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 75b8636cff7e35a52131898f0b916ec63cbbcd91..e8a90b9bac62dd8cef1bcc43c4c403d8368b4ec6 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,690,Zenodo,2013,https://zenodo.org/
+cern.zenodo,713,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,222,figshare Academic Research System,2016,http://figshare.com/
+figshare.ars,227,figshare Academic Research System,2016,http://figshare.com/
 dryad.dryad,154,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 51ac2b4c292b88fc4127a2fb2cdf81c61d42420b..d0a68ceb3e100ee6238fcfcbbb83d11a54857b86 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2037
\ No newline at end of file
+2065
\ 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 6da7c5ee8bd6d5869f0c5668d7e52929a5c42b7c..3b9b5db85cbcde28367e076b06a5e38322d88e50 100644
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diff --git a/2-produce-graph/hist-last-datasets-by-client.png b/2-produce-graph/hist-last-datasets-by-client.png
index 408c6c731bc0394e1f11a134b312f1126582d08a..40a20f3d3a1468b9ca6f4fe607b74d2b33e31a42 100644
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diff --git a/2-produce-graph/hist-quantity-year-type.png b/2-produce-graph/hist-quantity-year-type.png
index 67aae8e73852aaa3aa0daa60b2bf117816cd2067..8daf3bb07aaf57ae04a1f6e5f0075be17de4e963 100644
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diff --git a/2-produce-graph/pie--datacite-client.png b/2-produce-graph/pie--datacite-client.png
index 7b459bf5c3d0953d25ab5d34edb378586bbafedf..a3c1b80cbfd4ef94f6b17ee35c0062506a45db53 100644
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diff --git a/2-produce-graph/pie--datacite-type.png b/2-produce-graph/pie--datacite-type.png
index f6a04cc0aa977d3d3b20a707525bdc4de2fd70d7..29b91187ab22d92c407fef8ab4a9c5e5906a4c55 100644
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diff --git a/dois-uga.csv b/dois-uga.csv
index a3245786d61cbd56f3675e135d072b7ad1134242..01d4f52da789e3260e31bc66e3600db6a189744a 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -5746,3 +5746,61 @@ model_output_surverse_10MN.csv : output from the model for the reference simula
 10.6084/m9.figshare.23822166,Dataset key for the replication experiment from Mirror exposure following visual body-size adaptation does not affect own body image,The Royal Society,2023,,Dataset,Creative Commons Attribution 4.0 International,Key for the dataset for the replication experiment,mds,True,findable,0,0,0,0,0,2023-08-02T11:18:29.000Z,2023-08-02T11:18:30.000Z,figshare.ars,otjm,"Cognitive Science not elsewhere classified,Psychology and Cognitive Sciences not elsewhere classified","[{'subject': 'Cognitive Science not elsewhere classified'}, {'subject': 'Psychology and Cognitive Sciences not elsewhere classified'}]",['1002 Bytes'],
 10.26302/sshade/experiment_rc_20200618_000,"VIS reflectance spectra collected during electron irradiation experiments of salty fine-grained ice particles (spherical, 5 µm average diameter) prepared by freezing solutions of NaCl with different concentrations.",SSHADE/BYPASS (OSUG Data Center),2023,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.","Spherical salty ice particles are produced by spraying droplets of NaCl solutions into liquid nitrogen with the SPIPA-A setup and 9mm-thick samples are produced from this material. The samples are then introduced into the MEFISTO chamber, placed on a liquid nitrogen cooling plate, and the chamber is evacuated to high vacuum. The samples can then be bombarded with energetic electrons at different energies and fluxes and VIS hyperspectral images are collected.",mds,True,findable,0,0,0,0,0,2023-07-31T13:28:50.000Z,2023-07-31T13:28:50.000Z,inist.sshade,mgeg,"laboratory measurement,biconical reflection,imaging,Vis,Visible,reflectance factor,water ice,NaCl hydrate,laboratory,inorganic molecular solid,chloride","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'biconical reflection', 'subjectScheme': 'main'}, {'subject': 'imaging', 'subjectScheme': 'main'}, {'subject': 'Vis', 'subjectScheme': 'variables'}, {'subject': 'Visible', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'water ice', 'subjectScheme': 'name'}, {'subject': 'NaCl hydrate', 'subjectScheme': 'name'}, {'subject': 'laboratory', 'subjectScheme': 'origin'}, {'subject': 'inorganic molecular solid', 'subjectScheme': 'compound type'}, {'subject': 'chloride', 'subjectScheme': 'compound type'}]",['14 spectra'],['ASCII']
 10.5281/zenodo.10165854,Thickness map of the Patagonian Icefields,Zenodo,2023,en,Dataset,Creative Commons Attribution 4.0 International,"Ice thickness field for the Patagonian icefields relying on mass-conservation approach, which assimilates both glacier retreat data as well as an abundant record of direct thickness measurements. The thickness map has a time stamp of 2000. This map is provided together with error estimates and the basal topography beneath the icefields based on c-SRTM (v2.1) (Farr, T. et al. The Shuttle Radar Topography Mission. Reviews of Geophysics 45 (2007), http://dx.doi.org/10.1029/2005RG000183.)",api,True,findable,0,0,0,0,0,2023-11-21T10:31:14.000Z,2023-11-21T10:31:14.000Z,cern.zenodo,cern,"Patagonia,glacier,icefield,thickness","[{'subject': 'Patagonia'}, {'subject': 'glacier'}, {'subject': 'icefield'}, {'subject': 'thickness'}]",,
