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 aa9031ee65c57b20554345b780d2fa10ef6fae56..ef791cbf3abaddd22cf088688fb11e7d50b9002f 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,894,Zenodo,2013,https://zenodo.org/
-inist.sshade,527,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
+cern.zenodo,905,Zenodo,2013,https://zenodo.org/
+inist.sshade,530,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
 figshare.ars,380,figshare Academic Research System,2016,http://figshare.com/
 inist.osug,275,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
 dryad.dryad,168,DRYAD,2018,https://datadryad.org
 inist.resif,101,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
-rdg.prod,84,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
+rdg.prod,85,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
 inist.humanum,75,NAKALA,2020,https://nakala.fr
 inist.persyval,64,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 4e1d6981b24b3a7819b5a37fe2382022771d3b9d..79eded728c0d9f4cb616c9f7d87bbd9c76c030f8 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2712
\ No newline at end of file
+2727
\ 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 9c48d4f449b5c86776044d4dfb4b9bf9b863dc1e..3148bb67ec28383a1ac8c14dcf2d5ac54a72c5ff 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 25277d7327ba58b55e3489fa548d8bb81b315d64..f40f2f152b11c854e5471731a1bdd88d1943ead5 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 715a8a8c2fb2eb9c2f34de07918b8e3487deea9a..d2b22eebcff5fbb3286633194a1d7aa301fba827 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 a318b5f9f5d5bead7fde4def0616d9c025a82a9f..f88a32cf465939814d5c37e30ce309587f8a8f85 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 4899734a50b9518cf00bf32b983425027d8737d3..b38374a8c3f5c96cb666f8e119c3edd677c17484 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 537f6ceab4bf55c440341718d9c42253f53cb6bf..3907bc15f05e90715abf7714de6d94c13264ebda 100644
--- a/dois-uga--last-500.csv
+++ b/dois-uga--last-500.csv
@@ -1,4 +1,18 @@
 doi,client,resourceTypeGeneral,created,publisher,rights,sizes
+10.26302/sshade/experiment_lb_20241211_001,inist.sshade,Dataset,2025-03-28,SSHADE/GhoSST (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['20 spectra']
+10.5281/zenodo.15096524,cern.zenodo,Dataset,2025-03-27,Haute Ecole Pédagogique du Valais (HEP-VS) ; Laboratoire du développement sensori-moteur affectif et social (SMAS) ; Faculté de psychologie et des sciences de l'éducation (UNIGE) ; Université Savoir Mont-Blanc ; Laboratoire de Psychologie et NeuroCognition (Université Grenoble Alpes),Creative Commons Attribution Non Commercial No Derivatives 4.0 International,
+10.5281/zenodo.15094802,cern.zenodo,Dataset,2025-03-27,Zenodo,Creative Commons Attribution 4.0 International,
+10.26302/sshade/experiment_ps_20101901_001,inist.sshade,Dataset,2025-03-26,SSHADE/FAME (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['3 spectra']
+10.26302/sshade/experiment_ps_20101901_004,inist.sshade,Dataset,2025-03-26,SSHADE/FAME (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['3 spectra']
+10.5281/zenodo.15084804,cern.zenodo,Software,2025-03-25,Zenodo,Apache License 2.0,
+10.5281/zenodo.15084456,cern.zenodo,ComputationalNotebook,2025-03-25,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15083111,cern.zenodo,Model,2025-03-25,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15076715,cern.zenodo,ComputationalNotebook,2025-03-25,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15081726,cern.zenodo,Other,2025-03-25,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15078946,cern.zenodo,Other,2025-03-24,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15076537,cern.zenodo,Other,2025-03-24,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15074964,cern.zenodo,Dataset,2025-03-24,Zenodo,Creative Commons Attribution 4.0 International,
+10.57745/2jrrro,rdg.prod,Dataset,2025-03-21,Recherche Data Gouv,,
 10.5281/zenodo.15064810,cern.zenodo,Software,2025-03-21,Zenodo,CeCILL-B Free Software License Agreement,
