diff --git a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
index 08a056a8544820b9007ab3ea40541ca2def689a0..f247233ea5ae591489ac3746071b92c329ef6872 100644
--- a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+++ b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
@@ -1,11 +1,11 @@
 client,count,name,year,url
-cern.zenodo,719,Zenodo,2013,https://zenodo.org/
+cern.zenodo,721,Zenodo,2013,https://zenodo.org/
 inist.sshade,475,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
 figshare.ars,260,figshare Academic Research System,2016,http://figshare.com/
 inist.osug,238,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
-dryad.dryad,158,DRYAD,2018,https://datadryad.org
+dryad.dryad,159,DRYAD,2018,https://datadryad.org
 inist.resif,80,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
-inist.humanum,57,NAKALA,2020,https://nakala.fr
+inist.humanum,58,NAKALA,2020,https://nakala.fr
 inist.persyval,55,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
 rdg.prod,53,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
 fmsh.prod,28,Fondation Maison des sciences de l'homme,2023,
@@ -19,7 +19,7 @@ ugraz.unipub,2,unipub,2019,http://unipub.uni-graz.at
 bl.nerc,2,NERC Environmental Data Service,2011,https://eds.ukri.org
 tug.openlib,1,TU Graz OPEN Library,2020,https://openlib.tugraz.at/
 crui.ingv,1,Istituto Nazionale di Geofisica e Vulcanologia (INGV),2013,http://data.ingv.it/
-repod.dbuw,1,Dane Badawcze UW,2023,
+repod.dbuw,1,University of Warsaw Research Data Repository,2023,https://danebadawcze.uw.edu.pl/
 estdoi.ttu,1,TalTech,2019,https://digikogu.taltech.ee
 inist.ird,1,IRD,2016,
 inist.eost,1,Ecole et Observatoire des Sciences de la Terre,2017,https://eost.unistra.fr/en/
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index 376a9aff56a3e4a6ae4e091d92b9b1a65b65618f..3da63362a0c282d4db6b1fb99733cd0e2fb42221 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2176
\ No newline at end of file
+2180
\ No newline at end of file
diff --git a/2-produce-graph/hist-evol-datasets-per-repo.png b/2-produce-graph/hist-evol-datasets-per-repo.png
index e6c362f1c5ad42b7314b692c5e8fb97579bffe63..9b3eb77a301cce704a952c83a3b131cf9d9b62d8 100644
Binary files a/2-produce-graph/hist-evol-datasets-per-repo.png and b/2-produce-graph/hist-evol-datasets-per-repo.png differ
diff --git a/2-produce-graph/hist-last-datasets-by-client.png b/2-produce-graph/hist-last-datasets-by-client.png
index eb57e95293681095f09521096cedf6aa29063aea..38a05da1b9165c258f2cca42a332c1751b8974db 100644
Binary files a/2-produce-graph/hist-last-datasets-by-client.png and b/2-produce-graph/hist-last-datasets-by-client.png differ
diff --git a/2-produce-graph/hist-quantity-year-type.png b/2-produce-graph/hist-quantity-year-type.png
index 7206fab79ad9d844458ff99836d99fd7b8a6243c..aee80365269fb55af72c8ee96101d26980eccb20 100644
Binary files a/2-produce-graph/hist-quantity-year-type.png and b/2-produce-graph/hist-quantity-year-type.png differ
diff --git a/2-produce-graph/pie--datacite-client.png b/2-produce-graph/pie--datacite-client.png
index 712de10a905806311ac5251bb6b2919fc23b0adf..706811c8c2c37f50dd2e7caf05048017c0e04256 100644
Binary files a/2-produce-graph/pie--datacite-client.png and b/2-produce-graph/pie--datacite-client.png differ
diff --git a/2-produce-graph/pie--datacite-type.png b/2-produce-graph/pie--datacite-type.png
index 130a172c02afc7f33d98f2cf86bb678ccc22c4d1..704a304431d43f4734066d6fb8212f965fa268ef 100644
Binary files a/2-produce-graph/pie--datacite-type.png and b/2-produce-graph/pie--datacite-type.png differ
diff --git a/dois-uga.csv b/dois-uga.csv
index 636d87d6127fa1553540a730c8e99616562c1a91..86a84056eb556e91f1f992709dbf612ba25a66ec 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -9924,3 +9924,42 @@ For the bottommost measurements, because the density profile is less consistent
 10.57745/sqmqp1,Survey data and visualisation script of the administrative burden of Galaxy small-scale admins,Recherche Data Gouv,2024,,Dataset,,"Main publication Poll report and form on HAL Authors The raw data was generated by the poll respondents The authors of this Dataset, excluding Vlad Visan, are such respondents. There are also other respondents who chose to remain anonymous The script was written by Vlad Visan The raw format was adapted to a numerical format by Vlad Visan Overall description A poll took place in February 2024, to understand the administrative burden of using Galaxy, specifically for small-scale admins. Context Useful to anyone considering using Galaxy Done as part of the technology monitoring phase of the ""Gestionnaire de workflows"" (Workflow Management System) project of the OSUG LabEx File descriptions raw_data_names_removed.tsv Raw poll answers. With any personally identifiable information redacted. SSA-Poll-19-Feb-2024-Filtered-Numerical.tab This numerically filtered format is required by the script The transformation could be done automatically in the