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 a03432aeff8218be8724868f323dc3c76b7d2db7..e91e329f3bc650719b77e96fc2e35dcd9cff145b 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,793,Zenodo,2013,https://zenodo.org/
+cern.zenodo,799,Zenodo,2013,https://zenodo.org/
 inist.sshade,496,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
-figshare.ars,359,figshare Academic Research System,2016,http://figshare.com/
+figshare.ars,361,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,163,DRYAD,2018,https://datadryad.org
 inist.resif,93,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
-rdg.prod,65,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
+rdg.prod,68,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
 inist.humanum,65,NAKALA,2020,https://nakala.fr
 inist.persyval,61,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
 fmsh.prod,28,Fondation Maison des sciences de l'homme,2023,
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index 1c9d6bd480b8f617fe364c3824b54d239b5db058..5827dc196f8e5daeae9235daf7b283dd4c360c15 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2427
\ No newline at end of file
+2438
\ 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 74676243ddf1c5f215795c51a3c95c48db43f278..fb8d1115400610615cddeeb01cb4388b5b11fcd2 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 ba85de981cc40dd70dc6649a567845e00f54be1b..76a4d6e5c7f10b5c247fec339d9812cce42d4613 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 c7e2f96b802da2f4ebe3a32f469158d6df732821..6819ae377c13a3ae5e1d72449c6deaa0468c1272 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 ae7e3b3e4f60ee0728171c662ae606c29dc02dec..c1b86f7b8bcff8536ce6643d5621fcbee33362a2 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 137c318fff3d2cca35179bb2d1f66f9ad8e0d72a..308a6a8e1e4383c1b82c3049508492cdcc0efc9a 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--last-500.csv b/dois-uga--last-500.csv
index 5a055bfca551c9c34d3cbabb313497fc4043a578..dc18ae313cb6503e5f5349c3a609fc7e383c1bb9 100644
--- a/dois-uga--last-500.csv
+++ b/dois-uga--last-500.csv
@@ -1,9 +1,18 @@
 doi,client,resourceTypeGeneral,created,publisher,rights,sizes
+10.5281/zenodo.14051167,cern.zenodo,Other,2024-11-07,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14045604,cern.zenodo,Dataset,2024-11-07,Zenodo,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.c.7528784,figshare.ars,Collection,2024-11-07,figshare,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.27627620,figshare.ars,Dataset,2024-11-07,figshare,Creative Commons Attribution 4.0 International,['208073 Bytes']
+10.5281/zenodo.14039282,cern.zenodo,Dataset,2024-11-05,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14038923,cern.zenodo,Dataset,2024-11-05,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14036125,cern.zenodo,Software,2024-11-04,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14035431,cern.zenodo,Dataset,2024-11-04,Zenodo,Creative Commons Attribution 4.0 International,
 10.57760/sciencedb.16149,cnic.sciencedb,Dataset,2024-11-04,Science Data Bank,Creative Commons Attribution Non Commercial 4.0 International,"['2433253276 bytes', '4 files']"
 10.5281/zenodo.14003384,cern.zenodo,Dataset,2024-11-01,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.14016979,cern.zenodo,Other,2024-10-31,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.14016634,cern.zenodo,Other,2024-10-31,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.14013195,cern.zenodo,Dataset,2024-10-30,Zenodo,Creative Commons Attribution 4.0 International,
+10.57745/rvc6wq,rdg.prod,Dataset,2024-10-30,Recherche Data Gouv,,
 10.1594/pangaea.972515,pangaea.repository,Dataset,2024-10-30,PANGAEA,Creative Commons Attribution 4.0 International,['264 data points']