 10.15778/resif.f1,inist.resif,Dataset,2025-03-21,Epos-France Seismological Data Centre,Creative Commons Attribution 4.0 International,"['Approximately 80 active stations with one channel each', '2GB/day']"
 10.5281/zenodo.15058310,cern.zenodo,Dataset,2025-03-20,Zenodo,Creative Commons Attribution 4.0 International,
@@ -16,6 +30,7 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.26302/sshade/experiment_zed_20250224_01,inist.sshade,Dataset,2025-03-18,SSHADE/DAYSY (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['6 spectra']
 10.26302/sshade/experiment_mr_20241002_001,inist.sshade,Dataset,2025-03-18,SSHADE/FAME (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['2 spectra']
 10.5281/zenodo.13151800,cern.zenodo,Dataset,2025-03-17,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.10255142,cern.zenodo,Software,2025-03-16,Zenodo,Apache License 2.0,
 10.14470/3v875927,tib.gfz,Dataset,2025-03-14,GFZ Data Services,"embargoed access,Creative Commons Attribution 4.0 International",['495GB']
 10.5281/zenodo.15023210,cern.zenodo,Dataset,2025-03-14,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.14876809,cern.zenodo,Dataset,2025-03-14,Zenodo,Creative Commons Attribution 4.0 International,
@@ -346,8 +361,8 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.5281/zenodo.13785330,cern.zenodo,Dataset,2024-09-18,Zenodo,Creative Commons Attribution 4.0 International,
 10.26302/sshade/experiment_op_20050205_001,inist.sshade,Dataset,2024-09-13,SSHADE/FAME (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['1 spectrum']
 10.26302/sshade/experiment_op_20170206_001,inist.sshade,Dataset,2024-09-13,SSHADE/FAME (OSUG Data Center),"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",['1 spectrum']
-10.6084/m9.figshare.27012151,figshare.ars,Text,2024-09-13,figshare,Creative Commons Attribution 4.0 International,['450818 Bytes']
 10.6084/m9.figshare.c.7447186,figshare.ars,Collection,2024-09-13,figshare,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.27012151,figshare.ars,Text,2024-09-13,figshare,Creative Commons Attribution 4.0 International,['450818 Bytes']
 10.5281/zenodo.13748961,cern.zenodo,Text,2024-09-11,Zenodo,"Creative Commons Attribution 4.0 International,Creative Commons Attribution Share Alike 4.0 International",
 10.6084/m9.figshare.c.6585842,figshare.ars,Collection,2024-09-11,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26985715,figshare.ars,Text,2024-09-11,figshare,Creative Commons Attribution 4.0 International,['25284 Bytes']
@@ -393,8 +408,8 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.57760/sciencedb.11705,cnic.sciencedb,Dataset,2024-08-15,Science Data Bank,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"['99398887984 bytes', '14 files']"
 10.6084/m9.figshare.26722614,figshare.ars,Text,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['12341 Bytes']
 10.6084/m9.figshare.25854698,figshare.ars,Image,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['464136 Bytes']
-10.6084/m9.figshare.c.7204785,figshare.ars,Collection,2024-08-15,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26713777,figshare.ars,Dataset,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['553168 Bytes']
+10.6084/m9.figshare.c.7204785,figshare.ars,Collection,2024-08-15,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.25711209,figshare.ars,Text,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['33449 Bytes']
 10.6084/m9.figshare.c.7116481,figshare.ars,Collection,2024-08-15,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26691925,figshare.ars,Text,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['2061125 Bytes']
@@ -521,18 +536,3 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.5281/zenodo.12697960,cern.zenodo,Software,2024-07-09,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.12664035,cern.zenodo,Dataset,2024-07-08,Zenodo,Creative Commons Attribution 4.0 International,
 10.57745/ikymka,rdg.prod,Dataset,2024-07-08,Recherche Data Gouv,,
-10.5061/dryad.v6wwpzh2j,dryad.dryad,Dataset,2024-07-05,Dryad,Creative Commons Zero v1.0 Universal,['1556461926 bytes']
