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 d5daddf581c1723822ac02c36282de78f6abc8f9..88232ebc42ce924a5ddad8df3b413c75e4f82594 100644
--- a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+++ b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
@@ -1,10 +1,10 @@
 client,count,name,year,url
-cern.zenodo,878,Zenodo,2013,https://zenodo.org/
-inist.sshade,521,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
+cern.zenodo,885,Zenodo,2013,https://zenodo.org/
+inist.sshade,522,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,97,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
+inist.resif,99,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
 rdg.prod,81,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,
@@ -14,30 +14,31 @@ pangaea.repository,18,PANGAEA,2020,https://www.pangaea.de/
 mcdy.dohrmi,14,dggv-e-publications,2020,https://www.dggv.de/publikationen/dggv-e-publikationen.html
 inist.cirm,7,Centre International de Rencontres Mathématiques,2017,
 figshare.sage,6,figshare SAGE Publications,2018,
-iris.iris,5,NSF Seismological Facility for the Advancement of Geoscience (SAGE) Operated by EarthScope Consortium (formerly Incorporated Research Institutions for Seismology),2018,http://www.iris.edu/hq/
+iris.iris,5,NSF Seismological Facility for the Advancement of Geoscience (SAGE),2018,http://www.iris.edu/hq/
 vqpf.dris,3,Direction des ressources et de l'information scientifique,2021,
+tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
 tib.repod,3,RepOD,2015,https://repod.icm.edu.pl/
 cnic.sciencedb,3,ScienceDB,2022,https://www.scidb.cn/en
-tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
-bl.nerc,2,NERC Environmental Data Service,2011,https://eds.ukri.org
-bl.mendeley,2,Mendeley Data,2015,https://data.mendeley.com/
 inist.eost,2,Ecole et Observatoire des Sciences de la Terre,2017,https://eost.unistra.fr/en/
+tib.gfz,2,GFZ Data Services,2011,https://dataservices.gfz-potsdam.de/portal/
+bl.mendeley,2,Mendeley Data,2015,https://data.mendeley.com/
+bl.nerc,2,NERC Environmental Data Service,2011,https://eds.ukri.org
+tug.openlib,2,TU Graz OPEN Library,2020,https://openlib.tugraz.at/
 crui.ingv,2,Istituto Nazionale di Geofisica e Vulcanologia (INGV),2013,http://data.ingv.it/
 ugraz.unipub,2,unipub,2019,http://unipub.uni-graz.at
-tug.openlib,2,TU Graz OPEN Library,2020,https://openlib.tugraz.at/
 ethz.sed,2,"Swiss Seismological Service, national earthquake monitoring and hazard center",2013,http://www.seismo.ethz.ch
 inist.opgc,1,Observatoire de Physique du Globe de Clermont-Ferrand,2017,
-inist.ird,1,IRD,2016,
+ethz.da-rd,1,ETHZ Data Archive - Research Data,2013,http://data-archive.ethz.ch
 ethz.zora,1,"Universität Zürich, ZORA",2013,https://www.zora.uzh.ch/
-repod.dbuw,1,University of Warsaw Research Data Repository,2023,https://danebadawcze.uw.edu.pl/
 estdoi.ttu,1,TalTech,2019,https://digikogu.taltech.ee
-ihumi.pub,1,IHU Méditerranée Infection,2020,
-ethz.da-rd,1,ETHZ Data Archive - Research Data,2013,http://data-archive.ethz.ch
+repod.dbuw,1,University of Warsaw Research Data Repository,2023,https://danebadawcze.uw.edu.pl/
+inist.ird,1,IRD,2016,
+inist.omp,1,Observatoire Midi-Pyrénées,2011,
+umass.uma,1,University of Massachusetts (UMass) Amherst,2018,https://scholarworks.umass.edu/
 edi.edi,1,Environmental Data Initiative,2017,https://portal.edirepository.org/nis/home.jsp
 bl.iita,1,International Institute of Tropical Agriculture datasets,2017,http://data.iita.org/
-tib.gfz,1,GFZ Data Services,2011,https://dataservices.gfz-potsdam.de/portal/
 ardcx.nci,1,National Computational Infrastructure,2020,
-inist.omp,1,Observatoire Midi-Pyrénées,2011,
-umass.uma,1,University of Massachusetts (UMass) Amherst,2018,https://scholarworks.umass.edu/
+ihumi.pub,1,IHU Méditerranée Infection,2020,
 inist.inrap,1,Institut national de recherches archéologiques préventives,2019,
 tib.mpdl,1,Max Planck Digital Library,2015,
+tudublin.arrow,1,ARROW@TU Dublin,2020,https://arrow.dit.ie/
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index 5f8a553d14e071f315587cab96e9e4e335cf04ad..3184df26f6d98e72d2a8c2429a1a2d4acc87e7ef 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2680
\ No newline at end of file
+2692
