and unsuitable areas as 0
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ 
+ Repository URL :
+ https://github.com/ERGOcode/Cumulative-Temperature-Mask
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, Cumulitative Temperature","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12819566,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTemp230813.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120027,27
+NO,2020_MOOD_StandardMappingandAnalysispolygons,"MOOD standard Mapping and Analysis polygons. 
+ 
+ Abstract:
+ 
+ Standard extents and polygons were defined at the start of the MOOD project. There sets of standatd geographies are provided: 
+  a) A series of decimal degree grids (0.05, 0.1, 0.2, 0.5 ) in moodgrids.zip 
+  b) raster extents in EPRS ( molandmasketrslaeaepsg3035.tif) and geographic projection (molatlonglandmask.tif) in moodpolygonsjanmasks21.zip; and 
+  c) standard Administrative unit Polygons adopted from the Vectornet Project : one with relatively equal sized units designed for mapping (vectornetMAPforMOODjan21.shp) and one more suited to analysing and entering data recorded by admin unit that has consistent NITS 3 or Gaul 2 polygons (VectornetDATAforMOODjan21.shp) also in moodpolygonsjanmasks21.zip
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ 
+ -73.2634658809999451,18.9632860000000392 : 69.0703202920000763,83.6274185180000700
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8","polygons, extents","ERGO
+","William Wint (william.wint@gmail.com)
+",2020,Yes,https://doi.org/10.5281/zenodo.12819564,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_moodPolygons_21.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120028,28
+NO,2020_WorldpopHumanPopulation_Age_Gender,"Worldpop Human 2020 population by Age and Gender. 
+ 
+ Abstract:
+ 
+ Human population estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasets for the MOOD extent. Gender age categories were summed to provide datasets for all males, all females, and the total population. 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Filenames are as follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = mimimum age; OO= Maximum age
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999995960000092,81.9999997120000046
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Temporal resolution:
+  The year 2020
+ 
+ Pixel values:
+ Human population estimates per pixel 
+ 
+ Source: 
+ Worldpop (www.worldpop.org) datasets
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8","Human population, age, gender","ERGO
+","William Wint (william.wint@gmail.com)
+",2020,Yes,https://doi.org/10.5281/zenodo.12819552,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/vcwpopp.png,,,"Tularaemia, TBE, WNV, USUTU, Covid, Lyme",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120029,29
+NO,2021_2022_MonthlyDistribution_WaterBodies,"Monthly water bodies for WNV modelling, August 21 - Sept 22. 
+ 
+ Abstract:
+ 
+ 300m (0.00025 deg) resolution raster datasets for monthly water body presence (Aug 21 to Sept 22) were extracted for the MOOD spatial extent from global data provided by the Copernicus Global Land Service (https://land.copernicus.eu/global/products/wb). These data were produced by the Sentinel 2 satellite. 
+ 
+  
+ 
+ File naming scheme: 
+ Details of data production are provided in the accompanying file CGLOPS2_PUM_WB300m_V2_I1.10.tif
+ 
+ mowatbod+ Month+Year for example for July 2021: mowatbodjul21
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ 
+ Spatial extent:
+ Extent -32.0000000000018474,18.0000000000007603 : 68.9999999999969162,79.9999999999999858
+ 
+ 
+ Spatial resolution:
+ 0.002499999 deg (approx. 300 m) 
+ 
+ 
+ Temporal resolution:
+ Monthly
+ 
+ 
+ Pixel values:
+  Unit: meter
+ 
+ Source: 
+ the Copernicus Global Land Service (https://land.copernicus.eu/global/products/wb)
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8","Sentinel, water body, monthly, 300m","ERGO
+","William Wint (william.wint@gmail.com)
+",2021 -2022,Yes,https://doi.org/10.5281/zenodo.12819239,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_mowaterbodmay22.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120030,30
+NO,2015_2020_ERA5_MonthlyPrecipitation_10k,"Monthly Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2015-2020 
+ 
+ Abstract:
+ 
+ Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2015 - 2020. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algorithms to 10km.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Filenames = mo+ era5precet+ month+ year : moera5prectSep-15.TIF 
+ 
+ More information can be found in this file: ERA5-Land monthly averaged data from 1981 to present.pdf 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0999979564505225,17.9499999999999886 : 68.9999956073235836,82.0499999999999972
+ 
+ Spatial resolution:
+ 10 km 
+ 
+ Pixel values:
+ unit: meter
+ 
+ Source: 
+ 
+ The European Centre of Medium Range Weather Forecasting (ERA5) 
+ 
+ Software used:
+ ArcMap 10.8","ERA5, Precipitation, 10km","ERGO
+","William Wint (william.wint@gmail.com)
+",2015-2020,Yes,https://doi.org/10.5281/zenodo.12819131,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_era__pr_21_10k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120031,31
+NO,2021_FloodRisk_JRC,"Floodrisk, 10 year return period, 1km. 
+ 
+ Abstract:
+ 
+ The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/collection/id-0054 has been windowed to the MOOD extent for access by project partners. According to JRC ""The maps have been developed using hydrological and hydrodynamic models, driven by the climatological data of the European and Global Flood Awareness Systems (EFAS and GloFAS). European-scale maps comprise most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries.""
+ 
+  
+ 
+ File naming scheme: 
+ 
+ flood risk in TIF format for 10 years : erfloodMapGL_rp10y1km.tif 
+ 
+ JRC Guide: JRC cdatafloodrisk.pdf 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m)
+ 
+ Temporal Resolution:
+ 
+ 10 year period
+ 
+ Pixel values:
+ unit: meter
+ 
+ Source: 
+ 
+  JRC: https://data.jrc.ec.europa.eu/collection/id-0054
+ 
+ Software used:
+ ArcMap 10.8","Floodrisk, JRC, 10year","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12799469,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_floodrisk_10year.png,,,"Leptospirosis, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120032,32
+NO,2021_HPAI_AvianHost_DistributionIndices,"Weighted Index and formatted distribution data for HPAI hosts. 
+ 
+ Abstract:
+ 
+ A series of descriptive metrics were produced as host distribution indices for use as covariates in HPAI spatial modelling. The following bird species were included. For each, disptribution maps were extracted from the Birdlife International datasets and idvidual ratster images were produced for breeding, passage, resident and passage distributions. 
+  Anas crecca, Anas penelope, Anal platyrhynchos, Anser anser, Aytha fuligula, Bucephala clangula, Cygnus color, Cygnus cygnus, Mareca penelope, Mareca strepera,
+  Combination images for all species were also produced , each giving the number of species per pixel for each category ( Anatidae passage, Anatidae breeding, Anatidae resident, Anatidae nonbreeding,.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Filenames = AIHOSTxxxxsum.tif where XXX = breeding, nobreeding, resident, passage)
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Pixel values:
+ Number of species per pixel for each category
+ 
+ Source: 
+ 
+ Birdlife International datasets
+ 
+ Software used:
+ ArcMap 10.8","HPAI, Bird Hosts, Birdlife International, distrubtions, indices","ERGO
+","William Wint (william.wint@gmail.com)
+",2021,Yes,https://doi.org/ 10.5281/zenodo.12799446,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/aihostpresabssum.png,,,HPAI,C0016627,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120033,33
+NO,2021_Veterinary_Hospital_FacilityDistributions,"Veterinary and Hospital distributions. 
+ 
+ Abstract:
+ 
+ These maps of veterinary facilities and hospital health care locations are derived from the open-source Open Street Maps (OSM). These are large datasets that underlie most of the satnav utilities, and are continuously updated by members of the public. They are compiled by GEOFABRIK, and can be downloaded from https://www.geofabric.de. Healthcare data have been further enhanced by the Global Health Sites Mapping Project (https://healthsites.io). 
+  
+ The data consist of locations (“points of interest”) and building outlines, and are made up of points and polygons that are stored separately in the OSM files. Each record has a series of descriptors (“tags”) such as ‘amenity’ which may include descriptions of a hospital or veterinary clinic. These tags are provided by the people who add the data to the maps and are multilingual and very variable. Most records contain additional more generic tags that can be used to identify classes of locations like hospitals or pharmacies.
+  
+  ERGO has taken the IO and OSM datasets, combined the polygon and point data, converted the polygons to points, and removed any duplicates (where building outlines also have point location records), to produce and global datasets for healthcare and regional datasets for healthcare and veterinary locations covering Europe and its neighboring countries. 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Full descriptions in Maps of Healthcare and Veterinary Locations.pptx. 
+ 
+ ESRI point shapefiles are as follows:
+  Healthcare Global: Ionodewaynodupallmay21.shp
+  Healthcare Regional: MONODWAYALLnodupMAY21.shp
+  Veterinary Regional: afeupolypointsnondupmergemay21.shp
+  
+  The regional maps are produced by tabulating the number of each healthcare amenity or veterinary locations within administrative zones, normalising by the administrative unit areas. The maps are produced from the following shape files:
+  Healthcare Regional: MOiohealthamenitycountadmin2may21.shp
+  Veterinary Regional: MONODWATALLVETSNODUPMAY21.shp
+  An arcGS 10.4 MPK project file (ERGOhealthvetforweb.mpk) has been provided to display the spatial data provided
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -73.2621536249999394,18.9632860000000392 : 69.0703202920000336,83.6274185180000842
+ 
+ Spatial resolution:
+ Shp files
+ 
+ Temporal Resolution:
+ 
+ Year 2021
+ 
+ Values:
+ 
+ Per Units (Number of facilities ): the number of each healthcare amenity or veterinary locations within administrative zones,
+ 
+ Source: 
+ 
+ Open Street Maps (OSM):
+ 
+ GEOFABRIK https://www.geofabric.de
+ 
+ Health care data https://healthsites.io
+ 
+ Software used:
+ ArcMap 10.4","Veterinary clinics, human hospitals, health, One health","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12799454,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_hospital_units.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120034,34
+NO,2021_WNV_AvianHost DistributionIndices,"BRT/RF ensemble spatial Models and Weighted seasonality code for WNV avian Hosts. 
+ 
+ Abstract:
+ 
+ A series of BRT spatial models were produced for WNV avian hosts (Corvus corax, Corvus corone, Corvus frugilegus, Corvus monedula, Corvus ruficollis, Passer domesticus, Pica pica, Turdus merula) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include polygon data from the Birdlife Internatioinal project and from the Global Biodiversity Information Facility (www.gbif.org) . In addition an ensemble model combining Random Forest and Boosted Regresion Trees spatial modelling outputs has been produced for an index of seasonal distributions of corvid presence categories ranked in decreasing order as follows Resident, Breeding, Non-Breeding, Passage Speces included : Corvus vorax, Corvus monedula, Corvus corona, Corvus frugilegus, Corvus ruficollis. 
+ 
+  
+ 
+  
+ 
+ File naming scheme: 
+ 
+ File name = blifecorvidssuminvcdBRTRFENS.tif 
+ 
+ description: wnvmodelsdesc.txt
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Temporal Resolution: 
+ 
+ Seasonal 
+ 
+ Pixel values:
+ an index of seasonal distributions
+ 
+ Source: 
+ 
+ The Birdlife Internatioinal project and from the Global Biodiversity Information Facility (www.gbif.org)
+ 
+ Software used:
+ ArcMap 10.8","BRT, spatial models, WNV, avian Hosts, ranked multi species index","ERGO
+","William Wint (william.wint@gmail.com)
+",2021,Yes,https://doi.org/10.5281/zenodo.12799425,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/WNVAvianhostDist21.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120035,35
+NO,2022_2023_SpatialModel_TBEHosts,"Updated Spatial Models Hosts: Cervus elephus, Dama dama, Capreolus capreolus, Cervus nippon, Apodemus falvicollis, Myodes glareolus, Lepus europeaus, Lepus timidus, 2022 and 2023. 
+ 
+ Abstract:
+ 
+ Ensembled spatial models were produced for four deer species (mooddeerensemblemodelsaug23.zip), two small mammal species (moemmaapofmyogMEANRFBRTAug22.zip), and two hare species (moemmalepeutiaug22MEANRFBRT.zip) by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include data from IUCN, from the Global Biodiversity Information Facility (www.gbif.org), and from earlier models ( Alexander, N.S., Morley, D, Medlock, J, Searle, K and Wint, W (2014), A First Attempt at Modelling Roe Deer (Capreolus capreolus) Distributions Over Europe. Open Health Data 2(1):e2, DOI:http://dx.doi.org/10.5334/ohd.ah and Wint, W, Morley, D, Medlock, J and Alexander, N.S (2014), A First Attempt at Modelling Red Deer (Cervus elaphus) Distributions Over Europe. Open Health Data 2(1):e1, DOI: http://dx.doi.org/10.5334/ohd.ag . 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ four deer species (mooddeerensemblemodelsaug23.zip) , two small mammal species (moemmaapofmyogMEANRFBRTAug22.zip) and two hare species (moemmalepeutiaug22MEANRFBRT.zip)
+ 
+  The output files are as follows: 
+  a) deer: mocapcapensrfbrt2223.tif = Capreolus capreolus (Roe deer) ; mocervelensrfbrt2223.tif = Cervus elephbus (Red deer) ; modamdamensrfbrt2223.tif = Dama dama (Fallow deer) ; and mosikapaensbrtrfaug23.tif = Cervus nippon (Sika deer) 
+  b) small mammals: moemmaapoflaaug22MEANRFBRT.tif = Apodemus flavicolis (Yellow necked mouse ; moemmamyoglaaug22MEANRFBRT.tif = Myodes glareolus (Bank vole)
+  c) hares: moemmalepeuaug22MEANRFBRT.tif = Lepus europaeus (European Hare) ; and moemmaleptiaug22MEANRFBRT.tif = Lepus timidus (Mountain Hare)
+ 
+  
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Pixel values:
+ Cluster number and Predicted probability of presence
+ 
+ Source: 
+ 
+ UCN, from the Global Biodiversity Information Facility (www.gbif.org)
+ 
+ Software used:
+ ArcMap 10.8","Spatial Models Hosts: Cervus elephus, Dama dama, Capreolus capreolus, Cervus nippon, Apodemus falvicollis, Myodes glareolus, Lepus europeaus, Lepus timidus","ERGO
+","William Wint (william.wint@gmail.com)
+","2022, 2023",Yes,https://doi.org/10.5281/zenodo.12799418,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_host.png,,,TBE,C0014061,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120036,36
+NO,2022_2023_SpatialModelVector_Ixodesticks,"Updated Spatial Models of Tick Vectors Ixodes ricinus and Ixodes persulcatus. 
+ 
+ Abstract:
+ 
+ Ensembled spatial models were produced for Ixodes ricinus ( allixric1xybal40kjul23MEANbrtrf.zip) and Ixodes persulcatus (ergopersulcatusmodelpresabsmay23.zip)by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of national databases from the UK, Spain and Finland
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Ixodes ricinus ( allixric1xybal40kjul23MEANbrtrf.zip)
+ 
+  Ixodes persulcatus (ergopersulcatusmodelpresabsmay23.zip)
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Ixodes ricinus: Extent -32.0000000000000000,10.0000000000000000 : 69.0000000000000000,82.0000000000000000
+ 
+ Ixodes persulcatus: Extent -5.0000000000000000,35.0000000000000000 : 68.9999999999999716,74.9999999999999858
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Pixel values:
+ unit: Probability (between 0 to 1)
+ 
+ Source: 
+ 
+ Global Biodiversity Information Facility (www.gbif.org) and a series of national databases from the UK, Spain and Finland
+ 
+ Software used:
+ ArcMap 10.8","Ixodes ricinus, ixodes persulcatus, tick vector, spatial distribution, ensemble model","ERGO
+","William Wint (william.wint@gmail.com)
+","2022, 2023",Yes,https://doi.org/10.5281/zenodo.12799404,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_vectors.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120037,37
+NO,2022_MultipleWildfowl_Species_DistributionIndices,"Multiple wildfowl host species distributions and weighted distribution indices. 
+ 
+ Abstract:
+ 
+ An exploratory unsupervised non-heirachical clustering was performed using the isocluster fucntion in Idrisi in at attempt to produce a spatial cluster derieved from the the annual abundances of 11 WNV avian host species (Accipiter gentilis, Alcedo atthis, Cersophilus duponti, Eremophila alpestris, Lullula arborea, Luscinia luscinia, Luscinia megarhynchos, Passer domestica, Pica pica. Streptopelia decaocto, Turdus merula). The input anundnace data were purchasd from the European Breeding Bird Atlas
+ 
+  
+ 
+ File naming scheme: 
+ 
+ idrisisioclusterwnv11sppmay22.TIF = Spatial model for 11 WNV vectors on May 22
+ 
+ Projection :
+ ETRS89_LAEA_Europe - Projected
+ 
+ Spatial extent:
+ Extent 896891.4768881000345573,912730.8245633002370596 : 7646891.4768880996853113,6862730.8245633002370596
+ 
+ Spatial resolution:
+ 50000 
+ 
+ Pixel values:
+ unit: Cluster number
+ 
+ Source: 
+ 
+ The input anundnace data were purchasd from the European Breeding Bird Atlas
+ 
+ Software used:
+ Idrisi","WNV, avian host abundance, classification","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/ 10.5281/zenodo.12799398,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_wildfowlSpeciesDist22.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120038,38
+NO,2022_SpatialModelHyalomma_marginatum_lusitanicum,"Spatial Models CCHF vectors Hyalomma marginatum and Hyalomma lusitanicum 2022. 
+ Abstract: 
+ Ensembled spatial models were produced for CCHf vectors Hyalomma marginatum and Hyalomma lusitanicum by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net) and from the Global Biodiversity Information Facility (www.gbif.org). 
+  
+  
+ The work was a collaboration with ECDC and the vector distributions produced by MOOD were used to mask CCHF models to improve the predicted distributions of the disease. The results are published in Messina JP, Wint GRW. The Spatial Distribution of Crimean-Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond. Insects. 2023 Sep 17;14(9):771. doi: 10.3390/insects14090771. PMID: 37754739; PMCID: PMC10532370.
+  
+ File naming scheme: 
+ Four zipped archives are provided: two for probability ((VNHYxxyyyyPROBJUL22 = unmasked, VNHYxxyyyyPROBMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum value of replicates, std=standard deviation of replicates) and two for Presence absence (=>0.5) (VNHYxxyyyyPAJUL22 = unmasked, VNHYxxyyyyPAMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum value of replicates). 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ Snapshot, using training data to 2022
+ Pixel values:
+  Cluster number and Predicted probability of presence
+ Source: 
+ point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), and from the Global Biodiversity Information Facility (www.gbif.org)
+ Software used:
+ The software used for modelling is VECMAP®
+ The software used for map production is ESRI ArcMap 10.8","CCHF, Hyalomma marginatum, Hyalomma lusitanicum, tick vectors, spatial models","ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12799382,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_SM_Hayalomma_max_jul22.png,,,CCHF,C0019099,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120039,39
+NO,2022_WeightedMammal_HostDistributionModels,"Weighted TBE Mammal Host distribution models, 2022. 
+ 
+ Abstract:
+ 
+ The distributions of the known TBE vector hosts were obtained from IUCN and the European Mammal Atlas and converted to presence or absence within standard 50 km Grid and then ranked from 1 to 4 according to their function as follows:
+  
+  Reservoir Host and virus amplifiers. Score 4 = Apodemus flavicolis (Yellow necked mouse), Myodes glareolus (Bank vole) 
+  Major Vector amplifiers and virus dilution. Score 3 = Capreolus capreolus (Roe deer) , cervus elephus (Red deer), Dama dama (Fallow Deer)
+  Minor Vector amplifiers and virus dilution. Score 2 = Alces alces (Moose) , Odocoelus Virginia (White-tailed deer)
+  Possible Vector Amplifiers Score 1= Lepus europeus (European hare), Lepus timidus (Mountain Hare)
+  Host Predators Score -1 Vulpes vulpes (Red Fox)
+  An equal number of zero (hosts absent) points were assigned randomly outside the known host ranges.
+  
+  The summed scores were then modelled using Random Forest and Boosted Regression Trees spatial modelling methods, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The outputs of the two methods were then ensembled as a simple mean.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ tbeweighetedhostsRFBRTGAUSENSEMBLEDec21.TIF The weighted host model in Dec 2021
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m)
+ 
+ Pixel values:
+ 
+ a weighted score
+ 
+ Source: 
+ 
+ IUCN and the European Mammal Atlas
  
- Parameter Fourier Variable Image values are
-  A0, A1, A2, A3, Index Value * 10
- ALL D1,D2,D3,Da Percentages
- ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100","Precipitaion, ERA5 ECMWF, Fourier Processed","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/TFAera5monPre1920,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_19_20_a1.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120024,24
-NO,2019_Herbaceous_Wetlands_Copernicus,"Copernicus global land cover, herbaceous wetlands, 10m and proportion herbaceous wetland (1km) in 2019. 
-
-Abstract: Landuse/landcover datasets are proivided through the Copernicus climate data service, (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963.). This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then ggregated to 1km resolution version which contans the proportion of each pixel that is assined asherbaceous wetland (erprobapropherbwet1km.tif)","wetlands, coperncius, Proba","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/HerbaceousWetlandsCopernicus21,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/cglopsherbwet.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120025,25
-NO,2019_SurfaceWater_PROBA_1k,"PROBA V 2019 permanent and Seasonal water 1km. 
-
-Abstract: These layers are extracted for the MOOd extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, & Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050). A nunber of kilometre resolution derivatives have been produced from the original 100m resolution datsest for seasonal and permanent water categories: distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif); percentage of seasonak water (erprobavLLC2019pcseaswat1km.tif); percentage permanent water (erprobavLLC2019pcpermwat1km.tif)","Proba, permnent, seasonal, water","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/SurfaceWaterProba1k19,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_surfacewater_Proba_1k_2019.png,,,"Avian Flu, Tularaemia, Leptospirosis, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120026,26
-NO,2020_2022_VIIRS_CumTemp_IxodesRicinus,"This dataset presents cumulative temperature masks that identify areas that are warm enough to stimulate tick questing activity, using a temperature threshold of 6 C. A series of scripts download and process VIIRS EO imagery of Land Surface Temperature to create temperature masks every 8 days using a combination of decadel (8-day) and daily satellite data. This dataset includes 2020,2021,2022, and 2023.
-
-These result in Boolean masks where suitable areas according to temperature limits on I. ricinus are identified as 1 and unsuitable areas as 0.  This mask can then be applied to the existing seasonal Tick model to make a more timely prediction of tick activity based on recent temperatures.
-
-Image acquisition
-
-Two different products are downloaded VIIRS Land Surface Temperature/Emissivity 8-day L3 Global 1 km SIN grid (VNP21A2, version 6) and VIIRS Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN grid (VNP21A1D, version 6). 
-
-processing
-The cumulative temperature mask is processed in two steps through two separate scripts:
-
-a. Acquisition of 1km MODIS Land Surface Temperature imagery from NASA's data repository.
-b. Importing of the imagery into a Suitable format from which regularly updated masks are calculated.
-","VIIRS, Cumulitative Temperature","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/VIIRSCumTempIxRicinus2022,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTemp230813.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120027,27
-NO,2020_MOOD_StandardMappingandAnalysispolygons,"MOOD standard Mapping and Analysis polygons. 
-
-Abstract: Standard extents and polygons were defined at the start of the MOOD project. There sets of standatd geographies are provided: 
- a) A series of decimal degree grids (0.05, 0.1, 0.2, 0.5 ) in moodgrids.zip 
- b) raster extents in EPRS ( molandmasketrslaeaepsg3035.tif) and geographic projection (molatlonglandmask.tif) in moodpolygonsjanmasks21.zip; and 
- c) standard Administrative unit Polygons adopted from the Vectornet Project : one with relatively equal sized units designed for mapping (vectornetMAPforMOODjan21.shp) and one more suited to analysing and entering data recorded by admin unit that has consistent NITS 3 or Gaul 2 polygons (VectornetDATAforMOODjan21.shp) also in in moodpolygonsjanmasks21.zip","polygons, extents","ERGO
+ Software used:
+ 
+ The software used for modelling is VECMAP®
+ 
+ Software used for map production is ESRI ArcMap 10.8","TBE hosts, weighted ,mean score, spatial model","ERGO
 ","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/moodpolygongrid20,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_moodPolygons_21.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120028,28
-NO,2020_WorldpopHumanPopulation_Age_Gender,"Worldpop Human 2020 population by Age and Gender. 
-
-Abstract: Human popoulation estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasests for the MOOD extent. Gender age categories were summed to provide datasets for all males and all females as well as total populations . Filenames are are follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = mimimum age; OO= Maximum age","Human population, age, gender","ERGO
+",2022,Yes,https://doi.org/10.5281/zenodo.12799370,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_smallMammal.png,,,TBE,C0014061,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120040,40
+NO,2023_SpatialModels_Mosquitovectors_WNV,"Spatial Models of moisquito vectors of WNV - Culex pipiens, Culex torrentium and Culex modestus. 
+ 
+ Abstract
+ 
+ Ensembled spatial models were produced for WNV vectors Culex pipiens (movmnpipiensrfbrtmeanfeb23.tif), Culex torrentium movmnpipiensrfbrtmeanfeb23.tif) , and Culex modestus (modcumodMEANrfbrt.tif) by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables (see Scharlemann et. al. 2008, https://doi.org/10.1371/journal.pone.0001408), , land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of published papers.
+ 
+  
+ 
+ File naming scheme :
+ 
+ Filename movmnpipiensrfbrtmeanfeb23.tif = spatial model for WNV vector Culex pipiens
+ 
+ Filename movmnpipiensrfbrtmeanfeb23.tif = spatial model for Culex torrentium) ,
+ 
+ Filename modcumodMEANrfbrt.tif= spatial model for Culex modestus
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.008333 deg (nominally 1km)
+ 
+ temporal resolution:
+ 
+ Snapshot, using training data to 2023”
+ 
+ Pixel values:
+  Predicted probability of presence
+ 
+ Source: 
+ 
+ The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of published papers.
+ 
+ Software used:
+ 
+ Software used for modelling is VECMAP®
+ 
+ Software used for map production is ESRI ArcMap 10.8","Vectors,WNV,Mosquito","ERGO
 ","William Wint (william.wint@gmail.com)
-",2020,Yes,https://tinyurl.com/worldPo20,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/vcwpopp.png,,,"Tularaemia, TBE, WNV, USUTU, Covid, Lyme",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120029,29
-NO,2021_2022_MonthlyDistribution_WaterBodies,"Monthly water bodies for WNV modelling, august 21 - sept 22. 
-
-Abstract: 300m (0.00025 deg) resolution raster datasets for monthly water body presence (aug 21 to sept 22) were extracted for the MOOD spatial extent from global data provided by the Copernicus Global Land Service (https://land.copernicus.eu/global/products/wb). These data were prodced by the Sentinel 2 satellite . Details of data production are provided in the accompanying file CGLOPS2_PUM_WB300m_V2_I1.10.tif","Sentinel, water body, monthly, 300m","ERGO
+",2023,Yes,https://doi.org/10.5281/zenodo.12723449,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_SM_Mos_WNV23.png,,,Mosquito borne Flaviviruses,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120041,41
+YES,An annotated Avian Influenza dataset from two event-based surveillance systems,"These dataset concerns Avian Influenza (AI) data from news items (articles) collected by two event-based surveillance systems; HealthMap and PADI-Web published between 2018 and 2019. Collected articles were manually annotated by relevance for epidemic intelligence purposes. A relevant article reports one epiemiological avian influenza event (outbreak) or more while, an irrelevant article is related to sanitary/political/economic measures or mentions another disease. This dataset can be used to train or evaluate classification approaches and classify these AI events by relevance. The structure of the dataset is as follow: EBS: name of the event-based surveillance systems that detected the event (HealthMap or PADI-Web in this case) Country: Name of the country where the event happened Place_name: Name of the administration, city or district where the event happened Disease_name: Name of the disease that is reported in the article Species_name: Name of the affected host that is reported in the article Alert_id: Event identifier Source: Name of the news outlet reporting the article. href: URL information of the article reporting the considered event. Note that multiple article can report same event. Issue_date: Date of the article publication Manualclass: Manual classification (Relevant or Irrelevant) lon: Longitude for the spacial entity lat: Lattitude fot the spacial entity English ",,INRAE & CIRAD,,2023,Datagouv,https://doi.org/10.57745/6R81RT,YES,,Etalab Open License 2.0,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120042,42
+YES,"Annotated datasets from PADI-web for event-based surveillance of Avian Influenza, African Swine Fever, and West-Nile Virus Disease","These datasets concern unstructured data (articles) from news items detected by an event-based surveillance system; PADI-Web, between 2022 and 2023. Collected articles were manually annotated by relevance for epidemic intelligence purposes with the help of two epidemiologists Extracted data include relevant articles (with two possible labels; epidemiological events or general information) and irrelevant information regarding three different diseases: Avian Influenza (AI), African Swine Fever (ASF) and West Nile Virus disease(WNV). This database is extensive as it deals with different types of diseases (zoonotic, cross-border and vectorial disease ) and can be used to train or evaluate classification approaches to automatically identify written text on these diseases events and classify them by relevance. The structure of the dataset is as follow: Alert_id: Article identifier. Note that each article has a unique ID, if an article reports multiple events, it is duplicated and each line represent one event. Title: Article's title given by the news outlet. hsource: URL of the news outlet reporting the article. Source: Name of the news outlet reporting the article. url: URL information of the article reporting the considered event. Note that multiple articles can report same event. Issue_date: Date of the article publication Country: Name of the country where the event happened Place_name: Name of the administration, city or district where the event happened, if none of these is mentionned in the text, the country's name is reported. Administrative_division: The administrative level at which the information is reported (country, department, city...) Disease_name: Name of the disease that is reported in the article Species_name: Name of the affected host that is reported in the article Manualclass: Manual classification (Relevant or Irrelevant) Lat: Place_name lattitude coordinates Lon: Place_name longitude coordinates ",,INRAE & CIRAD,,2023,Datagouv,https://doi.org/10.57745/99SNOZ,YES,,Etalab Open License 2.0,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,"HPAI, ASF, WNV",,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120043,43
+YES,Annotation of epidemiological information in animal disease-related news articles: guidelines and manually labelled corpus,"This dataset contains two files: (i) An annotated corpus (""epi_info_corpus‧xlsx"") containing 486 manually annotated sentences extracted from 32 animal disease-related news articles. These news articles were obtained from the database of an event-based biosurveillance system dedicated to animal health surveillance, PADI-web (https://padi-web.cirad.fr/en/). The first sheet (‘article_metadata’) provides metadata about the news articles : (1) id_article, the unique id of a news article, (2) title, the title of the news article, (3) source, the name of the news article website, (3) publication_date, the publication date of the news article (mm-dd-yyyy) and (4) URL, the web URL of the news article. The second sheet (‘annot_sentences’) contains the annotated sentences: each row corresponds to a sentence from a news article. Each sentence has two distinct labels, Event type and Information type. The set of columns is : (1) id_article, the id of the news article to which the sentence belongs, (2) id_sentence, the unique id of the sentence, indicating its position in the news content (integer ranging from 1 to n, n being the total number of sentences in the news article), (3) sentence_text, the sentence textual content, (4) event_type, the Event type label and (5) information_type, the Information type label. Event type labels indicate the relation between the sentence and the epidemiological context, i‧e. current event (CE), risk event (RE), old event (OE), general (G) and irrelevant (IR). Information type labels indicate the type of epidemiological information, i‧e descriptive epidemiology (DE), distribution (DI), preventive and control measures (PCM), economic and political consequences (EPC), transmission pathway (TP), concern and risk factors (CRF), general epidemiology (GE) and irrelevant (IR). (ii) The annotation guidelines (""epi_info_guidelines‧doc"") providing a detailed description of each category.",,CIRAD,,2022,Dataverse,https://doi.org/10.18167/DVN1/YGAKNB,YES,https://doi.org/10.1038/s41597-022-01743-2,CC BY 4.0 ,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120044,44
+YES,Annual terrestrial Human Footprint dataset from 1982 to 2000,Human footprint dataset extrapolated to past periods 1982--2000. For each pixel we fit a logit-model and then extrapolate it to past years to produce assumed Human footprint prior to year 2000. This assumes simple linear trends in Human footprint.,,OPENGEOHUB,,2022,Zenodo,https://zenodo.org/records/6636562 ,YES,N/A,cc4.0,,,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120045,45
+NO,Avian Influenza events affecting mammals from ProMED,"This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day. These are preprocessed and normalized events, which are extracted from ProMED as Epidemiological Surveillance Systems (EBS).",,LIRMM,,2023,,http://advanse.lirmm.fr/avianflu/,YES,,CC0,,http://advanse.lirmm.fr/avianflu/Logo-LIRMM-long_329x113.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120046,46
+NO,Avian Influenza events affecting mammals from ProMED,"This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day, extracted from WAHIS database and normalized to the ESA platform standard. Weekly updated. ",,INRAE,,2023,,,YES,,,,https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120047,47
+NO,Avian Influenza events affecting mammals from ProMED,This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day extracted from EBS Padi-web and normalized to the ESA platform standard. Weekly updated. ,,CIRAD,,2023,,,NO,,,,https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120048,48
+YES,Avian Influenza events from differents digital surveillance tools,"This dataset contains a set of Avian Influenza events affecting bird species from 2019 to 2021. These are preprocessed and normalized events, which are extracted from three different sources: EMPRES-i as Indicator-Based Surveillance (IBS), PADI-Web and ProMED as Epidemiological Surveillance Systems (EBS).",,INRAE & CIRAD,,2023,Datagouv,https://doi.org/10.57745/Y3XROX,YES,,CC0,,https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120049,49
+YES,Code and Data for: Crowding and the shape of COVID-19 epidemics,Code and Data for: Crowding and the shape of COVID-19 epidemics,,UOXF,,2020,Github/Zenodo,"https://github.com/Emergent-Epidemics/COVID_crowding
+https://zenodo.org/record/4056578#.X3IFF5NKiek",YES,https://doi.org/10.1038/s41591-020-1104-0,,"https://github.com/Emergent-Epidemics/COVID_crowding
+https://github.com/alsnhll/SIRNestedNetwork",https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41591-020-1104-0/MediaObjects/41591_2020_1104_Fig2_HTML.png?as=webp,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120050,50
+NO,Consensus_LandUse_Earthenv_1k_ER,"Earthenv consensus land use. 
+ 
+ Abstract: The Earthenv (www.earthenv.org) consensus land cover layers are a combination of a number of public domain land cover datasets, which are considered to be more representative than any of the component datasets. 12 land cover types are provided as proportions, each of which has been extracted for the MOOD extent.
+ 
+ File naming scheme:
+ 
+ The filenames are given in accompanying file earthenvnames.txt
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -24.0000000000000000,-38.5000000000000000 : 110.9999999999999432,86.9999999999999432
+ 
+ Spatial resolution:
+ 0.008333 deg (approx. 1000 m)
+ 
+ Accuracy:
+ 
+ Based on World Geodetic System 1984 ensemble (EPSG:6326), which has a limited accuracy of at best 2 meters.
+ 
+  
+ 
+ Source:
+ 
+  Earthenv (www.earthenv.org) consensus land cover layers
+ 
+ Software used:
+ ESRI ArcMap 10.8","Land Cover, Mood extent Consensus layer from www.earthenv.org","ERGO
 ","William Wint (william.wint@gmail.com)
-",2021 -2022,Yes,https://tinyurl.com/monthlywaterbodies2122,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_mowaterbodmay22.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120030,30
-NO,2021_ERA5_MonthlyPrecipitation_10k,"monthly Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2021. 
+",2022,Yes,https://doi.org/10.5281/zenodo.12722471,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_eelcbare11.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120051,51
+NO,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): 30 m resolution, WGS 84 (EPSG:4326) (original resolution and projection). 
 