+10.6084/m9.figshare.25282850,Additional file 1 of “Cooperation between physicians and physios fosters trust you know”: a qualitative study exploring patients’ experience with first-contact physiotherapy for low back pain in French primary care,figshare,2024,,Text,Creative Commons Attribution 4.0 International,Supplementary Materials 1.,mds,True,findable,0,0,40,0,0,2024-02-24T04:40:42.000Z,2024-02-24T04:40:42.000Z,figshare.ars,otjm,"Biological Sciences not elsewhere classified,Science Policy,Mental Health","[{'subject': 'Biological Sciences not elsewhere classified'}, {'subject': 'Science Policy'}, {'subject': 'Mental Health'}]",['14659 Bytes'],
+10.5281/zenodo.4288857,din14970/TVIPSconverter: tvipsconverter v0.1.3,Zenodo,2020,,Software,Open Access,"GUI converter for 4D-STEM or PED data from TVIPS cameras into .blo files, tiffs, or .hspy files.",mds,True,findable,0,0,0,0,0,2020-11-24T14:48:06.000Z,2020-11-24T14:48:07.000Z,cern.zenodo,cern,,,,
+10.6084/m9.figshare.c.7089974,“Cooperation between physicians and physios fosters trust you know”: a qualitative study exploring patients’ experience with first-contact physiotherapy for low back pain in French primary care,figshare,2024,,Collection,Creative Commons Attribution 4.0 International,"Abstract Background Physiotherapists working in collaboration with family physicians in French multidisciplinary primary healthcare clinics are now able to manage acute low back pain patients as first-contact practitioners in advanced practice roles. This includes medical act delegation such as making a medical diagnosis and prescribing medication. The aim of this study is to explore patients’ experience and perceptions when attending a first-contact physiotherapist (FCP) in an advanced practice collaborative primary care model for acute low back pain (LBP). Methods A qualitative study using semi-structured interviews was conducted. Patients that consulted a FCP for acute LBP care in new collaborative model were included. Interviews were transcribed verbatim and inductive thematic analysis was performed to generate themes related to patients’ experience and perceptions. Results Ten patients were interviewed (3 women, 7 men; mean age 36.5 ± 9.63 years). All LBP participants experienced important level of pain and disability. Four overarching themes related to patients’ experience with the new FCP model were formalized: 1) “Going to see a physiotherapist who specializes in painful movements, well that makes sense to me”, 2) “Physiotherapist offered to give me exercises to do at home to relieve the back pain”, 3) “I went there feeling confident”, 4) “The physiotherapist can do more than just send you to see more appropriate people”. Participants highlighted the need to receive timely and high-quality care and were receptive with being autonomously managed by a FCP. Overall, patients’ experiences with FCP model of care were positive. Participants were highly confident in the FCP’s ability to perform delegated medical tasks including making a medical diagnosis and prescribing oral medication such as analgesic drugs. Patients felt that a greater expansion of FCPs’ scope of practice was needed to improve the model. Conclusion Findings from this study can inform the implementation of FCP in countries where patients are not typically granted FCP by underlining that patients are favourable towards the advance practice model as such models support timely and high-quality care. Further research is needed to better determine the future advance practice physiotherapists’ scope of practice in French primary and secondary care settings.",mds,True,findable,0,0,0,0,0,2024-02-24T04:40:43.000Z,2024-02-24T04:40:44.000Z,figshare.ars,otjm,"Biological Sciences not elsewhere classified,Science Policy,Mental Health","[{'subject': 'Biological Sciences not elsewhere classified'}, {'subject': 'Science Policy'}, {'subject': 'Mental Health'}]",,
+10.6084/m9.figshare.25284801,Additional file 1 of Intra-breath changes in respiratory mechanics are sensitive to history of respiratory illness in preschool children: the SEPAGES cohort,figshare,2024,,Text,Creative Commons Attribution 4.0 International,Supplementary Material 1,mds,True,findable,0,0,0,0,0,2024-02-25T04:40:03.000Z,2024-02-25T04:40:04.000Z,figshare.ars,otjm,"Medicine,Genetics,FOS: Biological sciences,Sociology,FOS: Sociology,Cancer,Science Policy","[{'subject': 'Medicine'}, {'subject': 'Genetics'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Sociology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Cancer'}, {'subject': 'Science Policy'}]",['58940 Bytes'],