-10.5281/zenodo.12662313,cern.zenodo,InteractiveResource,2024-07-05,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.12620977,cern.zenodo,Dataset,2024-07-04,Zenodo,Creative Commons Attribution 4.0 International,
-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,
-10.57745/qg9n3a,rdg.prod,Dataset,2024-06-20,Recherche Data Gouv,,
-10.5281/zenodo.12170086,cern.zenodo,Other,2024-06-19,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.7981221,cern.zenodo,Dataset,2024-06-19,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.7031228,cern.zenodo,Software,2024-06-18,Zenodo,Creative Commons Attribution 4.0 International,
-10.34847/nkl.46d78788,inist.humanum,Report,2024-06-18,NAKALA - https://nakala.fr (Huma-Num - CNRS),Etalab Open License 2.0,['12199 bytes']
diff --git a/dois-uga.csv b/dois-uga.csv
index c8a6eac54587c420efcd2e9d0aff62a84d071171..ffde516357cadb6294e7ff342d0e084e50160f62 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -13630,3 +13630,93 @@ This data is stored in TIF format, with each period and mountain range forming a
 allow calculation at grazing angles in DORT",api,True,findable,0,0,0,0,0,2025-03-19T20:31:14.000Z,2025-03-19T20:31:15.000Z,cern.zenodo,cern,,,,,,,"['IsSupplementTo', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.1173103']]"
 10.5281/zenodo.15052860,morays-community/NEMO-ORCA1_AirSeaHeat,Zenodo,2025,,Software,Creative Commons Attribution 4.0 International,For doi,api,True,findable,0,0,1,0,1,2025-03-19T17:05:13.000Z,2025-03-19T17:05:13.000Z,cern.zenodo,cern,,,,,,,"['IsSupplementTo', 'Cites', 'IsVariantFormOf', 'Compiles', 'HasVersion']",
 10.26302/sshade/experiment_zed_20250226_01,"MIR spectra of CO2 ice irradiated by $H^+$ ions at 45 K, increasing irradiation doses",SSHADE/DAYSY (OSUG Data Center),2025,en,Dataset,"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.","Mid-IR spectra of CO2 ice, at 45 K, increasing irradiation doses: unirradiated and irradiated by $H^+$ ions at 30 keV and a total fluence of $8.10^{15} ions.cm{-2}$.",mds,True,findable,0,0,0,0,0,2025-03-18T10:29:22.000Z,2025-03-18T10:29:22.000Z,inist.sshade,mgeg,"laboratory measurement,transmission,macroscopic,MIR,Mid-Infrared,absorbance,CO2 ice,laboratory,inorganic molecular solid","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'transmission', 'subjectScheme': 'main'}, {'subject': 'macroscopic', 'subjectScheme': 'main'}, {'subject': 'MIR', 'subjectScheme': 'variables'}, {'subject': 'Mid-Infrared', 'subjectScheme': 'variables'}, {'subject': 'absorbance', 'subjectScheme': 'variables'}, {'subject': 'CO2 ice', 'subjectScheme': 'name'}, {'subject': 'laboratory', 'subjectScheme': 'origin'}, {'subject': 'inorganic molecular solid', 'subjectScheme': 'compound type'}]",['4 spectra'],['ASCII'],,,"['IsPartOf', 'IsPartOf']",
+10.5281/zenodo.15084804,Comodo.jl: A Julia Package for Computational Mechanics and Design,Zenodo,2025,,Software,Apache License 2.0,Formal tagged release for version 1.0.0,api,True,findable,0,0,0,0,1,2025-03-25T17:20:12.000Z,2025-03-25T17:20:12.000Z,cern.zenodo,cern,,,,,,,"['IsSupplementTo', 'HasVersion']",
+10.5281/zenodo.15096524,The development of specific emotion comprehension components in 1285 preschool children : dataset,Haute Ecole Pédagogique du Valais (HEP-VS) ; Laboratoire du développement sensori-moteur affectif et social (SMAS) ; Faculté de psychologie et des sciences de l'éducation (UNIGE) ; Université Savoir Mont-Blanc ; Laboratoire de Psychologie et NeuroCognition (Université Grenoble Alpes),2025,fr,Dataset,Creative Commons Attribution Non Commercial No Derivatives 4.0 International,"Livret et données additionnelles à la publication scientifique : Richard, S., Cavadini, T., Dalla-Libera, N. et al. The development of specific emotion comprehension components in 1285 preschool children. Sci Rep 15, 8562 (2025). https://doi.org/10.1038/s41598-025-90613-z",api,True,findable,0,0,0,0,1,2025-03-27T14:11:08.000Z,2025-03-27T14:11:08.000Z,cern.zenodo,cern,"Emotions,Child, Preschool","[{'subject': 'Emotions', 'subjectScheme': 'MeSH'}, {'subject': 'Child, Preschool', 'subjectScheme': 'MeSH'}]",,,,,"['IsSupplementTo', 'HasVersion']",
