diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 3601307f394559887cc692fad0941754914ea681..6a469fa4532bcb3c6dcf26f706de24330f137a48 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -25,7 +25,7 @@ actualisation_dois:
- git config user.name "${GITLAB_USER_NAME}"
- git config user.email "${GITLAB_USER_EMAIL}"
- git remote set-url --push origin "https://PUSH_TOKEN:${ACCESS_TOKEN}@gricad-gitlab.univ-grenoble-alpes.fr/${CI_PROJECT_PATH}.git"
- - git add -f dois-uga.csv 2-produce-graph/hist-evol-datasets-per-repo.png 2-produce-graph/hist-quantity-year-type.png 2-produce-graph/pie--datacite-client.png 2-produce-graph/pie--datacite-type.png 1-enrich-with-datacite/all_datacite_clients_for_uga.csv 1-enrich-with-datacite/nb-dois.txt
+ - git add -f dois-uga.csv 2-produce-graph/hist-evol-datasets-per-repo.png 2-produce-graph/hist-quantity-year-type.png 2-produce-graph/pie--datacite-client.png 2-produce-graph/pie--datacite-type.png 2-produce-graph/hist-last-datasets-by-client.png 1-enrich-with-datacite/all_datacite_clients_for_uga.csv 1-enrich-with-datacite/nb-dois.txt
- git commit -m "Execution du pipeline. Actualisation des dois et des graphes."
- git push origin HEAD:${CI_COMMIT_REF_NAME}
@@ -55,5 +55,6 @@ actualisation_dois:
- 2-produce-graph/hist-quantity-year-type.png
- 2-produce-graph/pie--datacite-client.png
- 2-produce-graph/pie--datacite-type.png
+ - 2-produce-graph/hist-last-datasets-by-client.png
- 1-enrich-with-datacite/nb-dois.txt
- 1-enrich-with-datacite/all_datacite_clients_for_uga.csv
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 0bf292925dcb39e58e741c86825846ec5db1b20f..c0547b0f0d8d1a3795f729d5b090436fab5d2891 100644
--- a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+++ b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
@@ -1,8 +1,8 @@
client,count,name,year,url
-cern.zenodo,992,Zenodo,2013,https://zenodo.org/
+cern.zenodo,994,Zenodo,2013,https://zenodo.org/
inist.sshade,469,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
inist.osug,238,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
-figshare.ars,225,figshare Academic Research System,2016,http://figshare.com/
+figshare.ars,228,figshare Academic Research System,2016,http://figshare.com/
dryad.dryad,157,DRYAD,2018,https://datadryad.org
inist.resif,78,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
inist.persyval,55,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index c022d29ac98f331fdae0711c3b9ac51e86d95f9f..7233853fbe7e68e9e3677fcc8ea3d81b3d256639 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2381
\ No newline at end of file
+2386
\ 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 15f6cff4ea50d85d2ad07bf051b5af5f918855b6..02b1f2434a3812b9ef61116fedfea244ee338190 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 f330de346530657f72aa9db572381e2a72ac29e0..8f7792b9ff1a504ab95e1cdff3bb89772d7dd549 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 7582d436f062b622fbb9a20fd8918b1de4fe53eb..d28b01899c05a4ea35727f6c6b4c4134d970abca 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 5a2d49d1cddeb0413c72f9ff87d2b635146282c5..19dcdf7bd48f9735058d2047bc637cb260950fb0 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 8889b4b15b0073a440a8f47f0cffd693a2194d6f..d9f3c88b3cf2198de463782d578ef2b5b97d43b5 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 ee9bf85bc644699dc14101bc9e3dd45d4428fe0e..4914b8dd157fc17d9529869c2954c7fa52b3e4d9 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -6513,3 +6513,20 @@ Note: Authors are listed in alphabetical order by last name.",api,True,findable,
10.5281/zenodo.7603974,Design of a Natural Circulation Experiment to Investigate Flow Stability,Zenodo,2023.0,en,ConferencePaper,Creative Commons Attribution 4.0 International,"A numerical methodology for the study of the stability characteristics of natural circulation systems using molten salts as working fluid is currently under development. This numerical methodology is intended as an aid tool for the design of passive decay heat removal systems for Molten Salt Reactors (MSRs). This paper presents the design of a natural circulation experiment that will be used to test this novel numerical methodology. The experiment has been designed