 10.1594/pangaea.972514,pangaea.repository,Dataset,2024-10-30,PANGAEA,Creative Commons Attribution 4.0 International,['9249 data points']
 10.1594/pangaea.972510,pangaea.repository,Dataset,2024-10-30,PANGAEA,Creative Commons Attribution 4.0 International,['20736 data points']
@@ -23,6 +32,7 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.5281/zenodo.13951667,cern.zenodo,Dataset,2024-10-18,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13940200,cern.zenodo,Software,2024-10-16,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13932813,cern.zenodo,Software,2024-10-15,Zenodo,Creative Commons Attribution 4.0 International,
+10.57745/owexy1,rdg.prod,Dataset,2024-10-14,Recherche Data Gouv,,
 10.5281/zenodo.13927580,cern.zenodo,Software,2024-10-14,Zenodo,Creative Commons Attribution 4.0 International,
 10.57745/izde4q,rdg.prod,Dataset,2024-10-11,Recherche Data Gouv,,
 10.15778/resif.zp2020,inist.resif,Dataset,2024-10-08,RESIF - Réseau Sismologique et géodésique Français,,"['10 stations, 41Go (miniseed format)']"
@@ -178,8 +188,8 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.6084/m9.figshare.26585829,figshare.ars,Text,2024-08-13,figshare,Creative Commons Attribution 4.0 International,['279842 Bytes']
 10.6084/m9.figshare.26585826,figshare.ars,Text,2024-08-13,figshare,Creative Commons Attribution 4.0 International,['105007 Bytes']
 10.6084/m9.figshare.26585823,figshare.ars,Text,2024-08-13,figshare,Creative Commons Attribution 4.0 International,['11099 Bytes']
-10.6084/m9.figshare.26577821,figshare.ars,Dataset,2024-08-13,figshare,Creative Commons Attribution 4.0 International,['56397 Bytes']
 10.6084/m9.figshare.c.6596504,figshare.ars,Collection,2024-08-13,figshare,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.26577821,figshare.ars,Dataset,2024-08-13,figshare,Creative Commons Attribution 4.0 International,['56397 Bytes']
 10.6084/m9.figshare.26567603,figshare.ars,Text,2024-08-13,figshare,Creative Commons Attribution 4.0 International,['360541 Bytes']
 10.6084/m9.figshare.c.6586928,figshare.ars,Collection,2024-08-13,figshare,Creative Commons Attribution 4.0 International,
 10.15778/resif.z42022,inist.resif,Dataset,2024-08-12,RESIF - Réseau Sismologique et géodésique Français,,"['98 stations, 280Go (miniseed format)']"
@@ -237,6 +247,7 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.34847/nkl.344e6396,inist.humanum,Text,2024-07-02,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['240022 Bytes']
 10.6084/m9.figshare.c.7306419,figshare.ars,Collection,2024-06-28,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26122626,figshare.ars,Text,2024-06-28,figshare,Creative Commons Attribution 4.0 International,['882103 Bytes']
+10.57745/j3xipw,rdg.prod,Dataset,2024-06-26,Recherche Data Gouv,,
 10.5281/zenodo.12528242,cern.zenodo,Dataset,2024-06-25,Zenodo,Creative Commons Attribution 4.0 International,
 10.7914/ts1a-7g40,iris.iris,Dataset,2024-06-21,International Federation of Digital Seismograph Networks,,['500000 MB']
 10.5281/zenodo.12205981,cern.zenodo,Dataset,2024-06-21,Zenodo,Creative Commons Attribution 4.0 International,
@@ -488,14 +499,3 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.5281/zenodo.10410540,cern.zenodo,Dataset,2023-12-22,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.10392425,cern.zenodo,Dataset,2023-12-18,Zenodo,Creative Commons Attribution 4.0 International,
 10.25577/m0dc-n549,inist.eost,Dataset,2023-12-18,"EOST UAR830, Université de Strasbourg, CNRS",Creative Commons Attribution 4.0 International,