-Abstract: Precipitation form the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2010 - 2021. the original data is at 0.25 degree resolution and was downscaled by ERA extraction algroithms to 10km.","ERA5, Precipitation, 10km","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/Era5Preciptation2110km,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_era__pr_21_10k.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120031,31
-NO,2021_FloodRisk_JRC,"Floodrisk, 10 year return period, 1km.  
+Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
 
-Abstract: The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/collection/id-0054 haas been windowed to the MOOD extent for access by project partners. Accorig to JRC ""The maps have been developed using hydrological and hydrodynamic models, driven by the climatological data of the European and Global Flood Awareness Systems (EFAS and GloFAS). European-scale maps comprise most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries.""","Floodrisk, JRC, 10year","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/10yearfloodriskJRC21,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_floodrisk_10year.png,,,"Leptospirosis, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120032,32
-NO,2021_HPAI_AvianHostDistributionIndices,"Weighted Index and formatted distribution data for HPAI hosts. 
+The data are provided in original resolution and projection.","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de)",2022,Data upon request,https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/f576cda8-d598-478c-b8fe-ad2634c927e8,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120052,52
+NO,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): 30 m resolution, reprojected to EU LAEA (original resolution). 
 
-Abstract: A series of descriptive metrics were produced as host distribution indices for use as covariates in HPAI spatial modelling. The following bird species were included. For each disptribution maps were extracted from the Birdlife International datasets and idvidual ratster images were produced for breeding, passage, resident and passage distributions. 
- Anas crecca, Anas penelope, Anal platyrhynchos, Anser anser, Aytha fuligula, Bucephala clangula, Cygnus color, Cygnus cygnus, Mareca penelope, Mareca strepera,
-  Combination images for all species were also produced , eachgiving the number of species per pixel for each category ( Anatidae passage, Anatidae breeding, Anatidae resident, Anatidae nonbreeding,. Filenames = AIHOSTxxxxsum.tif where XXX = breeding, nobreeding, resident, passage)","HPAI, Bird Hosts, Birdlife International, distrubtions, indices","ERGO
-","William Wint (william.wint@gmail.com)
-",2021,Yes,https://tinyurl.com/WeightedHPAIAvianHostDis21,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/aihostpresabssum.png,,,HPAI,C0016627,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120033,33
-NO,2021_Veterinary_Hospital_FacilityDistributions,"Veterinary and Hospital distributions. Full descriptions in Maps of Healthcare and Veterinary Locations.pptx. 
+Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
 
-Abstract: These maps of veterinearfy facilkities and hospital health care locations are derived from the open source Open Street Maps (OSM). These are large datasets that underlie most of the satnav utilities, and are continuously updated by members of the public. They are compiled by GEOFABRIK, and can be downloaded from https://www.geofabric.de. Health care data have been further enhanced by the Global Health Sites Mapping Project (https://healthsites.io). 
- 
- The data consist of locations (“points of interest”) and building outlines, and so are made up of points and polygon which are stored separately in the OSM files. Each record has a series of descriptors (“tags”) such as ‘amenity’ which may include descriptions such as hospital or veterinary clinic. These tags are provided by the people who add the data to the maps and o are multilingual and very variable. Most records contain additional more generic tags that can be used to identify classes of locations like hospitals or pharmacies.
-  
- ERGO has taken the IO and OSM datasets, combined the polygon and point data, converted the polygons to points and removed any duplicates (where building outlines also have point location records), to produce and global datasets for healthcare and regional datasets for healthcare and veterinary locations covering Europe and its neighbouring countries. These are provided as ESRI point shapefiles as follows:
-  Healthcare Global: Ionodewaynodupallmay21.shp
-  Healthcare Regional: MONODWAYALLnodupMAY21.shp
-  Veterinary Regional: afeupolypointsnondupmergemay21.shp
- 
- The regional maps are produced by tabulating the number of each healthcare amenity or veterinary locations within administrative zones, normalising by the administrative unit areas. The maps aare produced from the following shape files:
-  Healthcare Regional: MOiohealthamenitycountadmin2may21.shp
-  Veterinary Regional: MONODWATALLVETSNODUPMAY21.shp
-  An arcGS 10.4 MPK project file (ERGOhealthvetforweb.mpk) has been provided to dsplay the spatial data provided","Veterinary clinics, human hospitals, health, One health","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/VeterinaryHospitalDistu21,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_hospital_units.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120034,34
-NO,2021_WNV_AvianHost DistributionIndices,"BRT/RF ensemble spatial Models and Weighted seasonality code for WNV avian Hosts. 
+The data are provided in original resolution and reprojected to EU LAEA.","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de)",2022,Data upon request,https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/9a382836-47a9-4dac-ad62-f0021e455ab8,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120053,53
+YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 3 arc seconds (ca. 90 meter) resolution, WGS 84 (EPSG:4326). 
 
-Abstract: A series of BRT spatial models were produced for WNV avian hosts (Corvus corax, Corvus corone, Corvus frugilegus, Corvus monedula, Corvus ruficollis, Passer domesticus, Pica pica, Turdus merula) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include polygon data from the Birdlife Internatioinal project and from the Global Biodiversity Information Facility (www.gbif.org) . In addition an ensemble model combining Random Forest and Boosted Regresion Trees spatial modelling outputs has been produced for ann index of seasonal distributions of corvid presence categories ranked in decreasing order as follows Resident, Breeding, Non-Breeding, Passage Speces included : Corvus vorax, Corvus monedula, Corvus corona, Corvus frugilegus, Corvus ruficollis. File name = blifecorvidssuminvcdBRTRFENS.tif.","BRT, spatial models, WNV, avian Hosts, ranked multi species index","ERGO
-","William Wint (william.wint@gmail.com)
-",2021,Yes,https://tinyurl.com/WNVavianHostDist21,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/WNVAvianhostDist21.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120035,35
-NO,2022_2023_SpatialModel_TBEHosts,"Updated Spatial Models Hosts: Cervus elephus, Dama dama, Capreolus capreolus, Cervus nippon, Apodemus falvicollis, Myodes glareolus, Lepus europeaus, Lepus timidus, 2022 and 2024. 
-
-Abstract: Ensembled spatial models were produced for four deer species (mooddeerensemblemodelsaug23.zip) , two small mammaol species (moemmaapofmyogMEANRFBRTAug22.zip) and two hare species (moemmalepeutiaug22MEANRFBRT.zip) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include data from IUCN, from the Global Biodiversity Information Facility (www.gbif.org) and from earlier models ( Alexander, N.S., Morley, D, Medlock, J, Searle, K and Wint, W (2014), A First Attempt at Modelling Roe Deer (Capreolus capreolus) Distributions Over Europe. Open Health Data 2(1):e2, DOI:http://dx.doi.org/10.5334/ohd.ah and Wint, W, Morley, D, Medlock, J and Alexander, N.S (2014), A First Attempt at Modelling Red Deer (Cervus elaphus) Distributions Over Europe. Open Health Data 2(1):e1, DOI: http://dx.doi.org/10.5334/ohd.ag . 
- The output files are as follows: 
- a) deer: mocapcapensrfbrt2223.tif = Capreolus capreolus (Roe deer) ; mocervelensrfbrt2223.tif = Cervus elephbus (Red deer) ; modamdamensrfbrt2223.tif = Dama dama (Fallow deer) ; and mosikapaensbrtrfaug23.tif = Cervus nippon (Sika deer) 
- b) small mammals: moemmaapoflaaug22MEANRFBRT.tif = Apodemus flavicolis (Yellow necked mouse ; moemmamyoglaaug22MEANRFBRT.tif = Myodes glareolus (Bank vole)
- c) hares: moemmalepeuaug22MEANRFBRT.tif = Lepus europaeus (European Hare) ; and moemmaleptiaug22MEANRFBRT.tif = Lepus timidus (Mountain Hare)","Spatial Models Hosts: Cervus elephus, Dama dama, Capreolus capreolus, Cervus nippon, Apodemus falvicollis, Myodes glareolus, Lepus europeaus, Lepus timidus","ERGO
-","William Wint (william.wint@gmail.com)
-","2022, 2023",Yes,https://tinyurl.com/MOODupdatedHost2223,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_host.png,,,TBE,C0014061,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120036,36
-NO,2022_2023_SpatialModelVector_Ixodesticks,"Updated Spatial Models of Tick Vectors Ixodes ricinus and Ixodes persulcatus. 
+Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
 
-Abstract: Ensembled spatial models waer produced for Ixodes ricinus ( allixric1xybal40kjul23MEANbrtrf.zip) and Ixodes persulcatus (ergopersulcatusmodelpresabsmay23.zip)by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of national databases from the UK, Spain and Finland","Ixodes ricinus, ixodes persulcatus, tick vector, spatial distribution, ensemble model","ERGO
-","William Wint (william.wint@gmail.com)
-","2022, 2023",Yes,https://tinyurl.com/MOODupdatedVectors2223,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_vectors.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120037,37
-NO,2022_MultipleWildfowlSpecies_Distribution_Indices,"Multiple wildfowl host species distributions and weighted distribution indices. 
+The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020 ",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211701,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120054,54
+YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 100 m, reprojected to EU LAEA. 
 
-Abstract: An exploratory unsupervsed non-heirachical clustering was performed using the isocluster fucntion in Idrisi in at attempt to produce a spatial cluster derieved from the the annual abundances of 11 WNV avian host species (Accipiter gentilis, Alcedo atthis, Cersophilus duponti, Eremophila alpestris, Lullula arborea, Luscinia luscinia, Luscinia megarhynchos, Passer domestica. Pica pica. Streptopelia decaocto, Turdus merula). The input anundnace data were purchasd from the European Breeding Bird Atlas","WNV, avian host abundance, classification","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/WildfowlSpeciesDistInd22,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_wildfowlSpeciesDist22.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120038,38
-NO,2022_SpatialModelHyalomma_marginatum_lusitanicum,"Spatial Models CCHF vectors Hyalomma marginatum and Hyalomma lusitanicum 2022. 
+Abstract:The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
 
-Abstract: Ensembled spatial models waer produced for CCHf vectors Hyalomma marginatum and and Hyalomma lusitanicum by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), and from the Global Biodiversity Information Facility (www.gbif.org) . 
- 
-  Four zippoed archives are provided : two for probability ((VNHYxxyyyyPROBJUL22 = unmasked, VNHYxxyyyyPROBMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum vale of replicates, std=standard deviation of replicates) and two for Presence absence (=>0.5) (VNHYxxyyyyPAJUL22 = unmasked, VNHYxxyyyyPAMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum vale of replicates). 
- The work was a collaboration with ECDC and the vector distributionsproduced by MOOD were used to mask CCHF models to improve the predicted distribtions of the disease. The results are published in Messina JP, Wint GRW. The Spatial Distribution of Crimean-Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond. Insects. 2023 Sep 17;14(9):771. doi: 10.3390/insects14090771. PMID: 37754739; PMCID: PMC10532370.","CCHF, Hyalomma marginatum, Hyalomma lusitanicum, tick vectors, spatial models","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/SpatialModelHyalommaMI22,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_SM_Hayalomma_max_jul22.png,,,CCHF,C0019099,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120039,39
-NO,2022_WeightedMammal_HostDistributionModels,"Weighted TBE Mammal Host distribution models, 2022. 
+The Copernicus DEM for Europe at 100 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). ","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020 ",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211990,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120055,55
+YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 30 arc seconds (ca. 1000 meter) resolution, WGS 84 (EPSG:4326). 
 
-Abstract: The distributions of the known TBE vector hosts were obtained from IUCN and the European Mammal Atlas and covnverted to presence or absence within standard 50 km Grid and then ranked from 1 to 4 according to their function as follows:
- 
- Reservoir Host and virus amplifiers. Score 4 = Apodemus flavicolis (Yellow necked mouse), Myodes glareolus (Bank vole) 
- Major Vector amplifiers and virus dilution. Score 3 = Capreolus capreolus (Roe deer) , cervus elephus (Red deer), Dama dama (Fallow Deer)
- Minor Vector amplifiers and virus dilution. Score 2 = Alces alces (Moose) , Odocoelus virginia (White tailed deer)
- Possible Vector Amplifiers Score 1= Lepus europeus (European hare), Lepus timidus (Mountain Hare)
- Host Predators Score -1 Vulpes vulpes (Red Fox)
- An equal number of zero (hosts absent) points were assigned randommly outside the known host ranges.
- 
- The summed scores were then modeled using Random Forest and Boosted Regresion Trees spatial modelling methods, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. . The outputs of the two methodds were then ensembled as a simple mean.","TBE hosts, weighted ,mean score, spatial model","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/weightedmammalhosts22,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_smallMammal.png,,,TBE,C0014061,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120040,40
-NO,2023_SpatialModels_Mosquitovectors_WNV,"Spatial Models of moisquito vectors of WNV - Culex pipiens, Culex torrentium and Culex modestus. 
+Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
 
-Abstract: Ensembled spatial models waer produced for WNV vectors Culex pipiens (movmnpipiensrfbrtmeanfeb23.tif), Culex torrentium movmnpipiensrfbrtmeanfeb23.tif) , and Culex modestus (modcumodMEANrfbrt.tif) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of published papers","Vectors,WNV,Mosquito","ERGO
-","William Wint (william.wint@gmail.com)
-",2023,Yes,https://tinyurl.com/modlsmosquitovectorsWNV23,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_SM_Mos_WNV23.png,,,Mosquito borne Flaviviruses,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120041,41
-NO,Consensus_LandUse_Earthenv_1k_ER,"Earthenv consensus land use. 
+The Copernicus DEM for Europe at 30 arcsec (0:00:30 = 0.0083333333 ~ 1000 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020 ",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211553,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120056,56
+YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 1000 m, reprojected to EU LAEA. 
 
-Abstract: The Earthenv (www.earthenv.org) consensus land cover layers are a combination of a number of public domain land cover datasets, which are considered to be more reperesnative han any of the component datasets. 12 land cover types are provided as proportions, easch of which has been ettracted for the MOOD extent. the filenames are given in file eartnenvnames.txt)","Land Cover, Mood extent Consensus layer from www.earthenv.org","ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/EARTHENVLandCoverMOOD,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_eelcbare11.png,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120051,51
-NO,GMTED_elevation_1k,"GMTED elevation. 
+Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
 
-Abstract: This layer has been modified from the GMTED 90m resolution datasets to produced a 1km layer to match other mMOOD covariates. The data are minimum elevation plus 1000 to remove negative values",Elevation,"ERGO
-","William Wint (william.wint@gmail.com)
-",2022,Yes,https://tinyurl.com/GMTEDelevation1k,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_ElevationPlus1000.png,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120097,97
-NO,resistancebank.org,"resistancebank.org, an open-access repository for surveys of antimicrobial resistance in animals","AMR, Animals",ETH,Thomas P. Van Boeckel (thomas.van.boeckel@gmail.com),2020,yes,https://doi.org/10.1038/s41597-021-00978-9,YES,https://doi.org/10.1038/s41597-021-00978-9,CC0,,https://github.com/nicocriscuolo/resistancebank.org,,,AMR,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120116,116
-,epiCurve,R code for the functions developed to model the force of infection in the case of seasonal vector-borne pathogens,"vector-borne, force of infection",FEM,Giovanni Marini,,Yes,https://github.com/giomarini/epiCurve-repository,YES,,,https://github.com/giomarini/epiCurve-repository,,,,vector-borne pathogens,,Software,,,,,c4702d92-80b3-11ee-b962-0242ac120152,152
-,Covariates metadata extracted from literature: Tick-borne encephalitis,Dashboard created based on disease profile created by WP2,"TBE, scoping review","FEM, ISS",Francesca Dagostin,,Yes,https://lookerstudio.google.com/u/0/reporting/fb077df1-7da2-485b-a2d5-9f100cc82604/page/p_mzkhlspowc,,https://doi.org/10.2807/1560-7917.ES.2023.28.42.2300121,,,,,,"Tick borne diseases, ticks",,Dataset,Disease data,Dashboard,,,c4702d92-80b3-11ee-b962-0242ac120150,150
-,Covariates metadata extracted from literature: West Nile Virus,Dashboard created based on disease profile created by WP2,"WNV, scoping review, West Nile","FEM, ISS",Francesca Dagostin,,Yes,https://lookerstudio.google.com/u/0/reporting/25dbdec3-0352-4fbc-aced-c6c4ff5ef99d/page/p_mzkhlspowc,,10.1016/j.onehlt.2022.100478,,,,,,"WNE, WNV, West Nile",,Dataset,Disease data,Dashboard,,,c4702d92-80b3-11ee-b962-0242ac120151,151
-NO,COVID-19-line-list,COVID-19-line-list,,FEM/FBK,,2020,Github,https://github.com/Juan-ZJ/COVID-19-line-list,YES, https://doi.org/10.1016/S1473-3099(20)30230-9,,,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120062,62
-YES,Data for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China,"Data for the paper ""changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China"" published in Science.",,FEM/FBK,,2020,Zenodo,http://dx.doi.org/10.5281/zenodo.3754582,YES,https://doi.org/10.1126/science.abb8001,cc4.0,https://zenodo.org/record/3775672#.Y5ccWXbMKM9,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120067,67
-YES,"Code and data of article ""Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity a","Code and data used in the journal article ""Estimating SARS-CoV-2 
-infections and associated changes in COVID-19 severity and fatality and fatality""",COVID-19,FEM/FBK,Valentina Marziano,2023,Yes,https://doi.org/10.5281/zenodo.8006661,YES,https://doi.org/10.1111/irv.13181,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120146,146
-NO,Gazetteer Access Tool,MOOD data normalisation tool,"data normalisation, gazetteer, geospatial data",INESC-ID,Bruno Martins (bruno.g.martins@tecnico.ulisboa.pt),2023,Github,,YES,,GNU General Public License,https://github.com/bgmartins/gazetteer-access,,,,,,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120142,142
-NO,Avian Influenza events affecting mammals from ProMED,"This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day, extracted from WAHIS database and normalized to the ESA platform standard. Weekly updated. ",,INRAE,,2023,,,YES,,,,https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120047,47
-NO,EpiDCA,"EpiDCA is a generic method that aims to link epidemiological data extracted from EBS systems with their associated environmental risks factors, in order to classify the textual data detected by EBS  systems according to their relevance and timely detect outrbeak events","text-mining, risk mapping ",INRAE,el-bahdja.boudoua@inrae.fr,2023,Github,,YES,,,https://github.com/BBahdja/EpiDCA,,,,"Avian Influenza, African Swine Fever, West Nile Disease",,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120138,138
-YES,An annotated Avian Influenza dataset from two event-based surveillance systems,"These dataset concerns Avian Influenza (AI) data from news items (articles) collected by two event-based surveillance systems; HealthMap and PADI-Web published between 2018 and 2019. Collected articles were manually annotated by relevance for epidemic intelligence purposes. A relevant article reports one epiemiological avian influenza event (outbreak) or more while, an irrelevant article is related to sanitary/political/economic measures or mentions another disease. This dataset can be used to train or evaluate classification approaches and classify these AI events by relevance. The structure of the dataset is as follow: EBS: name of the event-based surveillance systems that detected the event (HealthMap or PADI-Web in this case) Country: Name of the country where the event happened Place_name: Name of the administration, city or district where the event happened Disease_name: Name of the disease that is reported in the article Species_name: Name of the affected host that is reported in the article Alert_id: Event identifier Source: Name of the news outlet reporting the article. href: URL information of the article reporting the considered event. Note that multiple article can report same event. Issue_date: Date of the article publication Manualclass: Manual classification (Relevant or Irrelevant) lon: Longitude for the spacial entity lat: Lattitude fot the spacial entity English ",,INRAE & CIRAD,,2023,Datagouv,https://doi.org/10.57745/6R81RT,YES,,Etalab Open License 2.0,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120042,42
-YES,"Annotated datasets from PADI-web for event-based surveillance of Avian Influenza, African Swine Fever, and West-Nile Virus Disease","These datasets concern unstructured data (articles) from news items detected by an event-based surveillance system; PADI-Web, between 2022 and 2023. Collected articles were manually annotated by relevance for epidemic intelligence purposes with the help of two epidemiologists Extracted data include relevant articles (with two possible labels; epidemiological events or general information) and irrelevant information regarding three different diseases: Avian Influenza (AI), African Swine Fever (ASF) and West Nile Virus disease(WNV). This database is extensive as it deals with different types of diseases (zoonotic, cross-border and vectorial disease ) and can be used to train or evaluate classification approaches to automatically identify written text on these diseases events and classify them by relevance. The structure of the dataset is as follow: Alert_id: Article identifier. Note that each article has a unique ID, if an article reports multiple events, it is duplicated and each line represent one event. Title: Article's title given by the news outlet. hsource: URL of the news outlet reporting the article. Source: Name of the news outlet reporting the article. url: URL information of the article reporting the considered event. Note that multiple articles can report same event. Issue_date: Date of the article publication Country: Name of the country where the event happened Place_name: Name of the administration, city or district where the event happened, if none of these is mentionned in the text, the country's name is reported. Administrative_division: The administrative level at which the information is reported (country, department, city...) Disease_name: Name of the disease that is reported in the article Species_name: Name of the affected host that is reported in the article Manualclass: Manual classification (Relevant or Irrelevant) Lat: Place_name lattitude coordinates Lon: Place_name longitude coordinates ",,INRAE & CIRAD,,2023,Datagouv,https://doi.org/10.57745/99SNOZ,YES,,Etalab Open License 2.0,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,"HPAI, ASF, WNV",,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120043,43
-YES,Avian Influenza events from differents digital surveillance tools,"This dataset contains a set of Avian Influenza events affecting bird species from 2019 to 2021. These are preprocessed and normalized events, which are extracted from three different sources: EMPRES-i as Indicator-Based Surveillance (IBS), PADI-Web and ProMED as Epidemiological Surveillance Systems (EBS).",,INRAE & CIRAD,,2023,Datagouv,https://doi.org/10.57745/Y3XROX,YES,,CC0,,https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120049,49
-YES,MOOD - News AMR dataset - Hackathon 2022,"This dataset has been collected from four Epidemiological Surveillance Systems (EBS) to be used in an hackathon dedicated to AMR (antimicrobial resistance) for the MOOD summer school in June 2022. The choosen EBS sources are ProMED, PADI-web, Healthmap and MedISys. The collected data are news dealing with epidemiological information or event. This dataset is composed of 4 sub-datasets for each chosen EBS. Each sub-dataset is annotated according to 3 main classes (New Information, General Information, Not Relevant). For each news labeled as New Information or General Information, another annotation is provided concerning host classification with 7 classes (Humans, Human-animal, Animals, Human-food, Food, Environment, and All). This second annotation provided 4 sub-datasets.",,INRAE & CIRAD,,2022,Datagouv,https://doi.org/10.57745/MPNSPH,YES,https://doi.org/10.1016/j.dib.2022.108870,CC0,,https://mood-h2020.eu/wp-content/uploads/2022/06/MOOD-blogevent-banner-1-1210x900.png,,,AMR,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120107,107
-NO,Linking disease data from different sources,"Framework to compare official and unofficial disease data. We compared the events collected from official and unofficial sources in terms of two aspects: 1) spatio-temporal analysis (how the events are geographically and temporally distributed), and 2) thematic entity analysis (what thematic entities are extracted from the events and how they are related to spatio-temporal analysis).",,INRAE & CIRAD,Nejat Arinik (arinik9@gmail.com),2023,Gitlab,,,,,https://gitlab.irstea.fr/umr-tetis/mood/compebs,,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120132,132
-NO,GeoNLPlify,"GeoNLPlify is a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises using Language Models. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks (such as PADI-web). ","NLP, text-mining",INRAE & CIRAD,remy.decoupes@inrae.fr,2023,Github,,YES,https://www.doi.org/10.3233/IDA-230040,GPL_3,https://github.com/remydecoupes/GeoNLPlify/tree/master,https://raw.githubusercontent.com/remydecoupes/GeoNLPlify/master/readme_ressources/geonlplify_example_schema.png,,,"HPAI, TBE, Leptospirosis, AMR, WNE, Lyme, Unknown disease",,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120137,137
-NO,SNEToolkit,Spatial named entity disambiguation toolkit,"NLP, text-mining",INRAE &CIRAD,Rodrique Kafando (rodrique.kafando@citadel.bf),2023,Github,,,https://doi.org/10.1016/j.softx.2023.101480,,https://github.com/ElsevierSoftwareX/SOFTX-D-23-00004,https://www.softxjournal.com/cms/attachment/8b0f35dd-0221-43fe-8515-096fae509127/gr5.jpg,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120131,131
-NO,MOOD Press Tweets Collector,"The MOOD Press Tweets Collector is a Python script designed to gather tweets. MOOD Epidemiologists have specified a set of keywords related to diseases and symptoms and a list of media (newspapers) to followed. Consequently, only tweets from the designated newspapers featuring keywords monitored by MOOD are collected. It's important to note that the script may no longer function due to the fact that the Twitter (or now 'X') API is no longer available for free","NLP, text-mining",INRAE &CIRAD,remy.decoupes@inrae.fr,2021,Gitlab,,YES,,CeCILL-B,https://gitlab.irstea.fr/umr-tetis/mood/mood-tetis-tweets-collect,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,Europe,,"COVID-19, HPAI, Unknown disease, AMR, TBE",,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120136,136
+The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211883,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120057,57
+YES,"Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset","These datasets contain a set of news articles in English, French and Spanish extracted from Medisys (i.e. advanced search) according the following criteria:
+(1) Keywords (at least): COVID-19, ncov2019, cov2019, coronavirus;
+(2) Keywords (all words): masque (French), mask (English), máscara (Spanish)
+(3) Periods: March 2020, May 2020, July 2020;
+(4) Countries: UK (English), Spain (Spanish), France (French). ",,CIRAD,,2020,Dataverse,https://dataverse.cirad.fr/dataset.xhtml?persistentId=doi:10.18167/DVN1/ZUA8MF,YES,https://doi.org/10.1016/j.dib.2020.106356,CC4.0,N/A,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120058,58
 NO,COVID-19 international cases as of Feb 13,"Data for the article ""Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study"" by Pinotti et al. PLoS Med 17(7): e1003193 (2020). This dataset contains a list of the first 288 international cases of COVID-19 outside China collected by the authors, with detailed case history including dates of travel and symptom onset, date of COVID-19 confirmation, date of hospital admission, date of case isolation, travel history, epidemiological link with other cases (clusters), and hospitalization history.",,INSERM,,2020,google sheet,The database was made publicly available by the authors: “COVID-19 international cases as of Feb 13.” https://docs.google.com/spreadsheets/d/1X_8KaA7l5B_JPpwwV3js1L6lgCRa3FoH-gMrTy2k4Gw/edit?usp=sharing.,YES,https://doi.org/10.1371/journal.pmed.1003193,CC BY 4.0 ,n/a,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,COVID-19,C5203670,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120059,59
+NO,COVID-19 Open Research Dataset,,,,,,,,NO,,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120060,60
 NO,COVID-19_influenza,"Data used for the article ""Global patterns and drivers of influenza decline during the COVID-19 pandemic"", F. Bonacina et al.,  International Journal of Infectious Diseases 2023, Weekly counts of influenza specimens by country from ​​FluNet influenza repository
 https://www.who.int/tools/flunet
 ",,INSERM,,2023,yes,,YES,https://doi.org/10.1016/j.ijid.2022.12.042,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120061,61
+NO,COVID-19-line-list,COVID-19-line-list,,FEM/FBK,,2020,Github,https://github.com/Juan-ZJ/COVID-19-line-list,YES, https://doi.org/10.1016/S1473-3099(20)30230-9,,,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120062,62
 NO,COVID-19/contact_tracing,"Data used for the article ""Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection"", J. Moreno et al., ScienceAdvances 2021, The list of municipalities of Metropolitan France with INSEE code, population size, number of schools of six different levels (from kindergarten to university), number of workplaces in given size categories (0 to 9, 10 to 49, 50 to 99, 100 to 499, 500 to 999, and over 1000 employees) from the 
 French National Statistical Institute (INSEE)
 www.insee.fr/fr/statistiques/4265429?sommaire=4265511
@@ -940,6 +2176,17 @@ NO,COVID-19/vaccination,"Code and data for ""Agent-based modelling of reactive v
 
  https://zenodo.org/records/5910314
 ",,INSERM,,2022,yes,,YES,https://doi.org/10.1038/s41467-022-29015-y,,https://zenodo.org/records/5910314,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120064,64
+NO,covid19_spell,"Files and scripts related to our study entitled ""Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence – Belgium as a study case""",,ULB/KU Leuven,,2021,yes,https://github.com/sdellicour/covid19_spell,NO,https://doi.org/10.1186/s12942-021-00281-1,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120065,65
+NO,data and code for Establishment & lineage dynamics of the SARS-CoV-2 epidemic in the UK,data and code for Establishment & lineage dynamics of the SARS-CoV-2 epidemic in the UK,,UOXF,,2021,,"http://www.cogconsortium.uk/data/
+http://www.ebi.ac.uk/ena/browser/view/PRJEB37886",YES,https://doi.org/10.1126/science.abf2946,,,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120066,66
+YES,Data for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China,"Data for the paper ""changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China"" published in Science.",,FEM/FBK,,2020,Zenodo,http://dx.doi.org/10.5281/zenodo.3754582,YES,https://doi.org/10.1126/science.abb8001,cc4.0,https://zenodo.org/record/3775672#.Y5ccWXbMKM9,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120067,67
+YES,,"This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article ""Dissemination of information in event-based surveillance, a case study of Avian Influenza"".
+
+The reports were published between 1 July 2018 and 30st June 2019 and described one or several avian influenza outbreaks.  We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official.
+
+In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter SE and receptor sources SR. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized).
+
+The script for data analyses is available at https://github.com/SarahVal/EBS-network.",,CIRAD,,2022,Zenodo,https://doi.org/10.5281/zenodo.7324144,YES,N/A,CC40,,Not on the publication page,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120068,68
 NO,EPIcx-bal/COVID-19/schools,"Data used fro the article ""Screening and vaccination against COVID-19 to minimise school closure: a modelling study"", E. Colosi et al., The Lancet Infectious Diseases 2022, De-identified individual data on school contacts from the SocioPatterns project http://www.sociopatterns.org/datasets/primary-school-temporal-network-data/ 
 http://www.sociopatterns.org/datasets/high-school-dynamic-contact-networks/
 