to obtain 2D-like Rayleigh-Bénard cells with laminar flow. Moreover, the experiment will allow to obtain flow conditions close to those encountered in a natural convection system with an internal heat source. As the focus of the methodology is placed on its capability to accurately describe the dynamic behavior of the system, the experiment has been designed to cover a significant range of operational conditions (Rayleigh number) and to obtain distinguishable flow states and the transitions between them. The experiment design robustness has been investigated by performing numerical studies considering various potential bias and uncertainties. In the presented case the principal bias and uncertainties are related to the heating and cooling mechanisms, the wall materials effects and the possible non-negligible interaction with the environment. Results from this study show that an experimental configuration using a flat-cavity geometry will provide meaningful results and a sufficiently complex behavior for testing the methodology without resorting to a system with a turbulent or highly 3D dynamic. Finally, the experiment can operate with a conventional fluid while retaining key phenomena specific to the molten salts.",api,True,findable,0.0,0.0,0.0,0.0,1.0,2024-02-22T12:59:14.000Z,2024-02-22T12:59:14.000Z,cern.zenodo,cern,,,,
10.5281/zenodo.10688119,Dataset related to article: Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials,Zenodo,2024.0,,Dataset,Creative Commons Attribution 4.0 International,This repository contains the dataset to train and test the GeTe Machine Learning Interatomic Potential (MLIP).  The computational details are given in the manuscript.  ,api,True,findable,0.0,0.0,0.0,0.0,1.0,2024-02-21T14:23:00.000Z,2024-02-21T14:23:01.000Z,cern.zenodo,cern,,,,
10.5281/zenodo.10634905,Network Design with Integer Frank Wolfe,MATH+ Cluster of Excellence,2024.0,,Software,Creative Commons Attribution 4.0 International,Github Repository for the project Network Design with Integer Frank Wolfe. Associated with the paper https://arxiv.org/abs/2402.00166.,api,True,findable,0.0,0.0,0.0,0.0,0.0,2024-02-21T16:38:47.000Z,2024-02-21T16:38:48.000Z,cern.zenodo,cern,,,,
+10.6084/m9.figshare.12991753,Additional file 1 of Association between Neu5Gc carbohydrate and serum antibodies against it provides the molecular link to cancer: French NutriNet-Santé study,figshare,2020.0,,Text,Creative Commons Attribution 4.0 International,"Additional file 1: Figure S1. Measurements of anti-Neu5Gc IgG in 120 study cohort by ELISA. Figure S2. Distribution of Neu5Gc intake by food source. Figure S3. Increased levels and diversity of anti-Neu5Gc IgG with higher Neu5Gc intake. Figure S4. Anti-Neu5Gc IgG response in patients with infectious mononucleosis and controls. Figure S5. Characteristics of affinity-purified anti-Neu5Gc antibodies of women 45-60. Figure S6. International cancer risk according to national meat intake. Table S1. Sialic acid content (Neu5Ac and Neu5Gc) in common French food items measured by DMB-HPLC. Table S2. Daily Neu5Gc intake in NutriNet-Santé participants (May 2009 through May 2015) with a minimum of six 24-hour dietary records (total 16,149 participants). Table S3. List of glycans printed on glycan microarray and their characteristics. Table S4. Gcemic index.",mds,True,findable,0.0,0.0,0.0,1.0,0.0,2020-09-23T03:27:32.000Z,2020-09-23T03:27:39.000Z,figshare.ars,otjm,"Biochemistry,Neuroscience,Physiology,FOS: Biological sciences,Biotechnology,Chemical Sciences not elsewhere classified,Ecology,Immunology,FOS: Clinical medicine,Mathematical Sciences not elsewhere classified,Cancer,Science Policy,Infectious Diseases,FOS: Health sciences","[{'subject': 'Biochemistry'}, {'subject': 'Neuroscience'}, {'subject': 'Physiology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biotechnology'}, {'subject': 'Chemical Sciences not elsewhere classified'}, {'subject': 'Ecology'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Mathematical Sciences not elsewhere classified'}, {'subject': 'Cancer'}, {'subject': 'Science Policy'}, {'subject': 'Infectious Diseases'}, {'subject': 'FOS: Health sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['1295476 Bytes'],