\ 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 774f7c6a1535d065f5416ed82eedb101db411aff..6fc2b7bdfd295b4b6ea4be23eec0b2a50b0ebc49 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 7a2bdff8ca50e2ecf82a7c396654fcabd4421ec3..05b137aef0646a098429e1e15456b78ed09b97d0 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 3cbce88174f4fd3d2578db7aaf150faf13d51ff8..0d778e8999198db5fc89fc980bcb0bb7aa15af59 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 a16def36f41360919edbe0c26c5ea66041e18ed2..7ddea15eb18c8706640dc45646f8b1ca9f98587b 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 98f9a859d556a428fa93f217c231ed9554db2f18..e88b65526ec57a73f94de51927e77a3793268111 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 19ae36d98cdc2afd1332c3d1090f84f1b0c3a46c..29a6acd6b55c386bdc57bd48a45e59b8e8a04423 100644
--- a/dois-uga--last-500.csv
+++ b/dois-uga--last-500.csv
@@ -1,4 +1,15 @@
 doi,client,resourceTypeGeneral,created,publisher,rights,sizes
+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,
+10.5281/zenodo.15016412,cern.zenodo,Software,2025-03-13,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15016334,cern.zenodo,Software,2025-03-13,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.15013109,cern.zenodo,Other,2025-03-12,Zenodo,,
+10.26302/sshade/experiment_lb_20250307_001,inist.sshade,Dataset,2025-03-11,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.",['4 spectra']
+10.5281/zenodo.14833853,cern.zenodo,Dataset,2025-03-11,Zenodo,Creative Commons Attribution 4.0 International,
+10.15778/resif.2l2018,inist.resif,Dataset,2025-03-10,RESIF - Réseau Sismologique et géodésique Français,"Open Access,Creative Commons Attribution 4.0 International","['96 stations, 555Go (miniseed format)']"
+10.15778/resif.6j2018,inist.resif,Dataset,2025-03-10,RESIF - Réseau Sismologique et géodésique Français,"Open Access,Creative Commons Attribution 4.0 International","['341 stations, 1723Go (miniseed format)']"
+10.21427/3yhz-9j83,tudublin.arrow,Other,2025-03-10,Technological University Dublin,,
 10.15778/resif.z82016,inist.resif,Dataset,2025-03-07,RESIF - Réseau Sismologique et géodésique Français,,"['10 stations, 10Go (miniseed format)']"
 10.15778/resif.9m2022,inist.resif,Dataset,2025-03-07,RESIF - Réseau Sismologique et géodésique Français,"Open Access,Creative Commons Attribution 4.0 International","['48 stations, 138Go (miniseed format)']"
 10.24350/cirm.v.20308503,inist.cirm,Audiovisual,2025-03-07,CIRM,Creative Commons Attribution Non Commercial No Derivatives 4.0 International,
@@ -315,8 +326,8 @@ 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.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']
+10.6084/m9.figshare.c.7447186,figshare.ars,Collection,2024-09-13,figshare,Creative Commons Attribution 4.0 International,
 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.26985715,figshare.ars,Text,2024-09-11,figshare,Creative Commons Attribution 4.0 International,['25284 Bytes']
 10.6084/m9.figshare.c.6585842,figshare.ars,Collection,2024-09-11,figshare,Creative Commons Attribution 4.0 International,
@@ -362,8 +373,8 @@ 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.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.26713777,figshare.ars,Dataset,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['553168 Bytes']
 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']
@@ -422,8 +433,8 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.6084/m9.figshare.26635410,figshare.ars,Text,2024-08-14,figshare,Creative Commons Attribution 4.0 International,['39961 Bytes']
 10.6084/m9.figshare.c.6905671,figshare.ars,Collection,2024-08-14,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26633925,figshare.ars,Text,2024-08-14,figshare,Creative Commons Attribution 4.0 International,['557382 Bytes']
-10.6084/m9.figshare.c.6889784,figshare.ars,Collection,2024-08-14,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26629135,figshare.ars,Text,2024-08-14,figshare,Creative Commons Attribution 4.0 International,['792543 Bytes']
+10.6084/m9.figshare.c.6889784,figshare.ars,Collection,2024-08-14,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.c.6880688,figshare.ars,Collection,2024-08-14,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26626966,figshare.ars,Text,2024-08-14,figshare,Creative Commons Attribution 4.0 International,['4519412 Bytes']
 10.6084/m9.figshare.c.6683920,figshare.ars,Collection,2024-08-13,figshare,Creative Commons Attribution 4.0 International,
@@ -437,10 +448,10 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 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.c.6586928,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)']"
 10.12686/eshm20-output,ethz.sed,Dataset,2024-08-12,EFEHR (European Facilities of Earthquake Hazard and Risk),Creative Commons Attribution 4.0 International,['529MB']
