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 7fdde2976b61aa0cbeaba700860bf0b3c15e9daa..9118e3fde1fd980daef5e489f7faef5b18cf521c 100644
--- a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+++ b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
@@ -1,5 +1,5 @@
 client,count,name,year,url
-cern.zenodo,798,Zenodo,2013,https://zenodo.org/
+cern.zenodo,809,Zenodo,2013,https://zenodo.org/
 inist.sshade,474,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
 figshare.ars,255,figshare Academic Research System,2016,http://figshare.com/
 inist.osug,238,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index cf548ec489e56015fa9c0738bb68c609eac2028d..65024880e24a86561c4ad00d71dd39c1c98a4d68 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2219
\ No newline at end of file
+2230
\ 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 64e4f7a4c5b5d365d515d9bc9119dded9687cfe2..a8c9a551aece57931aa4b88f45989674ba24b5ae 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 32599a1d3f08d9895341e20775caf875a92aabdf..251ac2f97577433d5dd3ab896f1164b0c9798206 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 3639f813d44fecbed6b25976fba98a9bf2cb1bd5..bc495422a576f0f08b6302e0e663e16b2467c5c8 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 1c1f01621dd280e5e49b339ade19a5ff58cee386..a22168ef4932c6e8853d86b3d5f15ac895ff755d 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 59efdc9b0abd5bb9260612f0fd15c0ed0b8f5b00..c9362d593cdaaefefc70c3af1313719c8c00ba2a 100644
Binary files a/2-produce-graph/pie--datacite-type.png and b/2-produce-graph/pie--datacite-type.png differ
diff --git a/dois-uga.csv b/dois-uga.csv
index 6a9e4d9fb40293e371f17b11767e3ef499c9a358..4321b218c69bff19b0d7315a3ee381c0759de689 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -8488,3 +8488,93 @@ Bibliography
 
 2: D.Coquenet, C. Chatelain, T. Paquet: DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence pp. 1–17 (2023).",api,True,findable,0,0,0,0,1,2024-04-30T13:04:08.000Z,2024-04-30T13:04:08.000Z,cern.zenodo,cern,"handwriting text recognition,document understanding,named entity recognition,information extraction","[{'subject': 'handwriting text recognition'}, {'subject': 'document understanding'}, {'subject': 'named entity recognition'}, {'subject': 'information extraction'}]",,
 10.5281/zenodo.11106596,Nonequilibrium Andreev resonances in ultraclean graphene Andreev interferometers,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,,api,True,findable,0,0,0,0,0,2024-05-02T20:26:03.000Z,2024-05-02T20:26:03.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.11165024,A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset,Zenodo,2024,en,Dataset,Creative Commons Attribution 4.0 International,"Simulated sydromes measurement of quantum surface code error correction.Used for the paper: ""A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction"".
+
+File names: d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples>
+
+The file format is a csv file with the following columns:- label: binary label (0: no error, 1: error)- syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))- quantity: number of samples for this label + syndrome sequence
+
+Only distance 3 avaiable with 10M sample for each physical fault rate.
+
+The data generation rely on Stim.",api,True,findable,0,0,0,0,0,2024-05-09T18:01:53.000Z,2024-05-09T18:01:53.000Z,cern.zenodo,cern,Quantum computers,"[{'subject': 'Quantum computers', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.11165023,A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset,Zenodo,2024,en,Dataset,Creative Commons Attribution 4.0 International,"Simulated sydromes measurement of quantum surface code error correction.Used for the paper: ""A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction"".
+
+File names: d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples>
+
+Each file is formatted as csv with the following columns:
+
+
+
+label: binary label (0: no error, 1: error)
+
+syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))
+
+quantity: number of samples for this label + syndrome sequence
+
+
+Only distance 3 is currently available with 10M samples for each physical fault rate.
+
+The data generation relies on Stim.",api,True,findable,0,0,0,0,0,2024-05-09T18:03:05.000Z,2024-05-09T18:03:06.000Z,cern.zenodo,cern,Quantum computers,"[{'subject': 'Quantum computers', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.11181682,NeoGeographyToolkit/StereoPipeline: 2024-05-12-daily-build,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html,api,True,findable,0,0,0,0,0,2024-05-12T16:27:38.000Z,2024-05-12T16:27:38.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.11173632,"Artifact data of article ""Light-weight prediction for improving energy consumption in HPC platforms"", Euro-Par 2024",Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Please refer to the artifact-overview.pdf file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.",api,True,findable,0,0,0,0,0,2024-05-10T13:26:18.000Z,2024-05-10T13:26:18.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.11173631,"Artifact data of article ""Light-weight prediction for improving energy consumption in HPC platforms"", Euro-Par 2024",Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Please refer to the artifact-overview.pdf file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.",api,True,findable,0,0,0,0,0,2024-05-10T13:26:18.000Z,2024-05-10T13:26:18.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.11126561,Green Function Database in ak135 for synthetic cross-correlation computation in WMSAN.,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"## DescriptionThis file is an HDF5 file containing synthetic seismic waveforms computed with AxiSEM in an axisymmetric Earth in model ak135f.It contains waveforms at various distances for a vertical point force source of 1E20 N.