@@ -977,131 +2224,61 @@ https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.9.2100133
 vaccinations from DataGouv.fr
 https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1
 
-Presence at workplaces from Google Mobility Reports
-https://www.google.com/covid19/mobility?hl=fr
-
-Relative change in the daily number of visitors to places of work, based on Google location data from Google Mobility Reports
-https://www.google.com/covid19/mobility?hl=fr
-",,INSERM,,2021,yes,,YES,https://doi.org/10.2807/1560-7917.ES.2021.26.15.2100272,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120072,72
-NO,EPIcx-lab/COVID-19/Impact_school_reopening/,"Data and code for: Modelling safe protocols for reopening schools during the COVID-19 pandemic in France. Nat Commun 12, 1073 (2021)
-Laura Di Domenico, Giulia Pullano, Chiara E. Sabbatini, Pierre-Yves Boëlle & Vittoria Colizza
-https://doi.org/10.1038/s41467-021-21249-6",,INSERM,,2021,Github,https://github.com/EPIcx-lab/COVID-19/tree/master/Impact_school_reopening,YES,https://doi.org/10.1038/s41467-021-21249-6,CC BY 4.0 ,https://github.com/EPIcx-lab/COVID-19/tree/master/Impact_school_reopening,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,COVID-19,C5203670,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120073,73
-NO,EPIcx-lab/COVID-19/importation_risk_Africa,"Data for the article ""Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study"", M. Gilbert, G. Pullano et al., The Lancet 2020, China human population data per province and case data 
-http://www.citypopulation.de/en/china/cities/
-Coronavirus COVID-19 global cases.
-https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
-International health regulations (2005): State Party Self-Assessment Annual Reporting Tool. World Health Organization, Geneva2018
-https://apps.who.int/iris/handle/10665/272432
-",,INSERM,,2020,yes,,YES,https://doi.org/10.1016/S0140-6736(20)30411-6,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120074,74
-NO,EPIcx-lab/COVID-19/importation_lockdown,"Data for the article ""Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies"", L. Di Domenico, G.Pullano et al., BMC 2020 Demographic and age profile data. Population légale de ’Île-de-France. INSEE.fr. https://www.insee.fr/fr/statistiques/4270719. 
-
-Hospital admission data, Données hospitalières relatives à l’épidémie de COVID-19 
-https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
-
-
-Contact matrices from literature
-Béraud, G. et al. The French connection: the first large population-based contact survey in France relevant for the spread of infectious diseases. PLoS ONE 10, e0133203 (2015). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0133203",,INSERM,,2020,yes,,YES,https://doi.org/10.1186/s12916-020-01698-4,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120075,75
-NO,EPIcx-lab/COVID-19/importation_risk_Europe,"Data for the article ""Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020"" G. Pullano, F. Pinotti , et al., Eurosurveillance 2020, The data contains de-identified and aggregated domestic population movement data (2013–2015) derived from Baidu Location-Based",,INSERM,,2020,yes,,YES,https://doi.org/10.2807/1560-7917.ES.2020.25.4.2000057,CC BY 4.0,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120076,76
-NO,EPIcx-lab/COVID-19/importation_risk_summer_Delta,"Data for the article ""Projecting the COVID-19 epidemic risk in France for the summer 2021"", Mazzoli et al., Journal of Travel Medecine 2021 Incidence time series at Data Gouv. www.data.gouv.fr/en/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/
-Hospitalized time series at Data Gouv. www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
-Variants screening data
-https://geodes.santepubliquefrance.fr/#view=map2&c=indicator
-
-Vaccination time series from Assurance Maladie France
-https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1
-
-Facebook Stay Put Data
-https://dataforgood.fb.com/
-",,INSERM,,2021,yes,,YES,https://doi.org/10.1093/jtm/taab129,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120077,77
-NO,EPIcx-lab/COVID-19/mobility_effect,"Data for the article ""Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study"", G. Pullano, E. Valdano et al., The Lancet Digital Health 2020 Regional hospitalisation data and COVID-19-related deaths from Santé Publique France. Geodes-Indicateurs: cartes, données et graphiques.
-https://geodes.santepubliquefrance.fr/#c=indicator&f=0&i=covid_hospit.hosp&s=2020-04-09&t=a01&view=map1
-
-Census data from the French National Statistical Institute (INSEE)
-https://www.insee.fr
-
-Employment data and regional socio-economic indicators from French National Statistical Institute (INSEE)
-https://www.insee.fr
-
-Employment data from Direction de l'Animation de la Recherche
-Activité et conditions d'emploi de la main-d'œuvre pendant la crise sanitaire Covid-19
-https://dares.travail-emploi.gouv.fr/IMG/pdf/dares_acemo_covid19_synthese_17-04-2020.pdf
-",,INSERM,,2020,yes,,YES,https://doi.org/10.1016/S2589-7500(20)30243-0,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120078,78
-NO,EPIcx-lab/COVID-19/socio_economic,"Data for the article: ""Highlighting socio-economic constraints on mobility reductions during COVID-19 restrictions in France can inform effective and equitable pandemic response"", E. Valdano et a., Journal of Travel Medicine 2021, Retail stores per 100,000 residents from the French National Statistical Institute (INSEE)
-https://www.insee.fr/fr/statistiques/3568602?sommaire=3568656#consulter
-",,INSERM,,2021,yes,,YES,https://doi.org/10.1093/jtm/taab045,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120079,79
-NO,EPIcx-lab/COVID-19/Underdetection_France/,"Data and code for: Pullano, G., Di Domenico, L., Sabbatini, C.E. et al. 
-Underdetection of COVID-19 cases in France threatens epidemic control. 
-Nature (2020). https://doi.org/10.1038/s41586-020-03095-6",,INSERM,,2021,Github,https://github.com/EPIcx-lab/COVID-19/tree/master/Underdetection_France,YES,https://doi.org/10.1038/s41586-020-03095-6,CC BY 4.0 ,https://github.com/EPIcx-lab/COVID-19/tree/master/Underdetection_France,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,COVID-19,C5203670,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120080,80
-YES,Supplementary Video 1 from Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases,"Sketch of one simulation of a spread in the office data set, for R0=3 and the symptomatic testing protocol, for the dynamical network (left panels), heterogeneous network (middle panels) and contact matrix of distributions (right panel) representations. The nodes representing individuals are arranged according to their department and colored according to their disease compartment. The time is discretized in timesteps of 15 minutes, with contacts changing at every time step for the dynamical network representation, and from day to day in the contact matrix of distributions representation. Links are highlighted in red when a contagion event occurs. No contacts occur outside of office hours. The bottom panels show the evolution over time of the nodes in each compartment (putting together the prodromic, subclinical and clinical compartments for simplicity).",,INSERM,,2022,Figshare,http://dx.doi.org/10.6084/m9.figshare.20043288,NO,http://dx.doi.org/https://doi.org/10.1098/rsif.2022.0164,cc4.0,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120120,120
-YES,Supplementary Video 2 from Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases,"Sketch of one simulation of a spread in the office data set, for R0=3 and the weekly testing protocol with adherence 75%, for the dynamical network (left panels), heterogeneous network (middle panels) and contact matrix of distributions (right panel) representations. The nodes representing individuals are arranged according to their department and colored according to their disease compartment. The time is discretized in timesteps of 15 minutes, with contacts changing at every time step for the dynamical network representation, and from day to day in the contact matrix of distributions representation. Links are highlighted in red when a contagion event occurs. No contacts occur outside of office hours. The bottom panels show the evolution over time of the nodes in each compartment (putting together the prodromic, subclinical and clinical compartments for simplicity).",,INSERM,,2022,Figshare,http://dx.doi.org/10.6084/m9.figshare.20043291.v1,NO,http://dx.doi.org/https://doi.org/10.1098/rsif.2022.0165,cc4.0,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120121,121
-NO,"Dataset of the diseases' covariates extracted from the literature: Leptospirosis, Influenza A and Chikungunya dataset","
-The dataset contains quantitative data on the human, animal, vector and environmental
- covariates associated with influenza A, Chikungunya, and Leptospirosis 
-retrieved from scientific papers through a standardized search on 
-PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data
- on the association between disease and covariates, language (English or
- other EU languages), time frame (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, modelling studies with no data), lacking denominators or reference populations, unavailable full-texts, referring to data older than 2000 or gathered outside Europe, were excluded. The final time frame covered a period from 2000 to 2022. 
-
-
-The important human, animal, vector and environmental
- covariates were extracted with the associated quantitative information,
- and the related information on the diseases. The covariates were submitted to a revision process and labelled according to a labelling system agreed upon among a group of experts within the MOOD (grant agreement No 874850; https://mood-h2020.eu/) project consortium. ","Scoping review, disease covariates","ISS, FEM",Claudia Cataldo,2024,yes,"
-https://doi.org/10.5281/zenodo.11241409",YES,,,,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/scoping-review-ql.png,,,"Leptospirosis, Influenza A, Chikungunya",,Dataset,Disease data,,,MPo,c4702d92-80b3-11ee-b962-0242ac120143,143
-NO,Dataset of the diseases' covariates extracted from the literature: Tularemia dataset,"
-The dataset contains quantitative data on the human, animal, vector and environmental covariates associated with Tularemia
- retrieved from scientific papers through a standardized search on 
-PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data
- on the association between disease and covariates, language (English or
- other EU languages), time frame (30 years), 
-geographical location (Europe), and publication type. Studies without 
-data or with non-original or duplicated data (reviews, editorials, 
-letters, modelling studies with no data), lacking denominators or reference populations, unavailable full-texts, referring to data older than 2000 or gathered outside Europe, were excluded. The final time frame covered a period from 2000 to 2022. 
-
-
-The important human, animal, vector and environmental covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were submitted to a revision process and labelled according to a labelling system agreed upon among a group of experts within the MOOD (grant agreement No 874850; https://mood-h2020.eu/) project consortium. ","Scoping review, disease covariates","ISS, FEM",Claudia Cataldo,2024,yes,https://doi.org/10.5281/zenodo.11241750,YES,,,,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/scoping-review-ql.png,,,Tularaemia,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac12094,94
-NO,Avian Influenza events affecting mammals from ProMED,"This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day. These are preprocessed and normalized events, which are extracted from ProMED as Epidemiological Surveillance Systems (EBS).",,LIRMM,,2023,,http://advanse.lirmm.fr/avianflu/,YES,,CC0,,http://advanse.lirmm.fr/avianflu/Logo-LIRMM-long_329x113.png,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120046,46
-NO,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): 30 m resolution, WGS 84 (EPSG:4326) (original resolution and projection). 
-
-Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
-
-The data are provided in original resolution and projection.","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
-Haas Julia (haas@mundialis.de),
-Neteler Markus (neteler@mundialis.de)",2022,Data upon request,https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/f576cda8-d598-478c-b8fe-ad2634c927e8,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120052,52
-NO,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): 30 m resolution, reprojected to EU LAEA (original resolution). 
-
-Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
+Presence at workplaces from Google Mobility Reports
+https://www.google.com/covid19/mobility?hl=fr
 
-The data are provided in original resolution and reprojected to EU LAEA.","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
-Haas Julia (haas@mundialis.de),
-Neteler Markus (neteler@mundialis.de)",2022,Data upon request,https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/9a382836-47a9-4dac-ad62-f0021e455ab8,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120053,53
-YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 3 arc seconds (ca. 90 meter) resolution, WGS 84 (EPSG:4326). 
+Relative change in the daily number of visitors to places of work, based on Google location data from Google Mobility Reports
+https://www.google.com/covid19/mobility?hl=fr
+",,INSERM,,2021,yes,,YES,https://doi.org/10.2807/1560-7917.ES.2021.26.15.2100272,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120072,72
+NO,EPIcx-lab/COVID-19/Impact_school_reopening/,"Data and code for: Modelling safe protocols for reopening schools during the COVID-19 pandemic in France. Nat Commun 12, 1073 (2021)
+Laura Di Domenico, Giulia Pullano, Chiara E. Sabbatini, Pierre-Yves Boëlle & Vittoria Colizza
+https://doi.org/10.1038/s41467-021-21249-6",,INSERM,,2021,Github,https://github.com/EPIcx-lab/COVID-19/tree/master/Impact_school_reopening,YES,https://doi.org/10.1038/s41467-021-21249-6,CC BY 4.0 ,https://github.com/EPIcx-lab/COVID-19/tree/master/Impact_school_reopening,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,COVID-19,C5203670,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120073,73
+NO,EPIcx-lab/COVID-19/importation_risk_Africa,"Data for the article ""Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study"", M. Gilbert, G. Pullano et al., The Lancet 2020, China human population data per province and case data 
+http://www.citypopulation.de/en/china/cities/
+Coronavirus COVID-19 global cases.
+https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
+International health regulations (2005): State Party Self-Assessment Annual Reporting Tool. World Health Organization, Geneva2018
+https://apps.who.int/iris/handle/10665/272432
+",,INSERM,,2020,yes,,YES,https://doi.org/10.1016/S0140-6736(20)30411-6,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120074,74
+NO,EPIcx-lab/COVID-19/importation_lockdown,"Data for the article ""Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies"", L. Di Domenico, G.Pullano et al., BMC 2020 Demographic and age profile data. Population légale de ’Île-de-France. INSEE.fr. https://www.insee.fr/fr/statistiques/4270719. 
 
-Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
+Hospital admission data, Données hospitalières relatives à l’épidémie de COVID-19 
+https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
 
-The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020 ",mundialis,"Metz Markus (metz@mundialis.de),
-Haas Julia (haas@mundialis.de),
-Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211701,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120054,54
-YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 100 m, reprojected to EU LAEA. 
 
-Abstract:The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
+Contact matrices from literature
+Béraud, G. et al. The French connection: the first large population-based contact survey in France relevant for the spread of infectious diseases. PLoS ONE 10, e0133203 (2015). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0133203",,INSERM,,2020,yes,,YES,https://doi.org/10.1186/s12916-020-01698-4,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120075,75
+NO,EPIcx-lab/COVID-19/importation_risk_Europe,"Data for the article ""Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020"" G. Pullano, F. Pinotti , et al., Eurosurveillance 2020, The data contains de-identified and aggregated domestic population movement data (2013–2015) derived from Baidu Location-Based",,INSERM,,2020,yes,,YES,https://doi.org/10.2807/1560-7917.ES.2020.25.4.2000057,CC BY 4.0,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120076,76
+NO,EPIcx-lab/COVID-19/importation_risk_summer_Delta,"Data for the article ""Projecting the COVID-19 epidemic risk in France for the summer 2021"", Mazzoli et al., Journal of Travel Medecine 2021 Incidence time series at Data Gouv. www.data.gouv.fr/en/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/
+Hospitalized time series at Data Gouv. www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
+Variants screening data
+https://geodes.santepubliquefrance.fr/#view=map2&c=indicator
 
-The Copernicus DEM for Europe at 100 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). ","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020 ",mundialis,"Metz Markus (metz@mundialis.de),
-Haas Julia (haas@mundialis.de),
-Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211990,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120055,55
-YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 30 arc seconds (ca. 1000 meter) resolution, WGS 84 (EPSG:4326). 
+Vaccination time series from Assurance Maladie France
+https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-personnes-vaccinees-contre-la-covid-19-1
 
-Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
+Facebook Stay Put Data
+https://dataforgood.fb.com/
+",,INSERM,,2021,yes,,YES,https://doi.org/10.1093/jtm/taab129,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120077,77
+NO,EPIcx-lab/COVID-19/mobility_effect,"Data for the article ""Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study"", G. Pullano, E. Valdano et al., The Lancet Digital Health 2020 Regional hospitalisation data and COVID-19-related deaths from Santé Publique France. Geodes-Indicateurs: cartes, données et graphiques.
+https://geodes.santepubliquefrance.fr/#c=indicator&f=0&i=covid_hospit.hosp&s=2020-04-09&t=a01&view=map1
 
-The Copernicus DEM for Europe at 30 arcsec (0:00:30 = 0.0083333333 ~ 1000 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020 ",mundialis,"Metz Markus (metz@mundialis.de),
-Haas Julia (haas@mundialis.de),
-Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211553,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120056,56
-YES,Copernicus Digital Elevation Model (DEM),"Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset, (2020 release): resampled to 1000 m, reprojected to EU LAEA. 
+Census data from the French National Statistical Institute (INSEE)
+https://www.insee.fr
 
-Abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.
+Employment data and regional socio-economic indicators from French National Statistical Institute (INSEE)
+https://www.insee.fr
 
-The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).","digital terrain model, elevation, radar, radar remote sensing, space, remote sensing, Copernicus DEM, environment, DSM, DEM, geomorphometry, Europe, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
-Haas Julia (haas@mundialis.de),
-Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6211883,YES,,CC-BY-SA 4.0,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120057,57
+Employment data from Direction de l'Animation de la Recherche
+Activité et conditions d'emploi de la main-d'œuvre pendant la crise sanitaire Covid-19
+https://dares.travail-emploi.gouv.fr/IMG/pdf/dares_acemo_covid19_synthese_17-04-2020.pdf
+",,INSERM,,2020,yes,,YES,https://doi.org/10.1016/S2589-7500(20)30243-0,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120078,78
+NO,EPIcx-lab/COVID-19/socio_economic,"Data for the article: ""Highlighting socio-economic constraints on mobility reductions during COVID-19 restrictions in France can inform effective and equitable pandemic response"", E. Valdano et a., Journal of Travel Medicine 2021, Retail stores per 100,000 residents from the French National Statistical Institute (INSEE)
+https://www.insee.fr/fr/statistiques/3568602?sommaire=3568656#consulter
+",,INSERM,,2021,yes,,YES,https://doi.org/10.1093/jtm/taab045,,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120079,79
+NO,EPIcx-lab/COVID-19/Underdetection_France/,"Data and code for: Pullano, G., Di Domenico, L., Sabbatini, C.E. et al. 
+Underdetection of COVID-19 cases in France threatens epidemic control. 
+Nature (2020). https://doi.org/10.1038/s41586-020-03095-6",,INSERM,,2021,Github,https://github.com/EPIcx-lab/COVID-19/tree/master/Underdetection_France,YES,https://doi.org/10.1038/s41586-020-03095-6,CC BY 4.0 ,https://github.com/EPIcx-lab/COVID-19/tree/master/Underdetection_France,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,COVID-19,C5203670,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120080,80
 NO,"ERA5-Land, Air Temperature (2m)","ERA5-Land Air Temperature (2m), enhanced to 1 km, LAEA Europe (EPSG:3035): Daily, min/mean/max of air temperature (2m), 2000 - 2020
 
 Abstract: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. 
@@ -1175,7 +2352,7 @@ Relative humidity (rh2m) has been calculated from air temperature 2 m above grou
 
 The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month.
 The data have been reprojected to EU LAEA.","relative humidity, geospatial analysis, environment, meteorology, Europe, ERA5-Land, CHELSA, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de), Haas Julia (haas@mundialis.de), Neteler Markus (neteler@mundialis.de), Wint William (william.wint@zoo.ox.ac.uk), Jones Peter (p.jones@cgiar.org)",2022,Zenodo,https://doi.org/10.5281/zenodo.7427010,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120086,86
-YES,"ERA5-Land, Relative Humidity","ERA5-Land Relative Humidity calculated from ERA5-Land variables (temperature, dewpoint temperature, pressure), 30 arc seconds, WGS 84 (EPSG:4326), 2000 – 12/2022: Monthly average of relative humidity. 
+YES,"ERA5-Land, Relative Humidity","ERA5-Land Relative Humidity calculated from ERA5-Land variables (temperature, dewpoint temperature, pressure), 30 arc seconds, WGS 84 (EPSG:4326), 2000 – 2023: Monthly average of relative humidity. 
 
 Abstract: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.
 
@@ -1185,12 +2362,12 @@ The resulting relative humidity has been aggregated to monthly averages.","relat
 Haas Julia (haas@mundialis.de),
 Neteler Markus (neteler@mundialis.de),
 Wint William (william.wint@zoo.ox.ac.uk),
-Jones Peter (p.jones@cgiar.org)",2023,Zenodo,https://doi.org/10.5281/zenodo.10100967,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120087,87
-YES,"ERA5-Land, Relative Humidity","ERA5-Land Relative Humidity calculated from ERA5-Land variables (temperature, dewpoint temperature, pressure), 1000 m, EU LAEA (EPSG:3035), 2000 – 12/2022: Monthly average of relative humidity","relative humidity, geospatial analysis, environment, meteorology, Europe, ERA5-Land, CHELSA, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
+Jones Peter (p.jones@cgiar.org)",2024,Zenodo,https://doi.org/10.5281/zenodo.6146383,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120087,87
+YES,"ERA5-Land, Relative Humidity","ERA5-Land Relative Humidity calculated from ERA5-Land variables (temperature, dewpoint temperature, pressure), 1000 m, EU LAEA (EPSG:3035), 2000 – 2023: Monthly average of relative humidity","relative humidity, geospatial analysis, environment, meteorology, Europe, ERA5-Land, CHELSA, MOOD-H2020",mundialis,"Metz Markus (metz@mundialis.de),
 Haas Julia (haas@mundialis.de),
 Neteler Markus (neteler@mundialis.de),
 Wint William (william.wint@zoo.ox.ac.uk),
-Jones Peter (p.jones@cgiar.org)",2023,Zenodo,https://doi.org/10.5281/zenodo.10101390,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120088,88
+Jones Peter (p.jones@cgiar.org)",2024,Zenodo,https://doi.org/10.5281/zenodo.7427021,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120088,88
 NO,"ERA5-Land, Surface Temperature","ERA5-Land Surface Temperature, enhanced to 1 km, LAEA Europe (EPSG:3035): Daily min/mean/max of surface temperature, 2000 - 2020 
 
 Abstract: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.
@@ -1237,11 +2414,67 @@ Data available is the weekly average of daily sums and the weekly sum of daily s
 Haas Julia (haas@mundialis.de),
 Neteler Markus (neteler@mundialis.de),
 Kröber Felix ",2022,Zenodo,https://doi.org/10.5281/zenodo.6559048,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,,"Tularaemia, Leptospirosis, Mosquito borne Flaviviruses",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120092,92
+YES,"European West Nile virus outbreak event data identified by PADI-web, media monitoring tool","This repository contains 3 datasets related to human and animal West Nile virus geo-referenced outbreak locations in Europe, as well as climate and vector covariates captured by PADI-web media monitoring tool in online news articles, between 2010 and 2022. The data is available both as raw and curated datasets, composed of free text news articles describing West Nile virus outbreaks and extracted information on outbreaks. The data can be useful for spatial risk assessment of West Nile virus emergence in Europe, as well as improving information extraction (i.e., outbreak locations and dates, hosts, vectors, climate factors etc.) from outbreak related news articles. (2022-07-25)","Text-mining, information extraction, manual curation, West Nile, outbreaks, epidemic intelligence, risk mapping",CIRAD,"Elena Arsevska, elena.arsevska@cirad.fr",2022,Dataverse,https://doi.org/10.18167/DVN1/ZNVEPK,YES,"Not yer, undergping","CC0 - ""Public Domain Dedication""",No,Not on the publication page,,,WNV,C0043124,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120093,93
+NO,Dataset of the diseases' covariates extracted from the literature: Tularemia dataset,"
+The dataset contains quantitative data on the human, animal, vector and environmental covariates associated with Tularemia
+ retrieved from scientific papers through a standardized search on 
+PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data
+ on the association between disease and covariates, language (English or
+ other EU languages), time frame (30 years), 
+geographical location (Europe), and publication type. Studies without 
+data or with non-original or duplicated data (reviews, editorials, 
+letters, modelling studies with no data), lacking denominators or reference populations, unavailable full-texts, referring to data older than 2000 or gathered outside Europe, were excluded. The final time frame covered a period from 2000 to 2022. 
+
+
+The important human, animal, vector and environmental covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were submitted to a revision process and labelled according to a labelling system agreed upon among a group of experts within the MOOD (grant agreement No 874850; https://mood-h2020.eu/) project consortium. ","Scoping review, disease covariates","ISS, FEM",Claudia Cataldo,2024,yes,https://doi.org/10.5281/zenodo.11241750,YES,,,,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/scoping-review-ql.png,,,Tularaemia,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac12094,94
+NO,"files and scripts related to our study entitled ""Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing""","This report gathers all the input files and scripts related to our study entitled ""Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing"" (now on biorXiv): BEAST XML files of the continuous phylogeographic and skygrid-GLM analyses, as well as R scripts and related files needed to run all the landscape phylogeographic testing analyses. Continuous phylogeographic and phylodynamic (skygrid-GLM) inferences were performed with the Bayesian methods implemented in the open-source program BEAST. Subsequent dispersal statistics estimation and landscape phylogeographic analyses were implemented and performed with R functions available in the package ""seraphim"".",,ULB,,2020,Github,https://github.com/sdellicour/wnv_north_america,YES,https://doi.org/10.1038/s41467-020-19122-z,,https://github.com/sdellicour/wnv_north_america,,,,Viral diseases,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120095,95
 YES,Global Daylength Map,"Daily maps for global daylight length at 30 arc seconds resolution (2022). 
 
 Abstract: Daily maps for global daylight length, calculated for the year 2022.
 
 For each day within the year 2022, the photoperiod (sunshine hours on flat terrain) are calculated using the SOLPOS algorithm developed by the National Renewable Energy Laboratory (NREL), USA. Resultant values have been converted from hours to minutes.","daylength, photoperiod, environment, meteorology, global, MOOD-H2020",mundialis,Metz Markus (metz@mundialis.de),2022,Zenodo,https://doi.org/10.5281/zenodo.7249037,YES,,CC-BY-SA 4.0,,,,,"Lyme, TBE",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120096,96
+NO,GMTED_Elevation_Plus_1000,"Overview:
+ 
+ This layer has been modified from the GMTED 90m resolution datasets to produce a 1km layer to match other MOOD covariates. The data are minimum elevation in metres plus 1000 to remove negative values.
+ 
+  
+ 
+ File naming scheme:
+ 
+ vcmi30grdp1k.TIF and vcmi30grdp1k.TFW 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -24.0000000000000000,-38.5000000000000000 : 110.9999999999999432,86.9999999999999432
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m)
+ 
+ Accuracy:
+ 
+ Based on World Geodetic System 1984 ensemble (EPSG:6326), which has a limited accuracy of at best 2 meters.
+ 
+ Pixel values:
+ unit: meter
+ 
+ Source: 
+ 
+ Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset
+ 
+ Software used:
+ ArcMap 10.8",Elevation,"ERGO
+","William Wint (william.wint@gmail.com)
+",2022,Yes,https://doi.org/10.5281/zenodo.12722181,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/Overview_ElevationPlus1000.png,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120097,97
+YES,Keywords for PADI-web implemented in Ocean Indian,"Keywords for 3 diseases (Leptospirosis, Dengue, Influenza) and syndromic surveillance in 4 languages",,CIRAD,,2023,Dataverse,https://doi.org/10.18167/DVN1/E7WMAO,YES,,,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120098,98
+YES,Labeled Entities from Social Media Data Related to Avian Influenza Disease,"This dataset is composed of spatial (ie. location) and thematic entities (ie. disease, symptoms, virus) concerning avian influenza in social media textual data, in English. It was created from three corpora: - The first one is composed of 10 transcriptions of YouTube videos and 70 tweets annotated manually by an annotator. - The second corpus is composed of the same textual data as corpus 1 but annotated automatically with Named Entity Recognition (NER) tools. These two corpora are create to do an evaluation of the NER tools and apply them to a larger corpus. - The third corpus is composed of 100 transcriptions of YouTube videos automatically annoted with NER tools. The aim of the annotation task was to recognize spatial information, as the name of cities and epidemiological information, as the name of diseases. An annotation guideline was created in order to have an unified annotation and help the annotators. This dataset can be used to train or evaluate natural language processing approaches such as specialized entity recognition.",,CIRAD,,2021,Dataverse,https://doi.org/10.15454/GR5EFS,YES,https://doi.org/10.1016/j.dib.2022.108317,CC0,,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png?size=140,,,HPAI,C0016627,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120099,99
+YES,"Long-term MODIS LST day-time and night-time temperatures, sd and differences at 1 km based on the 2000–2020 time series
+                                        ","Layers include: Land Surface Temperature daytime monthly median value 2000–2017, Land Surface Temperature daytime monthly sd value 2000–2017, Land Surface Temperature daytime monthly day-night difference 2000–2017. Derived using the data.table package and quantile function in R. We derived four standard statistics: (1) lower 2.5% probability (l.025), median (m), upper 97.5% probability (u.975) and standard deviation (sd). Updated long-term values for 2000–2022+ are pending.
+
+Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this python tutorial. Basically models are fitted for each pixel and the model parameters are saved as images.
+
+For more info about the MODIS LST product see: https://lpdaac.usgs.gov/products/mod11a2v006/. Antarctica is not included.",,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6458406,YES,N/A,,,Not on the publication page,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120100,100
 NO,MODIS v6.0 Land Surface Temperature (LST),"Gap-filled MODIS LST data: Daily average, minimum and maximum gap-filled MODIS LST data from MOD11A1/MYD11A1, 1 km resolution, Sinusoidal, 2003-2019
 (spatial extent of central Europe, smaller than MOOD area).
 