-10.5281/zenodo.8390941,cern.zenodo,Dataset,2023-12-18,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.10400475,cern.zenodo,Dataset,2023-12-18,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.8091093,cern.zenodo,Dataset,2023-12-16,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.10370096,cern.zenodo,Dataset,2023-12-13,Zenodo,Creative Commons Attribution 4.0 International,
-10.48380/5662-yk90,mcdy.dohrmi,Text,2023-12-11,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),,
-10.48380/7gdm-j630,mcdy.dohrmi,Text,2023-12-11,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),,
-10.48380/6fh7-7m80,mcdy.dohrmi,Text,2023-12-11,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),,
-10.48380/gq45-dv88,mcdy.dohrmi,Text,2023-12-11,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),,
-10.48380/2gzr-9q70,mcdy.dohrmi,Text,2023-12-11,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),,
-10.48380/cwsp-mj37,mcdy.dohrmi,Text,2023-12-11,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),,
-10.5281/zenodo.10262983,cern.zenodo,Text,2023-12-11,Unite! Alliance Publications,Creative Commons Attribution 4.0 International,
diff --git a/dois-uga.csv b/dois-uga.csv
index fe5cef1c6c7211dbc41c0df78bde291a449db260..a35c007cb6254ab8f07653f8dc11fac865ab1c6f 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -11482,3 +11482,53 @@ MARTeam: MARv3.11, GitLab [data set], https://gitlab.com/Mar-Group/MARv3# (last
  ",api,True,findable,0,0,0,0,1,2024-10-28T13:59:49.000Z,2024-10-28T13:59:49.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
 10.1594/pangaea.972515,Continuous Antarctic Carbon monoxide (CO) record (spline from 3 ice cores) from 1820 to 1995 CE,PANGAEA,2024,,Dataset,Creative Commons Attribution 4.0 International,"Continuous ice core carbon monoxide (CO) mixing ratios are presented for three West Antarctic Cores (Jurassic, Bryan Coast, and Dyer Plateau). Data cover from 1820 CE to 1995 CE. For each core, data are presented integrated at 10-second intervals from an original acquisition rate of 4 Hz. Data were measured continuously utilising Optical Feedback Cavity Enhanced Spectroscopy connected to a continuous ice core melting system at the British Antarctic Survey. A smoothed spline composed of the bottom 5th percentile of each record is also presented. A percentile re-sampling method is required to remove the impact of in situ production. The spline is used to interpret Southern Hemisphere CO variability from the pre-industrial with a particular focus on biomass burning.",mds,True,findable,0,0,1,0,0,2024-10-30T01:08:37.000Z,2024-10-30T01:08:37.000Z,pangaea.repository,pangaea,"biomass burning,carbon monoxide,Ice core,pre-industrial,Southern Hemisphere,Gas age,Carbon monoxide,Carbon monoxide, uncertainty,Ice drill,Average composite","[{'subject': 'biomass burning'}, {'subject': 'carbon monoxide'}, {'subject': 'Ice core'}, {'subject': 'pre-industrial'}, {'subject': 'Southern Hemisphere'}, {'subject': 'Gas age', 'subjectScheme': 'Parameter'}, {'subject': 'Carbon monoxide', 'subjectScheme': 'Parameter'}, {'subject': 'Carbon monoxide, uncertainty', 'subjectScheme': 'Parameter'}, {'subject': 'Ice drill', 'subjectScheme': 'Method'}, {'subject': 'Average composite', 'subjectScheme': 'Method'}]",['264 data points'],['text/tab-separated-values'],,,"['IsPartOf', 'References']",