+10.5281/zenodo.15083111,Structure predictions of a protein complex between ferredoxin and aldehyde:ferredoxin oxidoreductase from the bacterium Clostridium autoethanogenum.,Zenodo,2025,,Model,Creative Commons Attribution 4.0 International,"Structural predictions performed by AlphaFold2 of a complex composed of the ferredoxin of the bacterium Clostridium autoethanogenum (WP_013236834) and the aldehyde:ferredoxin oxidoreductase of the same organism (WP_013238665). The pdb output of AlphaFold2 are provided, without any modification.",api,True,findable,0,0,0,0,1,2025-03-25T12:47:42.000Z,2025-03-25T12:47:42.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.5281/zenodo.10255142,sbi reloaded: a toolkit for simulation-based inference workflows,Zenodo,2025,,Software,Apache License 2.0,"Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.",api,True,findable,0,0,0,0,0,2025-03-16T12:00:09.000Z,2025-03-16T12:00:09.000Z,cern.zenodo,cern,,,,,,,"['IsSupplementTo', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.10255142']]"
+10.26302/sshade/experiment_lb_20241211_001,"Vis-NIR and Mid-IR reflectance spectra, Mid-IR transmission and Raman spectra of several preparations of the Oued Chebeika 002 meteorite (CI chondrite)",SSHADE/GhoSST (OSUG Data Center),2025,en,Dataset,"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.","Mid-IR and Vis-NIR reflectance spectra (i = 0°, e = 30°) - recorded with two different instruments - of raw chips and powdered sample, Mid-IR transmission of matrix fragments pressed on diamonds, and Raman emission spectra of matrix fragments pressed on glass slide of the Oued Chebeika 002 meteorite (CI chondrite)",mds,True,findable,0,0,0,0,0,2025-03-28T10:26:11.000Z,2025-03-28T10:26:12.000Z,inist.sshade,mgeg,"laboratory measurement,bidirectional reflection,macroscopic,biconical reflection,transmission,microscopy,Raman scattering,Vis,Visible,NIR,Near-Infrared,MIR,Mid-Infrared,FIR,Far-Infrared,reflectance factor,absorbance,Raman scattering intensity,bulk OC002,extraterrestrial,complex organic-mineral mix,carbonaceous chondrite,CI","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'bidirectional reflection', 'subjectScheme': 'main'}, {'subject': 'macroscopic', 'subjectScheme': 'main'}, {'subject': 'biconical reflection', 'subjectScheme': 'main'}, {'subject': 'transmission', 'subjectScheme': 'main'}, {'subject': 'microscopy', 'subjectScheme': 'main'}, {'subject': 'Raman scattering', 'subjectScheme': 'main'}, {'subject': 'Vis', 'subjectScheme': 'variables'}, {'subject': 'Visible', 'subjectScheme': 'variables'}, {'subject': 'NIR', 'subjectScheme': 'variables'}, {'subject': 'Near-Infrared', 'subjectScheme': 'variables'}, {'subject': 'MIR', 'subjectScheme': 'variables'}, {'subject': 'Mid-Infrared', 'subjectScheme': 'variables'}, {'subject': 'FIR', 'subjectScheme': 'variables'}, {'subject': 'Far-Infrared', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'absorbance', 'subjectScheme': 'variables'}, {'subject': 'Raman scattering intensity', 'subjectScheme': 'variables'}, {'subject': 'bulk OC002', 'subjectScheme': 'name'}, {'subject': 'extraterrestrial', 'subjectScheme': 'family'}, {'subject': 'complex organic-mineral mix', 'subjectScheme': 'compound type'}, {'subject': 'carbonaceous chondrite', 'subjectScheme': 'meteorite group'}, {'subject': 'CI', 'subjectScheme': 'meteorite class'}]",['20 spectra'],['ASCII'],,,"['IsPartOf', 'IsPartOf']",
+10.5281/zenodo.15076537,CAD Files: An open laboratory blade strike rig to evaluate the risk of injury and mortality to fish and to test passive sensors,Zenodo,2025,,Other,Creative Commons Attribution 4.0 International,"These are the CAD files accompanying the publication titled, ""An open laboratory blade strike rig to evaluate the risk of injury and mortality to fish and to test passive sensors"". The files are intended for use with Autodesk Inventor 2024 (or higher), for which a free educational license is available.",api,True,findable,0,0,0,1,0,2025-03-24T17:53:36.000Z,2025-03-24T17:53:36.000Z,cern.zenodo,cern,"fish passage,turbine passage,fish-friendly turbine,fish injury,sensor probe","[{'subject': 'fish passage'}, {'subject': 'turbine passage'}, {'subject': 'fish-friendly turbine'}, {'subject': 'fish injury'}, {'subject': 'sensor probe'}]",,,,,"['IsSupplementTo', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.15076537']]"