 10.5281/zenodo.7447726,cern.zenodo,Dataset,2024-08-12,Zenodo,Creative Commons Attribution 4.0 International,
@@ -454,8 +465,8 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.5281/zenodo.13234729,cern.zenodo,Text,2024-08-06,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13194009,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13194007,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.13189234,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13189238,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.13189234,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13189236,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5061/dryad.wdbrv15xr,dryad.dryad,Dataset,2024-08-02,Dryad,Creative Commons Zero v1.0 Universal,['482590 bytes']
 10.5281/zenodo.13164857,cern.zenodo,Dataset,2024-08-02,Zenodo,Creative Commons Attribution 4.0 International,
@@ -525,14 +536,3 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.34847/nkl.d8cei718,inist.humanum,Audiovisual,2024-06-05,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['12661479 Bytes']
 10.34847/nkl.bf113ij9,inist.humanum,Audiovisual,2024-06-05,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['49397800 Bytes']
 10.34847/nkl.805dw61i,inist.humanum,Audiovisual,2024-06-05,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['43348923 Bytes']
-10.57745/fkc6wp,rdg.prod,Dataset,2024-06-04,Recherche Data Gouv,,
-10.5281/zenodo.11474161,cern.zenodo,Software,2024-06-04,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.11206158,cern.zenodo,Dataset,2024-06-04,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.11454276,cern.zenodo,Dataset,2024-06-04,Zenodo,Creative Commons Attribution 4.0 International,
-10.57745/njabdi,rdg.prod,Dataset,2024-06-03,Recherche Data Gouv,,
-10.34847/nkl.7cf5asv8,inist.humanum,Text,2024-06-03,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['102823 Bytes']
-10.34847/nkl.ed0ej9ev,inist.humanum,Text,2024-06-03,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['306355 Bytes']
-10.34847/nkl.db46kss9,inist.humanum,Text,2024-06-03,NAKALA - https://nakala.fr (Huma-Num - CNRS),,['85635 Bytes']
-10.5281/zenodo.11402791,cern.zenodo,Dataset,2024-05-31,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.11401732,cern.zenodo,Dataset,2024-05-31,Zenodo,Creative Commons Attribution 4.0 International,
-10.57745/sl7zcw,rdg.prod,Dataset,2024-05-31,Recherche Data Gouv,,
diff --git a/dois-uga.csv b/dois-uga.csv
index ceca6a2a14cdf61eaa95d79d22edcbd687c01f7d..13cae06a52d79c23882c76119a2346e26a51efed 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -13409,3 +13409,145 @@ Distances between sensors: from 10m to 500m Continuous recordings; Sampling Freq
 10.26302/sshade/experiment_lb_20240821_001,"Mid-IR absorbance spectra, normalized and baseline-corrected, of matrix and IOM fragments extracted from unshocked and shocked Murchison (CM2 chondrite) acquired under vacuum and at T = 80°C",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.","Normalized Mid-IR absorbance spectra (baseline corrected) of matrix and IOM fragments extracted from unshocked and shocked (at 5, 10, 20 and 40 GPa) Murchison (CM2 chondrite) acquired under vacuum and at T = 80°C",mds,True,findable,0,0,0,0,0,2025-03-06T16:17:55.000Z,2025-03-06T16:17:55.000Z,inist.sshade,mgeg,"laboratory measurement,transmission,microscopy,MIR,Mid-Infrared,absorbance,matrix Murchison IPAG,matrix Murchison after a 5Gpa shock,matrix Murchison after a 10 GPa shock,matrix Murchison after a 20 GPa shock,matrix Murchison after a 40 GPa shock,IOM CsF/HF of unshocked Murchison,IOM CsF/HF of Murchison shocked at 5 GPa,IOM CsF/HF of Murchison shocked at 10 GPa,IOM CsF/HF of Murchison shocked at 20 GPa,IOM CsF/HF of Murchison shocked at 40 GPa,IOM CsF/HF of Murchison shocked at 50 GPa,extraterrestrial,complex organic-mineral mix,organic molecular solid,carbonaceous chondrite,CM","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'transmission', 'subjectScheme': 'main'}, {'subject': 'microscopy', 'subjectScheme': 'main'}, {'subject': 'MIR', 'subjectScheme': 'variables'}, {'subject': 'Mid-Infrared', 'subjectScheme': 'variables'}, {'subject': 'absorbance', 'subjectScheme': 'variables'}, {'subject': 'matrix Murchison IPAG', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 5Gpa shock', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 10 GPa shock', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 20 GPa shock', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 40 GPa shock', 'subjectScheme': 'name'}, {'subject': 'IOM CsF/HF of unshocked Murchison', 