 10.25577/fr1v-0577,Expertises bâtimentaires EMS-98 de bâtiments endommagés par le séisme de La Laigne du 16 juin 2023,"EOST UAR830, Université de Strasbourg, CNRS",2024,fr,Dataset,Creative Commons Attribution 4.0 International,"Ce jeu de données présente les résultats d’expertises bâtimentaires EMS-98, réalisées par le Groupe d’intervention macrosismique (GIM/Epos-France : EOST UAR830 &amp; ITES, RAP/ISterre, OMP/IRAP, IRSN) piloté par le BCSF-Rénass du 21 au 27 juin 2023 après le séisme de La Laigne du 16 juin 2023 (M=5,3).<br>Ce travail de terrain a été placé sous l’observation de deux experts en macrosismique d’EDF (Aix-en-Provence) et du CEA (Bruyères-le-Chatel).<br>Il s’agit principalement du degré de dommage EMS-98 (de 1 à 5) associé à la vulnérabilité sismique EMS-98 (A à F) du bâtiment.<br>Ces expertises ont été réalisées majoritairement à partir de la rue pour des raisons de sécurité, selon les critères de l’échelle macrosismique européenne EMS-98.<br>Les dommages aux bâtiments des deux communes de La Laigne (17201) et Cram-Chaban (17132) ont été relevés de façon complète, les autres communes expertisées ont été traitées par échantillonnage sur les dommages les plus importants indiqués par les mairies dans différentes classes de vulnérabilité (département 17 : Benon, Bouhet, Courçon, Ferrières, La Grève-sur-Mignon, Landrais, Le Gué-d'Alleré, Marans, Saint-Georges-du-Bois, Saint-Jean-de-Liversay, Saint-Pierre-d'Amilly, Saint-Saturnin-du-Bois, Saint-Sauveur-d'Aunis, Surgères, Vouhé; département 79 : Arçais, Mauzé-sur-le-Mignon, Saint-Hilaire-la-Palud).<br>Deux post-traitements ont été réalisés, l’un pour vérifier si les dommages expertisés n’étaient pas déjà présents avant le séisme grâce aux images Google Street View, l’autre pour confirmer les degrés de dommages et les vulnérabilités estimées à partir des données collectées par le GIM.<br>Les données sont géolocalisées avec une précision de 3 décimales (déviation possible de la localisation jusqu’à 100m).",fabricaForm,True,findable,0,0,0,0,0,2024-10-28T16:03:22.000Z,2024-10-29T15:30:50.000Z,inist.eost,jbru,"macrosismique,intensité,EMS-98,séisme,tremblement de terre,La Laigne,dommages aux constructions,vulnérabilité,structure,enquête de terrain,Charente-Maritime,catastrophe naturelle","[{'subject': 'macrosismique'}, {'subject': 'intensité'}, {'subject': 'EMS-98'}, {'subject': 'séisme'}, {'subject': 'tremblement de terre'}, {'subject': 'La Laigne'}, {'subject': 'dommages aux constructions'}, {'subject': 'vulnérabilité'}, {'subject': 'structure'}, {'subject': 'enquête de terrain'}, {'subject': 'Charente-Maritime'}, {'subject': 'catastrophe naturelle'}]",,['text/csv'],,,"['IsSourceOf', 'IsDocumentedBy', 'IsDocumentedBy', 'IsDocumentedBy']",
+10.57745/owexy1,Data to estimate urban seismic damages and debris from building-level simulations,Recherche Data Gouv,2024,,Dataset,,This dataset contains informations about buildings in Beirut in shapefile format used for estimation of seismic damage and debris at the urban scale. The buildings are in polygon format and the data includes attributes related to their height obtained from the treatment of satellite images. The format is compatible with most GIS software.,mds,True,findable,16,2,0,0,0,2024-10-14T12:50:18.000Z,2024-11-05T10:11:22.000Z,rdg.prod,rdg,,,,,,,['HasPart'],
+10.5281/zenodo.14039282,Metadata and georeferencing of the publications in the academic journal 'L'Espace Géographique' (1972-2020),Zenodo,2024,en,Dataset,Creative Commons Attribution 4.0 International,"The zip file contains two tab-separated files (.tsv):
+
+
+
+""netscity_espacegeo.tsv""
+
+
+29 columns, 1645 lines
+
+It contains the metadata of 1451 articles and ""positions de recherche"" issued in the academic journal 'L'Espace Géographique' between 1972 and 2020. The affiliations of the authors have been geocoded using the web application NETSCITY. This dataset combines the result of the geocoding process (geographical coordinates) for each publication with geographical enrichments (urban areas and countries from which the publications have been authored). Additionnal information are available: titles of the publications, sections of the journal in which the publications were published, publication year, authors names, affiliations.
