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 d0e2a9e9ff5869edf8e6994064c1f94e2699f6ab..98bb7aa4ac5c99b39ff038e8d98bfc5c51d811ea 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,757,Zenodo,2013,https://zenodo.org/
-inist.sshade,472,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
+cern.zenodo,763,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
-dryad.dryad,157,DRYAD,2018,https://datadryad.org
+dryad.dryad,159,DRYAD,2018,https://datadryad.org
 inist.resif,80,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
 inist.persyval,55,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
 rdg.prod,43,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index 848b2e7e2dfe5915735fec17ffcfe343065576f9..93271173b38addae4c8d9ba960cd3423320a236a 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2171
\ No newline at end of file
+2181
\ 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 e811eba497c20c0a039143c8c23b01eeadabb777..84bdf9eb5165ad8b55d1f41a0241265fee1d79d0 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 0bd7ab807244e7f5729b78e0366d6528eb056fb9..a484b707eb4059b19a3550981ad81f2cbaa9a50c 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 40c09da50a2d0876958ec728ca30230778deb8e5..8d54acaa888b8deb58d3e221da694e2602681176 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 a9ef5f6d5cb5a0e56af486286736fccf801ba07a..8a50c09724dfa9fe5b1f0ec70a03f4dd137595dd 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 34141751a7a095d0fcfa7cab6ab0babf4dc5954a..dd220637a1922f92e486874674b656e6426f55e1 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 8349e0accdaa138733d42ea27988d8ad9d94ae48..05a7a5e99ba8ddaff318f8ff708d4919d409e60c 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -7339,3 +7339,191 @@ edc3018 fix(english) workaround several bugs in example extraction
 2210fce Update versions for release
 aa0c8b8 Merge branch 'release/3.1.4'",api,True,findable,0,0,0,1,0,2024-04-01T12:48:04.000Z,2024-04-01T12:48:04.000Z,cern.zenodo,cern,,,,
 10.5905/ethz-1007-760,"Software for examples of ""Peak Time-Windowed Mean Estimation using Convex Optimization""","ETH Zurich; GIPSA-lab, Univ. Grenoble Alpes, CNRS, Grenoble INP, LAAS-CNRS, Czech Technical University",2024,,Software,,,api,True,findable,0,0,0,0,0,2024-04-03T05:15:01.000Z,2024-04-03T05:15:01.000Z,ethz.da-rd,stdp,,,,['MATLAB']
+10.5061/dryad.zkh1893hw,Vegetation changes with climate change in the Grandes Rousses mountain range,Dryad,2024,en,Dataset,Creative Commons Zero v1.0 Universal,"Questions: We assessed interactions between climate change, bedrock types
+ and snow cover duration on the trajectories of taxonomic and functional
+ composition of subalpine plant communities. We predict (i) an increase in
+ species richness on siliceous bedrock due to a reduced competition and a
+ decrease in richness on calcareous bedrock due to increasing drought
+ stress, (ii) decreasing snow cover duration should induce a higher shrub
+ encroachment in hollows as compared to ridges (iii) increasing growing
+ season temperature should induce taller sizes and more conservative growth
+ traits, in particular in hollows. Location: Subalpine belt of the Grandes
+ Rousses mountain range, south western Alps (France). Methods: 189
+ vegetation plots were sampled in 1997 and 2017-2018. The duration of snow
+ cover was assessed during two years in 1995-1997 and five functional
+ traits were measured on 108 species in 2021. We performed multivariate
+ analyses, quantified community weighted-means (CWM) of traits and used
+ ANOVAs to detect responses to local-scale factors and changes in snow
+ cover, temperature and precipitation since 1997 according to a nearby
+ meteorological station. Results: Overall, taxonomic composition weakly
+ changed and changes were more dependent on the position of communities
+ along the snow cover duration gradient than on their bedrock type. The
+ abundance of drought-tolerant species increased at the border of hollows
+ and there was, over all communities, a slight increase in the abundance of
+ dwarf shrubs and tall herbaceous species, a strong decrease in short
+ herbaceous species and, thus, an overall decrease in species richness.