+10.6084/m9.figshare.12991759,Additional file 3 of Association between Neu5Gc carbohydrate and serum antibodies against it provides the molecular link to cancer: French NutriNet-Santé study,figshare,2020.0,,Dataset,Creative Commons Attribution 4.0 International,Additional file 3: Supplementary data file S2. Glycan microarray.,mds,True,findable,0.0,0.0,0.0,1.0,0.0,2020-09-23T03:28:12.000Z,2020-09-23T03:28:17.000Z,figshare.ars,otjm,"Biochemistry,Neuroscience,Physiology,FOS: Biological sciences,Biotechnology,Chemical Sciences not elsewhere classified,Ecology,Immunology,FOS: Clinical medicine,Mathematical Sciences not elsewhere classified,Cancer,Science Policy,Infectious Diseases,FOS: Health sciences","[{'subject': 'Biochemistry'}, {'subject': 'Neuroscience'}, {'subject': 'Physiology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biotechnology'}, {'subject': 'Chemical Sciences not elsewhere classified'}, {'subject': 'Ecology'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Mathematical Sciences not elsewhere classified'}, {'subject': 'Cancer'}, {'subject': 'Science Policy'}, {'subject': 'Infectious Diseases'}, {'subject': 'FOS: Health sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['134190 Bytes'],
+10.5281/zenodo.10511344,"Supplementary Information for ""Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning""",Zenodo,2024.0,,ComputationalNotebook,Creative Commons Attribution 4.0 International,"This record consists of the results from the work 'Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning', DOI:10.1021/jacs.3c13687 (arxiv DOI: 10.26434/chemrxiv-2023-sw9kv).Â
+
+Training sets selected by Regression-Tree based Active Learning (RT-AL), as well as MAE values on test sets are provided as a benchmark for MOF datasets.
+
+Descriptors computed for each dataset are also provided.
+
+The codes and a comprehensive example of the usage of RT-AL is provided at https://github.com/AshnaJose/Regression-Tree-based-Active-Learning-for-MOFs.",api,True,findable,0.0,0.0,0.0,1.0,1.0,2024-02-23T11:09:09.000Z,2024-02-23T11:09:09.000Z,cern.zenodo,cern,,,,
+10.6084/m9.figshare.12991756,Additional file 2 of Association between Neu5Gc carbohydrate and serum antibodies against it provides the molecular link to cancer: French NutriNet-Santé study,figshare,2020.0,,Dataset,Creative Commons Attribution 4.0 International,Additional file 2: Supplementary data file S1. National world meat and cancer.,mds,True,findable,0.0,0.0,0.0,1.0,0.0,2020-09-23T03:27:38.000Z,2020-09-23T03:27:44.000Z,figshare.ars,otjm,"Biochemistry,Neuroscience,Physiology,FOS: Biological sciences,Biotechnology,Chemical Sciences not elsewhere classified,Ecology,Immunology,FOS: Clinical medicine,Mathematical Sciences not elsewhere classified,Cancer,Science Policy,Infectious Diseases,FOS: Health sciences","[{'subject': 'Biochemistry'}, {'subject': 'Neuroscience'}, {'subject': 'Physiology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biotechnology'}, {'subject': 'Chemical Sciences not elsewhere classified'}, {'subject': 'Ecology'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Mathematical Sciences not elsewhere classified'}, {'subject': 'Cancer'}, {'subject': 'Science Policy'}, {'subject': 'Infectious Diseases'}, {'subject': 'FOS: Health sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['29593 Bytes'],
+10.5281/zenodo.10511345,"Supplementary Information for ""Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning""",Zenodo,2024.0,,ComputationalNotebook,Creative Commons Attribution 4.0 International,"This record consists of the results from the work 'Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning', DOI:10.1021/jacs.3c13687 (arxiv DOI: 10.26434/chemrxiv-2023-sw9kv).Â
+
+Training sets selected by Regression-Tree based Active Learning (RT-AL), as well as MAE values on test sets are provided as a benchmark for MOF datasets.
+
+Descriptors computed for each dataset are also provided.
+
+The codes and a comprehensive example of the usage of RT-AL is provided at https://github.com/AshnaJose/Regression-Tree-based-Active-Learning-for-MOFs.",api,True,findable,0.0,0.0,0.0,1.0,0.0,2024-02-23T11:09:09.000Z,2024-02-23T11:09:09.000Z,cern.zenodo,cern,,,,