'subjectScheme': 'name'}, {'subject': 'IOM CsF/HF of Murchison shocked at 5 GPa', 'subjectScheme': 'name'}, {'subject': 'IOM CsF/HF of Murchison shocked at 10 GPa', 'subjectScheme': 'name'}, {'subject': 'IOM CsF/HF of Murchison shocked at 20 GPa', 'subjectScheme': 'name'}, {'subject': 'IOM CsF/HF of Murchison shocked at 40 GPa', 'subjectScheme': 'name'}, {'subject': 'IOM CsF/HF of Murchison shocked at 50 GPa', 'subjectScheme': 'name'}, {'subject': 'extraterrestrial', 'subjectScheme': 'origin'}, {'subject': 'complex organic-mineral mix', 'subjectScheme': 'compound type'}, {'subject': 'organic molecular solid', 'subjectScheme': 'compound type'}, {'subject': 'carbonaceous chondrite', 'subjectScheme': 'meteorite group'}, {'subject': 'CM', 'subjectScheme': 'meteorite class'}]",['15 spectra'],['ASCII'],,,"['IsPartOf', 'IsPartOf']",
 10.18709/perscido.2025.03.ds417,Smartphone and Inertial Measurement Units  data for vehicular transportation analysis,PerSCiDO,2025,,Dataset,,"The ""PercepTrans Data"" database comprises raw sensor data collected from smartphones and inertial measurement units (IMUs) during 113 transportation trips in Johannesburg and Durban, South Africa. This dataset aims to characterize the movement of vehicles across various transportation modes (car, minibus taxis, bus, ride hailing services). Passengers carrying the sensors had restricted movement, effectively linking the sensor data to the vehicle's motion. The data is intended for analysis of vehicle movement, including assessing driving quality and identifying mismatches between perceived and actual driving performance.",api,True,findable,0,0,0,1,0,2025-03-07T07:55:20.000Z,2025-03-07T07:55:20.000Z,inist.persyval,vcob,"Information Technology,Behavioural Sciences,Engineering,Social Sciences","[{'subject': 'Information Technology', 'subjectScheme': 'http://www.radar-projekt.org/display/Information_Technology'}, {'subject': 'Behavioural Sciences', 'subjectScheme': 'http://www.radar-projekt.org/display/Behavioural_Sciences'}, {'subject': 'Engineering', 'subjectScheme': 'http://www.radar-projekt.org/display/Engineering'}, {'subject': 'Social Sciences', 'subjectScheme': 'http://www.radar-projekt.org/display/Social_Sciences'}]",['500 Mo'],['CSV'],,,"['IsCitedBy', 'IsCitedBy']",
 10.5281/zenodo.14959913,robertxa/Berger-Sassenage: Zenodo Release,Zenodo,2025,,Software,Creative Commons Attribution 4.0 International,Système karstique du gouffre Berger,api,True,findable,0,0,0,0,0,2025-03-03T10:50:34.000Z,2025-03-03T10:50:34.000Z,cern.zenodo,cern,,,,,,,"['IsSupplementTo', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.14959913']]"
+10.5281/zenodo.14876809,Photochromic Control in Hybrid Perovskite Photovoltaics,Zenodo,2025,en,Dataset,Creative Commons Attribution 4.0 International,"Dataset corresponding to “Photochromic Control in Hybrid Perovskite Photovoltaics” published in Advanced Material (DOI: 10.1002/adma.202420143)
+
+Figure 2: UV-vis, PL and TAS spectra raw data
+
+Figure 3:  KPFM scan image and line profile raw data
+
+Figure 4: Devices parameters box plot and stability tests raw data
+
+Figure S1: XRD pattern raw data of aging perovskite films
+
+Figure S2: XPS raw data of the control and treated mixed cation perovskite
+
+Figure S3: GIWAXS raw data of the control and treated mixed cation perovskite before and after irradiation
+
+Figure S4: TAS raw data of the control and treated mixed cation perovskite with 500 nm pump
+
+Figure S5: TAS raw data of the control and treated mixed cation perovskite with 380 nm pump
+
+Figure S6: SEM image of the treated surface and cross-section
+
+Figure S7: XPS spectra raw data of reference films(MAPbBr3) and reference with SINO with and without UV irradiation
+
+Figure S8: Surface height image of the control and treated mixed cation perovskite
+
+Figure S10: EQE raw data and devices parameters of MAPbBr3 based perovskite treated with SINO
+
+Figure S11:  TRPL and PL raw data of  reference films(MAPbBr3) and reference with SINO with and without UV irradiation
+
+Figure S12: The UPS raw data of the MAPbBr3 based reference perovskite film and the reference treated with SINO
+
+Figure S14: Value of DoH raw data from J-V curves extracted from the photocurrent transient (forward and reversed bias) as a function of scan rate for control and treated mixed cation perovskite before and after light soaking.
+
+Figure S15: Cole-cole and Nyquist plots of electrochemical impedance spectroscopy raw data for control and treated mixed cation perovskite before and after light soaking.
