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 f2b7d96d7c585abc6c206fc5ae689e4188c8f786..22b04846b68f48e2cdc56ebae4878649ae650aac 100644
--- a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+++ b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
@@ -1,9 +1,9 @@
 client,count,name,year,url
-cern.zenodo,705,Zenodo,2013,https://zenodo.org/
+cern.zenodo,713,Zenodo,2013,https://zenodo.org/
 inist.sshade,475,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
 figshare.ars,258,figshare Academic Research System,2016,http://figshare.com/
 inist.osug,238,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
-dryad.dryad,157,DRYAD,2018,https://datadryad.org
+dryad.dryad,158,DRYAD,2018,https://datadryad.org
 inist.resif,80,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
 inist.humanum,57,Huma-Num,2020,https://nakala.fr
 inist.persyval,55,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
@@ -12,25 +12,25 @@ fmsh.prod,28,Fondation Maison des sciences de l'homme,2023,
 mcdy.dohrmi,12,dggv-e-publications,2020,https://www.dggv.de/publikationen/dggv-e-publikationen.html
 figshare.sage,6,figshare SAGE Publications,2018,
 iris.iris,4,Incorporated Research Institutions for Seismology,2018,http://www.iris.edu/hq/
-tib.repod,3,RepOD,2015,
 tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
+tib.repod,3,RepOD,2015,
 vqpf.dris,3,Direction des ressources et de l'information scientifique,2021,
 ugraz.unipub,2,unipub,2019,http://unipub.uni-graz.at
 bl.nerc,2,NERC Environmental Data Service,2011,https://eds.ukri.org
-repod.dbuw,1,Dane Badawcze UW,2023,
-bl.mendeley,1,Mendeley Data,2015,https://data.mendeley.com/
-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,
-ihumi.pub,1,IHU Méditerranée Infection,2020,
-inist.opgc,1,Observatoire de Physique du Globe de Clermont-Ferrand,2017,
-ethz.da-rd,1,ETHZ Data Archive - Research Data,2013,http://data-archive.ethz.ch
+tug.openlib,1,TU Graz OPEN Library,2020,https://openlib.tugraz.at/
 crui.ingv,1,Istituto Nazionale di Geofisica e Vulcanologia (INGV),2013,http://data.ingv.it/
+repod.dbuw,1,Dane Badawcze UW,2023,
+estdoi.ttu,1,TalTech,2019,https://digikogu.taltech.ee
+inist.ird,1,IRD,2016,
 inist.eost,1,Ecole et Observatoire des Sciences de la Terre,2017,https://eost.unistra.fr/en/
 ethz.zora,1,"Universität Zürich, ZORA",2013,https://www.zora.uzh.ch/
-inist.ird,1,IRD,2016,
-estdoi.ttu,1,TalTech,2019,https://digikogu.taltech.ee
-tug.openlib,1,TU Graz OPEN Library,2020,https://openlib.tugraz.at/
+inist.opgc,1,Observatoire de Physique du Globe de Clermont-Ferrand,2017,
+ethz.da-rd,1,ETHZ Data Archive - Research Data,2013,http://data-archive.ethz.ch
+bl.mendeley,1,Mendeley Data,2015,https://data.mendeley.com/
+ihumi.pub,1,IHU Méditerranée Infection,2020,
+inist.omp,1,Observatoire Midi-Pyrénées,2011,
+ardcx.nci,1,National Computational Infrastructure,2020,
+tib.gfz,1,GFZ Data Services,2011,https://dataservices.gfz-potsdam.de/portal/
+bl.iita,1,International Institute of Tropical Agriculture datasets,2017,http://data.iita.org/
+edi.edi,1,Environmental Data Initiative,2017,https://portal.edirepository.org/nis/home.jsp
 umass.uma,1,University of Massachusetts (UMass) Amherst,2018,https://scholarworks.umass.edu/
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index d2efa6b8955f6f04db7ebabf351cf64313af4dcb..2e8bf645b7126114fef6d1ba8e442a3196cf6852 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2157
\ No newline at end of file
+2166