@@ -1286,76 +2519,279 @@ Source data:
 The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.","NDVI, EVI, vegetation index, MOOD-H2020, Europe",mundialis,"Metz Markus (metz@mundialis.de),
 Haas Julia (haas@mundialis.de),
 Neteler Markus (neteler@mundialis.de)",2022,Zenodo,https://doi.org/10.5281/zenodo.6573852,YES,,LP DAAC,,,,,"Lyme, TBE, WNV, USUTU",,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120105,105
-NO,COVID-19 Scatterplots,"Statistical position of the counties or federal states in relation to the statewide or national averages of the 7-day incidence per 100,000 inhabitants and the percentage change in weekly new cases over the last week","scatterplot, Covid-19",mundialis,mundialis (info@mundialis.de),2021,yes,https://apps.mundialis.de/mood/covid19plots/,,https://mood-h2020.eu/covid-19-materials/covid-19-scatterplots/,,,,,,COVID-19,,Software,Disease data,Dashboard,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120147,147
-NO,Extension of actinia for COG import,cloud based geoprocessing platform,"covariates, geoprocessing, cloud",mundialis,mundialis (info@mundialis.de),2023,Github,https://github.com/actinia-org/,YES,https://doi.org/10.5281/zenodo.8305131,GPL 3,https://github.com/actinia-org/,https://www.mundialis.de/wp-content/uploads/2022/03/actinia_logo.png,,,,,Software,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120148,148
-YES,Annual terrestrial Human Footprint dataset from 1982 to 2000,Human footprint dataset extrapolated to past periods 1982--2000. For each pixel we fit a logit-model and then extrapolate it to past years to produce assumed Human footprint prior to year 2000. This assumes simple linear trends in Human footprint.,,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6458580 https://zenodo.org/records/6636562 ,YES,N/A,cc4.0,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120045,45
-YES,"Long-term MODIS LST day-time and night-time temperatures, sd and differences at 1 km based on the 2000–2020 time series
-                                        ","Layers include: Land Surface Temperature daytime monthly median value 2000–2017, Land Surface Temperature daytime monthly sd value 2000–2017, Land Surface Temperature daytime monthly day-night difference 2000–2017. Derived using the data.table package and quantile function in R. We derived four standard statistics: (1) lower 2.5% probability (l.025), median (m), upper 97.5% probability (u.975) and standard deviation (sd). Updated long-term values for 2000–2022+ are pending.
-
-Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this python tutorial. Basically models are fitted for each pixel and the model parameters are saved as images.
-
-For more info about the MODIS LST product see: https://lpdaac.usgs.gov/products/mod11a2v006/. Antarctica is not included.",,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6458406,YES,N/A,,,Not on the publication page,,,"Tularaemia, Lyme, TBE, WNV, USUTU, Mosquito borne Flaviviruses",,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120100,100
-YES,"Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim","Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (https://doi.org/10.5281/zenodo.2615278). Downscaled to 1 km resolution using gdalwarp (cubic splines) and combined with WorldClim (https://worldclim.org/data/worldclim21.html) and CHELSA Climate (https://chelsa-climate.org/downloads/) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the monthly IMERGE dataset, however this requires transformation and is available only for limited span of years.",,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6458580,YES,N/A,CC4.0,,Not on the publication page,,,"Tularaemia, Leptospirosis, Mosquito borne Flaviviruses",,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120106,106
-YES,SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs,This is a GeoTIFF version of the SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture data set v1.1 (Brocca et al. 2019). Conversion steps are available here. ,,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6459152,YES,https://doi.org/10.5194/essd-11-1583-2019,,,Not on the publication page,,,"Tularaemia, Leptospirosis, Mosquito borne Flaviviruses","C0041351, C0023364, ",Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120119,119
-NO,EpiNorm,MOOD structured data normalisation tool,data normalisation,SIB Swiss Institute of Bioinformatics,"Orlin Topalov (orlin.topalov@sib.swiss), Dmitry Kuznetsov (dmitry.kuznetsov@sib.swiss), Robin Engler (robin.engler@sib.swiss)",2023,Github,,YES,,GNU General Public License,https://github.com/sib-swiss/epinorm,https://www.sib.swiss/themes/custom/sib/logo.svg,,,"HPAI, TBE, WNE",,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120141,141
+YES,"Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim","Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (https://doi.org/10.5281/zenodo.2615278). Downscaled to 1 km resolution using gdalwarp (cubic splines) and combined with WorldClim (https://worldclim.org/data/worldclim21.html) and CHELSA Climate (https://chelsa-climate.org/downloads/) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the monthly IMERGE dataset, however this requires transformation and is available only for limited span of years.",,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6458580,YES,N/A,CC4.0,,Not on the publication page,,,"Tularaemia, Leptospirosis, Mosquito borne Flaviviruses",,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120106,106
+YES,MOOD - News AMR dataset - Hackathon 2022,"This dataset has been collected from four Epidemiological Surveillance Systems (EBS) to be used in an hackathon dedicated to AMR (antimicrobial resistance) for the MOOD summer school in June 2022. The choosen EBS sources are ProMED, PADI-web, Healthmap and MedISys. The collected data are news dealing with epidemiological information or event. This dataset is composed of 4 sub-datasets for each chosen EBS. Each sub-dataset is annotated according to 3 main classes (New Information, General Information, Not Relevant). For each news labeled as New Information or General Information, another annotation is provided concerning host classification with 7 classes (Humans, Human-animal, Animals, Human-food, Food, Environment, and All). This second annotation provided 4 sub-datasets.",,INRAE & CIRAD,,2022,Datagouv,https://doi.org/10.57745/MPNSPH,YES,https://doi.org/10.1016/j.dib.2022.108870,CC0,,https://mood-h2020.eu/wp-content/uploads/2022/06/MOOD-blogevent-banner-1-1210x900.png,,,AMR,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120107,107
+YES,MOOD Maps of Google community mobility change during the COVID-19 outbreak,"The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The data and the PDF files were originally posted at https://www.google.com/covid19/mobility/.
+ 
+ Abstract: The original Google data has been processed so that the data for each period is mappable using global standardised shapefiles for Europe and its neighbours. The daily values from the original datasets have been converted to 3 day moving avergages and were produced twice a week from early Febrary 2020 until October 2022. The data are exressed as a percentage of a baseline value defined as the value caluculated in January 2020. The data are available for a number of location categories: Workplaces, residence, parks and natural areas, retail and recreation, gricery and pharmacy and transit stations.","Google, human mobility, Covid19",ERGO,William Wint (william.wint@gmail.com),2020-2022,Figshare,https://doi.org/10.6084/m9.figshare.12130980.v155,YES,"Unknown but the data have beed downlaoded 94000 times,  so probably","CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,Not on the publication page,,,COVID-19,C5203670,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120108,109
+NO,new_york_variants,"Files and scripts related to our study entitled ""Variant-specific introduction and dispersal dynamics of SARS-CoV-2 in New York City – from Alpha to Omicron""",,ULB/KU Leuven,,2023,yes,https://github.com/sdellicour/new_york_variants,NO,https://doi.org/10.1371/journal.ppat.1011348,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120110,110
 NO,Novel-SARS-CoV-2-P1-Lineage-in-Brazi,Novel-SARS-CoV-2-P1-Lineage-in-Brazi,,SOTON,,2021,Github,https://github.com/CADDE-CENTRE/Novel-SARS-CoV-2-P1-Lineage-in-Brazil,YES,https://doi.org/10.1126/science.abh2644,,https://github.com/CADDE-CENTRE/Novel-SARS-CoV-2-P1-Lineage-in-Brazil,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120111,111
+NO,PADI-web,"PADI-web (Platform for Automated extraction of Disease Information from the web) is an automated biosurveillance system dedicated to the monitoring of online news sources for the detection of animal health infectious events. PADI-web automatically collects news with customised queries, classifies them and extracts epidemiological information (diseases, dates, symptoms, hosts and locations). In order to identify relevant news with PADI-web, specific models using machine learning approaches and labeled data have been integrated for monitoring plant diseases (e.g. Xylella fastidiosa). A limited version is available with free access for research or web monitoring. We warmly invite you to cite the project (cf. Publications). To get access to advanced functionalities and the extended data, please do not hesitate to contact us at padi-web@cirad.fr.","Epidemiology, health",CIRAD,Mathieu.Roche@cirad.fr,,yes,https://plant.padi-web.cirad.fr/,YES,https://doi.org/10.1016/j.onehlt.2021.100357,https://padi-web.cirad.fr/en/privacy-policy/,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,"HPAI, TBE, Leptospirosis, AMR, WNE, Lyme, Unknown disease",,Service,,,,,c4702d92-80b3-11ee-b962-0242ac120112,112
+YES,PADI-web corpus used for the EpidBioELECTRA approach,"This dataset contains a set of news articles in English related to animal disease outbreaks, that have been used to train and evaluate EpidBioELECTRA epidemiological classifier and explainer. It is composed of 70,707 articles in csv format found in several folders (relevant folder contains 34,015 news articles labelled relevant, while irrelevant folder contains 36,692 irrelevant articles), with information about the article itself (publication date, title, content, url, etc.). Thematic feature folder contains relevant and irrelevant labelled thematic features (disease, host, location, cases, etc) as contained in relevant and irrelevant documents by sentence id organized in year and month of the article. These labels were machine generated by PADIWeb classifier. ",,CIRAD,,2023,Dataverse,https://doi.org/10.18167/DVN1/WD1UC2,YES,,CC BY 4.0 ,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120113,113
+YES,PADI-web COVID-19 corpus: news articles manually labelled,"This dataset contains two files: a corpus of 275 manually labelled COVID-2019-news articles retrieved by PADI-web from Dec. 31, 2019, to Jan. 26, 2020 (padiweb_covid19.xlsx), and the multi-term expressions extracted from the news articles content using a text-mining tool, BIOTEX (biotex_covid19.xlsx). ",,CIRAD,,2020,Dataverse,https://doi.org/10.18167/DVN1/MSLEFC,YES,https://doi.org/10.1016/j.compag.2019.105163,,,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120114,114
+NO,PhyCovA,"Files and scripts related to our study entitled ""PhyCovA – a tool for exploring covariates of pathogen spread” - software development)""",,ULB/KU Leuven,,2022,yes,https://github.com/TimBlokker/PhyCovA,NO,https://doi.org/10.1093/ve/veac015,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120115,115
+NO,resistancebank.org,"resistancebank.org, an open-access repository for surveys of antimicrobial resistance in animals","AMR, Animals",ETH,Thomas P. Van Boeckel (thomas.van.boeckel@gmail.com),2020,yes,https://doi.org/10.1038/s41597-021-00978-9,YES,https://doi.org/10.1038/s41597-021-00978-9,CC0,,https://github.com/nicocriscuolo/resistancebank.org,,,AMR,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120116,116
+NO,sars_cov_2_pipeline,"Files and scripts related to our study entitled ""A phylodynamic workflow to rapidly gain insights into the dispersal history and dynamics of SARS-CoV-2 lineages""",,ULB/KU Leuven,,2021,yes,https://github.com/sdellicour/sars_cov_2_pipeline,NO,https://doi.org/10.1093/molbev/msaa284,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120117,117
+NO,SARS-CoV-2_EUR_PHYLOGEOGRAPHY,"Files and scripts related to our study entitled ""Untangling introductions and persistence in COVID-19 resurgence in Europe""",,ULB/KU Leuven,,2021,yes,https://github.com/phylogeography/SARS-CoV-2_EUR_PHYLOGEOGRAPHY,NO,"https://doi.org/10.1038/s41586-021-03754-2
+",,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120118,118
+YES,SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs,This is a GeoTIFF version of the SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture data set v1.1 (Brocca et al. 2019). Conversion steps are available here. ,,OPENGEOHUB,,2022,Zenodo,http://dx.doi.org/10.5281/zenodo.6459152,YES,https://doi.org/10.5194/essd-11-1583-2019,,,Not on the publication page,,,"Tularaemia, Leptospirosis, Mosquito borne Flaviviruses","C0041351, C0023364, ",Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120119,119
+YES,Supplementary Video 1 from Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases,"Sketch of one simulation of a spread in the office data set, for R0=3 and the symptomatic testing protocol, for the dynamical network (left panels), heterogeneous network (middle panels) and contact matrix of distributions (right panel) representations. The nodes representing individuals are arranged according to their department and colored according to their disease compartment. The time is discretized in timesteps of 15 minutes, with contacts changing at every time step for the dynamical network representation, and from day to day in the contact matrix of distributions representation. Links are highlighted in red when a contagion event occurs. No contacts occur outside of office hours. The bottom panels show the evolution over time of the nodes in each compartment (putting together the prodromic, subclinical and clinical compartments for simplicity).",,INSERM,,2022,Figshare,http://dx.doi.org/10.6084/m9.figshare.20043288,NO,http://dx.doi.org/https://doi.org/10.1098/rsif.2022.0164,cc4.0,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120120,120
+YES,Supplementary Video 2 from Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases,"Sketch of one simulation of a spread in the office data set, for R0=3 and the weekly testing protocol with adherence 75%, for the dynamical network (left panels), heterogeneous network (middle panels) and contact matrix of distributions (right panel) representations. The nodes representing individuals are arranged according to their department and colored according to their disease compartment. The time is discretized in timesteps of 15 minutes, with contacts changing at every time step for the dynamical network representation, and from day to day in the contact matrix of distributions representation. Links are highlighted in red when a contagion event occurs. No contacts occur outside of office hours. The bottom panels show the evolution over time of the nodes in each compartment (putting together the prodromic, subclinical and clinical compartments for simplicity).",,INSERM,,2022,Figshare,http://dx.doi.org/10.6084/m9.figshare.20043291.v1,NO,http://dx.doi.org/https://doi.org/10.1098/rsif.2022.0165,cc4.0,,https://www.inserm.fr/wp-content/uploads/2021/01/logo-inserm.svg,,,,,Dataset,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120121,121
 NO,Datasets of clinical and laboratory data of COVID-19,,,SOTON,,2021,Github,https://github.com/gubb673/MC-SEIR,YES,https://doi.org/10.1038/s41562-021-01063-2,,https://github.com/gubb673/MC-SEIR,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120122,122
 NO,Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories,COVID-19 intervention and climate data and the analysing R code,COVID-19,SOTON,Shengjie Lai (shengjie.lai@soton.ac.uk),2022,Github,https://github.com/wxl1379457192/Vaccine-NPIs-in-EuropeV2,YES,https://doi.org/10.1038/s41467-022-30897-1,cc4.0,https://github.com/wxl1379457192/Vaccine-NPIs-in-EuropeV2,https://www.nature.com/articles/s41467-022-30897-1/figures/1,Europe,,COVID-19,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120123,123
 NO,Prior water availability modifies the effect of heavy rainfall on dengue transmission,Dengue and climate data from southern China,Dengue,SOTON,Shengjie Lai (shengjie.lai@soton.ac.uk),2023,Github,https://github.com/qu-cheng/dengue_heavyrain,YES,https://doi.org/10.3389/fpubh.2023.1287678,cc4.0,https://github.com/qu-cheng/dengue_heavyrain,https://www.frontiersin.org/files/Articles/1287678/fpubh-11-1287678-HTML/image_m/fpubh-11-1287678-g004.jpg,Southern China ,,Dengue,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120124,124
+NO,COVID-19 Tweet dataset,"COVID-19 Tweets for the period of January 2020 for UK, Italy and France",,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2021,Github,https://github.com/mehtab-alam/spatial_opinion_mining/tree/master/datasets,YES,"https://doi.org/10.5220/0010887800003123
+
+https://doi.org/10.1016/j.ijid.2021.12.065",,https://github.com/mehtab-alam/spatial_opinion_mining/tree/master,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,"UK, Italy, France",,COVID-19,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120125,125
+NO,PADI-web AI corpus: news articles/sources manually labelled,Avian Influenza relevant articles and irrelevant articles +  manually labelled relevant and irrelevant sources,,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2023,Github,https://github.com/mehtab-alam/data_quality/tree/master/datasets,YES,"https://doi.org/10.1007/978-3-031-04447-2_18 
+
+https://doi.org/10.1109/C-CODE58145.2023.10139883",,https://github.com/mehtab-alam/data_quality/tree/master,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,HPAI,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120126,126
+NO,PADI-web diseases corpus of events,"Events related articles of AI, TBE, COVID-19, Lyme, AMR",,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2022,Github,https://github.com/mehtab-alam/RSI_Disease_Dataset/tree/master,YES,"https://doi.org/10.1080/15230406.2023.2264753
+
+https://doi.org/10.5194/agile-giss-3-16-2022",,https://github.com/mehtab-alam/GeospaCy,https://padi-web.cirad.fr/media/images/logo_padi.width-500.jpg,,,"HPAI, TBE, COVID-19, Lyme, AMR",,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120127,127
+NO,Polygons dataset for Relative spatial information,Polygons geojson of relative spatial information for various cities across Europe and UK,,CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2022,Github,https://github.com/mehtab-alam/RSI-Tagger/tree/master/geojson,YES,"https://doi.org/10.1080/15230406.2023.2264753
+
+https://doi.org/10.5194/agile-giss-3-16-2022",,https://github.com/mehtab-alam/GeospaCy,https://www.tandfonline.com/cms/asset/e0162320-3e8c-48d0-a81b-a8e48d943828/tcag_a_2264753_f0003_c.jpg,,,,,Dataset,Disease data,,,Public,c4702d92-80b3-11ee-b962-0242ac120128,128
 NO,EpidBioBERT,BioSurveillance Document Classifier ,"NLP, text-mining",Strathmore University (Kenya) and CIRAD,Edmond Odhiambo Menya (edmondmenya@gmail.com),2022,Github,,,,,https://github.com/menya-edmond/EpidBioBERT,,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120129,129
 NO,EpidBioELECTRA,Enhanced and Explainable BioSurveillance Document classifier,"NLP, text-mining",Strathmore University (Kenya) and CIRAD,Edmond Odhiambo Menya (edmondmenya@gmail.com),2023,Github,,,,,https://github.com/menya-edmond/EpidBioELECTRA,,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120130,130
-NO,"files and scripts related to our study entitled ""Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing""","This report gathers all the input files and scripts related to our study entitled ""Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing"" (now on biorXiv): BEAST XML files of the continuous phylogeographic and skygrid-GLM analyses, as well as R scripts and related files needed to run all the landscape phylogeographic testing analyses. Continuous phylogeographic and phylodynamic (skygrid-GLM) inferences were performed with the Bayesian methods implemented in the open-source program BEAST. Subsequent dispersal statistics estimation and landscape phylogeographic analyses were implemented and performed with R functions available in the package ""seraphim"".",,ULB,,2020,Github,https://github.com/sdellicour/wnv_north_america,YES,https://doi.org/10.1038/s41467-020-19122-z,,https://github.com/sdellicour/wnv_north_america,,,,Viral diseases,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120095,95
-NO,covid19_spell,"Files and scripts related to our study entitled ""Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence – Belgium as a study case""",,ULB/KU Leuven,,2021,yes,https://github.com/sdellicour/covid19_spell,NO,https://doi.org/10.1186/s12942-021-00281-1,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120065,65
-NO,new_york_variants,"Files and scripts related to our study entitled ""Variant-specific introduction and dispersal dynamics of SARS-CoV-2 in New York City – from Alpha to Omicron""",,ULB/KU Leuven,,2023,yes,https://github.com/sdellicour/new_york_variants,NO,https://doi.org/10.1371/journal.ppat.1011348,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120110,110
-NO,PhyCovA,"Files and scripts related to our study entitled ""PhyCovA – a tool for exploring covariates of pathogen spread” - software development)""",,ULB/KU Leuven,,2022,yes,https://github.com/TimBlokker/PhyCovA,NO,https://doi.org/10.1093/ve/veac015,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120115,115
-NO,sars_cov_2_pipeline,"Files and scripts related to our study entitled ""A phylodynamic workflow to rapidly gain insights into the dispersal history and dynamics of SARS-CoV-2 lineages""",,ULB/KU Leuven,,2021,yes,https://github.com/sdellicour/sars_cov_2_pipeline,NO,https://doi.org/10.1093/molbev/msaa284,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120117,117
-NO,SARS-CoV-2_EUR_PHYLOGEOGRAPHY,"Files and scripts related to our study entitled ""Untangling introductions and persistence in COVID-19 resurgence in Europe""",,ULB/KU Leuven,,2021,yes,https://github.com/phylogeography/SARS-CoV-2_EUR_PHYLOGEOGRAPHY,NO,"https://doi.org/10.1038/s41586-021-03754-2
-",,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120118,118
-YES,Code and Data for: Crowding and the shape of COVID-19 epidemics,Code and Data for: Crowding and the shape of COVID-19 epidemics,,UOXF,,2020,Github/Zenodo,"https://github.com/Emergent-Epidemics/COVID_crowding
-https://zenodo.org/record/4056578#.X3IFF5NKiek",YES,https://doi.org/10.1038/s41591-020-1104-0,,"https://github.com/Emergent-Epidemics/COVID_crowding
-https://github.com/alsnhll/SIRNestedNetwork",https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41591-020-1104-0/MediaObjects/41591_2020_1104_Fig2_HTML.png?as=webp,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120050,50
-NO,data and code for Establishment & lineage dynamics of the SARS-CoV-2 epidemic in the UK,data and code for Establishment & lineage dynamics of the SARS-CoV-2 epidemic in the UK,,UOXF,,2021,,"http://www.cogconsortium.uk/data/
-http://www.ebi.ac.uk/ena/browser/view/PRJEB37886",YES,https://doi.org/10.1126/science.abf2946,,,,,,COVID-19,C5203670,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120066,66
-NO,COVID-19 Open Research Dataset,,,,,,,,NO,,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120060,60
+NO,SNEToolkit,Spatial named entity disambiguation toolkit,"NLP, text-mining",INRAE &CIRAD,Rodrique Kafando (rodrique.kafando@citadel.bf),2023,Github,,,https://doi.org/10.1016/j.softx.2023.101480,,https://github.com/ElsevierSoftwareX/SOFTX-D-23-00004,https://www.softxjournal.com/cms/attachment/8b0f35dd-0221-43fe-8515-096fae509127/gr5.jpg,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120131,131
+NO,Linking disease data from different sources,"Framework to compare official and unofficial disease data. We compared the events collected from official and unofficial sources in terms of two aspects: 1) spatio-temporal analysis (how the events are geographically and temporally distributed), and 2) thematic entity analysis (what thematic entities are extracted from the events and how they are related to spatio-temporal analysis).",,INRAE & CIRAD,Nejat Arinik (arinik9@gmail.com),2023,Gitlab,,,,,https://gitlab.irstea.fr/umr-tetis/mood/compebs,,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120132,132
+NO,Spatial Opinion Mining of COVID-19 Tweets,Spatial Opinion Mining of COVID-19 Tweets through H-TFIDF and other features,"NLP, text-mining",CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2021,Github,,,,,https://github.com/mehtab-alam/spatial_opinion_mining/tree/master,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120133,133
+NO,Data quality: Classification of news articles,Avian Influenza relevant articles and irrelevant articles classification,"NLP, text-mining",CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2023,Github,,,,,https://github.com/mehtab-alam/data_quality/tree/master,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120134,134
+NO,GeospaCy,Relative spatial information extraction and its geographical referencing,"NLP, text-mining",CIRAD,Mehtab Alam SYED (mehtab-alam.syed@cirad.fr),2023,Github,,,,,https://github.com/mehtab-alam/GeospaCy,https://www.tandfonline.com/cms/asset/e0162320-3e8c-48d0-a81b-a8e48d943828/tcag_a_2264753_f0003_c.jpg,,,,,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120135,135
+NO,MOOD Press Tweets Collector,"The MOOD Press Tweets Collector is a Python script designed to gather tweets. MOOD Epidemiologists have specified a set of keywords related to diseases and symptoms and a list of media (newspapers) to followed. Consequently, only tweets from the designated newspapers featuring keywords monitored by MOOD are collected. It's important to note that the script may no longer function due to the fact that the Twitter (or now 'X') API is no longer available for free","NLP, text-mining",INRAE &CIRAD,remy.decoupes@inrae.fr,2021,Gitlab,,YES,,CeCILL-B,https://gitlab.irstea.fr/umr-tetis/mood/mood-tetis-tweets-collect,https://cdn.cms-twdigitalassets.com/content/dam/about-twitter/x/brand-toolkit/logo-black.png.twimg.2560.png,Europe,,"COVID-19, HPAI, Unknown disease, AMR, TBE",,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120136,136
+NO,GeoNLPlify,"GeoNLPlify is a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises using Language Models. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks (such as PADI-web). ","NLP, text-mining",INRAE & CIRAD,remy.decoupes@inrae.fr,2023,Github,,YES,https://www.doi.org/10.3233/IDA-230040,GPL_3,https://github.com/remydecoupes/GeoNLPlify/tree/master,https://raw.githubusercontent.com/remydecoupes/GeoNLPlify/master/readme_ressources/geonlplify_example_schema.png,,,"HPAI, TBE, Leptospirosis, AMR, WNE, Lyme, Unknown disease",,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120137,137
+NO,EpiDCA,"EpiDCA is a generic method that aims to link epidemiological data extracted from EBS systems with their associated environmental risks factors, in order to classify the textual data detected by EBS  systems according to their relevance and timely detect outrbeak events","text-mining, risk mapping ",INRAE,el-bahdja.boudoua@inrae.fr,2023,Github,,YES,,,https://github.com/BBahdja/EpiDCA,,,,"Avian Influenza, African Swine Fever, West Nile Disease",,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120138,138
+NO,arbocartoR app,Modeling the risk of emergence of aedes-borne diseases - Shiny interface,"simulation, aedes, arboviroses",Cirad,Hammami Pachka (pachka.hammami@cirad;fr),2023,Github,,YES,,GNU General Public License,https://forgemia.inra.fr/sk8/sk8-apps/sa/astre/arbocarto-r-app https://arbocarto-r-app.sk8.inrae.fr/,https://collaboratif.cirad.fr/share/proxy/alfresco-noauth/api/internal/shared/node/dAlwJZfsRn6IYbW7iAhNtQ/content/thumbnails/imgpreview?c=force&lastModified=imgpreview%3A1696497093303,,,"Dengue, Chikungunya, Zika",,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120139,139
+NO,arbocartoR package,Modeling the risk of emergence of aedes-borne diseases - R package,"simulation, aedes, arboviroses",Cirad,Hammami Pachka (pachka.hammami@cirad;fr),2023,Github,,YES,,Creative Commons Attribution 4.0 International Public License,https://forgemia.inra.fr/umr-astre/arbocartoR,https://collaboratif.cirad.fr/share/proxy/alfresco-noauth/api/internal/shared/node/dAlwJZfsRn6IYbW7iAhNtQ/content/thumbnails/imgpreview?c=force&lastModified=imgpreview%3A1696497093303,,,"Dengue, Chikungunya, Zika",,Software,,,,Public,c4702d92-80b3-11ee-b962-0242ac120140,140
+NO,EpiNorm,MOOD structured data normalisation tool,data normalisation,SIB Swiss Institute of Bioinformatics,"Orlin Topalov (orlin.topalov@sib.swiss), Dmitry Kuznetsov (dmitry.kuznetsov@sib.swiss), Robin Engler (robin.engler@sib.swiss)",2023,Github,,YES,,GNU General Public License,https://github.com/sib-swiss/epinorm,https://www.sib.swiss/themes/custom/sib/logo.svg,,,"HPAI, TBE, WNE",,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120141,141
+NO,Gazetteer Access Tool,MOOD data normalisation tool,"data normalisation, gazetteer, geospatial data",INESC-ID,Bruno Martins (bruno.g.martins@tecnico.ulisboa.pt),2023,Github,,YES,,GNU General Public License,https://github.com/bgmartins/gazetteer-access,,,,,,Software,,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120142,142
+NO,"Dataset of the diseases' covariates extracted from the literature: Leptospirosis, Influenza A and Chikungunya dataset","
+The dataset contains quantitative data on the human, animal, vector and environmental
+ covariates associated with influenza A, Chikungunya, and Leptospirosis 
+retrieved from scientific papers through a standardized search on 
+PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data
+ on the association between disease and covariates, language (English or
+ other EU languages), time frame (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, modelling studies with no data), lacking denominators or reference populations, unavailable full-texts, referring to data older than 2000 or gathered outside Europe, were excluded. The final time frame covered a period from 2000 to 2022. 
+
+
+The important human, animal, vector and environmental
+ covariates were extracted with the associated quantitative information,
+ and the related information on the diseases. The covariates were submitted to a revision process and labelled according to a labelling system agreed upon among a group of experts within the MOOD (grant agreement No 874850; https://mood-h2020.eu/) project consortium. ","Scoping review, disease covariates","ISS, FEM",Claudia Cataldo,2024,yes,"
+https://doi.org/10.5281/zenodo.11241409",YES,,,,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/scoping-review-ql.png,,,"Leptospirosis, Influenza A, Chikungunya",,Dataset,Disease data,,,MPo,c4702d92-80b3-11ee-b962-0242ac120143,143
+NO,RESAPATH,"Data set of antimicrobial resistance in bacterial pathogens isolated from diseased animals (all species) in France, aggregated at departement level and per month","AMR, Diseased animals",ANSES,Géraldine Cazeau,2006-2022,No,,YES, ,,,https://shiny-public.anses.fr/ENresapath2/,France – data available at the geographical department level,,AMR,,Dataset,Disease data,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120144,144
+YES,Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021,"Covariates for the UK, code of the model","ticks, companion animals, pets",CIRAD/ OGH,Elena Arsevska,2014-2021,Yes,https://doi.org/10.5281/zenodo.7625174,YES,https://doi.org/10.1186/s13071-023-06094-4,,,,UK,,"Tick borne diseases, ticks",,Dataset,Covariate,,Used,Public,c4702d92-80b3-11ee-b962-0242ac120145,145
+YES,"Code and data of article ""Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity a","Code and data used in the journal article ""Estimating SARS-CoV-2 
+infections and associated changes in COVID-19 severity and fatality and fatality""",COVID-19,FEM/FBK,Valentina Marziano,2023,Yes,https://doi.org/10.5281/zenodo.8006661,YES,https://doi.org/10.1111/irv.13181,,,,,,,,Dataset,Disease data,,,,c4702d92-80b3-11ee-b962-0242ac120146,146
+NO,COVID-19 Scatterplots,"Statistical position of the counties or federal states in relation to the statewide or national averages of the 7-day incidence per 100,000 inhabitants and the percentage change in weekly new cases over the last week","scatterplot, Covid-19",mundialis,mundialis (info@mundialis.de),2021,yes,https://apps.mundialis.de/mood/covid19plots/,,https://mood-h2020.eu/covid-19-materials/covid-19-scatterplots/,,,,,,COVID-19,,Software,Disease data,Dashboard,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120147,147
+NO,Extension of actinia for COG import,cloud based geoprocessing platform,"covariates, geoprocessing, cloud",mundialis,mundialis (info@mundialis.de),2023,Github,https://github.com/actinia-org/,YES,https://doi.org/10.5281/zenodo.8305131,GPL 3,https://github.com/actinia-org/,https://www.mundialis.de/wp-content/uploads/2022/03/actinia_logo.png,,,,,Software,Disease data,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120148,148
 YES,Replication Data for: Genomic surveillance reveals the spread patterns of SARS-CoV-2 in coastal Kenya during the first two waves,,,,,2022,Yes,"https://doi.org/10.7910/DVN/4ZZYIM
 https://github.com/george-githinji/sars-cov-2-early-phase-manuscript",YES,https://doi.org/10.1101/2021.07.01.21259583,,,,,,,,,,,,,c4702d92-80b3-11ee-b962-0242ac120149,149
+,Covariates metadata extracted from literature: Tick-borne encephalitis,Dashboard created based on disease profile created by WP2,"TBE, scoping review","FEM, ISS",Francesca Dagostin,,Yes,https://lookerstudio.google.com/u/0/reporting/fb077df1-7da2-485b-a2d5-9f100cc82604/page/p_mzkhlspowc,,https://doi.org/10.2807/1560-7917.ES.2023.28.42.2300121,,,,,,"Tick borne diseases, ticks",,Dataset,Disease data,Dashboard,,,c4702d92-80b3-11ee-b962-0242ac120150,150
+,Covariates metadata extracted from literature: West Nile Virus,Dashboard created based on disease profile created by WP2,"WNV, scoping review, West Nile","FEM, ISS",Francesca Dagostin,,Yes,https://lookerstudio.google.com/u/0/reporting/25dbdec3-0352-4fbc-aced-c6c4ff5ef99d/page/p_mzkhlspowc,,10.1016/j.onehlt.2022.100478,,,,,,"WNE, WNV, West Nile",,Dataset,Disease data,Dashboard,,,c4702d92-80b3-11ee-b962-0242ac120151,151
+,epiCurve,R code for the functions developed to model the force of infection in the case of seasonal vector-borne pathogens,"vector-borne, force of infection",FEM,Giovanni Marini,,Yes,https://github.com/giomarini/epiCurve-repository,YES,,,https://github.com/giomarini/epiCurve-repository,,,,vector-borne pathogens,,Software,,,,,c4702d92-80b3-11ee-b962-0242ac120152,152
 NO,Poultry intensification and emergence of Highly Pathogenic Avian Influenza: past and the future.,Files and scripts related to our study -manuscript in preparation,"HPAI, Conversion, poultry intensification, risk mapping",ULB,,,,,,,,,,,,,,,,,,,c4702d92-80b3-11ee-b962-0242ac120153,153
 NO,Insights from the worldwide risk mapping of H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onward,Files and scripts related to our study -manuscript in preparation,"HPAI, Wild birds, Poultry birds, interface, risk mapping",ULB,,,,,,,,,,,,,,,,,,,c4702d92-80b3-11ee-b962-0242ac120154,154
 NO,Combining phylogeographic and niche modelling approaches to investigate the ecological drivers of TBEV at the Palearctic scale,Files and scripts related to our study -ongoing,"TBEV, Subtypes, phylogeography",ULB,,,,,,,,,,,,,,,,,,,c4702d92-80b3-11ee-b962-0242ac120155,155
-NO,2024_VIIRS_CumTemp_IxodesRicinus,"This dataset presents cumulative temperature masks that identify areas that are warm enough to stimulate tick questing activity, using a temperature threshold of 6 C. A series of scripts download and process VIIRS EO imagery of Land Surface Temperature to create temperature masks every 8 days using a combination of decadel (8-day) and daily satellite data. This dataset includes 2024, It is an update for previous one.
+NO,2024_VIIRS_CumTemp_IxodesRicinus,"Abstract:
+ 
+ This dataset presents cumulative temperature masks that identify areas that are warm enough to stimulate tick questing activity, using a temperature threshold of 6 C. A series of scripts download and process VIIRS EO imagery of Land Surface Temperature to create temperature masks every 8 days using a combination of decadal (8-day) and daily satellite data. This dataset is an updated version for 2024.
  