+
+Figure S16: Impedance spectroscopy (high frequency) raw data for control and treated mixed cation perovskite before and after light soaking.",api,True,findable,0,0,0,0,0,2025-03-14T03:45:10.000Z,2025-03-14T03:45:10.000Z,cern.zenodo,cern,"photochromic materials,metal halide perovskites,perovskite solar cells","[{'subject': 'photochromic materials'}, {'subject': 'metal halide perovskites'}, {'subject': 'perovskite solar cells'}]",,,,,"['IsOriginalFormOf', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.14876809']]"
+10.5281/zenodo.10304655,It's time to go - Drivers and plasticity of migration phenology in a short-distance migratory ungulate,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Files provided in the repository consist of two dataframes (.Rdata format) and two scripts (.R format) to perform analyses and making figures presented in the paper: ""Drivers and plasticity of migration phenology in a short-distance migratory ungulate, the Alpine ibex Capra ibex""  
+
+Abstract: 
+
+Recurring events like migrations are an important part of the biological cycles of species. Understanding the factors influencing the timing of such events is crucial for determining how species face the pervasive consequences of climate change in highly seasonal environments. Relying on data from 406 GPS-collared Alpine ibex Capra ibex monitored across 17 populations, we investigated the environmental and individual drivers of short-distance migration in this emblematic mountain ungulate. Notably, we assessed how variations in the onset of spring and autumn affected the timing of migration and the extent to which ibex exhibited behavioral plasticity in their response. We found that vegetation phenology, including spring growth and autumn senescence, along with snow dynamics - snowmelt in spring, first snowfall in autumn - were the main drivers of the timing of migration.
+
+In spring, ibex migration timing was synchronized with the peak of vegetation growth, but more in males than in females. Specifically, a shift of 10 days in vegetation growth peak delayed migration by 6.4 days for males and 2.7 days for females. This led to increased differences in migration timing between sexes when the vegetation growth peak occurred early or late in the season. In addition, ibex delayed migration timing when the length of growing season was longer and when the date of snowmelt on ibex summer range occurred later. Similarly, in autumn, prolonged vegetation senescence and delayed first snowfall led to later migration.
+
+Overall, most of the response to inter-annual variations in vegetation and snow phenology could be explained by individual behavioral plasticity. Nonetheless, females appear to be less plastic than males in their timing of spring migration, likely due to the parturition period following migration forcing them to trade off foraging needs with predation risk. As the identified drivers of ibex migration are known to be and will continue to be largely impacted by climate change, the capacity of ibex to respond to such rapid changes could differ between sexes. 
+
+ 
+
+ ",api,True,findable,0,0,0,0,0,2024-03-18T15:43:36.000Z,2024-03-18T15:43:36.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.10304655']]"
+10.14470/3v875927,"EnvSeis project, Kåfjor",GFZ Data Services,2025,en,Dataset,"embargoed access,Creative Commons Attribution 4.0 International","This network of sixteen geophones and six broadbands was installed in Kåfjord, Troms og Finnmark, Norway, to study two rockslides: Njárgavárri and Indre Nordneset. Each study site had three broadbands from September 2023 to June 2025. In addition, were installed and recording: September – November 2023: six geophones on each site; April – August 2024: four geophones at Njárgavárri and ten at Indre Nordneset. The geophones were installed locally around the rockslides while the broadbands were installed one to a few kilometers from the rockslides (except for one of them directly at Indre Nordneset). The geophones in Njárgavárri were first installed as two triangular antennas of four stations each (three in triangle and one in the middle) and were then replaced by a small aperture array around the most active part of the unstable slope. The goal was to record all activities: rock falls, cracking and creeping movements. In Indre Nordneset, the geophone stations were placed in a small aperture array all around the main scarp and surface of failure to record the cracking activity. The geophones are of type 3-D Geophone PE-6/B with DATA-CUBE3 (built-in GPS). The broadbands are of type STS-2.5 with EDR-10 digitizers. Sampling frequency was 400 Hz for geophone stations, 200 Hz broadbands. Gain was at 16 (15.258789 nV/count) for the geophone stations, set on high (100 nV/bit) for the broadband stations. Waveform data is