+10.5281/zenodo.4029598,"Supplementary material for ""Port-Hamiltonian modeling, discretization and feedback control of a circular water tank""",Zenodo,2020,,Software,Open Access,"This archive presents the source codes and numerical results of our paper ""Port-Hamiltonian modeling, discretization and feedback control of a circular water tank"", presented at the 2019 IEEE 58th Conference on Decision and Control (CDC), in Lyon, France. The paper is available here. https://github.com/flavioluiz/Circular-Tank-PFEM-CDC-supplementary-material The following codes are provided: <code>codes/Saint_Venant1D.py</code>: reduced model of the circular tank, considering radial symmetry (Figures 1 and 2 of the paper) <code>codes/Saint_Venant2D.py</code>: nonlinear 2D model with feedback control (Figures 3 and 4 of the paper) <code>codes/AnimateSurf.py</code>: auxiliary file used to obtain the animation. A GitHub with the codes is available here. <strong>How to install and run the code?</strong> The numerical FEM model is obtained thanks to FEniCS. Firstly, you need to install it. We suggest installing it from Anaconda, as described here (check the part FEniCS on Anaconda). Once installed, you need to activate the FEniCs environment: <pre>your@user:~$ conda activate fenicsproject</pre> Then, you just need to run the Python script on the environment: <pre>(fenicsproject) your@user:~$ python Saint_Venant2D.py</pre> The scripts were tested using Python 3.7, and FEniCS 2018.1.0. <strong>Acknowledgements</strong> This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled ""Interconnected of Infinite-Dimensional systems for Heterogeneous Media"", nº ANR-16-CE92-0028. Further information is available here.",mds,True,findable,0,0,0,0,0,2020-09-14T23:14:43.000Z,2020-09-14T23:14:43.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.7732341,pyxem/orix: orix 0.11.1,Zenodo,2023,,Software,Open Access,"orix 0.11.1 is a patch 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. Fixed Initialization of a crystal map with a phase list with fewer phases than in the phase ID array given returns a map with a new phase list with correct phase IDs.",mds,True,findable,0,0,0,0,0,2023-03-14T09:12:02.000Z,2023-03-14T09:12:02.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.4302006,"Supplementary material for ""A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws""",Zenodo,2020,,Dataset,Open Access,"This archive contains supplementary material for the paper ""A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available here. The following codes are provided: <code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation <code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation <code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression <code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation <code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case <code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case A GitHub with the codes and a few instructions on usage is available here. <strong>Acknowledgements</strong> This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled ""Interconnected of Infinite-Dimensional systems for Heterogeneous Media"", nº ANR-16-CE92-0028. Further information is available here.",mds,True,findable,0,0,1,0,0,2020-12-02T11:54:54.000Z,2020-12-02T11:54:56.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.35230,trunk 1.12.0,Zenodo,2015,,Software,Open Access,git-repository for Yade project,mds,True,findable,0,0,1,0,0,2015-12-11T12:32:15.000Z,2015-12-11T12:32:16.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.8025653,Accelerated exploration of multinary systems,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This repository contains the datasets produced from the characterizations of the quinary Nb-Ti-Zr-Cr-Mo, and predictions made by Machine Learning models. <strong>Experimental work</strong> Gradients of composition were characterized by: EDX for composition evaluation, with an error of 1% on atomic and mass composition nanoindentation for the measurement of the elastic modulus (E) and hardness (H) EBSD : from each map we extract the Confidence Index CI and Image Quality IQ that are indicator of crystallinity. CI is also used to define phase classes (0 for amorphous, 1 for crystalline) XRD: from each diffractogram we extract a phase class (0 for amorphous, 1 for crystalline): raw data are available in XRD.zip Different datasets are built: Raw_data associate to each composition the EBSD CI, IQ, EBSD phase class, and the elastic modulus (E) and hardness (H) computed by the software TestWork Analysis without any correction. For each composition, 5 measurement replications were performed. Raw_data_corrected contains the EBSD CI, IQ, EBSD phase class, and the 5 replications