+ There were important overall changes in CWM of size traits, in particular
+ leaf area which increased the most in hollows irrespective of bedrock
+ types. Conclusion: In this subalpine site the effects of decreasing snow
+ cover duration overwhelmed the effects of bedrocks, which may explain the
+ overall increase in competitive species and decrease in species richness.",mds,True,findable,0,0,0,0,0,2024-04-12T17:10:17.000Z,2024-04-12T17:10:18.000Z,dryad.dryad,dryad,"French Alps,Bedrock types,Climate change,community composition,functional composition,Snow cover duration,FOS: Biological sciences,FOS: Biological sciences,Subalpine belt","[{'subject': 'French Alps'}, {'subject': 'Bedrock types'}, {'subject': 'Climate change', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}, {'subject': 'community composition'}, {'subject': 'functional composition'}, {'subject': 'Snow cover duration'}, {'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': 'Subalpine belt'}]",['146662 bytes'],
+10.26302/sshade/experiment_zed_20230103_02,FIR spectra of phyllosilicate pellets irradiated by He+ and Ar+ ions,SSHADE/DAYSY (OSUG Data Center),2024,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.",FIR spectra of phyllosilicate pellets irradiated by $Ar^+$ or $He^+$.,mds,True,findable,0,0,2,0,0,2024-04-10T15:37:24.000Z,2024-04-10T15:37:24.000Z,inist.sshade,mgeg,"laboratory measurement,confocal reflection,micro-imaging,FIR,Far-Infrared,reflectance factor,Serpentine Rawhide,Serpentine UB-N,Saponite Griffithite,mineral,natural terrestrial,phyllosilicate","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'confocal reflection', 'subjectScheme': 'main'}, {'subject': 'micro-imaging', 'subjectScheme': 'main'}, {'subject': 'FIR', 'subjectScheme': 'variables'}, {'subject': 'Far-Infrared', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'Serpentine Rawhide', 'subjectScheme': 'name'}, {'subject': 'Serpentine UB-N', 'subjectScheme': 'name'}, {'subject': 'Saponite Griffithite', 'subjectScheme': 'name'}, {'subject': 'mineral', 'subjectScheme': 'family'}, {'subject': 'natural terrestrial', 'subjectScheme': 'origin'}, {'subject': 'phyllosilicate', 'subjectScheme': 'compound type'}]",['9 spectra'],['ASCII']
+10.5281/zenodo.10951600,Response to Sea Surface Temperature and Primary Productivity to change in Earth's Orbit Eccentricity - Simulations,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"This dataset contains ocean and ocean biogeochemistry outputs from modeling experiments with present-day geography and various Earth's orbit confiurations. The set of simulation targets the role of Eccentricity on the tropical ocean sea surface temperature and primary productivity (Beaufort & Sarr, 2024) . The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed formulti-millennial climate simulations, GMD) and offline version of PISCESv2 model (Aumont et al., 2015 - PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, GMD). It includes 4 simulations. Data are monthly averages over the last 100 years of the simulations.
+
+Complementary outputs (4 simulations) can be found at https://www.seanoe.org/data/00728/84031/ (Beaufort et al., 2022)",api,True,findable,0,0,0,0,0,2024-04-11T20:07:00.000Z,2024-04-11T20:07:01.000Z,cern.zenodo,cern,Paleoclimatology,"[{'subject': 'Paleoclimatology', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.10958419,NNXRD-mfraction datasets,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Datasets from ""Neural networks for rapid phase quantification of Cultural Heritage X-ray powder diffraction data"" needed to run the codes at: https://github.com/polinev/NNXRD-mfraction.
+
+Some more information regarding the files:
+
+
+
+ Mock-up:
+
+
+
+rep-6_layer_0_0002_powder_short-iback_clean_corr_new.h5 = sample file with all XRD patterns pretreated
+
+dataset_mockup_rep6.pickle = dataset for neural network training
+
+best_val_loss_model_pp/h_mockup_rep6 = best model after NN training
+
+mockup_rep6_rebuilt.pickle = XRD patterns rebuilt from predictions
+
+REP6_seq_topas.h5 = data treated with serial Rietveld refinement
+
+
+
+Historical sample:
+
+
+
+S2018_157_sinogram_layer02_XRD_powder_pack_half1_short-iback_clean.h5 = data pretreated
+
+S2018_157_sinogram_layer02_XRD_powder_pack_half1-back_clean_corr_mask.h5 = other sample file that contains metadata used in the code (mask and contour)
+
+dataset_historical_sample_S157.pickle = dataset for NN training
+
+best_val_loss_model_pp/h_article.h5 = best model after NN training
+
+patterns_rebuilt_S157.pickle = XRD patterns rebuilt from predictions",api,True,findable,0,0,0,0,0,2024-04-11T07:31:59.000Z,2024-04-11T07:32:00.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.10966629,pyxem/orix: orix 0.12.0,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,"orix 0.12.0 is a minor release of orix, an open-source Python library for handling orientations, rotations and crystal symmetry.
