Merge branch 'main' of...

Merge branch 'main' of https://gricad-gitlab.univ-grenoble-alpes.fr/mlarrieu/open-research-data-monitor-back

.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

1-enrich-with-datacite/all_datacite_clients_for_uga.csv

 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,

1-enrich-with-datacite/nb-dois.txt

-2381
+2386
\ No newline at end of file

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). 
+
+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). 
+
+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,,,,