+10.5281/zenodo.15094802,Supplementary data and scripts for analysis of acid-stress induced LdcI clustering in E. coli,Zenodo,2025,,Dataset,Creative Commons Attribution 4.0 International,"This repository holds a tabular file (S1_file.xlsx) )documenting the data used for data analysis in Kirchner et la.: 
+
+Filamentation-driven peripheral clustering of the inducible lysine decarboxylase is crucial for {E. coli} acid stress response
+
+Page one of the .xlsx file holds statistics on the size and location of clusters detected in WT and 3M E. coli cells after acid-stress induced LdcI production. Data was recorded using dSTORM. Also included is raw data on cell culture growth and medium pH during growth (see Suppl. Fig. 3 in the Manuscript). Page 2 holds data on plasmids, strains and primers used to create the LdcI-3M producing E. coli strain. 
+
+The focal-analyse python script is an all-in-one script which was used to get the statistics from .txt files put out by FOCAL3D (https://github.com/MilsteinLab/FOCAL3D). It can be run in a conda environment set up with the here included environment.yml file to extract and measure cluster sizes, as well as do the distance ratio analysis described in the manuscript (see Methods). ",api,True,findable,0,0,0,0,1,2025-03-27T12:23:09.000Z,2025-03-27T12:23:09.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.26302/sshade/experiment_ps_20101901_001,Ni K edge XAS transmission of Ni oxides at 300K,SSHADE/FAME (OSUG Data Center),2025,en,Dataset,"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",,mds,True,findable,0,0,0,0,0,2025-03-26T08:48:16.000Z,2025-03-26T08:48:16.000Z,inist.sshade,mgeg,"laboratory measurement,transmission,None,hard X,hard X-rays,absorbance,BN powder,LiNiO2,AgNiO2,NiO,solid,commercial,non-oxide ceramic,oxide","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'transmission', 'subjectScheme': 'main'}, {'subject': 'None', 'subjectScheme': 'main'}, {'subject': 'hard X', 'subjectScheme': 'variables'}, {'subject': 'hard X-rays', 'subjectScheme': 'variables'}, {'subject': 'absorbance', 'subjectScheme': 'variables'}, {'subject': 'BN powder', 'subjectScheme': 'name'}, {'subject': 'LiNiO2', 'subjectScheme': 'name'}, {'subject': 'AgNiO2', 'subjectScheme': 'name'}, {'subject': 'NiO', 'subjectScheme': 'name'}, {'subject': 'solid', 'subjectScheme': 'family'}, {'subject': 'commercial', 'subjectScheme': 'origin'}, {'subject': 'non-oxide ceramic', 'subjectScheme': 'compound type'}, {'subject': 'oxide', 'subjectScheme': 'compound type'}]",['3 spectra'],['ASCII'],,,"['IsPartOf', 'IsPartOf']",
+10.5281/zenodo.15081726,ODIN2025 - Oral and Dental Image aNalysis challenges,Zenodo,2025,,Other,Creative Commons Attribution 4.0 International,"Computer-aided diagnosis tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation [1*]. In dental applications, Cone-Beam Computed Tomography (CBCT) and Intra-oral Scan (IOS) are 3D imaging techniques widely used for surgical planning and simulation [2*, 3*, 4*]. CBCT provides information on dental and maxillofacial structures, whereas IOS provides highly accurate surface information on tooth crowns and gingiva surfaces. In particular, segmentation of anatomical structures (e.g., teeth, pharynx, mandible) from CBCT and registration between CBCT and IOS is an essential prerequisite for surgical planning for dental implants or orthognathic surgery [1*, 5*]. Although the medical imaging community has proposed many solutions, these methods are trained and verified on a small, often private, amount of data, and their performances do not meet clinical requirements.