+
+
+
+""metadata.tsv""
+
+
+7 columns, 29 lines
+
+It contains a description for each variable of the dataset: ""netscity_espacegeo.tsv""
+
+ ",api,True,findable,0,0,0,0,1,2024-11-05T12:01:04.000Z,2024-11-05T12:01:05.000Z,cern.zenodo,cern,"spatial coordinates,Cultural and economic geography,Espace géographique","[{'subject': 'spatial coordinates'}, {'subject': 'Cultural and economic geography', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Espace géographique'}]",,,,,['HasVersion'],
+10.5281/zenodo.14045604,Data and code for the publication of a surge-specific DEM workflow on ASTER DEMs.,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"This repository is associated to the publication submitted with the title ""Glacier surge monitoring from temporally dense elevation time series: application to an ASTER dataset over the Karakoram region"".It contains elevation change maps produced by the workflow, the Python script of the workflow, and vector outlines used in the study.
+
+_______________________________Content of the data repository:
+
+1) Elevation change maps (raster **.tif files)    Regional maps of the elevation changes over 3 years periods, interpolated results in metre (m).    Name: dh_[date1]_[date2].tif    2) Surge-affected areas (vector **.gpkg files)    Surge-affected areas drawn manually of four selected glacier surges, divided into reservoir and receiving areas.    3) Python script dem_processing_publi.py    Script with the implementation of the presented workflow.",api,True,findable,0,0,0,0,0,2024-11-07T09:43:16.000Z,2024-11-07T09:43:16.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14045604']]"
+10.57745/j3xipw,Messages et enseignements du projet Explore2,Recherche Data Gouv,2024,,Dataset,,"Ce document résume les principales conclusions sur l'évolution de variables climatiques et hydrologiques (surface et souterrain) et des aléas hydroclimatiques, obtenues à l'issue du projet Explore2. Il s'achève par des messages et enseignements du projet Explore2 sur le volet scientifique et le volet accompagnement des acteurs.",mds,True,findable,584,279,0,0,0,2024-06-26T19:01:12.000Z,2024-06-28T09:00:51.000Z,rdg.prod,rdg,,,,,,,"['HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart']",
+10.5281/zenodo.14038923,Dataset for: Anomalous Subkelvin Thermal Frequency Shifts of Ultranarrow Linewidth Solid State Emitters,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Dataset supporting figures for the peer-reviewed article, ""Anomalous Subkelvin Thermal Frequency Shifts of Ultranarrow Linewidth Solid State Emitters"".",api,True,findable,0,0,0,0,1,2024-11-05T09:30:20.000Z,2024-11-05T09:30:21.000Z,cern.zenodo,cern,,,,,,,"['IsPublishedIn', 'HasVersion', 'IsPartOf']",
+10.6084/m9.figshare.27627620,Additional file 1 of Adoption and perception of prescribable digital health applications (DiGA) and the advancing digitalization among German internal medicine physicians: a cross-sectional survey study,figshare,2024,,Dataset,Creative Commons Attribution 4.0 International,Supplementary Material 1.,mds,True,findable,0,0,0,1,0,2024-11-07T08:45:05.000Z,2024-11-07T08:45:06.000Z,figshare.ars,otjm,"Medicine,Biotechnology,Sociology,FOS: Sociology,Cancer,Science Policy","[{'subject': 'Medicine'}, {'subject': 'Biotechnology'}, {'subject': 'Sociology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Cancer'}, {'subject': 'Science Policy'}]",['208073 Bytes'],,,,"['IsIdenticalTo', 'IsSupplementTo']","[['IsIdenticalTo', '10.6084/m9.figshare.27627620']]"
+10.5281/zenodo.14051167,Multiple Sclerosis Spinal Cord Lesions Detection from MultiSequence MRIs Challenge (MS-Multi-Spine),Zenodo,2024,,Other,Creative Commons Attribution 4.0 International,"Multiple Sclerosis (MS) is a common and potentially debilitating disease affecting around 3 million persons in the world. Currently, Magnetic Resonance Imaging (MRI) plays a central role in this context and in particular allows the identification of MS lesions in the central nervous system. The identification of these lesions on a given MRI image is a complex and mentally demanding task that often leads to an underestimation of disease activity, even for most experienced radiologists. There is thus a need for automated tools that can provide clinicians an aid for accurate and robust identification and quantification of MS lesions. To date, the medical imaging community concentrated its efforts toward the segmentation of the lesions in brain MRI. For this purpose, over the past years, several challenges have been organized to assess the ability of automated methods to detect multiple sclerosis (MS) lesions as compared to manual delineation (The longitudinal lesion challenge; https://smart-stats-tools.org/lesion-challenge, MSSeg: https://portal.fli-iam.irisa.fr/msseg-challenge/, MSSeg2 https://portal.fli-iam.irisa.fr/msseg-2/). These have allowed the community to explore innovative directions. The proposed MS-Multi-Spine challenge aims at offering the possibility to the medical imaging community to extend their methods to spinal cord lesions. This is an innovative challenge both from a clinical and methodological perspective.