future, but there are some subtleties: ""-1"" denotes ""ignore/invalid"" Some empty answers have to manually be converted to ""0"" I manually changed one answer that was ""0"" to ""-1"" after reading the associated comment which made it clear that ""invalid"" was more appropriate numericalCsvImportAndGenerateCharts.R The script parses the data, and creates one distribution/histogram graph per column It expects a filtered version, with only the numerical fields. Form-V2.pdf Survey questions, with several errors corrected: End-user assistance questions were worded wrongly Various spelling/wording mistakes",mds,True,findable,3,0,0,0,0,2024-05-21T14:06:25.000Z,2024-06-28T13:34:00.000Z,rdg.prod,rdg,,,,,,,"['HasPart', 'HasPart', 'HasPart', 'HasPart']",
 10.5281/zenodo.7728982,NH4_method_in_low_concentrated_environment-size_correction_and_calibration_scripts,Zenodo,2023,,Software,Creative Commons Attribution 4.0 International,Scripts used for size correction and calibration of NH4 samples,mds,True,findable,0,0,0,0,0,2023-03-13T13:38:23.000Z,2023-03-13T13:38:23.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.7728982']]"
 10.6084/m9.figshare.26122626,Additional file 1 of Impact of intensive prone position therapy on outcomes in intubated patients with ARDS related to COVID-19,figshare,2024,,Text,Creative Commons Attribution 4.0 International,Additional file 1. Additional information about the baseline characteristics and the statistical analysis. Additional Tables and Figures. Figure 1. Timeline and study period considered after ICU admission. Table 1. Proportion of missing data for each variable included in the analysis. Figure 2. Distribution of cumulative duration of prone positioning during the first 48 h after ICU admission. Figure 3. Evolution of the PaO2/FiO2 ratio during the first 28-days according to the prone strategy. Figure 4. Evolution of the static compliance during the first 28-days according to the prone strategy. Figure 5. Evolution of the SOFA score during the first 28-days according to the prone strategy. Table 2a. Estimated hazard ratio from a multivariate Cox model including day-60 survival associated with multiple variables in both the multiple imputation and complete case populations. b. Estimated hazard ratio from a multivariate Cox model including day-28 survival associated with multiple variables in both the multiple imputation and complete case populations. c. Estimated hazard ratio from a multivariate Cox model including day-90 survival associated with multiple variables in both the multiple imputation and complete case populations. Table 3. Estimated hazard ratio of the day-60 survival associated with the prone therapy strategy according to the prone position therapy strategy and the PaO2/FiO2 ratio at ICU admission before and after weighting in both multiple imputation and complete case population. Figure 6. Flow chart study included all ICU patients experiencing prone therapy during ICU stay. Table 4. Estimated hazard ratio of the day-60 survival associated with the prone therapy strategy including all patients experiencing prone therapy during ICU stay before and after weighting in both multiple imputation and complete case population. Figure 7a. Kaplan Meier curves according to prone therapy strategy including all patients experiencing prone therapy during ICU stay before weighting adjustment in complete case population. b. Kaplan Meier curves according to prone therapy strategy including all patients experiencing prone therapy during ICU stay after weighting adjustment in complete case population.,mds,True,findable,0,0,33,0,0,2024-06-28T04:01:14.000Z,2024-06-28T04:01:15.000Z,figshare.ars,otjm,"Medicine,Physiology,FOS: Biological sciences,Sociology,FOS: Sociology,Immunology,FOS: Clinical medicine,Biological Sciences not elsewhere classified,Developmental Biology,Science Policy","[{'subject': 'Medicine'}, {'subject': 'Physiology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Sociology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biological Sciences not elsewhere classified'}, {'subject': 'Developmental Biology'}, {'subject': 'Science Policy'}]",['882103 Bytes'],,,,"['References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References', 'References']",
+10.5281/zenodo.12620977,Regional Assessments of Glacier Mass Change (RAGMAC) experiment dataset,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"This repository contains the dataset that was distributed to the participants of the Regional Assessments of Glacier Mass Change (RAGMAC) workinggroup of the International Association of Cryospheric Science (IACS, 2023).
+
+In short, the dataset is divided in 5 study sites: Hintereisferner (Austrian Alps - HEF_AT), Grosser Aletschgletscher (Swiss Alps - ALE_CH), Vestre Svartisen ice cap (Scandes, Norway - VES_NO), Baltoro glacier (Karakoram, Pakistan - BAL_PK), and the Northern Patagonian Icefield (Andes, Chile - NPI_CL).