  These result in Boolean masks where suitable areas according to temperature limits on I. ricinus are identified as 1 and unsuitable areas as 0. This mask can then be applied to the existing seasonal Tick model to make a more timely prediction of tick activity based on recent temperatures.
  
- Image acquisition
+ This dataset will be updated every couple of months by the end of the year 2024.
+ 
+ Image acquisition:
  
  Two different products are downloaded VIIRS Land Surface Temperature/Emissivity 8-day L3 Global 1 km SIN grid (VNP21A2, version 6) and VIIRS Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN grid (VNP21A1D, version 6). 
  
- processing
+ Processing:
  The cumulative temperature mask is processed in two steps through two separate scripts:
  
- a. Acquisition of 1km MODIS Land Surface Temperature imagery from NASA's data repository.
- b. Importing of the imagery into a Suitable format from which regularly updated masks are calculated.","VIIRS, Cumulitative Temperature",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://tinyurl.com/VIIRSCumTempIxRicinus2024,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTemp240329.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120156,156
-NO,2023_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022,"This dataset compares cumulative temperature masks of year 2023 with the average in 2020,2021, and 2022. 
- The input data is taken from Cumulitative temperature masks calculated in those years and then turn it to integer numbers of 0 to 3 to present which of these years it was suitibale. 
- The data can be classified as: 
+ a. Acquisition of 1km VIIRS Land Surface Temperature imagery from NASA's data repository.
+ b. Importing of the imagery into a suitable format from which regularly updated masks are calculated.
+ 
+ File naming schema:
+ 
+  ER+ year + 8-day number (46 in total) for example: ER2433C68.tif 24 refers to the year 2024 and 33 (decadal number) in this example refers to the 22nd of September.
+ 
+ ERCumTemp + year + month+ day: ERCumTemp240109.jpg, 240109 refers to the 8-day starting 9th of January.
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day for year 2024
+ 
+ 
+ Pixel values:
+ 
+ Suitable areas as 1 and unsuitable areas as 0
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ Repository URL :
+ https://github.com/ERGOcode/Cumulative-Temperature-Mask
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, Cumulitative Temperature",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://doi.org/ 10.5281/zenodo.13221618,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTemp240329.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120156,156
+NO,2023_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022,"Abstract : 
+ 
+ This dataset compares cumulative temperature masks of the year 2023 with the average in 2020,2021, and 2022. 
+  The input data is taken from Cumulative temperature masks calculated in those years and then turned to integer numbers of 0 to 3 to present which of these years were suitable. 
+  The data can be classified as: 
+  1 for unsuitable areas
+  0 for Suitable in 2023
+  2 for Suitable in 2020-2023
+  3 for Suitable in 2020-2022
+ 
+  
+ 
+ File naming schema:
+ 
+  ER23ti202122 + 8-day number (46 in total) for example, ER23to20212208C68F.tif, 08 in this example refers to the 6th of March.
+ 
+ ERCumTepdif23 + month+ day: ERCumTempdif230306.jpg, 0306 refers to the 8-day starting that day. 
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day
+ 
+ Pixel values:
+ 
   1 for unsuitable areas
-  0 for Suitable on 2023
-  2 for Suitable on 2020-2023
-  3 for Suitable on 2020-2022","VIIRS, Cumulitative Temperature",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://tinyurl.com/VIIRSCumTempDif23to202122,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTempdif230618.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120157,157
-NO,2024_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022,"This dataset compares cumulative temperature masks of year 2024 with the average in 2020,2021, and 2022. 
- The input data is taken from Cumulitative temperature masks calculated in those years and then turn it to integer numbers of 0 to 3 to present which of these years it was suitibale. 
- The data can be classified as: 
+  0 for Suitable in 2023
+  2 for Suitable in 2020-2023
+  3 for Suitable in 2020-2022
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ 
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, Cumulitative Temperature",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://doi.org/10.5281/zenodo.13221630,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTempdif230618.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120157,157
+NO,2024_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022,"Abstract : 
+ 
+ This dataset compares cumulative temperature masks of the year 2024 with the average in 2020,2021, and 2022. 
+  The input data is taken from Cumulative temperature masks calculated in those years and then turned to integer numbers of 0 to 3 to present which of these years were suitable. 
+  The data can be classified as: 
   1 for unsuitable areas
-  0 for Suitable on 2024
-  2 for Suitable on 2020-2024
-  3 for Suitable on 2020-2022","VIIRS, Cumulitative Temperature",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://tinyurl.com/VIIRSCumTempDif24to202122,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTempdif240330.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120158,158
-NO,2024_Weeklyspecies_ridhness_abundance_selectedWNV_birdhosts_ Ebirddata,"Raw weekly abundance data at 3km resoultion was downloaded from EBIRD (https://science.ebird.org/en/use-ebird-data) for 12 listed WNV bird hosts.  These data were converetd to presence absence, and these then combined toi give a species number for each week.   The abundnace data were also proceeed to provided summed and meaned abundnace for each week.  
-Files as follows
-e4ebirdweeklyabundancemarini:	all weekly abundance datasets at 3km resolution.  						
-e4ebirdweeklyPAMarini:	abundance with missing recoded to 0.  This is based of ad hoc checks of weekly datasets against the birdlife  species ranges,  which suggest that the maximum extents of combined weekly abundance distributions match the rage boundares fairly well 						
-e4ebirdweeklyspprichnesMariniSUMMEANweeklymarini:	summed and mean weekly presence absence for all species.  If a species is missing a weekly dataset it is removed from the Mean value calculations for that week.  To maintain a constant maximum presenxe number in the summed number of present species,  missing weeks are filled with last valid presence week  up to halfway through the gap in availability, then with the first avaiklable distribution after the gap						
-Species list: Carrion Crow; Wood Pigeon, European Collared Dove; European Blackbird; European Jackdaw; Europoean Jay; European Kestrel; European Magpie; Herring Gull, Hooded Crow; House Sparrow; Little Owl; 							
-							
-							
-Data obtained from Ebird  https://science.ebird.org/en/status-and-trends/species/							
-Subsequent processing by William.wint@gmail.com,  Environmental Research Group Oxford Ltd,  for the MOOD project							","WNV,  Ebird, Species richness,  weekly abundance",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://tinyurl.com/MOOD-WNV-BIRDHOSTweekly,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/overview_image003_WNV_2024.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120159,159
+  0 for Suitable in 2024
+  2 for Suitable in 2020-2024
+  3 for Suitable in 2020-2022
+ 
+ This dataset will be updated every couple of months by the end of the year 2024.
+ 
+ File naming schema:
+ 
+  ER24ti202122 + 8-day number (46 in total) for example : ER24to20212208C68F.tif, 08 in this example refers to 6th of March
+ 
+  ERCumTepdif24 + month+ day: ERCumTempdif240306.jpg, 0306 refers to the 8-day starting that day. 
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day
+ 
+ Pixel values:
+ 
+ 1 for unsuitable areas
+  0 for Suitable in 2024
+  2 for Suitable in 2020-2024
+  3 for Suitable in 2020-2022
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ 
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8","VIIRS, Cumulitative Temperature",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://doi.org/10.5281/zenodo.13221658,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/ERCumTempdif240330.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120158,158
+NO,2024_Weeklyspecies_richness_abundance_selectedWNV_birdhosts_ Ebirddata,"Abstract:
+ Raw weekly abundance data at 3km resolution was downloaded from EBIRD (https://science.ebird.org/en/use-ebird-data) for 12 listed WNV bird hosts. These data were converted to presence-absence and then combined to give a species number each week. The abundance data were also processed to provide the Sum and Mean abundance for every week. 
+  
+ If a species is missing, a weekly dataset is removed from the Mean value calculations for that week. To maintain a constant maximum presence number in the summed number of present species, missing weeks are filled with the last valid presence week up to halfway through the gap in availability, then with the first available distribution after the gap.
+  
+ Species list: Carrion Crow; Wood Pigeon, European Collared Dove; European Blackbird; European Jackdaw; European Jay; European Kestrel; European Magpie; Herring Gull, Hooded Crow; House Sparrow; Little Owl.
+  
+  
+  
+ File naming scheme: 
+ 
+ Files are:
+ e4ebirdweeklyabundancemarini: all weekly abundance datasets at 3km resolution. 
+ e4ebirdweeklyPAMarini: abundance with missing recoded to 0. This is based on ad hoc checks of weekly datasets against the birdlife species ranges, which suggest that the maximum extents of combined weekly abundance distributions match the range boundaries fairly well.
+ e4ebirdweeklyspprichnesMariniSUMMEANweeklymarini: summed and mean weekly presence-absence for all species. 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 3km
+ 
+ Pixel values:
+ Number of species per pixel for each category
+ 
+ Source: 
+ 
+ Data obtained from Ebird https://science.ebird.org/en/status-and-trends/species/
+ 
+ Software used:
+ ArcMap 10.8","WNV,  Ebird, Species richness,  weekly abundance",ERGO,William Wint (william.wint@gmail.com),2024,Yes,https://doi.org/10.5281/zenodo.13221671,YES,,"CC-BY 4.0 Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.",,https://gitlab.irstea.fr/umr-tetis/mood/geonetwork-insertion/-/raw/master/readme.img/ergo/overview_image003_WNV_2024.jpg,,,,,Dataset,Covariate,,,,c4702d92-80b3-11ee-b962-0242ac120159,159
+,"ERA5-Land, Total Precipitation","ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
+
+Abstract: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.
+
+Total precipitation:
+Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.
+
+The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.","precipitation, MOOD-H2020, Mauritania, ERA5-Land",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de),
+Krisztian Lina (krisztian@mundialis.de) ",2024,Zenodo,https://doi.org/10.5281/zenodo.12189668 ,YES,,https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf,,,,, Rift Valley fever   ,,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120160,160
+,MODIS v6.1 NDVI,"Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023).
+
+Source data:
+- MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): https://lpdaac.usgs.gov/products/mod13a2v061/
+
+
+The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.","NDVI, vegetation index, MOOD-H2020, Mauritania",mundialis,"Metz Markus (metz@mundialis.de),
+Haas Julia (haas@mundialis.de),
+Neteler Markus (neteler@mundialis.de),
+Krisztian Lina (krisztian@mundialis.de) ",2024,Zenodo,https://doi.org/10.5281/zenodo.12188839,YES,,LP DAAC,,,,, Rift Valley fever   ,,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120161,161
+,CLMS Water Bodies,"Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
+
+Source data:
+- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m
+- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m
+
+Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.
+The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.","Water, Water bodies, MOOD-H2020, Mauritania ",mundialis,Krisztian Lina (krisztian@mundialis.de) ,2024,Zenodo,https://doi.org/10.5281/zenodo.12189682,YES,,Generated using European Union's Copernicus Land Monitoring Service information,,,,, Rift Valley fever   ,,Dataset,Covariate,,Prod.,Public,c4702d92-80b3-11ee-b962-0242ac120162,162
+,VectAbundance: a spatio-temporal database of Aedes mosquitoes observations,"Modelling approaches play a crucial role in supporting local public health agencies by estimating and forecasting vector abundance and seasonality. However, the reliability of these models is contingent on the availability of standardized, high-quality data. Addressing this need, our study focuses on collecting and harmonizing egg count observations of the mosquito Aedes albopictus, obtained through ovitraps in monitoring and surveillance efforts across Albania, France, Italy, and Switzerland from 2010 to 2022. We processed the raw observations to obtain a continuous time series of ovitraps observations allowing for an extensive geographical and temporal coverage of Ae. albopictus population dynamics. The resulting post-processed observations are stored in the open-access database VectAbundance.This initiative addresses the critical need for accessible, high-quality data, enhancing the reliability of modelling efforts and bolstering public health preparedness.","Mosquito, Aedes",FEM-UniTN,Daniele Da Re (daniele.dare@unitn.it),2024,Yes,https://zenodo.org/doi/10.5281/zenodo.10435687,YES,https://doi.org/10.1038/s41597-024-03482-y,,,,,,,,,Covariate,,,Public,c4702d92-80b3-11ee-b962-0242ac120163,163
diff --git a/xml_generated/.xml b/xml_generated/.xml
index b95e421..f0461d5 100644
--- a/xml_generated/.xml
+++ b/xml_generated/.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:33</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:46</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -136,7 +136,7 @@ The script for data analyses is available at https://github.com/SarahVal/EBS-net
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:33-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:46-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/1990_2015_GrossDomesticProduct.xml b/xml_generated/1990_2015_GrossDomesticProduct.xml
index 52c813e..bcb7044 100644
--- a/xml_generated/1990_2015_GrossDomesticProduct.xml
+++ b/xml_generated/1990_2015_GrossDomesticProduct.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:39</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:24</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,33 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>GDP 2015 (purchasing power parity, full description in document kummuetal2018sdata20184.pdf. 
-
-Abstract: a gridded data set of Gross domestic product (purchasing power parity) produced by Kummu, M. et al. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Sci. Data 5:180004 doi: 10.1038/sdata.2018.4 (2018) has been extracted for the MOOD extent</gco:CharacterString>
+        <gco:CharacterString>Abstract:
+ 
+ A gridded data set of Gross domestic product (purchasing power parity) produced by Kummu, M. et al. Gridded global dataset for Gross Domestic Product and Human Development Index over 1990–2015. Sci. Data 5:180004 doi: 10.1038/sdata.2018.4 (2018). ERGO has extracted the MOOD extent for this dataset. 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ full description in document kummuetal2018sdata20184.pdf. 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Source: 
+ 
+ Data obtained from GDP 2015 (purchasing power parity, full description in document kummuetal2018sdata20184.pdf). 
+ 
+ Software used:
+ ArcMap 10.8
+ 
+ License: CC-BY-SA 4.0
+ 
+ Processed by:
+ ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -96,7 +120,7 @@ Abstract: a gridded data set of Gross domestic product (purchasing power parity)
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/GDP9015</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13221047</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -136,7 +160,7 @@ Abstract: a gridded data set of Gross domestic product (purchasing power parity)
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:39-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:25-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2019_MODIS_Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml b/xml_generated/2001_2019_MODIS_Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
index 4a19988..9826767 100644
--- a/xml_generated/2001_2019_MODIS_Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2019_MODIS_Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:39</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:25</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,45 +30,80 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MIR) derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
- 
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
- 
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+        <gco:CharacterString>Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ MIR: Middle Infra-Red
+  
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100"</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -129,7 +164,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01191k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13134567</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -169,7 +204,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:39-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:25-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2019_MODIS_EVI_FourierProcessed_1k_ER.xml b/xml_generated/2001_2019_MODIS_EVI_FourierProcessed_1k_ER.xml
index 8f3be69..dff9fed 100644
--- a/xml_generated/2001_2019_MODIS_EVI_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2019_MODIS_EVI_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:39</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:25</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,46 +30,83 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Enhanced Vegetation Index (EVI) derived from the MOD13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of EVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
- 
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
- 
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+        <gco:CharacterString>Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ EVI: Enhanced Vegetation Index
+ 
+ 
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100"</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -136,7 +173,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01191k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13134567</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -176,7 +213,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:39-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:26-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2019_MODIS_LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml b/xml_generated/2001_2019_MODIS_LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
index a83cf76..6e21b25 100644
--- a/xml_generated/2001_2019_MODIS_LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2019_MODIS_LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:39</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:26</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,47 +30,83 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data 07 - DLST: Day-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
- 
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
- 
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+        <gco:CharacterString>Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ 
+ DLST: Day-time Land Surface Temperature
+ 
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, min Max Index Value * 10000
- 
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +173,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01191k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13134567</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +213,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:40-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:26-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2019_MODIS_LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml b/xml_generated/2001_2019_MODIS_LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
index 2a8dccf..5d2d210 100644
--- a/xml_generated/2001_2019_MODIS_LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2019_MODIS_LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:40</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:26</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,46 +30,83 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data 08 - NLST: Night-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
- 
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
- 
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+        <gco:CharacterString>Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ 
+ NLST: Night-time Land Surface Temperature
+ 
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -136,7 +173,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01191k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13134567</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -176,7 +213,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:40-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:26-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2019_MODIS_NDVI_Fourier_Processed_1k_ER.xml b/xml_generated/2001_2019_MODIS_NDVI_Fourier_Processed_1k_ER.xml
index ac8b137..8f87e1e 100644
--- a/xml_generated/2001_2019_MODIS_NDVI_Fourier_Processed_1k_ER.xml
+++ b/xml_generated/2001_2019_MODIS_NDVI_Fourier_Processed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:40</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:26</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,46 +30,82 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Normalised Difference Vegetation Index (NDVI) derived from the MOD13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of NDVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
- 
- The next two characters identify the channel:
- 03 - middle infra-red
- 07 - daytime land surface temperature
- 08 - nighttime land surface temperature
- 14 - NDVI: Normalised Difference Vegetation Index
- 15 - EVI: Enhanced Vegetation Index
- 
- The last two characters of each file name denote the output from Fourier processing:
- a0 - mean
- mn - minimum
- mx - maximum
- a1 - amplitude of annual cycle
- a2 - amplitude of bi-annual cycle
- a3 - amplitude of tri-annual cycle
- p1 - phase of annual cycle
- p2 - phase of bi-annual cycle
- p3 - phase of tri-annual cycle
- d1 - variance in annual cycle
- d2 - variance in bi-annual cycle
- d3 - variance in tri-annual cycle
- da - combined variance in annual, bi-annual, and tri-annual cycles
- vr - variance in raw data
+        <gco:CharacterString>Overview:
+ 
+ This is a set of images produced by Temporal Fourier Analysis of global MODIS data
+ 
+ NDVI: Normalised Difference Vegetation Index
+  
+ 
+ Abstract:
+ 
+ MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ 
+ This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.
+ 
+ A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility
+  
+ 
+ File naming scheme:
+ 
+ 
+  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+  
+  The next two characters identify the channel:
+  03 - middle infra-red
+  07 - daytime land surface temperature
+  08 - nighttime land surface temperature
+  14 - NDVI: Normalised Difference Vegetation Index
+  15 - EVI: Enhanced Vegetation Index
+  
+  The last two characters of each file name denote the output from Fourier processing:
+  a0 - mean
+  mn - minimum
+  mx - maximum
+  a1 - amplitude of annual cycle
+  a2 - amplitude of bi-annual cycle
+  a3 - amplitude of tri-annual cycle
+  p1 - phase of annual cycle
+  p2 - phase of bi-annual cycle
+  p3 - phase of tri-annual cycle
+  d1 - variance in annual cycle
+  d2 - variance in bi-annual cycle
+  d3 - variance in tri-annual cycle
+  da - combined variance in annual, bi-annual, and tri-annual cycles
+  vr - variance in raw data
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day from 2001 to 2019
+ 
+ 
+ Pixel values
  
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100"</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ MODIS NASA :MOD11A2 and MOD13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -136,7 +172,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01191k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13134567</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -176,7 +212,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:40-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:27-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2021__MODIS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml b/xml_generated/2001_2021__MODIS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
index e9f5f50..fb7fd25 100644
--- a/xml_generated/2001_2021__MODIS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2021__MODIS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:40</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:27</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,13 +30,24 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MIR) derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+  
+ MIR: Middle Infra-Red
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -64,11 +75,12 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
- NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 10000
+ NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000
+ NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100"</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -129,7 +141,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01211k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.10078149</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -169,7 +181,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:41-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:27-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2021__MODIS__EVI_FourierProcessed_1k_ER.xml b/xml_generated/2001_2021__MODIS__EVI_FourierProcessed_1k_ER.xml
index efdd7d1..86adf41 100644
--- a/xml_generated/2001_2021__MODIS__EVI_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2021__MODIS__EVI_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:41</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:27</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,14 +30,23 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Enhanced Vegetation Index (EVI) derived from the MOD13A2 Product from USGS for the period 2001 -2021 and is an update of hthe previous 2011 - 2019 series. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of EVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2019, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
+ EVI: Enhanced Vegetation Index
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -70,7 +79,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100"</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +146,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01211k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.10078149</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +186,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:41-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:27-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2021__MODIS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml b/xml_generated/2001_2021__MODIS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
index 3c72a67..49aa810 100644
--- a/xml_generated/2001_2021__MODIS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2021__MODIS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:41</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:27</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,16 +30,26 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data 07 - DLST: Day-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2021, as an update of the 2001-2019 series,, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
-  
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ DLST: Day-time Land Surface Temperature
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -137,7 +147,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01211k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.10078149</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +187,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:41-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:28-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2021__MODIS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml b/xml_generated/2001_2021__MODIS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
index a41ef2c..d2fe578 100644
--- a/xml_generated/2001_2021__MODIS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2021__MODIS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:41</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:28</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,14 +30,24 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data 08 - NLST: Night-time Land Surface Temperature derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 through to 2021, as an update of the 2001-2019 series,, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline from 2001-2021.
+ NLST: Night-time Land Surface Temperature
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -70,7 +80,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +147,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01211k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.10078149</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +187,7 @@ Abstract: Temperatures were extracted from MOD11A2 files for the years 2001 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:42-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:28-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2021__MODIS__NDVI_FourierProcessed_1k_ER.xml b/xml_generated/2001_2021__MODIS__NDVI_FourierProcessed_1k_ER.xml
index 71cbd50..83abc05 100644
--- a/xml_generated/2001_2021__MODIS__NDVI_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2001_2021__MODIS__NDVI_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:42</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:28</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,14 +30,24 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Normalised Difference Vegetation Index (NDVI) derived from the MOD13A2 Product from USGS. the period 2001 2021 and is an update of the previous 2011 - 2019 series The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD13A2 product contains an 16-day average of NDVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: the Index values were extracted from MOD13A2 files for the years 2001 through to 2021, as an update of the 2001-2019 series, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:
  
- File Names:
- The MO at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 21 refers to the year timeline from 2001-2021.
+ NDVI: Normalised Difference Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. 
+ 
+ Process:
+ Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ File names:
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.
  
  The next two characters identify the channel:
  03 - middle infra-red
@@ -70,7 +80,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100"</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +147,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfamodis01211k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.10078149</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +187,7 @@ Abstract: the Index values were extracted from MOD13A2 files for the years 2001
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:42-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:28-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2001_2022_ERA5_SoilMoisture_ER_5k.xml b/xml_generated/2001_2022_ERA5_SoilMoisture_ER_5k.xml
index 42f9ca0..c858f88 100644
--- a/xml_generated/2001_2022_ERA5_SoilMoisture_ER_5k.xml
+++ b/xml_generated/2001_2022_ERA5_SoilMoisture_ER_5k.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-04-11T18:59:50</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:28</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,37 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>soil moisture for WNV. 
-
-Abstract: Soil moisture data have been downloaded from ECWMF ERA5 reanalysis dataset then windowed to provide 5km ratser datasets for the MOOD extent for the years 2001 - 2022</gco:CharacterString>
+        <gco:CharacterString>Abstract:
+ 
+ Soil moisture data have been downloaded from the ECWMF ERA5 reanalysis dataset and then windowed to provide 5km raster datasets for the MOOD extent for the years 2001- 2022 (Oct 2022)
+ 
+  
+ 
+ File naming scheme: 
+ 
+ era5corsoilmoist+ Year+ Month +.tif
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 69.0000000000000000,82.0000000000000000
+ Spatial resolution:
+ 0.25 (5000m)
+ Temporal resolution:
+ Monthly from 2001 to 2022
+ 
+ 
+ Pixel values
+ 
+ Soil Moisture Precentage
+ 
+ Source: 
+  ECWMF ERA5 Soil Moisture
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +127,7 @@ Abstract: Soil moisture data have been downloaded from ECWMF ERA5 reanalysis dat
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/era5soilmoisture01225k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13123014</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +167,7 @@ Abstract: Soil moisture data have been downloaded from ECWMF ERA5 reanalysis dat
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-04-11T18:59:50-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:29-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2006_2022_MODIS_EnhancedVegetationIndex_5k_ER.xml b/xml_generated/2006_2022_MODIS_EnhancedVegetationIndex_5k_ER.xml
index da108a4..9e049a7 100644
--- a/xml_generated/2006_2022_MODIS_EnhancedVegetationIndex_5k_ER.xml
+++ b/xml_generated/2006_2022_MODIS_EnhancedVegetationIndex_5k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:42</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:29</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,40 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>MODIS 16 day Enhanced Vegetation Index, 5km, 2001-2021. 
-
-Abstract: This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. Itis designed for use with administraytive level analysis which need to used covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.</gco:CharacterString>
+        <gco:CharacterString>Abstract:
+ 
+ This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modeled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ There are two zip files. One includes data from 2006 to 2021, and the second is for 2022. 
+ 
+ the files name are: moeve5km16day + year + day (out of 365) : moeve5km16day2022049.tif
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 5k
+ Temporal resolution:
+ 16-day from 2006 to 2022
+ 
+ 
+ Pixel values:
+ 
+ Vegetation Index
+ 
+ Source: 
+ MODIS NASA : MOD13C1
+ 
+ 
+ Software used:
+  
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +130,7 @@ Abstract: This is a reduced 5km resolution version of the 1km data used for Four
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/modis0119</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13122989</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +170,7 @@ Abstract: This is a reduced 5km resolution version of the 1km data used for Four
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:42-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:29-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2010_2022_ERA5_MonthlyPrecipitation_5k.xml b/xml_generated/2010_2022_ERA5_MonthlyPrecipitation_5k.xml
index 46f08ff..f5a169e 100644
--- a/xml_generated/2010_2022_ERA5_MonthlyPrecipitation_5k.xml
+++ b/xml_generated/2010_2022_ERA5_MonthlyPrecipitation_5k.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:43</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:29</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,44 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2010 - 2022. 
-
-Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2022 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</gco:CharacterString>
+        <gco:CharacterString>Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2010 - 2022. 
+ 
+ Abstract:
+ 
+ Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting, for 2010 - 2022. The original data is at 0.25-degree resolution and was downscaled by ERA extraction algorithms. The daily data have been aggregated into decadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Monthly Precipitation: 2022 moeraprecmmmonthly2022.zip ; 2010 to 2021 moeraprecmmmonthly20102021.zip
+ 
+ Daily, decadal, and annual precipitation can be found here. 
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.25 (5000m) 
+ Temporal resolution:
+ Monthly from 2010 to 2022
+ 
+ Pixel values
+ 
+ Precipitation in meter
+ 
+ 
+ Source: 
+ 
+ ERA5 Precipitation by the European Centre for Medium-Range Weather Forecasting
+ 
+ 
+ Software used:
+  
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +134,7 @@ Abstract: Precipitation from the ERA5 reanalysis archive supplied by the Europea
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/Era5Preciptation10215km</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13122970</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +174,7 @@ Abstract: Precipitation from the ERA5 reanalysis archive supplied by the Europea
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:43-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:30-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2010_2022_MODIS_LandSurfaceTemperature_5k_ER.xml b/xml_generated/2010_2022_MODIS_LandSurfaceTemperature_5k_ER.xml
index 8f14727..d32a061 100644
--- a/xml_generated/2010_2022_MODIS_LandSurfaceTemperature_5k_ER.xml
+++ b/xml_generated/2010_2022_MODIS_LandSurfaceTemperature_5k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:43</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:30</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,50 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>MODIS daily, decadal monthly and annual land surface Temperature, 5km, 2010-2020. 
-
-Abstract: this is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. Itis designed for use with administraytive level analysis which need to used covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.</gco:CharacterString>
+        <gco:CharacterString>MODIS daily, decadal, and monthly land surface Temperature, 5km, 2010-2022. 
+ 
+ Abstract:
+ 
+ This is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for administrative-level analysis that uses covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive and windowed for the MOOD study area.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Monthly Day LST : 2022 MOODMonthlyDLST2022.zip ; 2010 to 2021 MOODMonthlyDLST20102021.zip
+ 
+ Monthly Night LST 2022 MOODMonthlyNLST2022.zip ; 2010 to 2021 MOODMonthlyNLST20102021.zip
+ 
+ Decadal Day LST: 2022 MOODMOD11c2Dekadaldlst2022.zip ; 2010 to 2021 MOODMOD11C2DEKADALDLST20102021.zip
+ 
+ Decadal Night LST: 2022 MOODMODC11dekadalnlst2022.zip ; 2010 to 2021 MOODMOD11C2DEKADALNLST20102021.zip
+ 
+ Daily Day LST: 2022 MOODMOD11C1DAILYDLST2022.zip ; 2010 to 2021 MOODMOD11C1DAILYDLST20102021.zip
+ 
+ Daily Night LST: 2022 MOODMOD11C1DAILYNLST2022.zip ; 2010 to 2021 MOODMOD11C1DAILYNLST20102021.zip
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 5k
+ Temporal resolution:
+ Daily, Decadal, and monthly from 2010 to 2022
+ 
+ Pixel values
+ 
+ Temperature Degree
+ 
+ 
+ Source: 
+ MODIS NASA : MOD11A1
+ 
+ 
+ Software used:
+  
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -96,7 +137,7 @@ Abstract: this is a reduced 5km resolution version of the 1km data used for Four
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/modis0119</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13122959</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -136,7 +177,7 @@ Abstract: this is a reduced 5km resolution version of the 1km data used for Four
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:43-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:30-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2012_2020__VIIRS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml b/xml_generated/2012_2020__VIIRS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
index 4c1e43e..fa3ab7d 100644
--- a/xml_generated/2012_2020__VIIRS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2012_2020__VIIRS__Channel3_MiddleInfraRed_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:43</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:30</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,15 +30,27 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global VIIRS data for Middle Infra Red (MIR) derived from the VNP13A2 Product from USGS. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area. VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP13A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ MIR: Middle Infra-Red
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
  