\ 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 6f7de59842e5d95457d6e79af1d63bad37344b52..9c1db6b8dfe383f3f88556f2df50c550a51be733 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 b90f03b98b2e1e7e63f9506da19d9a48ceea96a0..1c6ee8f48338f53916f81c258ce55c4d6e986459 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 d4d2d3990eadb4184acfbc2af55da2ce547e97a5..4b29425144c6614af4f65a7a07da58ac6549f8a6 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 8051297e057ca09f983657b503faaab8cf2b58ce..a7a1d8186f74b3d25c502c94827a9e06149cb23c 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 8778370406413cc951757fb3f0998e4e9212cb12..d318f235ba99ce85e1c536c487b494469c086aaf 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 86a6dc50d788eb01f17f35cd6110f5f65b80597e..a4f9008ad613fe9f878b3df209f94cf2fb11ce8b 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -9820,3 +9820,75 @@ Attached is also an R code to illustrate the statistical analyses performed in t
 10.34847/nkl.df7fuw6c,Footprint,NAKALA - https://nakala.fr (Huma-Num - CNRS),2024,,Audiovisual,,,api,True,findable,0,0,0,0,0,2024-06-05T07:43:46.000Z,2024-06-05T07:43:46.000Z,inist.humanum,jbru,,,['19348141 Bytes'],['video/quicktime'],,,,
 10.34847/nkl.70ecx2zf,The dragon stick,NAKALA - https://nakala.fr (Huma-Num - CNRS),2024,,Audiovisual,,,api,True,findable,0,0,0,0,0,2024-06-05T07:46:18.000Z,2024-06-05T07:46:18.000Z,inist.humanum,jbru,,,['65108954 Bytes'],['video/quicktime'],,,,
 10.57745/fkc6wp,Exploring the limits of Fe-rich chemistries in Na-based CaFe2O4-type postspinel oxides,Recherche Data Gouv,2024,,Dataset,,"Data set from Exploring the limits of Fe-rich chemistries in Na-based CaFe2O4 -type postspinel oxides. - Publication : Louise Benincasa, Mathieu Duttine, Céline Goujon, Murielle Legendre, Matthew Suchomel, et al.. Exploring the limits of Fe-rich chemistries in Na-based CaFe2O4 -type postspinel oxides. Inorganic Chemistry, 2024, 63 (22), pp.10373-10385. Hal-04601031 Dataset production context : This study of the NaFexRu2-xO4 system reveals a limited solid solution (1 ≤ x ≤ 1.3) adopting the CaFe2O4-type structure, in contrast to the NaFexTi2-xO4 system. Maintaining trivalent iron states for all compositions, detailed structural analysis reveals narrowed sodium ion diffusion channels with increasing Fe content, limiting the performance as positive electrode materials for sodium batteries. This fundamental insight guides the exploration of alternative NaM2O4-based positive electrode materials with a post spinel type structure.",mds,True,findable,8,0,0,0,0,2024-06-04T15:04:14.000Z,2024-06-05T13:12:43.000Z,rdg.prod,rdg,,,,,,,"['HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart']",
+10.5281/zenodo.11650896,Data of the Stochastic Grid Perturbation comparison with Location Uncertainty framework,Zenodo,2024,,Dataset,MIT License,"The archive outputs.tar.xz contain the outputs of the code which performs a Stochastic Grid Perturbation on a Quasi-Geostrophic model;
+
+The archive tmp_moments.tar.xz and tmp_moments_jet_centered.tar.xz contain the first four standarized spatial moments of the state variables.
+
+They are exploited in the paper ""Link between Stochastic Grid Perturbation and Location Uncertainty framework"".
+
+ ",api,True,findable,0,0,0,0,0,2024-06-14T07:04:18.000Z,2024-06-14T07:04:18.000Z,cern.zenodo,cern,,,,,,,"['HasVersion', 'HasVersion']",
+10.5281/zenodo.11554782,Global climatology of light-absorbing particle deposition on snow,Zenodo,2024,,Dataset,Open Government Licence - Canada,"Description
+
+Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. To better quantify the spatial variability of LAP deposition on snow, Gaillard et al. (2024) have generated a global climatology of LAP deposition on snow. It combines a climatological dataset of LAP deposition rates (Black Carbon (BC) and dust) over the period 1979-2015 (Zhao et al., 2018) with a global dataset of snow cover (Romanov, 2017) to obtain a climatology of LAP deposition rates on snow.
+
+The data are distributed in a NetCDF file (climato_tot_publish.nc). More details about the dataset and the file format are given in the file readme_climatology.pdf. 