per compositions of E and H corrected through Oliver and Pharr model. Compo_E_H_threshold correspond to Raw_data_corrected in which we have thresholded values of E and H. We removed all composition such that E &lt; 10 GPa and all H &lt; 2 GPa, as they correspond to nanoindentation test failures. Compo_E_wo_outliers and Compo_H_wo_outliers: Dixon test allows to identify outliers on E replications and H replications, that are removed to give each dataset. Each composition is associated to replications of E or H that were not identified as outliers. Averaged_data: each composition is associated to EBSD CI, IQ, EBSD phase class, and with average values of E and H replications without outliers. Data_averaged_mechanical_model: add to previous data the other mechanical properties computed with Galanov model from E and H experimental results: relative characteristic size of the elastic-plastic zone under the indenter \(x = \frac{b_s}{c}\), the constrain factor \(C\) – linking yield strength and hardness – and the ductility characteristic \(\delta_H\) – ratio of plastic deformation and total deformation. It also contains \(\frac{E²}{H}\). Database_XRD: each composition is associated to phase class defined from XRD diffractograms The dataset_initial.zipl contains the experimental results with an initial 20-gradients sets which screen preferably the center of Nb-Ti-Zr-Cr-Mo. It contains all the kind of datasets. The dataset_adding_binaries.zip contains the experimental results for the initial 20-gradients + additional binary gradients Nb-Ti binary 1), Nb-Cr (binary 2) and Cr-Mo (binary 3). It contains the data without outliers, averaged data and XRD database. <strong>Predictions of Machine Learning Models from experimental datasets</strong> Machine Learning models are trained to predict properties from compositions: Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN) models. Model assessment (i.e. choosing best hyper-parameters for each model) was performed on Compo_E_wo_outliers for E prediction, Compo_H_wo_outliers for H prediction, and on Averaged_data and Database_XRD for phase prediction. Results of model trainings are given in ModelAssessment.tar.gz. The best model of RF, NN and SVM are trained on all datasets: results are given in Train_model_xx.tar.gz. Training the same model with datasets with more or less outliers for E and H predictions allows to see the effect of outliers on the results. The best models of RF and NN are then trained adding iteratively the binaries: results are in tarball Train_model_xx_adding_binaries.tar.gz <strong><em>These tarball are to be used with PyTerK modules available here. </em></strong> The models then predict, for all atomic compositions of Nb-Ti-Zr-Cr-Mo, with 2%at steps, the associated properties: predictions_XX contain atomic compositions associated to predicted CI, IQ, EBSD phase class, XRD phase class, E, H, for each kind of model. Predictions_XX_mechanical_model contain the same data with other mechanical properties computed with Galanov model from E and H predictions: relative characteristic size of the elastic-plastic zone under the indenter \(x = \frac{b_s}{c}\), the constrain factor \(C\) – linking yield strength and hardness – and the ductility characteristic \(\delta_H\) – ratio of plastic deformation and total deformation. It also contains \(\frac{E²}{H}\). The prediction_initial.zip contains the predictions made for all the model families with initial datasets. The predictions_adding_binaries.zip the predictions made with the best model (determined with the initial dataset) trained with the initial dataset+ binaries",mds,True,findable,0,0,0,0,0,2023-07-02T19:43:27.000Z,2023-07-02T19:43:27.000Z,cern.zenodo,cern,"High Entropy Alloys,Combinatorial,Mixture Design,Multinary,Machine Learning","[{'subject': 'High Entropy Alloys'}, {'subject': 'Combinatorial'}, {'subject': 'Mixture Design'}, {'subject': 'Multinary'}, {'subject': 'Machine Learning'}]",,
+10.5281/zenodo.1443459,Parrot,Zenodo,2018,en,Dataset,"Creative Commons Attribution 4.0,Open Access","The netCDF files ""SF*.nc"" that can be found in the repository ""Parrot_experiment"" contain the experimental results of intense sediment transport experiments (sheet flow) carried out in the LEGI tilting flume with two sizes of uniformly distributed acrylic particles having median diameters of 1 mm (S1 experiment) and 3 mm (S3 experiment). The data contained in this repository are presented in Fromant et al. (2018). The files contain :
+
+    1/ Synchronised and colocated concentration and veclocity (streamwise component) profiles measurements collected with an Acoustic Concentration and Velocity Profiler (ACVP - Hurther et al., 2011).  <br>
+    2/ Concentration profiles time series collected with Conductivity and Concentration Profilers (Lanckriet et al., 2013), with two different vertical resolutions, 1 mm (CCP1mm) and 2mm (CCP2mm).