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -61,6 +73,22 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -69,7 +97,16 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -130,7 +167,7 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfaviirs12201k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12914013</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -170,7 +207,7 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:44-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:30-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2012_2020__VIIRS__EVI_FourierProcessed_1k_ER.xml b/xml_generated/2012_2020__VIIRS__EVI_FourierProcessed_1k_ER.xml
index a2291c9..0a46318 100644
--- a/xml_generated/2012_2020__VIIRS__EVI_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2012_2020__VIIRS__EVI_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:44</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:30</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,16 +30,28 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global VIIRS data for Enhanced Vegetation Index (EVI) derived from the VNP13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. TheVNP13A2 product contains an 16-day average of EVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ EVI: Enhanced Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
+ 
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -62,6 +74,22 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -70,7 +98,16 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +174,7 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfaviirs12201k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12914013</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +214,7 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:44-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:31-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2012_2020__VIIRS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml b/xml_generated/2012_2020__VIIRS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
index 838c0b6..5121cc9 100644
--- a/xml_generated/2012_2020__VIIRS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2012_2020__VIIRS__LandSurfaceDayTemperature_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:44</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:31</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,16 +30,29 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global VIIRS data 07 - DLST: Day-time Land Surface Temperature derived from the VNP21A2 Product from USGS. The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP21A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ DLST: Day-time Land Surface Temperature
+ 
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
+ 
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -62,6 +75,22 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -70,7 +99,16 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +175,7 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfaviirs12201k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12914013</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +215,7 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:44-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:31-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2012_2020__VIIRS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml b/xml_generated/2012_2020__VIIRS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
index cec98cf..eef9232 100644
--- a/xml_generated/2012_2020__VIIRS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
+++ b/xml_generated/2012_2020__VIIRS__LandSurfaceNightTemperature_FourierProcessed_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:47</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:31</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,16 +30,28 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global VIIRS data 08 - NLST: Night-time Land Surface Temperature derived from the VNP21A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP21A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ NLST: Night-time Land Surface Temperature
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
+ 
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -62,6 +74,22 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -70,7 +98,16 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -137,7 +174,7 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfaviirs12201k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12914013</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -177,7 +214,7 @@ Abstract: Temperatures were extracted from VNP21A2 files for the years 2012 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:47-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:31-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2012_2020__VIIRS__NDVI_FourierProcessed_ER.xml b/xml_generated/2012_2020__VIIRS__NDVI_FourierProcessed_ER.xml
index 0894b0a..8e611af 100644
--- a/xml_generated/2012_2020__VIIRS__NDVI_FourierProcessed_ER.xml
+++ b/xml_generated/2012_2020__VIIRS__NDVI_FourierProcessed_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:47</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:31</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,16 +30,28 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global VIIRS data for Normalised Difference Vegetation Index (NDVI) derived from the VNP13A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- VIIRS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The VNP13A2 product contains an 16-day average of NDVI at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture. 
-
-Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 through to 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed descripton of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
- Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was subset from global images. Idrisi rasters were converted to geotiff format in order to give data users more flexibility
- 
- File Names:
- The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+        <gco:CharacterString>Overview:
+ This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:
+ 
+ NDVI: Normalised Difference Vegetation Index
+ 
+ The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.
+ This series of VIIRS data has been updated to include imagery from 2012 to 2020. 
+ 
   
- 19 referes to he code give to PrecipitationThe next two characters identify the channel:
+ 
+ Abstract:
+ Image values were extracted from VIRRS imagery from 2012 to 2020. The day and night land temperature came from the 8-day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2, 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+ This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 
+ 
+ File naming scheme: 
+ 
+ 
+ The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 20 refers to the year timeline of 2012-2020.
+ 
+ The next two characters identify the channel:
  03 - middle infra-red
  07 - daytime land surface temperature
  08 - nighttime land surface temperature
@@ -62,6 +74,22 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  da - combined variance in annual, bi-annual, and tri-annual cycles
  vr - variance in raw data
  
+ 
+ Files can be accessed here as well (including Global files). 
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  8-day and 16-day for 2012 to 2020 
+ 
+ 
+ Pixel values
+ 
  Parameter Fourier Variable Image values are
  MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000
  LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50
@@ -70,7 +98,16 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
  NDVI (14) and EVI (15) VR Value * 10000
  ALL D1,D2,D3,Da Percentages
  ALL E1,E2,E3 Percentages
- ALL P1,P2.P3 Months*100. (Jan=100)</gco:CharacterString>
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP13A2
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -149,7 +186,7 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfaviirs12201k</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12914013</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -189,7 +226,7 @@ Abstract: Temperatures were extracted from VNP13A2 files for the years 2012 thro
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:47-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:32-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2013_FarmLabour_ER.xml b/xml_generated/2013_FarmLabour_ER.xml
index a54869a..f686282 100644
--- a/xml_generated/2013_FarmLabour_ER.xml
+++ b/xml_generated/2013_FarmLabour_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:28</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:32</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,14 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Farm Labour. 
-
-Abstract: Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by country. xlsx files.</gco:CharacterString>
+        <gco:CharacterString>Abstract:
+ 
+ Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by country. xlsx files.
+ 
+ Source: 
+ Eurostat farm Labour by NUTS 
+ Software used:
+ Excel</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +104,7 @@ Abstract: Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by cou
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/farmlabourer13</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12913705</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +144,7 @@ Abstract: Eurostat farm labour by NUTS areas. FAOSTAT agricultural labour by cou
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:29-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:32-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2015_MODIS_RelativeHumidity_FourierProcessed.xml b/xml_generated/2015_MODIS_RelativeHumidity_FourierProcessed.xml
index 37519a5..2ce3c8c 100644
--- a/xml_generated/2015_MODIS_RelativeHumidity_FourierProcessed.xml
+++ b/xml_generated/2015_MODIS_RelativeHumidity_FourierProcessed.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:42</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:29</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,8 +30,67 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MID) derived from the MOD11A2 Product from USGS. The imagery summarises a key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
- MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by view angle and land surface texture.</gco:CharacterString>
+        <gco:CharacterString>This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS Relative Humidity data:
+ 
+ Abstract :
+ 
+ This is a set of images produced by temporal Fourier analysis of global MODIS data for Middle Infra Red (MID) derived from the MOD11A2 Product from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.
+ MODIS is a sensor on two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of MIR at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture.
+ 
+  
+ 
+ The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
+ Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format to give data users more flexibility.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ The last two characters of each file name denote the output from Fourier processing:
+ a0 - mean
+ mn - minimum
+ mx - maximum
+ a1 - amplitude of annual cycle
+ a2 - amplitude of bi-annual cycle
+ a3 - amplitude of tri-annual cycle
+ p1 - phase of annual cycle
+ p2 - phase of bi-annual cycle
+ p3 - phase of tri-annual cycle
+ d1 - variance in annual cycle
+ d2 - variance in bi-annual cycle
+ d3 - variance in tri-annual cycle
+ da - combined variance in annual, bi-annual, and tri-annual cycles
+ vr - variance in raw data
+ 
+  
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+  Decadal 
+ 
+ Pixel values
+ 
+ Parameter Fourier Variable Image values are
+ RH values A0, A1, A2, A3, Min, Max, Vr Reflectance values 
+ 
+ ALL D1,D2,D3,Da Percentages
+ ALL E1,E2,E3 Percentages
+ ALL P1,P2.P3 Months*100. (Jan=100)
+ 
+ 
+ Source: 
+ Middle Infra Red (MID) derived from the MOD11A2(MODIS NASA) Product from USGS
+ 
+ 
+ Software used:
+ Codes for modelling are in Python and C++
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -98,7 +157,7 @@
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/tfarhmodis1021</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13122979</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -138,7 +197,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:43-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:29-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2017_RoadDensity_OSM_ER.xml b/xml_generated/2017_RoadDensity_OSM_ER.xml
index c82fb9f..6fb6a67 100644
--- a/xml_generated/2017_RoadDensity_OSM_ER.xml
+++ b/xml_generated/2017_RoadDensity_OSM_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:02</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:32</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,27 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Road density for unclassified, minor, major and autoroutes 2017. 
-
-Abstract: This dataset was produced by extracting all rood data from national level Open Steet Map archuves for 2017. The length of each type of road was calcluated for a series of 5 square km grids covering the MOOD study area.</gco:CharacterString>
+        <gco:CharacterString>Abstract:
+ 
+ This dataset was produced by extracting all road data from national-level Open Steet Map archives for 2017. The length of each type of road was calculated for a series of 5 square km grids covering the MOOD study area.
+ 
+  
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -24.5705863176307275,23.6115332000000535 : 44.5127470157025726,73.0115332000000308
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ The year 2017
+ Pixel values:
+  Km of road per square km 
+ Source: 
+ Open Street Map
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -102,7 +120,7 @@ Abstract: This dataset was produced by extracting all rood data from national le
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/RoadDensityerOSM17</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12913627</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -142,7 +160,7 @@ Abstract: This dataset was produced by extracting all rood data from national le
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:02-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:33-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2019_Herbaceous_Wetlands_Copernicus.xml b/xml_generated/2019_Herbaceous_Wetlands_Copernicus.xml
index 88a248e..eef9c68 100644
--- a/xml_generated/2019_Herbaceous_Wetlands_Copernicus.xml
+++ b/xml_generated/2019_Herbaceous_Wetlands_Copernicus.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:46</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:33</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,40 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Copernicus global land cover, herbaceous wetlands, 10m and proportion herbaceous wetland (1km) in 2019. 
-
-Abstract: Landuse/landcover datasets are proivided through the Copernicus climate data service, (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963.). This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then ggregated to 1km resolution version which contans the proportion of each pixel that is assined asherbaceous wetland (erprobapropherbwet1km.tif)</gco:CharacterString>
+        <gco:CharacterString>Copernicus global land cover, herbaceous wetlands, 100m, and proportion herbaceous wetland (1km) in 2019. 
+ 
+ Abstract:
+ 
+ Landuse/landcover datasets are provided through the Copernicus climate data service, (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963).
+ 
+ This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)
+ 
+  
+ 
+ File naming scheme: 
+ 
+ This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 100-meter and 1000-meter
+ 
+ Temporal resolution:
+ The year 2019
+ 
+ Pixel values:
+  The proportion of each pixel that is assigned as herbaceous wetland
+ 
+ Source: 
+ The Copernicus climate data service
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +130,7 @@ Abstract: Landuse/landcover datasets are proivided through the Copernicus climat
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/HerbaceousWetlandsCopernicus21</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12913360</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +170,7 @@ Abstract: Landuse/landcover datasets are proivided through the Copernicus climat
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:46-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:33-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2019_SurfaceWater_PROBA_1k.xml b/xml_generated/2019_SurfaceWater_PROBA_1k.xml
index 2bd1926..49bcf7b 100644
--- a/xml_generated/2019_SurfaceWater_PROBA_1k.xml
+++ b/xml_generated/2019_SurfaceWater_PROBA_1k.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:29</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:33</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,47 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>PROBA V 2019 permanent and Seasonal water 1km. 
-
-Abstract: These layers are extracted for the MOOd extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, &amp; Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050). A nunber of kilometre resolution derivatives have been produced from the original 100m resolution datsest for seasonal and permanent water categories: distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif); percentage of seasonak water (erprobavLLC2019pcseaswat1km.tif); percentage permanent water (erprobavLLC2019pcpermwat1km.tif)</gco:CharacterString>
+        <gco:CharacterString>PROBA V 2019 Permanent and Seasonal water 1km. 
+ 
+ Abstract:
+ 
+ These layers were extracted for the MOOD extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, &amp; Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050). 
+ 
+ Several kilometre resolution derivatives have been produced from the original 100m resolution dataset for seasonal and permanent water categories: 
+ 
+  
+ 
+ File naming scheme: 
+ Distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif); 
+ 
+ Percentage of seasonal water (erprobavLLC2019pcseaswat1km.tif);
+ 
+ Percentage of permanent water (erprobavLLC2019pcpermwat1km.tif)
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ 
+ Temporal resolution:
+ seasonal and permament 
+ 
+ 
+ Pixel values:
+ Meter and Percentage
+ 
+ 
+ Source: 
+ The Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -126,7 +164,7 @@ Abstract: These layers are extracted for the MOOd extent from the Copernicus Glo
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/SurfaceWaterProba1k19</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12819570</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -166,7 +204,7 @@ Abstract: These layers are extracted for the MOOd extent from the Copernicus Glo
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:29-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:34-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2020_MOOD_StandardMappingandAnalysispolygons.xml b/xml_generated/2020_MOOD_StandardMappingandAnalysispolygons.xml
index 01a8e5d..be40c4c 100644
--- a/xml_generated/2020_MOOD_StandardMappingandAnalysispolygons.xml
+++ b/xml_generated/2020_MOOD_StandardMappingandAnalysispolygons.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:03</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:34</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,11 +31,23 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>MOOD standard Mapping and Analysis polygons. 
-
-Abstract: Standard extents and polygons were defined at the start of the MOOD project. There sets of standatd geographies are provided: 
- a) A series of decimal degree grids (0.05, 0.1, 0.2, 0.5 ) in moodgrids.zip 
- b) raster extents in EPRS ( molandmasketrslaeaepsg3035.tif) and geographic projection (molatlonglandmask.tif) in moodpolygonsjanmasks21.zip; and 
- c) standard Administrative unit Polygons adopted from the Vectornet Project : one with relatively equal sized units designed for mapping (vectornetMAPforMOODjan21.shp) and one more suited to analysing and entering data recorded by admin unit that has consistent NITS 3 or Gaul 2 polygons (VectornetDATAforMOODjan21.shp) also in in moodpolygonsjanmasks21.zip</gco:CharacterString>
+ 
+ Abstract:
+ 
+ Standard extents and polygons were defined at the start of the MOOD project. There sets of standatd geographies are provided: 
+  a) A series of decimal degree grids (0.05, 0.1, 0.2, 0.5 ) in moodgrids.zip 
+  b) raster extents in EPRS ( molandmasketrslaeaepsg3035.tif) and geographic projection (molatlonglandmask.tif) in moodpolygonsjanmasks21.zip; and 
+  c) standard Administrative unit Polygons adopted from the Vectornet Project : one with relatively equal sized units designed for mapping (vectornetMAPforMOODjan21.shp) and one more suited to analysing and entering data recorded by admin unit that has consistent NITS 3 or Gaul 2 polygons (VectornetDATAforMOODjan21.shp) also in moodpolygonsjanmasks21.zip
+ 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ 
+ -73.2634658809999451,18.9632860000000392 : 69.0703202920000763,83.6274185180000700
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +111,7 @@ Abstract: Standard extents and polygons were defined at the start of the MOOD pr
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/moodpolygongrid20</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12819564</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +151,7 @@ Abstract: Standard extents and polygons were defined at the start of the MOOD pr
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:03-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:34-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2020_WorldpopHumanPopulation_Age_Gender.xml b/xml_generated/2020_WorldpopHumanPopulation_Age_Gender.xml
index 630964a..75895ed 100644
--- a/xml_generated/2020_WorldpopHumanPopulation_Age_Gender.xml
+++ b/xml_generated/2020_WorldpopHumanPopulation_Age_Gender.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:44</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:34</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,8 +31,38 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>Worldpop Human 2020 population by Age and Gender. 
-
-Abstract: Human popoulation estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasests for the MOOD extent. Gender age categories were summed to provide datasets for all males and all females as well as total populations . Filenames are are follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = mimimum age; OO= Maximum age</gco:CharacterString>
+ 
+ Abstract:
+ 
+ Human population estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasets for the MOOD extent. Gender age categories were summed to provide datasets for all males, all females, and the total population. 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Filenames are as follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = mimimum age; OO= Maximum age
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999995960000092,81.9999997120000046
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Temporal resolution:
+  The year 2020
+ 
+ Pixel values:
+ Human population estimates per pixel 
+ 
+ Source: 
+ Worldpop (www.worldpop.org) datasets
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -117,7 +147,7 @@ Abstract: Human popoulation estimates per pixel were extracted from MOOD partner
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/worldPo20</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12819552</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -157,7 +187,7 @@ Abstract: Human popoulation estimates per pixel were extracted from MOOD partner
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:45-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:35-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2021_2022_MonthlyDistribution_WaterBodies.xml b/xml_generated/2021_2022_MonthlyDistribution_WaterBodies.xml
index 6d028b9..71dbcb6 100644
--- a/xml_generated/2021_2022_MonthlyDistribution_WaterBodies.xml
+++ b/xml_generated/2021_2022_MonthlyDistribution_WaterBodies.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:54</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:35</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,45 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Monthly water bodies for WNV modelling, august 21 - sept 22. 
-
-Abstract: 300m (0.00025 deg) resolution raster datasets for monthly water body presence (aug 21 to sept 22) were extracted for the MOOD spatial extent from global data provided by the Copernicus Global Land Service (https://land.copernicus.eu/global/products/wb). These data were prodced by the Sentinel 2 satellite . Details of data production are provided in the accompanying file CGLOPS2_PUM_WB300m_V2_I1.10.tif</gco:CharacterString>
+        <gco:CharacterString>Monthly water bodies for WNV modelling, August 21 - Sept 22. 
+ 
+ Abstract:
+ 
+ 300m (0.00025 deg) resolution raster datasets for monthly water body presence (Aug 21 to Sept 22) were extracted for the MOOD spatial extent from global data provided by the Copernicus Global Land Service (https://land.copernicus.eu/global/products/wb). These data were produced by the Sentinel 2 satellite. 
+ 
+  
+ 
+ File naming scheme: 
+ Details of data production are provided in the accompanying file CGLOPS2_PUM_WB300m_V2_I1.10.tif
+ 
+ mowatbod+ Month+Year for example for July 2021: mowatbodjul21
+ 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ 
+ Spatial extent:
+ Extent -32.0000000000018474,18.0000000000007603 : 68.9999999999969162,79.9999999999999858
+ 
+ 
+ Spatial resolution:
+ 0.002499999 deg (approx. 300 m) 
+ 
+ 
+ Temporal resolution:
+ Monthly
+ 
+ 
+ Pixel values:
+  Unit: meter
+ 
+ Source: 
+ the Copernicus Global Land Service (https://land.copernicus.eu/global/products/wb)
+ 
+ 
+ Software used:
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -102,7 +138,7 @@ Abstract: 300m (0.00025 deg) resolution raster datasets for monthly water body p
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/monthlywaterbodies2122</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12819239</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -142,7 +178,7 @@ Abstract: 300m (0.00025 deg) resolution raster datasets for monthly water body p
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:54-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:35-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2021_FloodRisk_JRC.xml b/xml_generated/2021_FloodRisk_JRC.xml
index ba1bec1..71100d9 100644
--- a/xml_generated/2021_FloodRisk_JRC.xml
+++ b/xml_generated/2021_FloodRisk_JRC.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:55</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:36</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,9 +30,42 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Floodrisk, 10 year return period, 1km.  
-
-Abstract: The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/collection/id-0054 haas been windowed to the MOOD extent for access by project partners. Accorig to JRC "The maps have been developed using hydrological and hydrodynamic models, driven by the climatological data of the European and Global Flood Awareness Systems (EFAS and GloFAS). European-scale maps comprise most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries."</gco:CharacterString>
+        <gco:CharacterString>Floodrisk, 10 year return period, 1km. 
+ 
+ Abstract:
+ 
+ The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/collection/id-0054 has been windowed to the MOOD extent for access by project partners. According to JRC "The maps have been developed using hydrological and hydrodynamic models, driven by the climatological data of the European and Global Flood Awareness Systems (EFAS and GloFAS). European-scale maps comprise most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries."
+ 
+  
+ 
+ File naming scheme: 
+ 
+ flood risk in TIF format for 10 years : erfloodMapGL_rp10y1km.tif 
+ 
+ JRC Guide: JRC cdatafloodrisk.pdf 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m)
+ 
+ Temporal Resolution:
+ 
+ 10 year period
+ 
+ Pixel values:
+ unit: meter
+ 
+ Source: 
+ 
+  JRC: https://data.jrc.ec.europa.eu/collection/id-0054
+ 
+ Software used:
+ ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -105,7 +138,7 @@ Abstract: The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/c
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/10yearfloodriskJRC21</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799469</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -145,7 +178,7 @@ Abstract: The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/c
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:55-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:36-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2021_Veterinary_Hospital_FacilityDistributions.xml b/xml_generated/2021_Veterinary_Hospital_FacilityDistributions.xml
index 972bab3..32a0f67 100644
--- a/xml_generated/2021_Veterinary_Hospital_FacilityDistributions.xml
+++ b/xml_generated/2021_Veterinary_Hospital_FacilityDistributions.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:55</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:36</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,21 +30,61 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Veterinary and Hospital distributions. Full descriptions in Maps of Healthcare and Veterinary Locations.pptx. 
-
-Abstract: These maps of veterinearfy facilkities and hospital health care locations are derived from the open source Open Street Maps (OSM). These are large datasets that underlie most of the satnav utilities, and are continuously updated by members of the public. They are compiled by GEOFABRIK, and can be downloaded from https://www.geofabric.de. Health care data have been further enhanced by the Global Health Sites Mapping Project (https://healthsites.io). 
+        <gco:CharacterString>Veterinary and Hospital distributions. 
+ 
+ Abstract:
+ 
+ These maps of veterinary facilities and hospital health care locations are derived from the open-source Open Street Maps (OSM). These are large datasets that underlie most of the satnav utilities, and are continuously updated by members of the public. They are compiled by GEOFABRIK, and can be downloaded from https://www.geofabric.de. Healthcare data have been further enhanced by the Global Health Sites Mapping Project (https://healthsites.io). 
+  
+ The data consist of locations (“points of interest”) and building outlines, and are made up of points and polygons that are stored separately in the OSM files. Each record has a series of descriptors (“tags”) such as ‘amenity’ which may include descriptions of a hospital or veterinary clinic. These tags are provided by the people who add the data to the maps and are multilingual and very variable. Most records contain additional more generic tags that can be used to identify classes of locations like hospitals or pharmacies.
+  
+  ERGO has taken the IO and OSM datasets, combined the polygon and point data, converted the polygons to points, and removed any duplicates (where building outlines also have point location records), to produce and global datasets for healthcare and regional datasets for healthcare and veterinary locations covering Europe and its neighboring countries. 
  
- The data consist of locations (“points of interest”) and building outlines, and so are made up of points and polygon which are stored separately in the OSM files. Each record has a series of descriptors (“tags”) such as ‘amenity’ which may include descriptions such as hospital or veterinary clinic. These tags are provided by the people who add the data to the maps and o are multilingual and very variable. Most records contain additional more generic tags that can be used to identify classes of locations like hospitals or pharmacies.
   
- ERGO has taken the IO and OSM datasets, combined the polygon and point data, converted the polygons to points and removed any duplicates (where building outlines also have point location records), to produce and global datasets for healthcare and regional datasets for healthcare and veterinary locations covering Europe and its neighbouring countries. These are provided as ESRI point shapefiles as follows:
+ 
+ File naming scheme: 
+ 
+ Full descriptions in Maps of Healthcare and Veterinary Locations.pptx. 
+ 
+ ESRI point shapefiles are as follows:
   Healthcare Global: Ionodewaynodupallmay21.shp
   Healthcare Regional: MONODWAYALLnodupMAY21.shp
   Veterinary Regional: afeupolypointsnondupmergemay21.shp
- 
- The regional maps are produced by tabulating the number of each healthcare amenity or veterinary locations within administrative zones, normalising by the administrative unit areas. The maps aare produced from the following shape files:
+  
+  The regional maps are produced by tabulating the number of each healthcare amenity or veterinary locations within administrative zones, normalising by the administrative unit areas. The maps are produced from the following shape files:
   Healthcare Regional: MOiohealthamenitycountadmin2may21.shp
   Veterinary Regional: MONODWATALLVETSNODUPMAY21.shp
-  An arcGS 10.4 MPK project file (ERGOhealthvetforweb.mpk) has been provided to dsplay the spatial data provided</gco:CharacterString>
+  An arcGS 10.4 MPK project file (ERGOhealthvetforweb.mpk) has been provided to display the spatial data provided
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -73.2621536249999394,18.9632860000000392 : 69.0703202920000336,83.6274185180000842
+ 
+ Spatial resolution:
+ Shp files
+ 
+ Temporal Resolution:
+ 
+ Year 2021
+ 
+ Values:
+ 
+ Per Units (Number of facilities ): the number of each healthcare amenity or veterinary locations within administrative zones,
+ 
+ Source: 
+ 
+ Open Street Maps (OSM):
+ 
+ GEOFABRIK https://www.geofabric.de
+ 
+ Health care data https://healthsites.io
+ 
+ Software used:
+ ArcMap 10.4</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -114,7 +154,7 @@ Abstract: These maps of veterinearfy facilkities and hospital health care locati
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/VeterinaryHospitalDistu21</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799454</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -154,7 +194,7 @@ Abstract: These maps of veterinearfy facilkities and hospital health care locati
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:56-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:37-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2021_WNV_AvianHost_DistributionIndices.xml b/xml_generated/2021_WNV_AvianHost_DistributionIndices.xml
index d6588a3..75afe4c 100644
--- a/xml_generated/2021_WNV_AvianHost_DistributionIndices.xml
+++ b/xml_generated/2021_WNV_AvianHost_DistributionIndices.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:56</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:37</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,8 +31,45 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>BRT/RF ensemble spatial Models and Weighted seasonality code for WNV avian Hosts. 
-
-Abstract: A series of BRT spatial models were produced for WNV avian hosts (Corvus corax, Corvus corone, Corvus frugilegus, Corvus monedula, Corvus ruficollis, Passer domesticus, Pica pica, Turdus merula) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include polygon data from the Birdlife Internatioinal project and from the Global Biodiversity Information Facility (www.gbif.org) . In addition an ensemble model combining Random Forest and Boosted Regresion Trees spatial modelling outputs has been produced for ann index of seasonal distributions of corvid presence categories ranked in decreasing order as follows Resident, Breeding, Non-Breeding, Passage Speces included : Corvus vorax, Corvus monedula, Corvus corona, Corvus frugilegus, Corvus ruficollis. File name = blifecorvidssuminvcdBRTRFENS.tif.</gco:CharacterString>
+ 
+ Abstract:
+ 
+ A series of BRT spatial models were produced for WNV avian hosts (Corvus corax, Corvus corone, Corvus frugilegus, Corvus monedula, Corvus ruficollis, Passer domesticus, Pica pica, Turdus merula) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include polygon data from the Birdlife Internatioinal project and from the Global Biodiversity Information Facility (www.gbif.org) . In addition an ensemble model combining Random Forest and Boosted Regresion Trees spatial modelling outputs has been produced for an index of seasonal distributions of corvid presence categories ranked in decreasing order as follows Resident, Breeding, Non-Breeding, Passage Speces included : Corvus vorax, Corvus monedula, Corvus corona, Corvus frugilegus, Corvus ruficollis. 
+ 
+  
+ 
+  
+ 
+ File naming scheme: 
+ 
+ File name = blifecorvidssuminvcdBRTRFENS.tif 
+ 
+ description: wnvmodelsdesc.txt
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Temporal Resolution: 
+ 
+ Seasonal 
+ 
+ Pixel values:
+ an index of seasonal distributions
+ 
+ Source: 
+ 
+ The Birdlife Internatioinal project and from the Global Biodiversity Information Facility (www.gbif.org)
+ 
+ Software used:
+ ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -105,7 +142,7 @@ Abstract: A series of BRT spatial models were produced for WNV avian hosts (Corv
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/WNVavianHostDist21</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799425</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -145,7 +182,7 @@ Abstract: A series of BRT spatial models were produced for WNV avian hosts (Corv
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:56-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:37-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2022_2023_SpatialModelVector_Ixodesticks.xml b/xml_generated/2022_2023_SpatialModelVector_Ixodesticks.xml
index 40a440b..44d0107 100644
--- a/xml_generated/2022_2023_SpatialModelVector_Ixodesticks.xml
+++ b/xml_generated/2022_2023_SpatialModelVector_Ixodesticks.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:49</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:37</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,8 +31,41 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>Updated Spatial Models of Tick Vectors Ixodes ricinus and Ixodes persulcatus. 
-
-Abstract: Ensembled spatial models waer produced for Ixodes ricinus ( allixric1xybal40kjul23MEANbrtrf.zip) and Ixodes persulcatus (ergopersulcatusmodelpresabsmay23.zip)by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of national databases from the UK, Spain and Finland</gco:CharacterString>
+ 
+ Abstract:
+ 
+ Ensembled spatial models were produced for Ixodes ricinus ( allixric1xybal40kjul23MEANbrtrf.zip) and Ixodes persulcatus (ergopersulcatusmodelpresabsmay23.zip)by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of national databases from the UK, Spain and Finland
+ 
+  
+ 
+ File naming scheme: 
+ 
+ Ixodes ricinus ( allixric1xybal40kjul23MEANbrtrf.zip)
+ 
+  Ixodes persulcatus (ergopersulcatusmodelpresabsmay23.zip)
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Ixodes ricinus: Extent -32.0000000000000000,10.0000000000000000 : 69.0000000000000000,82.0000000000000000
+ 
+ Ixodes persulcatus: Extent -5.0000000000000000,35.0000000000000000 : 68.9999999999999716,74.9999999999999858
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Pixel values:
+ unit: Probability (between 0 to 1)
+ 
+ Source: 
+ 
+ Global Biodiversity Information Facility (www.gbif.org) and a series of national databases from the UK, Spain and Finland
+ 
+ Software used:
+ ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -105,7 +138,7 @@ Abstract: Ensembled spatial models waer produced for Ixodes ricinus ( allixric1x
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/MOODupdatedVectors2223</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799404</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -145,7 +178,7 @@ Abstract: Ensembled spatial models waer produced for Ixodes ricinus ( allixric1x
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:49-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:38-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2022_2023_SpatialModel_TBEHosts.xml b/xml_generated/2022_2023_SpatialModel_TBEHosts.xml
index 1772a01..4d65f52 100644
--- a/xml_generated/2022_2023_SpatialModel_TBEHosts.xml
+++ b/xml_generated/2022_2023_SpatialModel_TBEHosts.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:29</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:37</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,13 +30,45 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>Updated Spatial Models Hosts: Cervus elephus, Dama dama, Capreolus capreolus, Cervus nippon, Apodemus falvicollis, Myodes glareolus, Lepus europeaus, Lepus timidus, 2022 and 2024. 
-
-Abstract: Ensembled spatial models were produced for four deer species (mooddeerensemblemodelsaug23.zip) , two small mammaol species (moemmaapofmyogMEANRFBRTAug22.zip) and two hare species (moemmalepeutiaug22MEANRFBRT.zip) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include data from IUCN, from the Global Biodiversity Information Facility (www.gbif.org) and from earlier models ( Alexander, N.S., Morley, D, Medlock, J, Searle, K and Wint, W (2014), A First Attempt at Modelling Roe Deer (Capreolus capreolus) Distributions Over Europe. Open Health Data 2(1):e2, DOI:http://dx.doi.org/10.5334/ohd.ah and Wint, W, Morley, D, Medlock, J and Alexander, N.S (2014), A First Attempt at Modelling Red Deer (Cervus elaphus) Distributions Over Europe. Open Health Data 2(1):e1, DOI: http://dx.doi.org/10.5334/ohd.ag . 
- The output files are as follows: 
- a) deer: mocapcapensrfbrt2223.tif = Capreolus capreolus (Roe deer) ; mocervelensrfbrt2223.tif = Cervus elephbus (Red deer) ; modamdamensrfbrt2223.tif = Dama dama (Fallow deer) ; and mosikapaensbrtrfaug23.tif = Cervus nippon (Sika deer) 
- b) small mammals: moemmaapoflaaug22MEANRFBRT.tif = Apodemus flavicolis (Yellow necked mouse ; moemmamyoglaaug22MEANRFBRT.tif = Myodes glareolus (Bank vole)
- c) hares: moemmalepeuaug22MEANRFBRT.tif = Lepus europaeus (European Hare) ; and moemmaleptiaug22MEANRFBRT.tif = Lepus timidus (Mountain Hare)</gco:CharacterString>
+        <gco:CharacterString>Updated Spatial Models Hosts: Cervus elephus, Dama dama, Capreolus capreolus, Cervus nippon, Apodemus falvicollis, Myodes glareolus, Lepus europeaus, Lepus timidus, 2022 and 2023. 
+ 
+ Abstract:
+ 
+ Ensembled spatial models were produced for four deer species (mooddeerensemblemodelsaug23.zip), two small mammal species (moemmaapofmyogMEANRFBRTAug22.zip), and two hare species (moemmalepeutiaug22MEANRFBRT.zip) by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include data from IUCN, from the Global Biodiversity Information Facility (www.gbif.org), and from earlier models ( Alexander, N.S., Morley, D, Medlock, J, Searle, K and Wint, W (2014), A First Attempt at Modelling Roe Deer (Capreolus capreolus) Distributions Over Europe. Open Health Data 2(1):e2, DOI:http://dx.doi.org/10.5334/ohd.ah and Wint, W, Morley, D, Medlock, J and Alexander, N.S (2014), A First Attempt at Modelling Red Deer (Cervus elaphus) Distributions Over Europe. Open Health Data 2(1):e1, DOI: http://dx.doi.org/10.5334/ohd.ag . 
+ 
+  
+ 
+ File naming scheme: 
+ 
+ four deer species (mooddeerensemblemodelsaug23.zip) , two small mammal species (moemmaapofmyogMEANRFBRTAug22.zip) and two hare species (moemmalepeutiaug22MEANRFBRT.zip)
+ 
+  The output files are as follows: 
+  a) deer: mocapcapensrfbrt2223.tif = Capreolus capreolus (Roe deer) ; mocervelensrfbrt2223.tif = Cervus elephbus (Red deer) ; modamdamensrfbrt2223.tif = Dama dama (Fallow deer) ; and mosikapaensbrtrfaug23.tif = Cervus nippon (Sika deer) 
+  b) small mammals: moemmaapoflaaug22MEANRFBRT.tif = Apodemus flavicolis (Yellow necked mouse ; moemmamyoglaaug22MEANRFBRT.tif = Myodes glareolus (Bank vole)
+  c) hares: moemmalepeuaug22MEANRFBRT.tif = Lepus europaeus (European Hare) ; and moemmaleptiaug22MEANRFBRT.tif = Lepus timidus (Mountain Hare)
+ 
+  
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ 
+ Pixel values:
+ Cluster number and Predicted probability of presence
+ 
+ Source: 
+ 
+ UCN, from the Global Biodiversity Information Facility (www.gbif.org)
+ 
+ Software used:
+ ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -121,7 +153,7 @@ Abstract: Ensembled spatial models were produced for four deer species (mooddeer
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/MOODupdatedHost2223</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799418</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -161,7 +193,7 @@ Abstract: Ensembled spatial models were produced for four deer species (mooddeer
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:29-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:37-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2022_SpatialModelHyalomma_marginatum_lusitanicum.xml b/xml_generated/2022_SpatialModelHyalomma_marginatum_lusitanicum.xml
index 7a482ff..087b210 100644
--- a/xml_generated/2022_SpatialModelHyalomma_marginatum_lusitanicum.xml
+++ b/xml_generated/2022_SpatialModelHyalomma_marginatum_lusitanicum.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:49</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:38</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,11 +31,30 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>Spatial Models CCHF vectors Hyalomma marginatum and Hyalomma lusitanicum 2022. 
-
-Abstract: Ensembled spatial models waer produced for CCHf vectors Hyalomma marginatum and and Hyalomma lusitanicum by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), and from the Global Biodiversity Information Facility (www.gbif.org) . 
- 
-  Four zippoed archives are provided : two for probability ((VNHYxxyyyyPROBJUL22 = unmasked, VNHYxxyyyyPROBMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum vale of replicates, std=standard deviation of replicates) and two for Presence absence (=&gt;0.5) (VNHYxxyyyyPAJUL22 = unmasked, VNHYxxyyyyPAMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum vale of replicates). 
- The work was a collaboration with ECDC and the vector distributionsproduced by MOOD were used to mask CCHF models to improve the predicted distribtions of the disease. The results are published in Messina JP, Wint GRW. The Spatial Distribution of Crimean-Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond. Insects. 2023 Sep 17;14(9):771. doi: 10.3390/insects14090771. PMID: 37754739; PMCID: PMC10532370.</gco:CharacterString>
+ Abstract: 
+ Ensembled spatial models were produced for CCHf vectors Hyalomma marginatum and Hyalomma lusitanicum by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net) and from the Global Biodiversity Information Facility (www.gbif.org). 
+  
+  
+ The work was a collaboration with ECDC and the vector distributions produced by MOOD were used to mask CCHF models to improve the predicted distributions of the disease. The results are published in Messina JP, Wint GRW. The Spatial Distribution of Crimean-Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond. Insects. 2023 Sep 17;14(9):771. doi: 10.3390/insects14090771. PMID: 37754739; PMCID: PMC10532370.
+  
+ File naming scheme: 
+ Four zipped archives are provided: two for probability ((VNHYxxyyyyPROBJUL22 = unmasked, VNHYxxyyyyPROBMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum value of replicates, std=standard deviation of replicates) and two for Presence absence (=&gt;0.5) (VNHYxxyyyyPAJUL22 = unmasked, VNHYxxyyyyPAMASKEDJUL22= masked with calculated environmental suitability, XX: LU=lusitanica, MA=marginatum; yyyy=mean of replicates, max=max value of replicates, min = minimum value of replicates). 
+  
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ Snapshot, using training data to 2022
+ Pixel values:
+  Cluster number and Predicted probability of presence
+ Source: 
+ point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), and from the Global Biodiversity Information Facility (www.gbif.org)
+ Software used:
+ The software used for modelling is VECMAP®
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -108,7 +127,7 @@ Abstract: Ensembled spatial models waer produced for CCHf vectors Hyalomma margi
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/SpatialModelHyalommaMI22</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799382</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -148,7 +167,7 @@ Abstract: Ensembled spatial models waer produced for CCHf vectors Hyalomma margi
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:49-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:39-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2022_WeightedMammal_HostDistributionModels.xml b/xml_generated/2022_WeightedMammal_HostDistributionModels.xml
index 2db968a..a04f6cc 100644
--- a/xml_generated/2022_WeightedMammal_HostDistributionModels.xml
+++ b/xml_generated/2022_WeightedMammal_HostDistributionModels.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:45</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:39</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,17 +31,50 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>Weighted TBE Mammal Host distribution models, 2022. 
-
-Abstract: The distributions of the known TBE vector hosts were obtained from IUCN and the European Mammal Atlas and covnverted to presence or absence within standard 50 km Grid and then ranked from 1 to 4 according to their function as follows:
  