+
+For each site, we provide series of Digital Elevation Models (DEMs) derived from SRTM, ASTER and TanDEM-X sensors. Additionally, we provide glacier outlines extracted from the Randolph Glacier Inventory version 6, for all glaciers in the study area, and selected glaciers for which participants were required to calculate the geodetic mass balance.
+
+Additionally, we provide the airborne DEMs that were used as reference data to evaluate the spaceborne DEMs. These are available for the cases HEF_AT, ALE_CH and VES_NO, all included in the single zip file “validation_data.zip”.
+
+For details on the dataset, experiment and any reference to this dataset, please refer to the following publication: Piermattei et al. (2024) ""Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data"" The Cryosphere, DOI: 10.5194/egusphere-2023-2309 (to be updated upon final acceptance).
+
+NOTE: As of July 2024, the TanDEM-X DEMs cannot be publicly shared due to license restrictions. We are discussing future opportunities to share the data in a future version of this repository.
+
+ ",api,True,findable,0,0,0,0,2,2024-07-04T11:50:19.000Z,2024-07-04T11:50:20.000Z,cern.zenodo,cern,,,,,,,"['HasVersion', 'HasVersion', 'IsPartOf']",
+10.34847/nkl.344e6396,"Édition TEI de la pièce Ploutos d’Aristophane (Ve siècle avant J.-C.), traduite par André Charles Brotier (1751- 1798), 1789",NAKALA - https://nakala.fr (Huma-Num - CNRS),2024,fr,Text,,"La présente édition s’inscrit dans le cadre du projet Translatoscope de l'Université Grenoble Alpes, dédié à l'étude de l’histoire des traductions d’Aristophane. L’encodage du texte a été réalisé en suivant les recommandations de la Text Encoding Initiative (http://www.tei-c.org/Guidelines/P5/).",api,True,findable,0,0,0,0,0,2024-07-02T08:01:59.000Z,2024-07-02T08:02:00.000Z,inist.humanum,jbru,"TEI,Traduction,Aristophane,18e siècle,Français (langue),TEI,translation,Aristophanes,18th century,french,Theater–Greece,18th century,French language","[{'lang': 'fr', 'subject': 'TEI'}, {'lang': 'fr', 'subject': 'Traduction'}, {'lang': 'fr', 'subject': 'Aristophane'}, {'lang': 'fr', 'subject': '18e siècle'}, {'lang': 'fr', 'subject': 'Français (langue)'}, {'lang': 'en', 'subject': 'TEI'}, {'lang': 'en', 'subject': 'translation'}, {'lang': 'en', 'subject': 'Aristophanes'}, {'lang': 'en', 'subject': '18th century'}, {'lang': 'en', 'subject': 'french'}, {'subject': 'Theater–Greece'}, {'subject': '18th century'}, {'subject': 'French language'}]",['240022 Bytes'],['text/xml'],,,,
+10.5061/dryad.v6wwpzh2j,Data from: Unravelling large-scale patterns and drivers of biodiversity in dry rivers,Dryad,2023,en,Dataset,Creative Commons Zero v1.0 Universal,"We conducted a coordinated experiment and a metabarcoding approach on
+ environmental DNA targeting multiple taxa (i.e. Archaea, Bacteria, Fungi,
+ Algae, Protozoa, Nematoda, Arthropoda, and Streptophyta). Dry sediments
+ were collected from 84 non-perennial rivers across 19 countries on four
+ continents to investigate biodiversity patterns and drivers.",mds,True,findable,0,0,0,0,0,2024-07-05T20:50:03.000Z,2024-07-05T20:50:04.000Z,dryad.dryad,dryad,"FOS: Earth and related environmental sciences,FOS: Earth and related environmental sciences,metabarcoding,eDNA,intermittent rivers,Biodiversity","[{'subject': 'FOS: Earth and related environmental sciences', 'subjectScheme': 'fos'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'metabarcoding'}, {'subject': 'eDNA'}, {'subject': 'intermittent rivers'}, {'subject': 'Biodiversity', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}]",['1556461926 bytes'],,,,,
+10.5281/zenodo.12662313,Initiation à R Studio,Zenodo,2024,,InteractiveResource,Creative Commons Attribution 4.0 International,"Cet atelier propose une initiation au logiciel R et son interface R Studio.
+
+Les points suivants y sont abordés :
+
+
+
+Quels logiciels pour l'analyse de données ?
+
+Qu'est-ce que R et R Studio, et pourquoi utiliser ce logiciel.
+
+Premiers pas sur R Studio : exploration de données
+
+Bonus – Visualisation graphique
+
+Bonus  – Statistiques inférentielles
+
+Pour aller plus loin 
+
+
+Le fichier Excel utilisé dans les exercices ainsi que le script R et sa version .html sont accessibles ci-dessous. ",api,True,findable,0,0,0,0,0,2024-07-05T06:14:18.000Z,2024-07-05T06:14:18.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.12662313']]"