+
+1) Clinically, the presence of lesions in the spinal cord has a major prognostic value compared to brain lesions [1]. However, in clinical practice their detection represents a hard task for radiologists. Indeed, MS lesion detection/segmentation in spinal cord MRI is a complex task due to specific characteristics:
+
+
+
+the size of the anatomical structures of interest (around 1cm diameter) resulting in high occurrence of partial volume effects and thus less sharp gradients and contrasts between distinct normal appearing and pathological tissues;
+
+the occurrence of significant artifacts due to subjects motion and respiration.
+
+
+As a result, despite its clinical importance, spinal cord MRI is currently under-exploited in patients with MS.Providing clinicians with tools capable of reliably identifying these spinal cord lesions would therefore be a major added-value.
+
+2) Methodologically, spinal cord lesion detection raises a specific challenge. Indeed, in clinical practice, it is highly recommended to acquire at least two sequences among a set of available sequences, without specific guidelines to date. In practice, depending on the center and context, any combination of existing MR sequences can be provided. In this challenge, that represents a concrete complex case of multisequence datasets, we focus on four commonly used sequences: the sagittal T2 (that is always provided in the challenge and will be considered as the reference to segment), the sagittal STIR, the sagittal PSIR and the 3D MP2RAGE. From a methodological point ofview, this is a concrete and paradigmatic case of missing modalities setting where, depending on the case, some modalities may be missing both at inference or training time. To the best of our knowledge, such clinical datasets are still rarely available in medical imaging.
+
+[1] Early imaging predictors of long-term outcomes in relapse-onset multiple sclerosis Brownlee WJ, Altmann DR, Prados F, Miszkiel KA, Eshaghi A et al. Brain, 2019[2] 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis Wattjes MP, Ciccarelli O, Reich DS, Banwell B, de Stefano N, Enzinger C et al. Lancet Neurol., 2021",api,True,findable,0,0,0,0,0,2024-11-07T13:48:57.000Z,2024-11-07T13:48:58.000Z,cern.zenodo,cern,"Multiple Sclerosis,Spinal Cord,MRI,Lesion,Instance Segmentation,Detection,Missing Modalities,Missing Data,MICCAI 2025 challenge","[{'subject': 'Multiple Sclerosis'}, {'subject': 'Spinal Cord'}, {'subject': 'MRI'}, {'subject': 'Lesion'}, {'subject': 'Instance Segmentation'}, {'subject': 'Detection'}, {'subject': 'Missing Modalities'}, {'subject': 'Missing Data'}, {'subject': 'MICCAI 2025 challenge'}]",,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14051167']]"
+10.6084/m9.figshare.c.7528784,Adoption and perception of prescribable digital health applications (DiGA) and the advancing digitalization among German internal medicine physicians: a cross-sectional survey study,figshare,2024,,Collection,Creative Commons Attribution 4.0 International,"Abstract Background Therapeutic digital health applications (DiGAs) are expected to significantly enhance access to evidence-based care. Since 2020, German physicians and psychotherapists have been able to prescribe approved DiGAs, which are reimbursed by statutory health insurance. This study investigates the usage, knowledge and perception of DiGAs as well as the growing digitalization among internal medicine physicians in Germany. Methods A web-based survey was distributed at the 2024 annual congress of the German Society for Internal Medicine. Participants could respond by scanning a QR code or directly on a tablet. Results A total of 100 physicians completed the survey, with a mean age of 43.4 years. The majority were internal medicine physicians (85%). Of the respondents, 31% had already prescribed DiGAs, and 29% had tested one. Self-rated knowledge of DiGAs was low (median score 3.17/10). The main barriers identified were lack of knowledge about effective implementation (60%), lack of time