+
+The main objective of our challenges is to push further the research on 3D dental image analysis, applying the rigorous approaches to performance evaluation needed in the medical field and calling for the latest results and techniques, which are often neglected by application-specific papers.
+
+Our ODIN2025 initiative brings together two different challenge series, ToothFairy, and 3DTeethSeg, combining most of the image modalities involved in maxillofacial analysis, i.e., CBCTs and IOSs. Building on the success of our previous initiatives, with ODIN2025 we aim to further advance the understanding of cone beam computed tomography and intra-oral scans, addressing more complex data and structures and increasing the amount of training and testing sets. An ever-increasing effort in this edition is put into the clinical applicability of the participant solutions by considering not only performance but also typical constraints of daily clinical practice: time and resources.Through this collaborative effort, we aim to pave the way for future multimodal image analysis that can more accurately and efficiently inform clinical decision-making, from diagnostics to treatment planning and post-surgical evaluations. The continued advancement in 3D dental imaging, along with improved segmentation, registration, and integration of CBCT and IOS data, holds the potential to revolutionize the way dental procedures are performed, ultimately benefiting both patients and practitioners alike.
+
+[1*] Cui, Z., Fang, Y., Mei, L., Zhang, B., Yu, B., Liu, J., Jiang, C., Sun, Y., Ma, L., Huang, J., et al.: A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nature Communications 13(1), 2096 (2022) 1[2*] Flügge, T., Derksen, W., Te Poel, J., Hassan, B., Nelson, K., Wismeijer, D.: Registration of cone beam computed tomography data and intraoral surface scans–a prerequisite for guided implant surgery with cad/cam drilling guides. Clinical Oral Implants Research 28(9), 1113–1118 (2017) 1[3*] Jamjoom, F.Z., Kim, D.G., McGlumphy, E.A., Lee, D.J., Yilmaz, B.: Positional accuracy of a prosthetic treatment plan incorporated into a cone beam computed tomography scan using surface scan registration. The Journal of Prosthetic Dentistry 120(3), 367–374 (2018) 1[4*] Kim, S., Choi, Y., Na, J., Song, I.S., Lee, Y.S., Hwang, B.Y., Lim, H.K., Baek, S.J.: Best of both modalities: Fusing CBCT and intraoral scan data into a single tooth image. In: International Conference on Medical Image  Computing and Computer-Assisted Intervention. pp. 553–563. Springer (2024[5*] Rangel, F.A., Maal, T.J., de Koning, M.J., Bronkhorst, E.M., Berg´e, S.J., Kuijpers-Jagtman, A.M.: Integration of digital dental casts in cone beam computed tomography scans—a clinical validation study. Clinical Oral Investigations 22, 1215–1222 (2018).",api,True,findable,0,0,0,0,0,2025-03-25T08:50:28.000Z,2025-03-25T08:50:29.000Z,cern.zenodo,cern,"CBCTs,3D Intraoral Scan,Segmentation,Maxillofacial,3D Volumes,Digital Orthodontics,3D mesh,Dental Restoration,Latency Optimization,Interactive Segmentation,MICCAI 2025 challenge","[{'subject': 'CBCTs'}, {'subject': '3D Intraoral Scan'}, {'subject': 'Segmentation'}, {'subject': 'Maxillofacial'}, {'subject': '3D Volumes'}, {'subject': 'Digital Orthodontics'}, {'subject': '3D mesh'}, {'subject': 'Dental Restoration'}, {'subject': 'Latency Optimization'}, {'subject': 'Interactive Segmentation'}, {'subject': 'MICCAI 2025 challenge'}]",,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.15081726']]"