+
+ ",api,True,findable,0,0,0,0,0,2024-06-10T20:32:42.000Z,2024-06-10T20:32:43.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.11554782']]"
+10.5281/zenodo.11554925,Improved snow ageing parameter for large-scale albedo modelling with Crocus,Zenodo,2024,,Dataset,Open Government Licence - Canada,"Description
+
+Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models can be associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In Gaillard et al. (2024), a new strategy of intermediate complexitythat includes the effects of spatially variable LAP deposition on snow albedo was tested with the snowpack model Crocus. It relies on an optimization of the parameter that controls the evolution of snow albedo in the visible range. A global dataset of LAP-informed and spatially variable values of this parameter was constructed. These revised parameter values improved snow albedo simulations at the ten sites considered in the study by 10%, with the largest improvements found in the Arctic (more than 25%).
+
+The data are distributed in a NetCDF file (gamma_tot_publish.nc). More details about the dataset and the file format are given in the file readme_gamma.pdf. ",api,True,findable,0,0,0,0,0,2024-06-10T20:32:53.000Z,2024-06-10T20:32:53.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.11554925']]"
+10.5281/zenodo.11403191,Robust quantum dots charge autotuning using neural networks uncertainty - Output data,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Outputs of the model training and the offline autotuning experiments presented in the paper: ""Robust quantum dots charge autotuning using neural networks uncertainty"".
+
+For convenience, the results are splitted in several zipped files:
+
+
+
+run_outputs_light.zip: contains only settings and results text files (sufficient for compiling result tables).
+
+run_outputs_full_scan.zip: contains complete scan of the diagrams (for qualitative analyse)
+
+run_outputs_part<N>.zip: contains all autotuning simulation output, grouped by seed (images and video output types might vary between seeds)
+
+
+Each folder in the zipped files represent a run that includes:
+
+
+
+log file
+
+plots / images
+
+run settings
+
+performance results
+
+pytorch model parameters
+
+
+See README.txt for more information about the file strucutre.",api,True,findable,0,0,0,0,0,2024-06-10T19:25:50.000Z,2024-06-10T19:25:50.000Z,cern.zenodo,cern,,,,,,,"['IsDerivedFrom', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.11403191']]"
+10.5281/zenodo.11657789,French-Amr-Parser,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,"Code implementation of the article : Analyse sémantique AMR pour le français par transfert translingue (Kang et al., 2023) published at TALN 2023 ",api,True,findable,0,0,0,0,0,2024-06-14T14:18:39.000Z,2024-06-14T14:18:39.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.5281/zenodo.11594164,"Evaluation of CMIP5 and CMIP6 global climate models in the Arctic and Antarctic regions, atmosphere and surface ocean",Zenodo,2024,,Text,Creative Commons Attribution 4.0 International,"Large efforts are engaged to model climate-ice sheet interactions in order to estimate the contribution of Antarctica and Greenland to sea level in the next decades to centuries. Here we present a first-order evaluation of CMIP5 and CMIP6 climate models over both polar regions. We focus on large-scale atmospheric fields and surface ocean variables only. Our goal is to provide a first overview of climate model biases in polar regions, in order to use their outputs on an informed basis. We particularly target climate model outputs relevant for driving ice-sheet models and regional climate models.