for patient onboarding (27%), and concerns about patient adherence (21%). However, 92% believed that DiGAs could improve care, and 88% expressed interest in specific digital health training. The majority (64%) stated that digitalization had a positive impact on medical care and 39% of physicians expected their daily workload to decrease due to digitalization. In addition, 38% believed that the physician-patient relationship would improve as a result of digitalization. Conclusions While physicians widely acknowledged the potential benefits of DiGAs, adoption and understanding remain limited. Specific training in digital health is crucial to accelerate digitalization in internal medicine.",mds,True,findable,0,0,0,0,0,2024-11-07T08:45:06.000Z,2024-11-07T08:45:07.000Z,figshare.ars,otjm,"Medicine,Biotechnology,Sociology,FOS: Sociology,Cancer,Science Policy","[{'subject': 'Medicine'}, {'subject': 'Biotechnology'}, {'subject': 'Sociology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Cancer'}, {'subject': 'Science Policy'}]",,,,,,
+10.5281/zenodo.14036125,Triple AMR Parser,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,"This is the code implementation to train a sequence-to-sequence AMR parser. In this work, we represent an AMR graph as a set of triples for linearization. We train the parser with triple AMRs  and evaluate the weakness/strength of the linearization method.",api,True,findable,0,0,0,0,0,2024-11-04T17:18:05.000Z,2024-11-04T17:18:05.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14036125']]"
+10.57745/rvc6wq,Simulated data for searches for electroweakino dark matter in the monojet channel,Recherche Data Gouv,2024,,Dataset,,"Dataset associated with the ""Machine Learning Electroweakino Production"" publication (https://doi.org/10.48550/arXiv.2411.00093). In this study, we explore the possibility of enhancing searches for supersymmetric dark matter particles at the LHC in the monojet channel, by using Graph Neural Networks (GNNs). We train an ensemble of 10 networks for Wino- and Higgsino-like neutralinos, and we use it on Bino, Wino, and Higgsino test samples in order to derive the sensitivity achievable at the end of Run-3 and High Luminosity phases of the LHC. The dataset contains 5 folders: 1) wino_train, 2) wino_val, 3) higgsino_train, 4) higgsino_val, 5) test. Each ""train"" folder contains 10 files (archives) corresponding to an ensemble of 10 networks, for either Wino- or Higgsino-like neutralino. ""Val"" folders contain validation data for the ensemble, 10 files per each neutralino type. Validation and training data are all for the same mass point: neutralino mass 300 GeV and squark mass 2.2 TeV. The ""Test"" folder contains test data for SM, Binos, Higgsinos, and Winos. For neutralino test data, the archives contain all 30 mass points. For the test set, masses of neutralinos vary between 200 GeV and 1100 GeV, while the masses of squarks vary between 2.0 TeV and 3.0 TeV. Data was produced using Monte Carlo simulation methods, with MadGraph5, Pythia, and Delphes. The published data was subject to preselection, described in the associated article. All files are in the awkd0 format. Example code demonstrating how to read the files and use them for NN training can be found in the official repository of the project: https://github.com/Rav2/monojet",mds,True,findable,7,0,0,0,0,2024-10-30T13:09:16.000Z,2024-11-07T09:32:54.000Z,rdg.prod,rdg,,,,,,,"['HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart']",
+10.5281/zenodo.14035431,The Little Prince AMR Corpus (Expanded with Korean and Croatian),Zenodo,2024,ko,Dataset,Creative Commons Attribution 4.0 International,"We expanded The Little Prince AMR Corpus (https://amr.isi.edu/). The original data is available in English and Chinese. By manually aligning Korean and Croatian texts to English, we obtained multilingual The Little Corpus AMR Corpus. ",api,True,findable,0,0,0,0,0,2024-11-04T14:19:01.000Z,2024-11-04T14:19:02.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14035431']]"