+10.5281/zenodo.15074964,Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation,Zenodo,2025,en,Dataset,Creative Commons Attribution 4.0 International,"READ ME:
+
+Data required to run the codes attached to the paper ""Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation"" available on GitHub (https://github.com/VadimLimousin/StrAss-PINN/). The data requirements are: 
+
+
+
+To run the introductory notebook: params.in, vars.nc, coords_128_100j, mask_128_100j, psi1_128_100j, std_128_100j
+
+To run the full method: params.in, vars.nc, coords, mask, psi1, std",api,True,findable,0,0,0,0,1,2025-03-24T16:17:43.000Z,2025-03-24T16:17:43.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.5281/zenodo.15084456,Citation counts for reinterpretation tools (Fig. 4 of RiF white paper for ESPPU 2026),Zenodo,2025,,ComputationalNotebook,Creative Commons Attribution 4.0 International,"Jupyter notebook to count and visualise the number of (published) papers that cite a diversity of SM/BSM/Higgs reinterpretation tools. Used to create Figure 4 of the ESPPU2026 white paper on ""Reinterpretation and preservation of data and analyses in HEP"".
+
+Based on this notebook by Graeme Watt for counting Inspire records that have an associated HEPData record. ",api,True,findable,0,0,0,0,1,2025-03-25T16:39:54.000Z,2025-03-25T16:39:54.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.26302/sshade/experiment_ps_20101901_004,Co K edge XAS transmission of Co oxides at 300K,SSHADE/FAME (OSUG Data Center),2025,en,Dataset,"Any use of downloaded SSHADE data in a scientific or technical paper or a presentation is free but you should cite both SSHADE and the used data in the text ( 'first author' et al., year) with its full reference (with its DOI) in the main reference section of the paper (or in a special 'data citation' section) and, when available, the original paper(s) presenting the data.",,mds,True,findable,0,0,0,0,0,2025-03-26T08:46:13.000Z,2025-03-26T08:46:13.000Z,inist.sshade,mgeg,"laboratory measurement,transmission,None,hard X,hard X-rays,absorbance,BN powder,Co3O4,LiCoO2,Co(acetate)2,solid,commercial,non-oxide ceramic,oxide-hydroxide,oxide,organic salt","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'transmission', 'subjectScheme': 'main'}, {'subject': 'None', 'subjectScheme': 'main'}, {'subject': 'hard X', 'subjectScheme': 'variables'}, {'subject': 'hard X-rays', 'subjectScheme': 'variables'}, {'subject': 'absorbance', 'subjectScheme': 'variables'}, {'subject': 'BN powder', 'subjectScheme': 'name'}, {'subject': 'Co3O4', 'subjectScheme': 'name'}, {'subject': 'LiCoO2', 'subjectScheme': 'name'}, {'subject': 'Co(acetate)2', 'subjectScheme': 'name'}, {'subject': 'solid', 'subjectScheme': 'family'}, {'subject': 'commercial', 'subjectScheme': 'origin'}, {'subject': 'non-oxide ceramic', 'subjectScheme': 'compound type'}, {'subject': 'oxide-hydroxide', 'subjectScheme': 'compound type'}, {'subject': 'oxide', 'subjectScheme': 'compound type'}, {'subject': 'organic salt', 'subjectScheme': 'compound type'}]",['3 spectra'],['ASCII'],,,"['IsPartOf', 'IsPartOf']",
+10.57745/2jrrro,Replication Data for: Distributed network of smartphone sensors: a new tool for scientific field measurements,Recherche Data Gouv,2025,,Dataset,,"IMU (Accelerometer, Gyroscope, Magnetometer) data of mechanical waves measured with a smartphone fleet.",mds,True,findable,7,0,0,0,0,2025-03-21T20:00:38.000Z,2025-03-25T09:51:51.000Z,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', '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', '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', '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.15078946,CAD