+
+Details about the experimental protocol can be found in Revil-Baudard et al. (2015). More details regarding the experimental protocol and flow conditions can be found in Fromant et al. (2018).",mds,True,findable,0,0,0,0,0,2018-10-03T15:42:14.000Z,2018-10-03T15:42:15.000Z,cern.zenodo,cern,"Sediment Transport,Sheet-flow,Concentration measurement,ACVP,CCP","[{'subject': 'Sediment Transport'}, {'subject': 'Sheet-flow'}, {'subject': 'Concentration measurement'}, {'subject': 'ACVP'}, {'subject': 'CCP'}]",,
+10.5281/zenodo.7978514,Danaroth83/irca: v1.1,Zenodo,2023,,Software,Open Access,Added wavelength axis to spectra and transmittance responses.,mds,True,findable,0,0,0,0,0,2023-05-28T06:40:25.000Z,2023-05-28T06:40:26.000Z,cern.zenodo,cern,,,,
+10.6084/m9.figshare.13525202,Additional file 2 of Factors associated with survival of patients with solid Cancer alive after intensive care unit discharge between 2005 and 2013,figshare,2021,,Text,Creative Commons Attribution 4.0 International,Additional file 2: Supplementary Table 1. Patient Characteristics According To Previous Chemotherapy,mds,True,findable,0,0,32,1,0,2021-01-06T04:36:04.000Z,2021-01-06T04:36:05.000Z,figshare.ars,otjm,"Medicine,Microbiology,FOS: Biological sciences,Biotechnology,Chemical Sciences not elsewhere classified,Immunology,FOS: Clinical medicine,Biological Sciences not elsewhere classified,Cancer,Science Policy,Infectious Diseases,FOS: Health sciences","[{'subject': 'Medicine'}, {'subject': 'Microbiology'}, {'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': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biological 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)'}]",['14404 Bytes'],
+10.5281/zenodo.7331741,OPEN-NEXT/wp2.2_dev: D2.5 dashboard backend with support for Wikifactory and GitHub,Zenodo,2022,,Software,Open Access,This release contains the Open!Next M36 D2.5 dashboard <strong><em>backend</em></strong> code that can retrieve metadata from repositories hosted on GitHub and Wikifactory. The sample implementation of the <em>frontend</em> is now in this repository. Please read <code>README.md</code> in this repository and the D2.5 report for more information. The only change in this version is an update to the link to the demo instance of the API.,mds,True,findable,0,0,0,0,0,2022-11-17T17:46:01.000Z,2022-11-17T17:46:01.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.10695199,Environmental DNA highlights the influence of salinity and agricultural run-off on coastal fish assemblages in the Great Barrier Reef region,Zenodo,2024,,Other,Creative Commons Attribution 4.0 International,"Agricultural run-off in Australia's Mackay-Whitsunday region is a major source of nutrient and pesticide pollution to coastal and inshore ecosystems of the Great Barrier Reef. While the effects of run-off are well documented for the region's coral and seagrass habitats, the ecological impacts on estuaries, the direct recipients of run-off, are less known. This is particularly true for fish communities, which are shaped by the physico-chemical properties of coastal waterways that vary greatly in tropical regions. To address this knowledge gap, we used environmental DNA (eDNA) metabarcoding to examine fish assemblages at four locations (three estuaries and a harbour) subjected to varying levels of agricultural run-off during a wet and dry season. Pesticide and nutrient concentrations were markedly elevated during the sampled wet season with the influx of freshwater and agricultural run-off. Fish taxa richness significantly decreased in all three estuaries (F = 164.73, P = <0.001), along with pronounced changes in community composition (F = 46.68, P = 0.001) associated with environmental variables (largely salinity: 27.48% contribution to total variance). In contrast, the nearby Mackay Harbour exhibited a far more stable community structure, with no marked changes in fish assemblages observed between the sampled seasons. Among the four sampled locations, variation in fish community composition was more pronounced within the wet season (F = 2.5, P = 0.001). Notably, variation in the wet season was significantly correlated with agricultural contaminants (phosphorus: 6.25%, pesticides: 5.22%) alongside environmental variables (salinity: 5.61%, DOC: 5.57%). Historically contaminated and relatively unimpacted estuaries each demonstrated distinct fish communities, reflecting their associated catchment use. Our findings emphasise that while seasonal effects play a key role in shaping the community structure of fish in this region, agricultural contaminants are also important contributors in estuarine systems.",api,True,findable,0,0,0,0,0,2024-02-23T16:31:30.000Z,2024-02-23T16:31:31.000Z,cern.zenodo,cern,"eDNA,chemical analyses,species matrix","[{'subject': 'eDNA'}, {'subject': 'chemical analyses'}, {'subject': 'species matrix'}]",,