- Reservoir Host and virus amplifiers. Score 4 = Apodemus flavicolis (Yellow necked mouse), Myodes glareolus (Bank vole) 
- Major Vector amplifiers and virus dilution. Score 3 = Capreolus capreolus (Roe deer) , cervus elephus (Red deer), Dama dama (Fallow Deer)
- Minor Vector amplifiers and virus dilution. Score 2 = Alces alces (Moose) , Odocoelus virginia (White tailed deer)
- Possible Vector Amplifiers Score 1= Lepus europeus (European hare), Lepus timidus (Mountain Hare)
- Host Predators Score -1 Vulpes vulpes (Red Fox)
- An equal number of zero (hosts absent) points were assigned randommly outside the known host ranges.
+ Abstract:
  
- The summed scores were then modeled using Random Forest and Boosted Regresion Trees spatial modelling methods, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. . The outputs of the two methodds were then ensembled as a simple mean.</gco:CharacterString>
+ The distributions of the known TBE vector hosts were obtained from IUCN and the European Mammal Atlas and converted to presence or absence within standard 50 km Grid and then ranked from 1 to 4 according to their function as follows:
+  
+  Reservoir Host and virus amplifiers. Score 4 = Apodemus flavicolis (Yellow necked mouse), Myodes glareolus (Bank vole) 
+  Major Vector amplifiers and virus dilution. Score 3 = Capreolus capreolus (Roe deer) , cervus elephus (Red deer), Dama dama (Fallow Deer)
+  Minor Vector amplifiers and virus dilution. Score 2 = Alces alces (Moose) , Odocoelus Virginia (White-tailed deer)
+  Possible Vector Amplifiers Score 1= Lepus europeus (European hare), Lepus timidus (Mountain Hare)
+  Host Predators Score -1 Vulpes vulpes (Red Fox)
+  An equal number of zero (hosts absent) points were assigned randomly outside the known host ranges.
+  
+  The summed scores were then modelled using Random Forest and Boosted Regression Trees spatial modelling methods, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The outputs of the two methods were then ensembled as a simple mean.
+ 
+  
+ 
+ File naming scheme: 
+ 
+ tbeweighetedhostsRFBRTGAUSENSEMBLEDec21.TIF The weighted host model in Dec 2021
+ 
+  
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,18.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m)
+ 
+ Pixel values:
+ 
+ a weighted score
+ 
+ Source: 
+ 
+ IUCN and the European Mammal Atlas
+ 
+ Software used:
+ 
+ The software used for modelling is VECMAP®
+ 
+ Software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -111,7 +144,7 @@ Abstract: The distributions of the known TBE vector hosts were obtained from IUC
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/weightedmammalhosts22</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12799370</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -151,7 +184,7 @@ Abstract: The distributions of the known TBE vector hosts were obtained from IUC
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:45-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:39-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2023_SpatialModels_Mosquitovectors_WNV.xml b/xml_generated/2023_SpatialModels_Mosquitovectors_WNV.xml
index f864661..125bf46 100644
--- a/xml_generated/2023_SpatialModels_Mosquitovectors_WNV.xml
+++ b/xml_generated/2023_SpatialModels_Mosquitovectors_WNV.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:49</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:39</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,8 +31,46 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>Spatial Models of moisquito vectors of WNV - Culex pipiens, Culex torrentium and Culex modestus. 
-
-Abstract: Ensembled spatial models waer produced for WNV vectors Culex pipiens (movmnpipiensrfbrtmeanfeb23.tif), Culex torrentium movmnpipiensrfbrtmeanfeb23.tif) , and Culex modestus (modcumodMEANrfbrt.tif) by combining Random Forest and Boosted Regresion Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human poplation and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of published papers</gco:CharacterString>
+ 
+ Abstract
+ 
+ Ensembled spatial models were produced for WNV vectors Culex pipiens (movmnpipiensrfbrtmeanfeb23.tif), Culex torrentium movmnpipiensrfbrtmeanfeb23.tif) , and Culex modestus (modcumodMEANrfbrt.tif) by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables (see Scharlemann et. al. 2008, https://doi.org/10.1371/journal.pone.0001408), , land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of published papers.
+ 
+  
+ 
+ File naming scheme :
+ 
+ Filename movmnpipiensrfbrtmeanfeb23.tif = spatial model for WNV vector Culex pipiens
+ 
+ Filename movmnpipiensrfbrtmeanfeb23.tif = spatial model for Culex torrentium) ,
+ 
+ Filename modcumodMEANrfbrt.tif= spatial model for Culex modestus
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ 
+ Spatial resolution:
+ 0.008333 deg (nominally 1km)
+ 
+ temporal resolution:
+ 
+ Snapshot, using training data to 2023”
+ 
+ Pixel values:
+  Predicted probability of presence
+ 
+ Source: 
+ 
+ The training data offered to the model process include point location and polygon data from the VectorNet project (https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net), from the Global Biodiversity Information Facility (www.gbif.org) and a series of published papers.
+ 
+ Software used:
+ 
+ Software used for modelling is VECMAP®
+ 
+ Software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -96,7 +134,7 @@ Abstract: Ensembled spatial models waer produced for WNV vectors Culex pipiens (
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/modlsmosquitovectorsWNV23</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12723449</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -136,7 +174,7 @@ Abstract: Ensembled spatial models waer produced for WNV vectors Culex pipiens (
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:50-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:39-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2023_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml b/xml_generated/2023_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml
index 8e371d7..f17c84b 100644
--- a/xml_generated/2023_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml
+++ b/xml_generated/2023_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:05</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:09</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,13 +30,50 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This dataset compares cumulative temperature masks of year 2023 with the average in 2020,2021, and 2022. 
- The input data is taken from Cumulitative temperature masks calculated in those years and then turn it to integer numbers of 0 to 3 to present which of these years it was suitibale. 
- The data can be classified as: 
+        <gco:CharacterString>Abstract : 
+ 
+ This dataset compares cumulative temperature masks of the year 2023 with the average in 2020,2021, and 2022. 
+  The input data is taken from Cumulative temperature masks calculated in those years and then turned to integer numbers of 0 to 3 to present which of these years were suitable. 
+  The data can be classified as: 
   1 for unsuitable areas
-  0 for Suitable on 2023
-  2 for Suitable on 2020-2023
-  3 for Suitable on 2020-2022</gco:CharacterString>
+  0 for Suitable in 2023
+  2 for Suitable in 2020-2023
+  3 for Suitable in 2020-2022
+ 
+  
+ 
+ File naming schema:
+ 
+  ER23ti202122 + 8-day number (46 in total) for example, ER23to20212208C68F.tif, 08 in this example refers to the 6th of March.
+ 
+ ERCumTepdif23 + month+ day: ERCumTempdif230306.jpg, 0306 refers to the 8-day starting that day. 
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day
+ 
+ Pixel values:
+ 
+  1 for unsuitable areas
+  0 for Suitable in 2023
+  2 for Suitable in 2020-2023
+  3 for Suitable in 2020-2022
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ 
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +136,7 @@
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/VIIRSCumTempDif23to202122</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13221630</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +176,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:05-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:09-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus.xml b/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus.xml
index b1e9ea8..665d5ec 100644
--- a/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus.xml
+++ b/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:05</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:08</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,19 +30,55 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This dataset presents cumulative temperature masks that identify areas that are warm enough to stimulate tick questing activity, using a temperature threshold of 6 C. A series of scripts download and process VIIRS EO imagery of Land Surface Temperature to create temperature masks every 8 days using a combination of decadel (8-day) and daily satellite data. This dataset includes 2024, It is an update for previous one.
+        <gco:CharacterString>Abstract:
+ 
+ This dataset presents cumulative temperature masks that identify areas that are warm enough to stimulate tick questing activity, using a temperature threshold of 6 C. A series of scripts download and process VIIRS EO imagery of Land Surface Temperature to create temperature masks every 8 days using a combination of decadal (8-day) and daily satellite data. This dataset is an updated version for 2024.
  
  These result in Boolean masks where suitable areas according to temperature limits on I. ricinus are identified as 1 and unsuitable areas as 0. This mask can then be applied to the existing seasonal Tick model to make a more timely prediction of tick activity based on recent temperatures.
  
- Image acquisition
+ This dataset will be updated every couple of months by the end of the year 2024.
+ 
+ Image acquisition:
  
  Two different products are downloaded VIIRS Land Surface Temperature/Emissivity 8-day L3 Global 1 km SIN grid (VNP21A2, version 6) and VIIRS Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN grid (VNP21A1D, version 6). 
  
- processing
+ Processing:
  The cumulative temperature mask is processed in two steps through two separate scripts:
  