+
+See below, the changelog or the GitHub changelog for all updates from the previous release.
+
+Added
+
+
+
+Vector3d.from_path_ends() class method to get vectors between two vectors.
+
+Convenience function plot.format_labels() to get nicely formatted vector labels to use when plotting vectors.
+
+Two offsets in the stereographic coordinates (X, Y) can be given to StereographicPlot.text() to offset text coordinates.
+
+Explicit support for Python 3.11.
+
+Creating quaternions from neo-eulerian vectors via new class methods from_rodrigues() and from_homochoric(), replacing the now deprecated from_neo_euler(). from_rodrigues() accepts an angle parameter to allow passing Rodrigues-Frank vectors.
+
+Creating neo-eulerian vectors from quaternions via new methods to_axes_angles(), to_rodrigues() and to_homochoric(). Rodrigues-Frank vectors can be returned from to_rodrigues() by passing frank=True.
+
+inv() method for Quaternion, Rotation, Orientation, and Misorientation. For the three first, its behavior is identical to the inversion operator ~. For misorientations, it inverts the direction of the transformation. Convenient for chaining operations.
+
+The random() methods of Orientation and Misorientation now accept symmetry. A random() method is also added to Vector3d and Miller, the latter accepting a phase.
+
+Function orix.sampling.get_sample_reduced_fundamental() for sampling rotations that rotate the Z-vector (0, 0, 1) onto the fundamental sector of the Laue group of a given Symmetry.
+
+
+Changed
+
+
+
+The convention parameter in from_euler() and to_euler() will be removed in the next minor release, 0.13, instead of release 1.0 as previously stated.
+
+Allow passing a tuple of integers to reshape() methods of 3D objects.
+
+random() methods no longer accept a list as a valid shape: pass a tuple instead.
+
+Increase minimal version of Matplotlib to >= 3.5.
+
+
+Removed
+
+
+
+Support for Python 3.7.
+
+
+Deprecated
+
+
+
+Creating quaternions from neo-eulerian vectors via from_neo_euler() is deprecated and will be removed in v0.13. Use the existing from_axes_angles() and the new from_rodrigues() and from_homochoric() instead.
+
+
+Fixed
+
+
+
+Transparency of polar stereographic grid lines can now be controlled by Matplotlib's grid.alpha, just like the azimuth grid lines.
+
+Previously, Phase did not adjust atom positions when forcing Phase.structure.lattice.base to use the crystal axes alignment e1 || a, e3 || c*. This is now fixed.",api,True,findable,0,0,0,0,0,2024-04-12T17:23:52.000Z,2024-04-12T17:23:53.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.10969048,NeoGeographyToolkit/StereoPipeline: 2024-04-13-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,0,0,0,0,0,2024-04-13T18:42:52.000Z,2024-04-13T18:42:52.000Z,cern.zenodo,cern,,,,
+10.5061/dryad.3tx95x6pk,Data from: The importance of migratory drop-off for island colonization in birds,Dryad,2024,en,Dataset,Creative Commons Zero v1.0 Universal,"Seasonal migration is an underappreciated driver of animal
+ diversification. Changes in migratory behavior may favor the establishment
+ of sedentary founder populations and promote speciation if there is
+ sufficient reproductive isolation between sedentary and migratory
+ populations. From a systematic literature review, we here quantify the
+ role of migratory drop-off – the loss of migratory behavior – in promoting
+ speciation in birds on islands. We identify at least 157 independent
+ colonization events likely initiated by migratory species that led to
+ speciation, including 44 cases among recently extinct species. By
+ comparing, for all islands, the proportion of island endemic species that
+ derived from migratory drop-off with the proportion of migratory species
+ among potential colonizers, we showed that seasonal migration has a larger
+ effect on island endemic richness than direct dispersal. We also found
+ that the role of migration in island colonization increases with the
+ geographic isolation of islands. Furthermore, the success of speciation
+ events depends in part on species biogeographic and ecological factors,
+ here positively associated with greater range size and larger flock sizes.