available from the GEOFON data centre, under network code 8I.",mds,True,findable,0,0,0,0,0,2025-03-14T16:44:57.000Z,2025-03-14T16:44:58.000Z,tib.gfz,gfz,"rockslides,rock fall,creeping landslides,EARTH SCIENCE > SOLID EARTH > TECTONICS > EARTHQUAKES,EARTH SCIENCE > SOLID EARTH > TECTONICS > VOLCANIC ACTIVITY,In Situ/Laboratory Instruments > Magnetic/Motion Sensors > Seismometers,In Situ Land-based Platforms > GEOPHYSICAL STATIONS/NETWORKS,In Situ Land-based Platforms > GEOPHYSICAL STATIONS/NETWORKS > SEISMOLOGICAL STATIONS,Geophysics,FOS: Earth and related environmental sciences,Passive seismic,Seismometers,Velocity,MiniSEED,GIPP,MESI,Temporary,Polar/ice","[{'subject': 'rockslides'}, {'subject': 'rock fall'}, {'subject': 'creeping landslides'}, {'lang': 'en', 'subject': 'EARTH SCIENCE > SOLID EARTH > TECTONICS > EARTHQUAKES', 'schemeUri': 'https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords', 'subjectScheme': 'NASA/GCMD Earth Science Keywords'}, {'lang': 'en', 'subject': 'EARTH SCIENCE > SOLID EARTH > TECTONICS > VOLCANIC ACTIVITY', 'schemeUri': 'https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords', 'subjectScheme': 'NASA/GCMD Earth Science Keywords'}, {'lang': 'en', 'subject': 'In Situ/Laboratory Instruments > Magnetic/Motion Sensors > Seismometers', 'schemeUri': 'https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords', 'subjectScheme': 'GCMD Instruments'}, {'lang': 'en', 'subject': 'In Situ Land-based Platforms > GEOPHYSICAL STATIONS/NETWORKS', 'schemeUri': 'https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords', 'subjectScheme': 'GCMD Platforms'}, {'lang': 'en', 'subject': 'In Situ Land-based Platforms > GEOPHYSICAL STATIONS/NETWORKS > SEISMOLOGICAL STATIONS', 'schemeUri': 'https://gcmd.earthdata.nasa.gov/kms/concepts/concept_scheme/sciencekeywords', 'subjectScheme': 'GCMD Platforms'}, {'lang': 'en', 'subject': 'Geophysics', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'lang': 'en', 'subject': 'Passive seismic', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'Seismometers', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'Velocity', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'MiniSEED', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'GIPP', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'MESI', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'Temporary', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}, {'lang': 'en', 'subject': 'Polar/ice', 'schemeUri': 'https://geofon.gfz.de/cv/seisdata', 'subjectScheme': 'SeisData'}]",['495GB'],"['.mseed', 'XML']",,,"['HasMetadata', 'IsDescribedBy']",
+10.21427/3yhz-9j83,Lessons in Low-Tech: A Handbook for Sustainable Education,Technological University Dublin,2025,,Other,,,fabricaForm,True,findable,0,0,0,0,0,2025-03-10T14:39:33.000Z,2025-03-10T14:39:33.000Z,tudublin.arrow,tudublin,,,,,,,,
+10.5281/zenodo.15013109,Questionnaire: Assessing the Impact of AI and Digital Twins on Clinical Decision-Making in Hepatology and Hepatobiliary Surgery,Zenodo,2025,en,Other,,"Assessing the Impact of AI and Digital Twins on Clinical Decision-Making in Hepatology and Hepatobiliary Surgery
+
+This survey explores the impact of Artificial Intelligence (AI) and Digital Twins (DTs) on clinical decision-making in hepatology and liver surgery. It aims to assess how AI and Digital Twins influence physician autonomy, integrate patient preferences, and affect diagnostic and treatment decisions. 
+
+This survey is developed by:
+
+Mariia Myshkina1, Elisabetta Casabianca2, Anton Schnurpel3, Michael Tautenhahn3, Matthias König11Humboldt-University Berlin, Institute for Life Science, Institute for Theoretical Biology, Systems Medicine of the Liver, Berlin, Germany, https://livermetabolism.com2Faculty of Pharmacy, Grenoble Alpes University, Grenoble, France3Department of Visceral, Transplantation, Thoracic and Vascular Surgery, Leipzig University Hospital, GermanyContact: koenigmx@hu-berlin.de",api,True,findable,0,0,0,0,1,2025-03-12T14:32:23.000Z,2025-03-12T14:32:23.000Z,cern.zenodo,cern,"Liver,Hepatology,Artificial Intelligence,Digital Twin","[{'subject': 'Liver', 'subjectScheme': 'MeSH'}, {'subject': 'Hepatology', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Artificial Intelligence', 'subjectScheme': 'MeSH'}, {'subject': 'Digital Twin'}]",,,,,['HasVersion'],