Data: An open laboratory blade strike rig to evaluate the risk of injury and mortality to fish and to test passive sensors,Zenodo,2025,,Other,Creative Commons Attribution 4.0 International,"These are the CAD files accompanying the publication titled, ""An open laboratory blade strike rig to evaluate the risk of injury and mortality to fish and to test passive sensors"". The files are intended for use with Autodesk Inventor 2024 (or higher), for which a free educational license is available.",api,True,findable,0,0,0,0,0,2025-03-24T18:09:44.000Z,2025-03-24T18:09:44.000Z,cern.zenodo,cern,"fish passage,turbine passage,fish-friendly turbine,fish injury,sensor probe","[{'subject': 'fish passage'}, {'subject': 'turbine passage'}, {'subject': 'fish-friendly turbine'}, {'subject': 'fish injury'}, {'subject': 'sensor probe'}]",,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.15078946']]"
+10.5281/zenodo.15076715,"Code ""Reinterpretation Metadata Analysis""",Zenodo,2025,,ComputationalNotebook,Creative Commons Attribution 4.0 International,"Code ""Reinterpretation Metadata Analysis"" for producing Fig. 3 of the ESPPU 2025 contribution ""Reinterpretation and preservation of data and analyses in HEP""
+
+Description of the project
+
+This project, which is also available on GitLab, allows to plot the number of publications as well as citations per publication on Inspire HEP. They are further sub-categorised by whether they have a HEPData entry and whether reinterpretation material in an appropriate tool (CheckMATE, CMS Combine, MadAnalysis, Nuisance, pyHF-HistFactory, Rivet, or SModelS) is available.
+
+For Fig. 3, the following settings were used:
+
+
+
+Only ATLAS and CMS publications with centre-of-mass energy 13 TeV or 13.6 TeV are considered.
+
+
+
+Only peer-reviewed and published analyses are taken into account. This excludes, among others, performance, trigger, and software publications.
+
+The publication date (taken as the date of publication on arXiv) must lie between 2016 and 6 months prior to running the code (i.e. between January 2016 and September 2024).
+
+
+
+As tools, only those giving a reimplementation of the analysis logic (CheckMATE, MadAnalysis, Rivet, and SModelS) are taken into account.
+
+Publications are labelled as ""search"" if the term ""search"" appears in either title or abstract of the publication.
+
+
+Description of folders and files contained in reinterpretation_metadata_analysis.zip
+
+
+
+*.py: the python executables run to produce the plots
+
+*.csv: the intermediate database files
+
+plots/: directory of the produced plots
+
+
+Fig. 3 corresponds to plots/citation_count_without_self_citations_by_year_collATLAS,CMS_cms10+TeV_after16-01_before24-09_analysistypesAll_toolsImplementations.png and plots/citation_count_and_publications_by_year_collATLAS,CMS_cms10+TeV_after16-01_before24-09_analysistypesSearch_toolsImplementations.png.
+
+Instructions to reproduce Fig. 3
+
+# prepare environment
+python3 -m venv .venv
+source ./.venv/bin/activate
+pip install hepdata-cli matplotlib pandas tqdm
+
+# get information from inspire
+python3 get_inspire.py
+
+# amend with information from HEPData
+python3 get_hepdata.py
+
+# plot
+python3 plot.py --cms 10+TeV -t implementations -a 6 --min_date 2016-01-01 --no_mean 
+python3 plot.py --cms 10+TeV -t implementations -a 6 --min_date 2016-01-01 --no_mean --analysis_types search",api,True,findable,0,0,0,0,1,2025-03-25T09:47:34.000Z,2025-03-25T09:47:34.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],