+10.5281/zenodo.290428,"Reproducible Workflow for the ""Tuning Backfilling Queues"" article.",Zenodo,2017,,Software,"ISC License,Open Access","Tuning EASY-Backfilling Queues reproducible build.<br> ================================================== This repository contains the data, code, and workflow<br> necessary to build the ""Tuning EASY Backfilling Queues""<br> paper by Lelong, Reis and Trystram. The workflow is managed using zymake.<br> http://www-personal.umich.edu/~ebreck/code/zymake/ The dependencies are managed via the NIX package manager.<br> https://nixos.org/nix/ Please read the file 'zymakefile' to see the workflow. DEPENDENCIES:<br> =============<br> * NIX: curl https://nixos.org/nix/install | sh<br> * Ocaml packages are not yet managed by NIX.<br> To be installed using OPAM: batteries oasis cmdliner See file default.nix for a list of the dependencies<br> that will be managed by nix. RUNNING:<br> ======== The build is obtained by running the command ""make"" and should<br> take half a dozen days on a recent 200-core machine.<br> This will build the zymake tool under /zymake and<br> the simulator under ocs/. A dry run can be obtained by ""make dummy"". CONTACT:<br> ========<br> We do not provide other documentation for this workflow system.<br> Feel free to contact tuningqueues@valentinreis.com for any inquiry<br> including troubleshooting of reproducing the results. LICENSE:<br> ========<br> All code under ocs/ and misc/ is copyright of Valentin Reis and<br> distributed under the ISC license (see LICENSE.md), except<br> file ocs/src/binary_heap which is copyrighted by Jean-Christophe<br> Fillatre (see file for license).<br> The Zymake workflow system is copyright of Eric Breck(see files<br> for license)<br> The workflows in gz/ obtained via the Parallel Workload Acthive<br> (http://www.cs.huji.ac.il/labs/parallel/workload/) remain<br> property of their respective owners.<br>",mds,True,findable,0,0,0,0,0,2017-02-12T10:08:26.000Z,2017-02-12T10:08:27.000Z,cern.zenodo,cern,Scheduling Backfilling EASY Reproducible Workflow Zymake,[{'subject': 'Scheduling Backfilling EASY Reproducible Workflow Zymake'}],,
+10.5281/zenodo.1475283,SPARK_Stimulo_session_05072017_Grenoble,Zenodo,2018,es,Audiovisual,"Creative Commons Attribution Non Commercial 4.0 International,Open Access","Recording of a collaborative design session between designers and end-users.
+
+A design company invites a final consumer to provide feedback on the layout of user interface elements (lights, speakers, buttons) and the aesthetic design (colours, materials and finishes) of an industrial product. The session is conducted using a Spatial Augmented Reality (SAR) application which allows a real-time modification of the design contents. Language: Spanish.",mds,True,findable,0,0,0,0,0,2018-10-30T17:05:42.000Z,2018-10-30T17:05:43.000Z,cern.zenodo,cern,"SPARK,H2020,Collaborative design,Co-design,Spatial Augmented Reality,Augmented Reality,Mixed prototype,ICT,Creativity,Product design","[{'subject': 'SPARK'}, {'subject': 'H2020'}, {'subject': 'Collaborative design'}, {'subject': 'Co-design'}, {'subject': 'Spatial Augmented Reality'}, {'subject': 'Augmented Reality'}, {'subject': 'Mixed prototype'}, {'subject': 'ICT'}, {'subject': 'Creativity'}, {'subject': 'Product design'}]",,
+10.5281/zenodo.2590150,"Snow properties measurements (in situ &amp; retrived from satellite) at Dome C, East Antarctica Plateau",Zenodo,2019,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","The dataset contains the data inputs for the electromagnetic model:
+
+
+	the snow density profile down to 20 m depth (measured in 2010)
+	the snow SSA profile down to 20 m depth (measured in 2010)
+	the snow temperature profile down to 20 m depth (measured from 1 December 2006 to 4 October 2011)
+
+
+The dataset contains also the data retrieved from satellite:
+
+
+	the retrieved surface snow density from AMSR-E satellite (obtained from 18 June 2002 to 4 October 2011)
+
+
+The dataset contains finally the data measured in situ to compare with the data retrieved from satellite:
+
+
+	the surface snow density from CALVA program (measured from 3 February 2010 to 4 October 2011)
+	the surface snow density from PNRA program measured in snow pits (measured from 18 December 2007 to 4 October 2011)
+	the surface snow density from PNRA program measured next to stakes (measured from 9 May 2008 to 4 October 2011)",mds,True,findable,0,0,0,0,0,2019-03-11T11:13:13.000Z,2019-03-11T11:13:14.000Z,cern.zenodo,cern,Surface snow density ; Profiles of snow properties,[{'subject': 'Surface snow density ; Profiles of snow properties'}],,
+10.5281/zenodo.3819313,Programmers manual FlexGripPlus SASS SM 1.0,Zenodo,2020,,Other,"Creative Commons Attribution 4.0 International,Open Access",This document describes the op-code of the assembly language SASS of the G80 architecture used in the FlexGripPlus model. Every instruction is compatible with the CUDA Programming environment under the SM_1.0,mds,True,findable,0,0,0,0,0,2020-05-10T13:51:41.000Z,2020-05-10T13:51:42.000Z,cern.zenodo,cern,"SASS, GPGPU, G80, FlexGripPlus, Assembly Language","[{'subject': 'SASS, GPGPU, G80, FlexGripPlus, Assembly Language'}]",,