+
+## Parameters
+
+Distance range from 0° to 180° with a 0.1° step.Source location latitude  = 90°, longitude = 0°.Sampling frequency 1Hz. Duration 3600s.Dominant period 1s.## ArchitectureNetwork  ""L"" 
+
+Station ""SYNTH0000"" : station at distance = 0° from the source location.
+
+|-- NOISE_vertforce_dirac_0-ak135f_1.s_3600s.h5/
+│   └── L/
+│       └── SYNTH0000/│       └── ...│       └── SYNTH1800/│   └── _metadata/
+
+ ",api,True,findable,0,0,0,0,1,2024-05-07T13:55:40.000Z,2024-05-07T13:55:40.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.11164068,A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data,Zenodo,2024,en,Dataset,Creative Commons Attribution 4.0 International,"Simulation output data used to generate figures of the paper: ""A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction""",api,True,findable,0,0,0,0,0,2024-05-09T14:50:50.000Z,2024-05-09T14:50:51.000Z,cern.zenodo,cern,"Machine learning,Quantum computers,Nanoelectronics","[{'subject': 'Machine learning', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Quantum computers', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Nanoelectronics', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.11166209,A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset,Zenodo,2024,en,Dataset,Creative Commons Attribution 4.0 International,"Simulated sydromes measurement of quantum surface code error correction.Used for the paper: ""A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction"".
+
+File names: d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples>
+
+Each file is formatted as csv with the following columns:
+
+
+
+label: binary label (0: no error, 1: error)
+
+syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))
+
+quantity: number of samples for this label + syndrome sequence
+
+
+Only distance 3 is currently available with 10M samples for each physical fault rate.
+
+The data generation relies on Stim.",api,True,findable,0,0,0,0,0,2024-05-09T18:09:34.000Z,2024-05-09T18:09:34.000Z,cern.zenodo,cern,Quantum computers,"[{'subject': 'Quantum computers', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.11182079,easystats/insight: insight 0.19.11,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,"General
+
+
+
+More informative error message for get_varcov() when the requested
+vcov-function failed.
+
+
+Bug fixes
+
+
+
+Fixed issue with get_data() for coxme models when sourcewas set to
+""modelframe"".",api,True,findable,0,0,0,0,0,2024-05-12T19:44:28.000Z,2024-05-12T19:44:28.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.11164067,A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data,Zenodo,2024,en,Dataset,Creative Commons Attribution 4.0 International,"Simulation output data used to generate figures of the paper: ""A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction""",api,True,findable,0,0,0,0,0,2024-05-09T14:50:50.000Z,2024-05-09T14:50:51.000Z,cern.zenodo,cern,"Machine learning,Quantum computers,Nanoelectronics","[{'subject': 'Machine learning', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Quantum computers', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Nanoelectronics', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.11126562,Green Function Database in ak135 for synthetic cross-correlation computation in WMSAN.,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"## DescriptionThis file is an HDF5 file containing synthetic seismic waveforms computed with AxiSEM in an axisymmetric Earth in model ak135f.It contains waveforms at various distances for a vertical point force source of 1E20 N.
+
+## Parameters
+
+Distance range from 0° to 180° with a 0.1° step.Source location latitude  = 90°, longitude = 0°.Sampling frequency 1Hz. Duration 3600s.Dominant period 1s.## ArchitectureNetwork  ""L"" 
+
+Station ""SYNTH0000"" : station at distance = 0° from the source location.
+
+|-- NOISE_vertforce_dirac_0-ak135f_1.s_3600s.h5/
+│   └── L/
+│       └── SYNTH0000/│       └── ...│       └── SYNTH1800/│   └── _metadata/
+
+ ",api,True,findable,0,0,0,0,0,2024-05-07T13:55:40.000Z,2024-05-07T13:55:40.000Z,cern.zenodo,cern,,,,