+10.26302/sshade/experiment_lb_20250307_001,"Vis-NIR reflectance spectra (i=0°, e=30°) measured at ambient conditions of pressure and temperature of Murchison (CM2 chondrite) shocked at 5 GPa, 10 GPa, 20 GPa and 40 GPa",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.",,mds,True,findable,0,0,0,0,0,2025-03-11T16:19:41.000Z,2025-03-11T16:19:42.000Z,inist.sshade,mgeg,"laboratory measurement,bidirectional reflection,macroscopic,Vis,Visible,NIR,Near-Infrared,reflectance factor,matrix Murchison after a 5Gpa shock,chondrules Murchison after a 5Gpa shock,CAIs Murchison after a 5Gpa shock,matrix Murchison after a 10 GPa shock,chondrules Murchison after a 10 GPa shock,CAIs Murchison after a 10 GPa shock,matrix Murchison after a 20 GPa shock,chondrules Murchison after a 20 GPa shock,CAIs Murchison after a 20 GPa shock,matrix Murchison after a 40 GPa shock,chondrules Murchison after a 40 GPa shock,CAIs Murchison after a 40 GPa shock,extraterrestrial,complex organic-mineral mix,complex mineral mix,carbonaceous chondrite,CM","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'bidirectional reflection', 'subjectScheme': 'main'}, {'subject': 'macroscopic', 'subjectScheme': 'main'}, {'subject': 'Vis', 'subjectScheme': 'variables'}, {'subject': 'Visible', 'subjectScheme': 'variables'}, {'subject': 'NIR', 'subjectScheme': 'variables'}, {'subject': 'Near-Infrared', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'matrix Murchison after a 5Gpa shock', 'subjectScheme': 'name'}, {'subject': 'chondrules Murchison after a 5Gpa shock', 'subjectScheme': 'name'}, {'subject': 'CAIs Murchison after a 5Gpa shock', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 10 GPa shock', 'subjectScheme': 'name'}, {'subject': 'chondrules Murchison after a 10 GPa shock', 'subjectScheme': 'name'}, {'subject': 'CAIs Murchison after a 10 GPa shock', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 20 GPa shock', 'subjectScheme': 'name'}, {'subject': 'chondrules Murchison after a 20 GPa shock', 'subjectScheme': 'name'}, {'subject': 'CAIs Murchison after a 20 GPa shock', 'subjectScheme': 'name'}, {'subject': 'matrix Murchison after a 40 GPa shock', 'subjectScheme': 'name'}, {'subject': 'chondrules Murchison after a 40 GPa shock', 'subjectScheme': 'name'}, {'subject': 'CAIs Murchison after a 40 GPa shock', 'subjectScheme': 'name'}, {'subject': 'extraterrestrial', 'subjectScheme': 'family'}, {'subject': 'complex organic-mineral mix', 'subjectScheme': 'compound type'}, {'subject': 'complex mineral mix', 'subjectScheme': 'compound type'}, {'subject': 'carbonaceous chondrite', 'subjectScheme': 'meteorite group'}, {'subject': 'CM', 'subjectScheme': 'meteorite class'}]",['4 spectra'],['ASCII'],,,"['IsPartOf', 'IsPartOf']",
+10.5281/zenodo.15016334,Scripts used for the treatment of time-resolved in crystallo absorption spectroscopy data,Zenodo,2025,en,Software,Creative Commons Attribution 4.0 International,"These scripts were used to correct and analyse time-resolved in crystallo absorption spectroscopy data in the study of the cryptochrome photoreceptor from Chlamydomonas rheinhardtii (CraCRY). 
+
+The file architecture is as follows: data corresponding to each power level is stored in its own folder. Each pair of dark and time-point spectra is contained in its own subfolder.
+
+
+
+TRicOS_light-dark_CraCRY_article_withcorrection.py runs in a folder containing the pair of light and dark spectra.
+
+cracryfig_sci-adv_2025.py runs in a folder containing the concatenated outputs of TRicOS_light-dark_CraCRY_article_withcorrection.py, and produces the time-traces of neutral semiquinone over time.
+
+correlation_coefficient_analysis.py runs on the whole file architecture and calculates the pairwise correlation coefficient matrix for difference spectra, which is represented as a correlation map. 
+
+
+This version of the scripts was used during the first revision phase, in February 2025. ",api,True,findable,0,0,0,0,0,2025-03-13T08:19:39.000Z,2025-03-13T08:19:39.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.15016334']]"
+10.15778/resif.6j2018,"Nodes_Argostoli, temporary nodes experiment deployed at Argostoli for study spatial variation of ground structure, Greece (RESIF-SISMOB)",RESIF - Réseau Sismologique et géodésique Français,2018,,Dataset,"Open Access,Creative Commons Attribution 4.0 International",Numerous array noise measurements by using 65 3C-geophones were performed in the sedimentary valley of Argostoli (Greece) in order to provide fine image of the spatial variation of the near-surface ground structure (down to 20 m depth).,fabrica,True,findable,0,0,0,0,0,2025-03-10T17:47:23.000Z,2025-03-10T17:47:56.000Z,inist.resif,vcob,"site effects,seismic ambient vibration,dense arrays","[{'subject': 'site effects'}, {'subject': 'seismic ambient vibration'}, {'subject': 'dense arrays'}]","['341 stations, 1723Go (miniseed format)']","['Miniseed data', 'stationXML metadata']",,,['IsCitedBy'],
+10.5281/zenodo.15023210,"SmartStake SMB, air temperature, snow depth measurements at Argentière Glacier between 2019 and 2021",Zenodo,2025,,Dataset,Creative Commons Attribution 4.0 International,"Files Description:
+
+==================================ss_2019-21.smb:==================================
+
+Contains 30-min sampled values of recorded Surface Mass Balance (SMB) at the SmartStake device.