+10.5281/zenodo.7135091,spectralpython/spectral: Spectral Python (SPy) 0.23.1,Zenodo,2022,,Software,Open Access,"SPy 0.23.1 Release date: 2022.10.02 Bug Fixes [#143] Eigen{values,vectors} in a <code>GaussianStats</code> weren't sorted in descending order, which is inconsistent with <code>PrincipalComponents</code>. [#144] <code>SpyFile.load</code> was failing on Windows because numpy versions there did not support complex256. [#145] <code>unmix</code> was failing, due to an invalid reference to ""np.inv""",mds,True,findable,0,0,0,0,0,2022-10-02T16:01:03.000Z,2022-10-02T16:01:04.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.8424333,RENO - a multi-agent simulation tool for a renewable energy community,Zenodo,2023,en,Software,"Creative Commons Attribution 4.0 International,Open Access",A multi-agent simulation tool for simulating renewable energy communities in different configurations. GitLab up-to-date public repository: https://gricad-gitlab.univ-grenoble-alpes.fr/ploixs/energycommunitymodel,mds,True,findable,0,0,0,0,0,2023-10-10T06:42:43.000Z,2023-10-10T06:42:44.000Z,cern.zenodo,cern,"energy community,multi-agent,energy management,photovoltaic,self-consumption,self-sufficiency,human-centered control systems","[{'subject': 'energy community'}, {'subject': 'multi-agent'}, {'subject': 'energy management'}, {'subject': 'photovoltaic'}, {'subject': 'self-consumption'}, {'subject': 'self-sufficiency'}, {'subject': 'human-centered control systems'}]",,
+10.5281/zenodo.8404376,vispy/vispy: Version 0.14.1,Zenodo,2023,,Software,Open Access,<strong>Fixed bugs:</strong> return to oldest supported numpy #2535 (brisvag) <strong>Merged pull requests:</strong> Bump pypa/cibuildwheel from 2.16.0 to 2.16.1 #2534 (dependabot[bot]) Bump pypa/cibuildwheel from 2.15.0 to 2.16.0 #2531 (dependabot[bot]) Bump docker/setup-qemu-action from 2 to 3 #2529 (dependabot[bot]) Bump actions/checkout from 3 to 4 #2527 (dependabot[bot]),mds,True,findable,0,0,0,0,0,2023-10-03T22:23:30.000Z,2023-10-03T22:23:31.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.4715737,Research data: preparation of nanocellulose using non-aligned hemp bast fibers,Zenodo,2021,en,Dataset,"Creative Commons Attribution 4.0 International,Embargoed Access","The data files and archives contain experimental results and Optical, FESEM, and TEM micrographs of hemp fibers, raw and pretreated, and of the nanocellulose obtained from them. The files are named following this convention: Hemp_A_B.ext where A is the type of material: PT = fibers after all pretreatment steps PT(xx) = fibers after the pretreatment step indicated in the parenthesis PT_NC = fibers after all pretreatment steps and nanocellulose NC = nanocellulose and B is the type of measurement done: TEM = Transmission Electron Microscope FESEM = Field Emission Scanning Electron Microscope Dimensions = lengths and diameters of the fibers, as analyzed from the Optical, FESEM, and TEM micrographs with the software ImageJ XRD_bkgsub = X-ray diffraction measurements, with background subtracted FTIR = Fourier Transform Infrared Spectroscopy (in ATR mode) TGA = Thermogravimetric analysis The data will be made open as soon as possible depending on the publishing process of the article",mds,True,findable,0,0,0,0,0,2021-04-23T15:45:27.000Z,2021-04-23T15:45:28.000Z,cern.zenodo,cern,"Hemp,Nanocellulose","[{'subject': 'Hemp'}, {'subject': 'Nanocellulose'}]",,
+10.5281/zenodo.10695198,Environmental DNA highlights the influence of salinity and agricultural run-off on coastal fish assemblages in the Great Barrier Reef region,Zenodo,2024,,Software,MIT License,"Agricultural run-off in Australia's Mackay-Whitsunday region is a major source of nutrient and pesticide pollution to coastal and inshore ecosystems of the Great Barrier Reef. While the effects of run-off are well documented for the region's coral and seagrass habitats, the ecological impacts on estuaries, the direct recipients of run-off, are less known. This is particularly true for fish communities, which are shaped by the physico-chemical properties of coastal waterways that vary greatly in tropical regions. To address this knowledge gap, we used environmental DNA (eDNA) metabarcoding to examine fish assemblages at four locations (three estuaries and a harbour) subjected to varying levels of agricultural run-off during a wet and dry season. Pesticide and nutrient concentrations were markedly elevated during the sampled wet season with the influx of freshwater and agricultural run-off. Fish taxa richness significantly decreased in all three estuaries (F = 164.73, P = <0.001), along with pronounced changes in community composition (F = 46.68, P = 0.001) associated with environmental variables (largely salinity: 27.48% contribution to total variance). In contrast, the nearby