+
+We consider 9 (non-independent) variables : 850 hPa and 700 hPa annual and summer temperature, annual integrated water vapor, annual sea level pressure, annual 500hPa geopotential height, summer sea surface temperature, and winter sea ice concentration; over the Arctic (> 50°N) and the Antarctic (<40°S) regions. We use the ERA5 reanalysis as a reference, but we also consider 5 other reanalyses in the intercomparison to account for observational uncertainty. We define two sets of metrics. The first set of metrics, called “scaled rmse”, is the spatial root mean square error (RMSE) of time-mean variables for each region, that we divide by the median RMSE among all CMIP models. The second set of metrics, called “implausible fraction”, is the portion of the region where the difference between time-mean CMIP model and time-mean ERA5 is greater than three times the local interannual standard deviation. We find a strong relationship between the two sets of metrics. In addition, using the implausible fraction, we find that CMIP variables are significantly more implausible in the Antarctic than in the Arctic. It might be because of badly resolved processes or because of higher decadal variability in the South. Further work should include estimates of decadal variability in the implausibility computation.",api,True,findable,0,0,0,0,0,2024-06-12T01:07:16.000Z,2024-06-12T01:07:16.000Z,cern.zenodo,cern,,,,,,,"['HasPart', 'HasVersion', 'HasVersion']",
+10.5281/zenodo.11067075,Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition,Zenodo,2024,en,Model,Creative Commons Attribution 4.0 International,"Human Activity Recognition (HAR) based on the sensors of mobile/wearable devices aims to detect the physical activities performed by humans in their daily lives. Although supervised learning methods are the most effective in this task, their effectiveness is constrained to using a large amount of labeled data during training. While collecting raw unlabeled data can be relatively easy, annotating data is challenging due to costs, intrusiveness, and time constraints.To address these challenges, this paper explores alternative approaches for accurate HAR using a limited amount of labeled data. In particular, we have adapted recent Self-Supervised Learning (SSL) algorithms to the HAR domain and compared their effectiveness. We investigate three state-of-the-art SSL techniques of different families: contrastive, generative, and predictive. Additionally, we evaluate the impact of the underlying neural network on the recognition rate by comparing state-of-the-art CNN and transformer architectures.Our results show that a Masked Auto Encoder (MAE) approach significantly outperforms other SSL approaches, including SimCLR, commonly considered one of the best-performing SSL methods in the HAR domain.The code and the pre-trained SSL models are publicly available for further research and development.
+
+Pre-print paper available at:https://arxiv.org/abs/2404.15331",api,True,findable,0,0,0,0,0,2024-04-25T13:34:40.000Z,2024-04-25T13:34:41.000Z,cern.zenodo,cern,"Human Activity Recogniton,Pretrained Models","[{'subject': 'Human Activity Recogniton'}, {'subject': 'Pretrained Models'}]",,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.11067075']]"
+10.5061/dryad.jdfn2z3k4,Data from: Neural interactions in the human frontal cortex dissociate reward and punishment learning,Dryad,2024,en,Dataset,Creative Commons Zero v1.0 Universal,"How human prefrontal and insular regions interact while maximizing rewards
+ and minimizing punishments is unknown. Capitalizing on human intracranial
+ recordings, we demonstrate that the functional specificity toward reward
+ or punishment learning is better disentangled by interactions compared to
+ local representations. Prefrontal and insular cortices display
+ non-selective neural populations to reward and punishment. The
+ non-selective responses, however, give rise to context-specific interareal
+ interactions. We identify a reward subsystem with redundant interactions
+ between the orbitofrontal and ventromedial prefrontal cortices, with a
+ driving role of the latter. In addition, we find a punishment subsystem
+ with redundant interactions between the insular and dorsolateral cortices,
+ with a driving role of the insula. Finally, switching between reward and
+ punishment learning is mediated by synergistic interactions between the
+ two subsystems. These results provide a unifying explanation of
+ distributed cortical representations and interactions supporting reward
+ and punishment learning.",mds,True,findable,0,0,0,0,0,2024-06-12T09:54:38.000Z,2024-06-12T09:54:38.000Z,dryad.dryad,dryad,"preprocessed high-gamma activity data,FOS: Biological sciences,FOS: Biological sciences,Probabilistic learning,Reinforcement Learning","[{'subject': 'preprocessed high-gamma activity data'}, {'subject': 'FOS: Biological sciences', 'subjectScheme': 'fos'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Probabilistic learning'}, {'subject': 'Reinforcement Learning'}]",['233570710 bytes'],,,,['IsSupplementedBy'],
+10.5281/zenodo.11614593,"aUPaEU D2.1 Preliminary analysis and plan, outline of the acceleration services catalogue",Zenodo,2024,,Text,Creative Commons Attribution 4.0 International,"The central element is the creation of an acceleration services catalogue under Work Package 2 (WP2), involving stakeholders like university alliances and FOR-EU. The project also conducts a comparative analysis of strategies employed by European Alliances within the EU, ultimately aiming to bridge the gap between institutional transformations and acceleration services in European higher education and research. The document follows a structured approach, covering the transformation agenda, services exploration, the initial catalogue, and concluding with insights",api,True,findable,0,0,0,0,0,2024-06-12T11:04:39.000Z,2024-06-12T11:04:39.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.11614593']]"