+ These results highlight the importance of shifts in migratory behavior in
+ speciation process and calls for greater consideration of migratory
+ drop-off in the biogeographic distribution of birds.",mds,True,findable,0,0,0,0,0,2024-04-12T14:00:46.000Z,2024-04-12T14:00:47.000Z,dryad.dryad,dryad,"FOS: Biological sciences,FOS: Biological sciences,seasonal migration,extinct species,long distance dispersal,Island biogeography,Birds","[{'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': 'seasonal migration'}, {'subject': 'extinct species'}, {'subject': 'long distance dispersal'}, {'subject': 'Island biogeography', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}, {'subject': 'Birds', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}]",['763810 bytes'],
+10.5281/zenodo.10951601,Response to Sea Surface Temperature and Primary Productivity to change in Earth's Orbit Eccentricity - Simulations,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"This dataset contains ocean and ocean biogeochemistry outputs from modeling experiments with present-day geography and various Earth's orbit confiurations. The set of simulation targets the role of Eccentricity on the tropical ocean sea surface temperature and primary productivity (Beaufort & Sarr, 2024) . The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed formulti-millennial climate simulations, GMD) and offline version of PISCESv2 model (Aumont et al., 2015 - PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, GMD). It includes 4 simulations. Data are monthly averages over the last 100 years of the simulations.
+
+Complementary outputs (4 simulations) can be found at https://www.seanoe.org/data/00728/84031/ (Beaufort et al., 2022)",api,True,findable,0,0,0,0,0,2024-04-11T20:07:00.000Z,2024-04-11T20:07:00.000Z,cern.zenodo,cern,Paleoclimatology,"[{'subject': 'Paleoclimatology', 'subjectScheme': 'EuroSciVoc'}]",,
+10.5281/zenodo.10958418,NNXRD-mfraction datasets,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"Datasets from ""Neural networks for rapid phase quantification of Cultural Heritage X-ray powder diffraction data"" needed to run the codes at: https://github.com/polinev/NNXRD-mfraction.
+
+Some more information regarding the files:
+
+
+
+ Mock-up:
+
+
+
+rep-6_layer_0_0002_powder_short-iback_clean_corr_new.h5 = sample file with all XRD patterns pretreated
+
+dataset_mockup_rep6.pickle = dataset for neural network training
+
+best_val_loss_model_pp/h_mockup_rep6 = best model after NN training
+
+mockup_rep6_rebuilt.pickle = XRD patterns rebuilt from predictions
+
+REP6_seq_topas.h5 = data treated with serial Rietveld refinement
+
+
+
+Historical sample:
+
+
+
+S2018_157_sinogram_layer02_XRD_powder_pack_half1_short-iback_clean.h5 = data pretreated
+
+S2018_157_sinogram_layer02_XRD_powder_pack_half1-back_clean_corr_mask.h5 = other sample file that contains metadata used in the code (mask and contour)
+
+dataset_historical_sample_S157.pickle = dataset for NN training
+
+best_val_loss_model_pp/h_article.h5 = best model after NN training
+
+patterns_rebuilt_S157.pickle = XRD patterns rebuilt from predictions",api,True,findable,0,0,0,0,0,2024-04-11T07:31:59.000Z,2024-04-11T07:32:00.000Z,cern.zenodo,cern,,,,
+10.26302/sshade/experiment_zed_20230103_01,MIR spectra of phyllosilicate pellets irradiated by He+ and Ar+ ions,SSHADE/DAYSY (OSUG Data Center),2024,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.",MIR spectra of phyllosilicate pellets irradiated by $Ar^+$ or $He^+$.,mds,True,findable,0,0,2,0,0,2024-04-10T15:36:00.000Z,2024-04-10T15:36:01.000Z,inist.sshade,mgeg,"laboratory measurement,confocal reflection,micro-imaging,MIR,Mid-Infrared,reflectance factor,Serpentine Rawhide,Serpentine UB-N,Saponite Griffithite,mineral,natural terrestrial,phyllosilicate","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'confocal reflection', 'subjectScheme': 'main'}, {'subject': 'micro-imaging', 'subjectScheme': 'main'}, {'subject': 'MIR', 'subjectScheme': 'variables'}, {'subject': 'Mid-Infrared', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'Serpentine Rawhide', 'subjectScheme': 'name'}, {'subject': 'Serpentine UB-N', 'subjectScheme': 'name'}, {'subject': 'Saponite Griffithite', 'subjectScheme': 'name'}, {'subject': 'mineral', 'subjectScheme': 'family'}, {'subject': 'natural terrestrial', 'subjectScheme': 'origin'}, {'subject': 'phyllosilicate', 'subjectScheme': 'compound type'}]",['9 spectra'],['ASCII']