Mackay Harbour exhibited a far more stable community structure, with no marked changes in fish assemblages observed between the sampled seasons. Among the four sampled locations, variation in fish community composition was more pronounced within the wet season (F = 2.5, P = 0.001). Notably, variation in the wet season was significantly correlated with agricultural contaminants (phosphorus: 6.25%, pesticides: 5.22%) alongside environmental variables (salinity: 5.61%, DOC: 5.57%). Historically contaminated and relatively unimpacted estuaries each demonstrated distinct fish communities, reflecting their associated catchment use. Our findings emphasise that while seasonal effects play a key role in shaping the community structure of fish in this region, agricultural contaminants are also important contributors in estuarine systems.",api,True,findable,0,0,0,0,0,2024-02-23T16:31:12.000Z,2024-02-23T16:31:12.000Z,cern.zenodo,cern,"eDNA,chemical analyses,species matrix","[{'subject': 'eDNA'}, {'subject': 'chemical analyses'}, {'subject': 'species matrix'}]",,
+10.6084/m9.figshare.13525199,Additional file 1 of Factors associated with survival of patients with solid Cancer alive after intensive care unit discharge between 2005 and 2013,figshare,2021,,Image,Creative Commons Attribution 4.0 International,Additional file 1: Supplementary Figure 1. Estimation of survival according to previous chemotherapy (Kaplan Meier).,mds,True,findable,0,0,32,1,0,2021-01-06T04:35:50.000Z,2021-01-06T04:35:53.000Z,figshare.ars,otjm,"Medicine,Microbiology,FOS: Biological sciences,Biotechnology,Chemical Sciences not elsewhere classified,Immunology,FOS: Clinical medicine,Biological Sciences not elsewhere classified,Cancer,Science Policy,Infectious Diseases,FOS: Health sciences","[{'subject': 'Medicine'}, {'subject': 'Microbiology'}, {'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': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biological 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)'}]",['96708 Bytes'],
+10.5281/zenodo.10698942,NeoGeographyToolkit/StereoPipeline: 2024-02-23-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,0,0,0,1,0,2024-02-23T19:52:54.000Z,2024-02-23T19:52:54.000Z,cern.zenodo,cern,,,,
+10.5281/zenodo.10568799,"Voter Autrement 2022 - The Online Experiment (""Un Autre Vote'')",Zenodo,2024,en,Dataset,ODC Open Database License v1.0," In April 2022, we have run a voting experiment during the French presidential election. During this experiment, participants were asked to test several alternative voting methods to elect the French president, like scoring methods, instant-runoff voting, Borda with partial rankings, majority judgement and pairwise comparisons. The experiment was both carried out in situ in polling stations during the first round of the presidential election (using paper ballots), and online between April 8th (two days before the first round of the election) and May 7th (using a web application). A total of 2308 participants took part in the online experiment. This dataset contains the answers provided by the participants to the online experiment, with no other processsing than a basic transformation to a set of CSV files.
+
+The companion paper available on this repository describes the experimental protocol, the format of the files, and summarizes the precise conditions under which this dataset is available.",api,True,findable,0,0,0,0,0,2024-01-30T15:47:32.000Z,2024-01-30T15:47:32.000Z,cern.zenodo,cern,"Election,Social Choice,Voting,Experimental Voting,Comsoc","[{'subject': 'Election'}, {'subject': 'Social Choice'}, {'subject': 'Voting'}, {'subject': 'Experimental Voting'}, {'subject': 'Comsoc'}]",,
+10.5281/zenodo.1475271,SPARK_Artefice_session_05072017_Grenoble,Zenodo,2018,en,Audiovisual,"Creative Commons Attribution Non Commercial 4.0 International,Open Access","Recording of a collaborative design session between designers and clients.
+
+A design company receives its clients to discuss/contribute/co-design together. They develop new graphical layout options for the packaging of a tomato sauce product using an ICT application based on Spatial Augmented Reality, which allows for a real-time modification. Language: English.",mds,True,findable,0,0,0,0,0,2018-10-31T09:52:51.000Z,2018-10-31T09:52:52.000Z,cern.zenodo,cern,"SPARK,H2020,Collaborative design,Co-design,Spatial Augmented Reality,Augmented Reality,Mixed prototype,Creativity,ICT","[{'subject': 'SPARK'}, {'subject': 'H2020'}, {'subject': 'Collaborative design'}, {'subject': 'Co-design'}, {'subject': 'Spatial Augmented Reality'}, {'subject': 'Augmented Reality'}, {'subject': 'Mixed prototype'}, {'subject': 'Creativity'}, {'subject': 'ICT'}]",,
+10.5281/zenodo.7110117,mhmdjouni/AoAdmmAsc-python: v1,Zenodo,2022,,Software,Open Access,First working environment for tensor CPD based on AO-ADMM-ASC in Python,mds,True,findable,0,0,0,0,0,2022-09-24T12:49:59.000Z,2022-09-24T12:50:00.000Z,cern.zenodo,cern,,,,