- a. Acquisition of 1km MODIS Land Surface Temperature imagery from NASA's data repository.
- b. Importing of the imagery into a Suitable format from which regularly updated masks are calculated.</gco:CharacterString>
+ a. Acquisition of 1km VIIRS Land Surface Temperature imagery from NASA's data repository.
+ b. Importing of the imagery into a suitable format from which regularly updated masks are calculated.
+ 
+ File naming schema:
+ 
+  ER+ year + 8-day number (46 in total) for example: ER2433C68.tif 24 refers to the year 2024 and 33 (decadal number) in this example refers to the 22nd of September.
+ 
+ ERCumTemp + year + month+ day: ERCumTemp240109.jpg, 240109 refers to the 8-day starting 9th of January.
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day for year 2024
+ 
+ 
+ Pixel values:
+ 
+ Suitable areas as 1 and unsuitable areas as 0
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ Repository URL :
+ https://github.com/ERGOcode/Cumulative-Temperature-Mask
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -105,7 +141,7 @@
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/VIIRSCumTempIxRicinus2024</gmd:URL>
+                <gmd:URL>https://doi.org/ 10.5281/zenodo.13221618</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -145,7 +181,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:05-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:09-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml b/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml
index da6519d..d4dce3f 100644
--- a/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml
+++ b/xml_generated/2024_VIIRS_CumTemp_IxodesRicinus_Difference_2020to2022.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:05</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:09</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,13 +30,50 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This dataset compares cumulative temperature masks of year 2024 with the average in 2020,2021, and 2022. 
- The input data is taken from Cumulitative temperature masks calculated in those years and then turn it to integer numbers of 0 to 3 to present which of these years it was suitibale. 
- The data can be classified as: 
+        <gco:CharacterString>Abstract : 
+ 
+ This dataset compares cumulative temperature masks of the year 2024 with the average in 2020,2021, and 2022. 
+  The input data is taken from Cumulative temperature masks calculated in those years and then turned to integer numbers of 0 to 3 to present which of these years were suitable. 
+  The data can be classified as: 
   1 for unsuitable areas
-  0 for Suitable on 2024
-  2 for Suitable on 2020-2024
-  3 for Suitable on 2020-2022</gco:CharacterString>
+  0 for Suitable in 2024
+  2 for Suitable in 2020-2024
+  3 for Suitable in 2020-2022
+ 
+ This dataset will be updated every couple of months by the end of the year 2024.
+ 
+ File naming schema:
+ 
+  ER24ti202122 + 8-day number (46 in total) for example : ER24to20212208C68F.tif, 08 in this example refers to 6th of March
+ 
+  ERCumTepdif24 + month+ day: ERCumTempdif240306.jpg, 0306 refers to the 8-day starting that day. 
+ 
+ gif file presents time-series animation for a whole year. 
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ Spatial extent:
+ Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
+ Spatial resolution:
+ 0.0083333 deg (approx. 1000 m) 
+ Temporal resolution:
+ 8-day
+ 
+ Pixel values:
+ 
+ 1 for unsuitable areas
+  0 for Suitable in 2024
+  2 for Suitable in 2020-2024
+  3 for Suitable in 2020-2022
+ 
+ 
+ Source: 
+  VIIRS NASA :VNP21A2 and VNP21A1D
+ 
+ 
+ Software used:
+ Codes for modelling are in Python 
+ The software used for map production is ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -99,7 +136,7 @@
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/VIIRSCumTempDif24to202122</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.13221658</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -139,7 +176,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:06-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:09-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/An_annotated_Avian_Influenza_dataset_from_two_event-based_surveillance_systems.xml b/xml_generated/An_annotated_Avian_Influenza_dataset_from_two_event-based_surveillance_systems.xml
index 72ca4d5..3702fb3 100644
--- a/xml_generated/An_annotated_Avian_Influenza_dataset_from_two_event-based_surveillance_systems.xml
+++ b/xml_generated/An_annotated_Avian_Influenza_dataset_from_two_event-based_surveillance_systems.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:52</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:39</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:52-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:40-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Annotated_datasets_from_PADI-web_for_event-based_surveillance_of_Avian_Influenza,_African_Swine_Fever,_and_West-Nile_Virus_Disease.xml b/xml_generated/Annotated_datasets_from_PADI-web_for_event-based_surveillance_of_Avian_Influenza,_African_Swine_Fever,_and_West-Nile_Virus_Disease.xml
index b7f51bb..e1f3788 100644
--- a/xml_generated/Annotated_datasets_from_PADI-web_for_event-based_surveillance_of_Avian_Influenza,_African_Swine_Fever,_and_West-Nile_Virus_Disease.xml
+++ b/xml_generated/Annotated_datasets_from_PADI-web_for_event-based_surveillance_of_Avian_Influenza,_African_Swine_Fever,_and_West-Nile_Virus_Disease.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:52</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:40</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -136,7 +136,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:52-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:40-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Annotation_of_epidemiological_information_in_animal_disease-related_news_articles:_guidelines_and_manually_labelled_corpus.xml b/xml_generated/Annotation_of_epidemiological_information_in_animal_disease-related_news_articles:_guidelines_and_manually_labelled_corpus.xml
index 8be6733..fc22cae 100644
--- a/xml_generated/Annotation_of_epidemiological_information_in_animal_disease-related_news_articles:_guidelines_and_manually_labelled_corpus.xml
+++ b/xml_generated/Annotation_of_epidemiological_information_in_animal_disease-related_news_articles:_guidelines_and_manually_labelled_corpus.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:32</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:40</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:33-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:40-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Annual_terrestrial_Human_Footprint_dataset_from_1982_to_2000.xml b/xml_generated/Annual_terrestrial_Human_Footprint_dataset_from_1982_to_2000.xml
index b48dcb4..ff78278 100644
--- a/xml_generated/Annual_terrestrial_Human_Footprint_dataset_from_1982_to_2000.xml
+++ b/xml_generated/Annual_terrestrial_Human_Footprint_dataset_from_1982_to_2000.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:48</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:40</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -45,7 +45,7 @@
       <gmd:descriptiveKeywords>
         <gmd:MD_Keywords>
           <gmd:keyword>
-            <gco:CharacterString>Disease data</gco:CharacterString>
+            <gco:CharacterString>Covariate</gco:CharacterString>
           </gmd:keyword>
         </gmd:MD_Keywords>
       </gmd:descriptiveKeywords>
@@ -74,7 +74,7 @@
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>http://dx.doi.org/10.5281/zenodo.6458580 https://zenodo.org/records/6636562 </gmd:URL>
+                <gmd:URL>https://zenodo.org/records/6636562 </gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -114,7 +114,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:49-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:41-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Avian_Influenza_events_affecting_mammals_from_ProMED.xml b/xml_generated/Avian_Influenza_events_affecting_mammals_from_ProMED.xml
index 726ea4e..bb04192 100644
--- a/xml_generated/Avian_Influenza_events_affecting_mammals_from_ProMED.xml
+++ b/xml_generated/Avian_Influenza_events_affecting_mammals_from_ProMED.xml
@@ -1,6 +1,6 @@
 <gmd:MD_Metadata xmlns:gco="http://www.isotc211.org/2005/gco" xmlns:gfc="http://www.isotc211.org/2005/gfc" xmlns:gmd="http://www.isotc211.org/2005/gmd" xmlns:gmi="http://standards.iso.org/iso/19115/-2/gmi/1.0" xmlns:gmx="http://www.isotc211.org/2005/gmx" xmlns:gts="http://www.isotc211.org/2005/gts" xmlns:srv="http://www.isotc211.org/2005/srv" xmlns:gml="http://www.opengis.net/gml/3.2" xmlns:gmlcov="http://www.opengis.net/gmlcov/1.0" xmlns:gmlrgrid="http://www.opengis.net/gml/3.3/rgrid" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
   <gmd:fileIdentifier>
-    <gco:CharacterString>c4702d92-80b3-11ee-b962-0242ac120046</gco:CharacterString>
+    <gco:CharacterString>c4702d92-80b3-11ee-b962-0242ac120048</gco:CharacterString>
   </gmd:fileIdentifier>
   <gmd:language>
     <gmd:LanguageCode codeList="http://www.loc.gov/standards/iso639-2/" codeListValue="eng" codeSpace="ISO 639-2">English</gmd:LanguageCode>
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:01</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:41</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -30,12 +30,12 @@
         </gmd:CI_Citation>
       </gmd:citation>
       <gmd:abstract>
-        <gco:CharacterString>This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day. These are preprocessed and normalized events, which are extracted from ProMED as Epidemiological Surveillance Systems (EBS).</gco:CharacterString>
+        <gco:CharacterString>This dataset contains a set of Avian Influenza events affecting mammal species from 2020 to the present day extracted from EBS Padi-web and normalized to the ESA platform standard. Weekly updated. </gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
           <gmd:organisationName>
-            <gco:CharacterString>LIRMM</gco:CharacterString>
+            <gco:CharacterString>CIRAD</gco:CharacterString>
           </gmd:organisationName>
           <gmd:role>
             <gmd:CI_RoleCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_RoleCode" codeListValue="principalInvestigator" codeSpace="ISOTC211/19115">principalInvestigator</gmd:CI_RoleCode>
@@ -45,7 +45,7 @@
       <gmd:graphicOverview>
         <gmd:MD_BrowseGraphic>
           <gmd:fileName>
-            <gco:CharacterString>http://advanse.lirmm.fr/avianflu/Logo-LIRMM-long_329x113.png</gco:CharacterString>
+            <gco:CharacterString>https://promedmail.org/wp-content/uploads/2022/07/ProMed_logo-Full-Name.png</gco:CharacterString>
           </gmd:fileName>
           <gmd:fileDescription>
             <gco:CharacterString>thumbnail</gco:CharacterString>
@@ -62,13 +62,6 @@
           </gmd:keyword>
         </gmd:MD_Keywords>
       </gmd:descriptiveKeywords>
-      <gmd:resourceConstraints>
-        <gmd:MD_LegalConstraints>
-          <gmd:useLimitation>
-            <gco:CharacterString>CC0</gco:CharacterString>
-          </gmd:useLimitation>
-        </gmd:MD_LegalConstraints>
-      </gmd:resourceConstraints>
       <gmd:language>
         <gmd:LanguageCode codeList="http://www.loc.gov/standards/iso639-2/" codeListValue="eng" codeSpace="ISO 639-2">English</gmd:LanguageCode>
       </gmd:language>
@@ -87,7 +80,7 @@
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>http://advanse.lirmm.fr/avianflu/</gmd:URL>
+                <gmd:URL/>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -127,7 +120,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:01-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:42-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Avian_Influenza_events_from_differents_digital_surveillance_tools.xml b/xml_generated/Avian_Influenza_events_from_differents_digital_surveillance_tools.xml
index 925f275..7c9ea7c 100644
--- a/xml_generated/Avian_Influenza_events_from_differents_digital_surveillance_tools.xml
+++ b/xml_generated/Avian_Influenza_events_from_differents_digital_surveillance_tools.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:52</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:42</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:53-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:42-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/COVID-19-line-list.xml b/xml_generated/COVID-19-line-list.xml
index 0e36614..45c6d04 100644
--- a/xml_generated/COVID-19-line-list.xml
+++ b/xml_generated/COVID-19-line-list.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:50</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:44</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -110,7 +110,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:50-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:44-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/COVID-19_Tweet_dataset.xml b/xml_generated/COVID-19_Tweet_dataset.xml
index 02ab4b6..a0140df 100644
--- a/xml_generated/COVID-19_Tweet_dataset.xml
+++ b/xml_generated/COVID-19_Tweet_dataset.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:35</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:58</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -125,7 +125,7 @@ https://doi.org/10.1016/j.ijid.2021.12.065</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:36-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:59-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/COVID-19_contact_tracing.xml b/xml_generated/COVID-19_contact_tracing.xml
index e4fb1d2..3e9bfac 100644
--- a/xml_generated/COVID-19_contact_tracing.xml
+++ b/xml_generated/COVID-19_contact_tracing.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:57</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:44</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -141,7 +141,7 @@ Béraud, G. et al. The French connection: the first large population-based conta
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:58-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:44-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/COVID-19_influenza.xml b/xml_generated/COVID-19_influenza.xml
index 8dd277f..ffe6448 100644
--- a/xml_generated/COVID-19_influenza.xml
+++ b/xml_generated/COVID-19_influenza.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:57</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:43</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -122,7 +122,7 @@ https://www.who.int/tools/flunet
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:57-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:44-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/COVID-19_international_cases_as_of_Feb_13.xml b/xml_generated/COVID-19_international_cases_as_of_Feb_13.xml
index 8bd60d0..c6107cc 100644
--- a/xml_generated/COVID-19_international_cases_as_of_Feb_13.xml
+++ b/xml_generated/COVID-19_international_cases_as_of_Feb_13.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:57</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:43</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:57-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:43-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/COVID-19_vaccination.xml b/xml_generated/COVID-19_vaccination.xml
index 1a88bc4..2e3c890 100644
--- a/xml_generated/COVID-19_vaccination.xml
+++ b/xml_generated/COVID-19_vaccination.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:58</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:44</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -123,7 +123,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:58-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:45-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Code_and_Data_for:_Crowding_and_the_shape_of_COVID-19_epidemics.xml b/xml_generated/Code_and_Data_for:_Crowding_and_the_shape_of_COVID-19_epidemics.xml
index 9e2e7d7..55b6e33 100644
--- a/xml_generated/Code_and_Data_for:_Crowding_and_the_shape_of_COVID-19_epidemics.xml
+++ b/xml_generated/Code_and_Data_for:_Crowding_and_the_shape_of_COVID-19_epidemics.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:01</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:42</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -125,7 +125,7 @@ https://github.com/alsnhll/SIRNestedNetwork</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:02-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:42-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git "a/xml_generated/Code_and_data_of_article_\"Estimating_SARS-CoV-2_infections_and_associated_changes_in_COVID-19_severity_a.xml" "b/xml_generated/Code_and_data_of_article_\"Estimating_SARS-CoV-2_infections_and_associated_changes_in_COVID-19_severity_a.xml"
index 3105ac2..b2b63b0 100644
--- "a/xml_generated/Code_and_data_of_article_\"Estimating_SARS-CoV-2_infections_and_associated_changes_in_COVID-19_severity_a.xml"
+++ "b/xml_generated/Code_and_data_of_article_\"Estimating_SARS-CoV-2_infections_and_associated_changes_in_COVID-19_severity_a.xml"
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:51</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:06</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -111,7 +111,7 @@ infections and associated changes in COVID-19 severity and fatality and fatality
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:51-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:06-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Combining_phylogeographic_and_niche_modelling_approaches_to_investigate_the_ecological_drivers_of_TBEV_at_the_Palearctic_scale.xml b/xml_generated/Combining_phylogeographic_and_niche_modelling_approaches_to_investigate_the_ecological_drivers_of_TBEV_at_the_Palearctic_scale.xml
index 5a034c9..8f1619f 100644
--- a/xml_generated/Combining_phylogeographic_and_niche_modelling_approaches_to_investigate_the_ecological_drivers_of_TBEV_at_the_Palearctic_scale.xml
+++ b/xml_generated/Combining_phylogeographic_and_niche_modelling_approaches_to_investigate_the_ecological_drivers_of_TBEV_at_the_Palearctic_scale.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="" codeSpace="ISOTC211/19115"/>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:04</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:08</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -113,7 +113,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:05-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:08-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Consensus_LandUse_Earthenv_1k_ER.xml b/xml_generated/Consensus_LandUse_Earthenv_1k_ER.xml
index 5a51c6f..f61529f 100644
--- a/xml_generated/Consensus_LandUse_Earthenv_1k_ER.xml
+++ b/xml_generated/Consensus_LandUse_Earthenv_1k_ER.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:50</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:42</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -31,8 +31,34 @@
       </gmd:citation>
       <gmd:abstract>
         <gco:CharacterString>Earthenv consensus land use. 
-
-Abstract: The Earthenv (www.earthenv.org) consensus land cover layers are a combination of a number of public domain land cover datasets, which are considered to be more reperesnative han any of the component datasets. 12 land cover types are provided as proportions, easch of which has been ettracted for the MOOD extent. the filenames are given in file eartnenvnames.txt)</gco:CharacterString>
+ 
+ Abstract: The Earthenv (www.earthenv.org) consensus land cover layers are a combination of a number of public domain land cover datasets, which are considered to be more representative than any of the component datasets. 12 land cover types are provided as proportions, each of which has been extracted for the MOOD extent.
+ 
+ File naming scheme:
+ 
+ The filenames are given in accompanying file earthenvnames.txt
+ 
+ Projection + EPSG code:
+ Latitude-Longitude/WGS84 (EPSG: 4326)
+ 
+ Spatial extent:
+ Extent -24.0000000000000000,-38.5000000000000000 : 110.9999999999999432,86.9999999999999432
+ 
+ Spatial resolution:
+ 0.008333 deg (approx. 1000 m)
+ 
+ Accuracy:
+ 
+ Based on World Geodetic System 1984 ensemble (EPSG:6326), which has a limited accuracy of at best 2 meters.
+ 
+  
+ 
+ Source:
+ 
+  Earthenv (www.earthenv.org) consensus land cover layers
+ 
+ Software used:
+ ESRI ArcMap 10.8</gco:CharacterString>
       </gmd:abstract>
       <gmd:pointOfContact>
         <gmd:CI_ResponsibleParty>
@@ -96,7 +122,7 @@ Abstract: The Earthenv (www.earthenv.org) consensus land cover layers are a comb
           <gmd:onLine>
             <gmd:CI_OnlineResource>
               <gmd:linkage>
-                <gmd:URL>https://tinyurl.com/EARTHENVLandCoverMOOD</gmd:URL>
+                <gmd:URL>https://doi.org/10.5281/zenodo.12722471</gmd:URL>
               </gmd:linkage>
               <gmd:protocol>
                 <gco:CharacterString>WWW:LINK-1.0-http--link</gco:CharacterString>
@@ -136,7 +162,7 @@ Abstract: The Earthenv (www.earthenv.org) consensus land cover layers are a comb
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:50-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:43-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Covariates_metadata_extracted_from_literature:_Tick-borne_encephalitis.xml b/xml_generated/Covariates_metadata_extracted_from_literature:_Tick-borne_encephalitis.xml
index 04c02a0..35fc3d8 100644
--- a/xml_generated/Covariates_metadata_extracted_from_literature:_Tick-borne_encephalitis.xml
+++ b/xml_generated/Covariates_metadata_extracted_from_literature:_Tick-borne_encephalitis.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:29</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:06</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -119,7 +119,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:30-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:07-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Covariates_metadata_extracted_from_literature:_West_Nile_Virus.xml b/xml_generated/Covariates_metadata_extracted_from_literature:_West_Nile_Virus.xml
index 61763ef..24f902a 100644
--- a/xml_generated/Covariates_metadata_extracted_from_literature:_West_Nile_Virus.xml
+++ b/xml_generated/Covariates_metadata_extracted_from_literature:_West_Nile_Virus.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:50</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:07</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -119,7 +119,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:50-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:07-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Data_for_changes_in_contact_patterns_shape_the_dynamics_of_the_novel_coronavirus_disease_2019_outbreak_in_China.xml b/xml_generated/Data_for_changes_in_contact_patterns_shape_the_dynamics_of_the_novel_coronavirus_disease_2019_outbreak_in_China.xml
index b0db438..c6a0718 100644
--- a/xml_generated/Data_for_changes_in_contact_patterns_shape_the_dynamics_of_the_novel_coronavirus_disease_2019_outbreak_in_China.xml
+++ b/xml_generated/Data_for_changes_in_contact_patterns_shape_the_dynamics_of_the_novel_coronavirus_disease_2019_outbreak_in_China.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:50</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:45</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -117,7 +117,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:51-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:46-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Data_quality:_Classification_of_news_articles.xml b/xml_generated/Data_quality:_Classification_of_news_articles.xml
index fde2062..4afb7c8 100644
--- a/xml_generated/Data_quality:_Classification_of_news_articles.xml
+++ b/xml_generated/Data_quality:_Classification_of_news_articles.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:37</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:02</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -123,7 +123,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:37-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:02-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Leptospirosis,_Influenza_A_and_Chikungunya_dataset.xml b/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Leptospirosis,_Influenza_A_and_Chikungunya_dataset.xml
index 76b166b..a41b589 100644
--- a/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Leptospirosis,_Influenza_A_and_Chikungunya_dataset.xml
+++ b/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Leptospirosis,_Influenza_A_and_Chikungunya_dataset.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:00</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:05</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -147,7 +147,7 @@ https://doi.org/10.5281/zenodo.11241409</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:01-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:05-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Tularemia_dataset.xml b/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Tularemia_dataset.xml
index c24d2c8..ebb1f36 100644
--- a/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Tularemia_dataset.xml
+++ b/xml_generated/Dataset_of_the_diseases'_covariates_extracted_from_the_literature:_Tularemia_dataset.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:01</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:51</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -140,7 +140,7 @@ The important human, animal, vector and environmental covariates were extracted
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:01-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:52-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Dissemination_of_information_in_event-based_surveillance,_a_case_study_of_Avian_Influenza_-_dataset.xml b/xml_generated/Dissemination_of_information_in_event-based_surveillance,_a_case_study_of_Avian_Influenza_-_dataset.xml
index 0345d1c..fbf9616 100644
--- a/xml_generated/Dissemination_of_information_in_event-based_surveillance,_a_case_study_of_Avian_Influenza_-_dataset.xml
+++ b/xml_generated/Dissemination_of_information_in_event-based_surveillance,_a_case_study_of_Avian_Influenza_-_dataset.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:33</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:43</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -121,7 +121,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:33-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:43-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-bal_COVID-19_schools.xml b/xml_generated/EPIcx-bal_COVID-19_schools.xml
index 2beb2f2..8b2012d 100644
--- a/xml_generated/EPIcx-bal_COVID-19_schools.xml
+++ b/xml_generated/EPIcx-bal_COVID-19_schools.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:58</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:46</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@ https://www.ecdc.europa.eu/en/publications-data/data-covid-19-vaccination-eu-eea
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:59-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:46-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_Adherence_and_sustainability_.xml b/xml_generated/EPIcx-lab_COVID-19_Adherence_and_sustainability_.xml
index 7be7382..cda660b 100644
--- a/xml_generated/EPIcx-lab_COVID-19_Adherence_and_sustainability_.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_Adherence_and_sustainability_.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:59</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:47</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -132,7 +132,7 @@ Commun Med 1, 57 (2021). https://doi.org/10.1038/s43856-021-00057-5 </gco:Charac
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:59-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:47-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_Impact_school_reopening_.xml b/xml_generated/EPIcx-lab_COVID-19_Impact_school_reopening_.xml
index dd6f168..ce362a7 100644
--- a/xml_generated/EPIcx-lab_COVID-19_Impact_school_reopening_.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_Impact_school_reopening_.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:59</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:48</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -132,7 +132,7 @@ https://doi.org/10.1038/s41467-021-21249-6</gco:CharacterString>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:00-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:49-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_Underdetection_France_.xml b/xml_generated/EPIcx-lab_COVID-19_Underdetection_France_.xml
index 9db40da..e439b0e 100644
--- a/xml_generated/EPIcx-lab_COVID-19_Underdetection_France_.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_Underdetection_France_.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:30</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:51</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -132,7 +132,7 @@ Nature (2020). https://doi.org/10.1038/s41586-020-03095-6</gco:CharacterString>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:30-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:51-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_attitude.xml b/xml_generated/EPIcx-lab_COVID-19_attitude.xml
index 9df13ab..ee19134 100644
--- a/xml_generated/EPIcx-lab_COVID-19_attitude.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_attitude.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:59</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:47</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -133,7 +133,7 @@ https://influweb.org/welcome
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:59-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:47-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_curfew_B.1.1.7.xml b/xml_generated/EPIcx-lab_COVID-19_curfew_B.1.1.7.xml
index aa089eb..ead751a 100644
--- a/xml_generated/EPIcx-lab_COVID-19_curfew_B.1.1.7.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_curfew_B.1.1.7.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:59</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:47</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -137,7 +137,7 @@ https://www.google.com/covid19/mobility?hl=fr
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:59-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:48-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_importation_lockdown.xml b/xml_generated/EPIcx-lab_COVID-19_importation_lockdown.xml
index 8f8d080..acdde9d 100644
--- a/xml_generated/EPIcx-lab_COVID-19_importation_lockdown.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_importation_lockdown.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:53</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:49</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@ Béraud, G. et al. The French connection: the first large population-based conta
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:54-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:49-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_importation_risk_Africa.xml b/xml_generated/EPIcx-lab_COVID-19_importation_risk_Africa.xml
index 6090b30..5e4f14f 100644
--- a/xml_generated/EPIcx-lab_COVID-19_importation_risk_Africa.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_importation_risk_Africa.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:30</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:49</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -126,7 +126,7 @@ https://apps.who.int/iris/handle/10665/272432
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:30-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:49-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_importation_risk_Europe.xml b/xml_generated/EPIcx-lab_COVID-19_importation_risk_Europe.xml
index dda18c7..56e8e6e 100644
--- a/xml_generated/EPIcx-lab_COVID-19_importation_risk_Europe.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_importation_risk_Europe.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:02</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:49</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:03-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:50-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_importation_risk_summer_Delta.xml b/xml_generated/EPIcx-lab_COVID-19_importation_risk_summer_Delta.xml
index 108b920..8fb5ec3 100644
--- a/xml_generated/EPIcx-lab_COVID-19_importation_risk_summer_Delta.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_importation_risk_summer_Delta.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:00</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:50</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@ https://dataforgood.fb.com/
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:00-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:50-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_mobility_effect.xml b/xml_generated/EPIcx-lab_COVID-19_mobility_effect.xml
index 0663c2e..24efd05 100644
--- a/xml_generated/EPIcx-lab_COVID-19_mobility_effect.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_mobility_effect.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:45</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:50</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -132,7 +132,7 @@ https://dares.travail-emploi.gouv.fr/IMG/pdf/dares_acemo_covid19_synthese_17-04-
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:45-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:50-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EPIcx-lab_COVID-19_socio_economic.xml b/xml_generated/EPIcx-lab_COVID-19_socio_economic.xml
index 29ffa7e..5d3378f 100644
--- a/xml_generated/EPIcx-lab_COVID-19_socio_economic.xml
+++ b/xml_generated/EPIcx-lab_COVID-19_socio_economic.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:30</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:50</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -122,7 +122,7 @@ https://www.insee.fr/fr/statistiques/3568602?sommaire=3568656#consulter
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:30-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:51-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EpiDCA.xml b/xml_generated/EpiDCA.xml
index 5d4e3cf..7668518 100644
--- a/xml_generated/EpiDCA.xml
+++ b/xml_generated/EpiDCA.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:52</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:03</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -119,7 +119,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:52-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:03-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EpiNorm.xml b/xml_generated/EpiNorm.xml
index bde6627..a34632d 100644
--- a/xml_generated/EpiNorm.xml
+++ b/xml_generated/EpiNorm.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:31</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:04</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -136,7 +136,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:31-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:05-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EpidBioBERT.xml b/xml_generated/EpidBioBERT.xml
index 7bf3973..cce8787 100644
--- a/xml_generated/EpidBioBERT.xml
+++ b/xml_generated/EpidBioBERT.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:32</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:00</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -110,7 +110,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:32-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:00-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/EpidBioELECTRA.xml b/xml_generated/EpidBioELECTRA.xml
index ea2eb47..30224f5 100644
--- a/xml_generated/EpidBioELECTRA.xml
+++ b/xml_generated/EpidBioELECTRA.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:02</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:00</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -110,7 +110,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:02-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:01-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/European_West_Nile_virus_outbreak_event_data_identified_by_PADI-web,_media_monitoring_tool.xml b/xml_generated/European_West_Nile_virus_outbreak_event_data_identified_by_PADI-web,_media_monitoring_tool.xml
index 2735fa9..0b18f1b 100644
--- a/xml_generated/European_West_Nile_virus_outbreak_event_data_identified_by_PADI-web,_media_monitoring_tool.xml
+++ b/xml_generated/European_West_Nile_virus_outbreak_event_data_identified_by_PADI-web,_media_monitoring_tool.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:33</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:51</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -151,7 +151,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:34-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:51-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Gazetteer_Access_Tool.xml b/xml_generated/Gazetteer_Access_Tool.xml
index 7f395f0..f9dbc71 100644
--- a/xml_generated/Gazetteer_Access_Tool.xml
+++ b/xml_generated/Gazetteer_Access_Tool.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:51</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:05</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -120,7 +120,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:51-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:05-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/GeoNLPlify.xml b/xml_generated/GeoNLPlify.xml
index 40ff6e6..fb6b18e 100644
--- a/xml_generated/GeoNLPlify.xml
+++ b/xml_generated/GeoNLPlify.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:56</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:03</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -151,7 +151,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:56-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:03-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/GeospaCy.xml b/xml_generated/GeospaCy.xml
index acfb247..2ff08c5 100644
--- a/xml_generated/GeospaCy.xml
+++ b/xml_generated/GeospaCy.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:37</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:02</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -123,7 +123,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:37-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:02-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Insights_from_the_worldwide_risk_mapping_of_H5N1_and_H5Nx_in_the_light_of_epidemic_episodes_occurring_from_2020_onward.xml b/xml_generated/Insights_from_the_worldwide_risk_mapping_of_H5N1_and_H5Nx_in_the_light_of_epidemic_episodes_occurring_from_2020_onward.xml
index ef15e01..d9b9ab0 100644
--- a/xml_generated/Insights_from_the_worldwide_risk_mapping_of_H5N1_and_H5Nx_in_the_light_of_epidemic_episodes_occurring_from_2020_onward.xml
+++ b/xml_generated/Insights_from_the_worldwide_risk_mapping_of_H5N1_and_H5Nx_in_the_light_of_epidemic_episodes_occurring_from_2020_onward.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="" codeSpace="ISOTC211/19115"/>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:04</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:07</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -119,7 +119,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:04-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:08-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Keywords_for_PADI-web_implemented_in_Ocean_Indian.xml b/xml_generated/Keywords_for_PADI-web_implemented_in_Ocean_Indian.xml
index c807860..cb2756b 100644
--- a/xml_generated/Keywords_for_PADI-web_implemented_in_Ocean_Indian.xml
+++ b/xml_generated/Keywords_for_PADI-web_implemented_in_Ocean_Indian.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:34</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:52</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -120,7 +120,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:34-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:53-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Labeled_Entities_from_Social_Media_Data_Related_to_Avian_Influenza_Disease.xml b/xml_generated/Labeled_Entities_from_Social_Media_Data_Related_to_Avian_Influenza_Disease.xml
index 5be2308..0d818cb 100644
--- a/xml_generated/Labeled_Entities_from_Social_Media_Data_Related_to_Avian_Influenza_Disease.xml
+++ b/xml_generated/Labeled_Entities_from_Social_Media_Data_Related_to_Avian_Influenza_Disease.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:34</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:53</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:34-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:53-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Linking_disease_data_from_different_sources.xml b/xml_generated/Linking_disease_data_from_different_sources.xml
index 6716716..d834c8e 100644
--- a/xml_generated/Linking_disease_data_from_different_sources.xml
+++ b/xml_generated/Linking_disease_data_from_different_sources.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:53</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:01</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -103,7 +103,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:53-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:01-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git "a/xml_generated/Long-term_MODIS_LST_day-time_and_night-time_temperatures,_sd_and_differences_at_1_km_based_on_the_2000\342\200\2232020_time_series\n________________________________________.xml" "b/xml_generated/Long-term_MODIS_LST_day-time_and_night-time_temperatures,_sd_and_differences_at_1_km_based_on_the_2000\342\200\2232020_time_series\n________________________________________.xml"
index 83116df..6d4074d 100644
--- "a/xml_generated/Long-term_MODIS_LST_day-time_and_night-time_temperatures,_sd_and_differences_at_1_km_based_on_the_2000\342\200\2232020_time_series\n________________________________________.xml"
+++ "b/xml_generated/Long-term_MODIS_LST_day-time_and_night-time_temperatures,_sd_and_differences_at_1_km_based_on_the_2000\342\200\2232020_time_series\n________________________________________.xml"
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:48</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:53</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -81,7 +81,7 @@ For more info about the MODIS LST product see: https://lpdaac.usgs.gov/products/
             <gco:CharacterString>Mosquito borne Flaviviruses</gco:CharacterString>
           </gmd:keyword>
           <gmd:keyword>
-            <gco:CharacterString>Disease data</gco:CharacterString>
+            <gco:CharacterString>Covariate</gco:CharacterString>
           </gmd:keyword>
         </gmd:MD_Keywords>
       </gmd:descriptiveKeywords>
@@ -143,7 +143,7 @@ For more info about the MODIS LST product see: https://lpdaac.usgs.gov/products/
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:48-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:53-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/MOOD_-_News_AMR_dataset_-_Hackathon_2022.xml b/xml_generated/MOOD_-_News_AMR_dataset_-_Hackathon_2022.xml
index 4f9da7d..a832546 100644
--- a/xml_generated/MOOD_-_News_AMR_dataset_-_Hackathon_2022.xml
+++ b/xml_generated/MOOD_-_News_AMR_dataset_-_Hackathon_2022.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:53</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:54</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:53-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:54-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/MOOD_Press_Tweets_Collector.xml b/xml_generated/MOOD_Press_Tweets_Collector.xml
index 861b23d..0399f9a 100644
--- a/xml_generated/MOOD_Press_Tweets_Collector.xml
+++ b/xml_generated/MOOD_Press_Tweets_Collector.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:56</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:02</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -145,7 +145,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:57-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:03-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Monthly_precipitation_in_mm_at_1_km_resolution_(multisource_average)_based_on_SM2RAIN-ASCAT_2007-2021,_CHELSA_Climate_and_WorldClim.xml b/xml_generated/Monthly_precipitation_in_mm_at_1_km_resolution_(multisource_average)_based_on_SM2RAIN-ASCAT_2007-2021,_CHELSA_Climate_and_WorldClim.xml
index 61974ca..e4bef8e 100644
--- a/xml_generated/Monthly_precipitation_in_mm_at_1_km_resolution_(multisource_average)_based_on_SM2RAIN-ASCAT_2007-2021,_CHELSA_Climate_and_WorldClim.xml
+++ b/xml_generated/Monthly_precipitation_in_mm_at_1_km_resolution_(multisource_average)_based_on_SM2RAIN-ASCAT_2007-2021,_CHELSA_Climate_and_WorldClim.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:31</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:53</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -67,7 +67,7 @@
             <gco:CharacterString>Mosquito borne Flaviviruses</gco:CharacterString>
           </gmd:keyword>
           <gmd:keyword>
-            <gco:CharacterString>Disease data</gco:CharacterString>
+            <gco:CharacterString>Covariate</gco:CharacterString>
           </gmd:keyword>
         </gmd:MD_Keywords>
       </gmd:descriptiveKeywords>
@@ -136,7 +136,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:31-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:54-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Novel-SARS-CoV-2-P1-Lineage-in-Brazi.xml b/xml_generated/Novel-SARS-CoV-2-P1-Lineage-in-Brazi.xml
index acfc4fd..81a0a43 100644
--- a/xml_generated/Novel-SARS-CoV-2-P1-Lineage-in-Brazi.xml
+++ b/xml_generated/Novel-SARS-CoV-2-P1-Lineage-in-Brazi.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:49</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:54</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -110,7 +110,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:49-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:55-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/PADI-web.xml b/xml_generated/PADI-web.xml
index e432210..dee9df0 100644
--- a/xml_generated/PADI-web.xml
+++ b/xml_generated/PADI-web.xml
@@ -9,10 +9,10 @@
     <gmd:MD_CharacterSetCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_CharacterSetCode" codeListValue="utf8">utf8</gmd:MD_CharacterSetCode>
   </gmd:characterSet>
   <gmd:hierarchyLevel>
-    <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
+    <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="service" codeSpace="ISOTC211/19115">service</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:34</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:55</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -151,7 +151,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:35-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:55-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/PADI-web_AI_corpus:_news_articles_sources_manually_labelled.xml b/xml_generated/PADI-web_AI_corpus:_news_articles_sources_manually_labelled.xml
index 0f69ab2..873b4d3 100644
--- a/xml_generated/PADI-web_AI_corpus:_news_articles_sources_manually_labelled.xml
+++ b/xml_generated/PADI-web_AI_corpus:_news_articles_sources_manually_labelled.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:36</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:59</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -125,7 +125,7 @@ https://doi.org/10.1109/C-CODE58145.2023.10139883</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:36-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:59-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/PADI-web_COVID-19_corpus:_news_articles_manually_labelled.xml b/xml_generated/PADI-web_COVID-19_corpus:_news_articles_manually_labelled.xml
index faca3b1..fd9261f 100644
--- a/xml_generated/PADI-web_COVID-19_corpus:_news_articles_manually_labelled.xml
+++ b/xml_generated/PADI-web_COVID-19_corpus:_news_articles_manually_labelled.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:35</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:55</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -123,7 +123,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:35-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:56-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/PADI-web_corpus_used_for_the_EpidBioELECTRA_approach.xml b/xml_generated/PADI-web_corpus_used_for_the_EpidBioELECTRA_approach.xml
index 02b6c04..d0f577d 100644
--- a/xml_generated/PADI-web_corpus_used_for_the_EpidBioELECTRA_approach.xml
+++ b/xml_generated/PADI-web_corpus_used_for_the_EpidBioELECTRA_approach.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:35</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:55</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:35-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:55-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/PADI-web_diseases_corpus_of_events.xml b/xml_generated/PADI-web_diseases_corpus_of_events.xml
index d6fbc91..5743f4f 100644
--- a/xml_generated/PADI-web_diseases_corpus_of_events.xml
+++ b/xml_generated/PADI-web_diseases_corpus_of_events.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:36</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:59</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -137,7 +137,7 @@ https://doi.org/10.5194/agile-giss-3-16-2022</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:36-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:00-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/PhyCovA.xml b/xml_generated/PhyCovA.xml
index 71214fb..1f02bb3 100644
--- a/xml_generated/PhyCovA.xml
+++ b/xml_generated/PhyCovA.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:32</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:56</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -107,7 +107,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:32-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:56-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Polygons_dataset_for_Relative_spatial_information.xml b/xml_generated/Polygons_dataset_for_Relative_spatial_information.xml
index c171c81..3233702 100644
--- a/xml_generated/Polygons_dataset_for_Relative_spatial_information.xml
+++ b/xml_generated/Polygons_dataset_for_Relative_spatial_information.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:36</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:00</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -122,7 +122,7 @@ https://doi.org/10.5194/agile-giss-3-16-2022</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:36-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:00-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Poultry_intensification_and_emergence_of_Highly_Pathogenic_Avian_Influenza:_past_and_the_future..xml b/xml_generated/Poultry_intensification_and_emergence_of_Highly_Pathogenic_Avian_Influenza:_past_and_the_future..xml
index 3dca94a..79ce0ab 100644
--- a/xml_generated/Poultry_intensification_and_emergence_of_Highly_Pathogenic_Avian_Influenza:_past_and_the_future..xml
+++ b/xml_generated/Poultry_intensification_and_emergence_of_Highly_Pathogenic_Avian_Influenza:_past_and_the_future..xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="" codeSpace="ISOTC211/19115"/>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:45</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:07</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -116,7 +116,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:45-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:07-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Prior_water_availability_modifies_the_effect_of_heavy_rainfall_on_dengue_transmission.xml b/xml_generated/Prior_water_availability_modifies_the_effect_of_heavy_rainfall_on_dengue_transmission.xml
index 9bfc2e0..77ff85e 100644
--- a/xml_generated/Prior_water_availability_modifies_the_effect_of_heavy_rainfall_on_dengue_transmission.xml
+++ b/xml_generated/Prior_water_availability_modifies_the_effect_of_heavy_rainfall_on_dengue_transmission.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:31</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:58</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -130,7 +130,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:32-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:58-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/RESAPATH.xml b/xml_generated/RESAPATH.xml
index 4ba4bb6..afc4390 100644
--- a/xml_generated/RESAPATH.xml
+++ b/xml_generated/RESAPATH.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:32</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:05</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -126,7 +126,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:32-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:06-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git "a/xml_generated/Risk_factors_for_tick_attachment_in_companion_animals_in_Great_Britain:_a_spatiotemporal_analysis_covering_2014\342\200\2232021.xml" "b/xml_generated/Risk_factors_for_tick_attachment_in_companion_animals_in_Great_Britain:_a_spatiotemporal_analysis_covering_2014\342\200\2232021.xml"
index 9c7600a..40e316e 100644
--- "a/xml_generated/Risk_factors_for_tick_attachment_in_companion_animals_in_Great_Britain:_a_spatiotemporal_analysis_covering_2014\342\200\2232021.xml"
+++ "b/xml_generated/Risk_factors_for_tick_attachment_in_companion_animals_in_Great_Britain:_a_spatiotemporal_analysis_covering_2014\342\200\2232021.xml"
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:38</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:06</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -119,7 +119,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:38-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:06-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/SARS-CoV-2_EUR_PHYLOGEOGRAPHY.xml b/xml_generated/SARS-CoV-2_EUR_PHYLOGEOGRAPHY.xml
index 8c8cc42..9fcf474 100644
--- a/xml_generated/SARS-CoV-2_EUR_PHYLOGEOGRAPHY.xml
+++ b/xml_generated/SARS-CoV-2_EUR_PHYLOGEOGRAPHY.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:01</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:57</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -108,7 +108,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:01-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:57-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/SM2RAIN-ASCAT_(2007-2021)_global_daily_satellite_rainfall_including_aggregated_values_and_trend_parameters_as_10km_resolution_GeoTIFFs.xml b/xml_generated/SM2RAIN-ASCAT_(2007-2021)_global_daily_satellite_rainfall_including_aggregated_values_and_trend_parameters_as_10km_resolution_GeoTIFFs.xml
index 6dbaa0e..a76c029 100644
--- a/xml_generated/SM2RAIN-ASCAT_(2007-2021)_global_daily_satellite_rainfall_including_aggregated_values_and_trend_parameters_as_10km_resolution_GeoTIFFs.xml
+++ b/xml_generated/SM2RAIN-ASCAT_(2007-2021)_global_daily_satellite_rainfall_including_aggregated_values_and_trend_parameters_as_10km_resolution_GeoTIFFs.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:31</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:57</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -67,7 +67,7 @@
             <gco:CharacterString>Mosquito borne Flaviviruses</gco:CharacterString>
           </gmd:keyword>
           <gmd:keyword>
-            <gco:CharacterString>Disease data</gco:CharacterString>
+            <gco:CharacterString>Covariate</gco:CharacterString>
           </gmd:keyword>
         </gmd:MD_Keywords>
       </gmd:descriptiveKeywords>
@@ -129,7 +129,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:31-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:57-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/SNEToolkit.xml b/xml_generated/SNEToolkit.xml
index e4c8b53..978efa3 100644
--- a/xml_generated/SNEToolkit.xml
+++ b/xml_generated/SNEToolkit.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:56</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:01</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -123,7 +123,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:56-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:01-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Spatial_Opinion_Mining_of_COVID-19_Tweets.xml b/xml_generated/Spatial_Opinion_Mining_of_COVID-19_Tweets.xml
index 1dea693..89c56c4 100644
--- a/xml_generated/Spatial_Opinion_Mining_of_COVID-19_Tweets.xml
+++ b/xml_generated/Spatial_Opinion_Mining_of_COVID-19_Tweets.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:36</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:01</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -123,7 +123,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:37-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:02-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Supplementary_Video_1_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml b/xml_generated/Supplementary_Video_1_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml
index 70cad14..4e32096 100644
--- a/xml_generated/Supplementary_Video_1_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml
+++ b/xml_generated/Supplementary_Video_1_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:02</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:57</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:02-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:57-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Supplementary_Video_2_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml b/xml_generated/Supplementary_Video_2_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml
index 0088fac..bc6239a 100644
--- a/xml_generated/Supplementary_Video_2_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml
+++ b/xml_generated/Supplementary_Video_2_from_Impact_of_contact_data_resolution_on_the_evaluation_of_interventions_in_mathematical_models_of_infectious_diseases.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:00</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:57</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:00-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:58-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/Untangling_the_changing_impact_of_non-pharmaceutical_interventions_and_vaccination_on_European_COVID-19_trajectories.xml b/xml_generated/Untangling_the_changing_impact_of_non-pharmaceutical_interventions_and_vaccination_on_European_COVID-19_trajectories.xml
index d389769..3f20aab 100644
--- a/xml_generated/Untangling_the_changing_impact_of_non-pharmaceutical_interventions_and_vaccination_on_European_COVID-19_trajectories.xml
+++ b/xml_generated/Untangling_the_changing_impact_of_non-pharmaceutical_interventions_and_vaccination_on_European_COVID-19_trajectories.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:46</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:58</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -127,7 +127,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:47-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:58-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/arbocartoR_app.xml b/xml_generated/arbocartoR_app.xml
index ee96105..b98781f 100644
--- a/xml_generated/arbocartoR_app.xml
+++ b/xml_generated/arbocartoR_app.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:37</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:03</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -142,7 +142,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:38-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:04-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/arbocartoR_package.xml b/xml_generated/arbocartoR_package.xml
index 2b0e388..ae8db3a 100644
--- a/xml_generated/arbocartoR_package.xml
+++ b/xml_generated/arbocartoR_package.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:38</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:04</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -142,7 +142,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:38-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:04-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/covid19_spell.xml b/xml_generated/covid19_spell.xml
index 9cad99d..3d70c76 100644
--- a/xml_generated/covid19_spell.xml
+++ b/xml_generated/covid19_spell.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:53</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:45</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -107,7 +107,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:53-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:45-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/data_and_code_for_Establishment_&_lineage_dynamics_of_the_SARS-CoV-2_epidemic_in_the_UK.xml b/xml_generated/data_and_code_for_Establishment_&_lineage_dynamics_of_the_SARS-CoV-2_epidemic_in_the_UK.xml
index 7cf17ef..5276c62 100644
--- a/xml_generated/data_and_code_for_Establishment_&_lineage_dynamics_of_the_SARS-CoV-2_epidemic_in_the_UK.xml
+++ b/xml_generated/data_and_code_for_Establishment_&_lineage_dynamics_of_the_SARS-CoV-2_epidemic_in_the_UK.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:04</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:45</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -111,7 +111,7 @@ http://www.ebi.ac.uk/ena/browser/view/PRJEB37886</gmd:URL>
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:04-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:45-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/epiCurve.xml b/xml_generated/epiCurve.xml
index cb31b4d..09531ba 100644
--- a/xml_generated/epiCurve.xml
+++ b/xml_generated/epiCurve.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="software" codeSpace="ISOTC211/19115">software</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:45</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:33:07</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -113,7 +113,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:46-->
+  <!--Metadata Creation date/time: 2024-08-28T16:33:07-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git "a/xml_generated/files_and_scripts_related_to_our_study_entitled_\"Phylogeographic_and_phylodynamic_approaches_to_epidemiological_hypothesis_testing\".xml" "b/xml_generated/files_and_scripts_related_to_our_study_entitled_\"Phylogeographic_and_phylodynamic_approaches_to_epidemiological_hypothesis_testing\".xml"
index e03bc42..80f178e 100644
--- "a/xml_generated/files_and_scripts_related_to_our_study_entitled_\"Phylogeographic_and_phylodynamic_approaches_to_epidemiological_hypothesis_testing\".xml"
+++ "b/xml_generated/files_and_scripts_related_to_our_study_entitled_\"Phylogeographic_and_phylodynamic_approaches_to_epidemiological_hypothesis_testing\".xml"
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:46</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:52</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -110,7 +110,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:46-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:52-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/new_york_variants.xml b/xml_generated/new_york_variants.xml
index 3ee4abc..e440415 100644
--- a/xml_generated/new_york_variants.xml
+++ b/xml_generated/new_york_variants.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:28</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:54</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -107,7 +107,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:28-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:54-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/resistancebank.org.xml b/xml_generated/resistancebank.org.xml
index bfd204d..8401acc 100644
--- a/xml_generated/resistancebank.org.xml
+++ b/xml_generated/resistancebank.org.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:11:03</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:56</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -133,7 +133,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:11:04-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:56-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
diff --git a/xml_generated/sars_cov_2_pipeline.xml b/xml_generated/sars_cov_2_pipeline.xml
index 29074f7..5eb93c6 100644
--- a/xml_generated/sars_cov_2_pipeline.xml
+++ b/xml_generated/sars_cov_2_pipeline.xml
@@ -12,7 +12,7 @@
     <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MX_ScopeCode" codeListValue="dataset" codeSpace="ISOTC211/19115">dataset</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:dateStamp>
-    <gco:DateTime>2024-05-29T17:10:46</gco:DateTime>
+    <gco:DateTime>2024-08-28T16:32:56</gco:DateTime>
   </gmd:dateStamp>
   <gmd:metadataStandardName>
     <gco:CharacterString>ISO 19115:2003/19139</gco:CharacterString>
@@ -107,7 +107,7 @@
       </gmd:transferOptions>
     </gmd:MD_Distribution>
   </gmd:distributionInfo>
-  <!--Metadata Creation date/time: 2024-05-29T17:10:46-->
+  <!--Metadata Creation date/time: 2024-08-28T16:32:56-->
   <!--ISO 19139 XML generated by geometa R package - Version 0.6-2-->
   <!--ISO 19139 XML compliance: NO-->
   <!--geometa R package information:	Contact: Emmanuel Blondel emmanuel.blondel1@gmail.com	URL: https://github.com/eblondel/geometa/wiki	BugReports: https://github.com/eblondel/geometa/issues-->
-- 
GitLab