+
+Column 1 = DateTimeColumn 2 = SMB (m)
+
+================================SafranTemp1959-2022.dat:================================
+
+Contains daily records of air temperature.
+
+Column 1 = DateColumn 2 = Temperature (degree)
+
+================================SnowheightArg.xlsx:================================
+
+Contains pit measurements at the ablation zone of the glacier (2400 m a.s.l).
+
+Coordinates of the sites (balise) are given in NTF (Paris) / Lambert zone II
+
+Column 1 = site and two last digits of the yearColumn 2 = day
+
+Column 3 = monthColumn 4 = year
+
+Column 5 = snow depth (m)Column 6 = snow density (g cm^-3)",api,True,findable,0,0,0,0,1,2025-03-14T15:23:03.000Z,2025-03-14T15:23:04.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.5281/zenodo.15016412,Scripts used for the Singular Value Decomposition analysis of isomorphous difference density maps,Zenodo,2025,en,Software,Creative Commons Attribution 4.0 International,"These scripts were used to analyse a series of isomorphous difference density map in the study of the cryptochrome photoreceptor from Chlamydomonas rheinhardtii (CraCRY). 
+
+
+
+mask_mtz.sh is a bash script using Coot (9.1, provided with CCP4 8.0) to produce masked maps where only signal close to the C-terminal α-helix of CraCRY remained. It takes isomorphous difference maps (generated with the phenix.fobs_minus_fobs tool) as input
+
+SVD_gemmi.py then analyses them via Singular Value Decomposition, outputing time-invariant left Singular Vectors, or lSV) (in the form of ismomorphous difference maps) and right Singular Vector (in the form of a set of time-dependant scalars for each lSV, effectively a time-trace)
+
+
+Both scripts run within a folder containing the series of isomorphous difference maps. ",api,True,findable,0,0,0,0,0,2025-03-13T08:19:40.000Z,2025-03-13T08:19:40.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.15016412']]"
+10.5281/zenodo.14833853,DynamicGT: a dynamic-aware geometric transformer model to predict protein binding interfaces in flexible and disordered regions,Zenodo,2025,,Dataset,Creative Commons Attribution 4.0 International,"This dataset contains conformational Representatives of protein structures derived from two sources:
+
+
+
+Molecular Dynamics (MD) Simulations (three replicates of 500 microseconds for each entry) – Representative structures were selected based on RMSD clustering, with dynamically chosen cutoffs to retain between 20 to 50 representatives per trajectory.
+
+AlphaFlow-Generated Conformations – A distilled version of AlphaFlow was used to generate 25 conformations per protein.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ",api,True,findable,0,0,0,0,0,2025-03-11T08:53:21.000Z,2025-03-11T08:53:21.000Z,cern.zenodo,cern,"Molecular Dynamics Simulation,Alphaflow,protein structure,Graph Neural Networks","[{'subject': 'Molecular Dynamics Simulation', 'subjectScheme': 'MeSH'}, {'subject': 'Alphaflow'}, {'subject': 'protein structure'}, {'subject': 'Graph Neural Networks'}]",,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14833853']]"
+10.15778/resif.2l2018,"Membach, temporary nodes experiment deployed on the forested hill of Membach, Belgium (RESIF-SISMOB)",RESIF - Réseau Sismologique et géodésique Français,2018,,Dataset,"Open Access,Creative Commons Attribution 4.0 International","The LARGE-MEM project is a small-scale but dense noise-based study aiming at better understanding rainfall and groundwater storage effects on seismic velocities measured by a dense seismic network. The goal of LARGE-MEM is to demonstrate that the actual quantity and location of water in the subsurface can be estimated from measurements using a LargeN array. To achieve this, the data from superconducting and absolute gravimeters located in a gallery under the study site will be used to measure precisely the mass of water sampled by the seismic waves.",fabrica,True,findable,0,0,0,0,0,2025-03-10T17:57:34.000Z,2025-03-10T17:58:07.000Z,inist.resif,vcob,"seismic velocities,dense noise,Ambient seismic noise,groundwater storage,Membach,Dense nodal network,LARGE-MEM","[{'subject': 'seismic velocities'}, {'subject': 'dense noise'}, {'subject': 'Ambient seismic noise'}, {'subject': 'groundwater storage'}, {'subject': 'Membach'}, {'subject': 'Dense nodal network'}, {'subject': 'LARGE-MEM'}]","['96 stations, 555Go (miniseed format)']","['Miniseed data', 'stationXML metadata']",,,"['IsCitedBy', 'Obsoletes']",