diff --git a/0-collect-data/datacite-dois.txt b/0-collect-data/datacite-dois.txt
index 9b988636f1a8d95657c9ba3f88b8acc74989eb73..7821df915d10517edb5b2031956a74b2e88b9ae8 100644
--- a/0-collect-data/datacite-dois.txt
+++ b/0-collect-data/datacite-dois.txt
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diff --git a/0-collect-data/nakala-dois.txt b/0-collect-data/nakala-dois.txt
index 89884b8815c5544fcadc7c75193d24f68e80fe3c..a65bea54d6f14f19fde0e8ba909b3b4d7276a9fe 100644
--- a/0-collect-data/nakala-dois.txt
+++ b/0-collect-data/nakala-dois.txt
@@ -1,8 +1,8 @@
-10.34847/nkl.5bcck3cz
-10.34847/nkl.ca709965
 10.34847/nkl.a0fe865m
 10.34847/nkl.76abr599
 10.34847/nkl.6caam3dp
+10.34847/nkl.5bcck3cz
+10.34847/nkl.ca709965
 10.34847/nkl.ca8dmbdh
 10.34847/nkl.a5ae8y33
 10.34847/nkl.748eqz51
@@ -22,5 +22,11 @@
 10.34847/nkl.3dbc2mtb
 10.34847/nkl.bc2b1071
 10.34847/nkl.81dcdekj
-10.34847/nkl.ef903o6v
-10.34847/nkl.ae94a74k
+10.34847/nkl.b1cb3arm
+10.34847/nkl.c9e92or4
+10.34847/nkl.bf5f263z
+10.34847/nkl.9f85iol5
+10.34847/nkl.345bf9i7
+10.34847/nkl.9cd8hi4k
+10.34847/nkl.e1e41vdi
+10.34847/nkl.deb655as
diff --git a/0-collect-data/nakala-uga-users.txt b/0-collect-data/nakala-uga-users.txt
index 0403bbc44d57665ef178615ec49e89d31103576a..d71aae78495a0a012b283beb18f1be41e88ee85c 100644
--- a/0-collect-data/nakala-uga-users.txt
+++ b/0-collect-data/nakala-uga-users.txt
@@ -15,4 +15,6 @@ egreslou
 troulet
 mbeligne
 acarbonnelle
-annegf
\ No newline at end of file
+annegf
+tleduc
+abey
\ No newline at end of file
diff --git a/0-collect-data/nakala.py b/0-collect-data/nakala.py
index e2debe6bd3eba95de301bf6c3c42e9a1b38ec445..74cc96b55810a0bbf425411d74377278217b383d 100644
--- a/0-collect-data/nakala.py
+++ b/0-collect-data/nakala.py
@@ -104,8 +104,9 @@ for user in nakala_uga_users :
 with open("nakala-dois.txt", 'w') as fh :
     [fh.write(f"{line}\n") for line in all_dois]
 
-## print les autres utilisateurs trouvés7
-print("\n\n nakala new user finded ")
-for elem in other_user_finded : 
-    print("\t\telem")
+## print les autres utilisateurs trouvés
+if other_user_finded : 
+    print("\n\n nakala new user finded ")
+    for elem in other_user_finded : 
+        print(f"\t\t{elem}")
 
diff --git a/0-collect-data/rdg-dois.txt b/0-collect-data/rdg-dois.txt
index 6df36b535da70bc6764fe10860c3086b520f63f4..f99781363b17ebd62277557c169d6ba05ccbbd5d 100644
--- a/0-collect-data/rdg-dois.txt
+++ b/0-collect-data/rdg-dois.txt
@@ -1,51 +1,42 @@
-10.57745/QOA1QO
-10.57745/GZKUZS
-10.57745/J2A44Q
-10.15454/M7OK9E
-10.57745/QOA1QO
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-10.57745/J2A44Q
-10.57745/QOA1QO
-10.57745/B6PSX0
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-10.57745/TVAHUQ
-10.57745/BYWEA3
-10.57745/QCVYG3
-10.57745/NGC4J0
-10.57745/HZDPTT
-10.57745/69UNAM
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-10.57745/GZKUZS
+10.57745/52HT2L
+10.57745/7RFNNP
+10.57745/UOGRPY
+10.57745/GRHRZJ
+10.57745/7HF7KG
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-10.57745/LUTMNE
+10.57745/69UNAM
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-10.57745/LXTWNG
+10.57745/R1NIKK
+10.57745/QCVYG3
+10.57745/KTFZQD
+10.57745/B6PSX0
+10.15454/M7OK9E
+10.57745/CM2WOI
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+10.57745/NGC4J0
+10.57745/IZHDPC
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-10.57745/RUQLJL
-10.15454/8UIA76
-10.57745/OVCWQN
+10.57745/TVAHUQ
+10.57745/BYWEA3
+10.15454/O93984
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-10.57745/KTFZQD
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diff --git a/0-collect-data/zenodo-dois.txt b/0-collect-data/zenodo-dois.txt
index e11068cfd156296d3b46c52fd3e58262ed3c1263..611b108c3ebee7d2e4e1ff119df94a392a9aaf98 100644
--- a/0-collect-data/zenodo-dois.txt
+++ b/0-collect-data/zenodo-dois.txt
@@ -1,1027 +1,1035 @@
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-10.5061/dryad.6226d
-10.1051/0004-6361/202244633
-10.1016/j.pss.2022.105536
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diff --git a/2-produce-graph/pie--datacite-client.png b/2-produce-graph/pie--datacite-client.png
index 8a6c0f3651d9dcfe7b5d247791fcefcc4ee47e14..1265c5ef5d24b5ec3361f09f56e611fffb3a18d3 100644
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diff --git a/2-produce-graph/pie--datacite-type.png b/2-produce-graph/pie--datacite-type.png
new file mode 100644
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diff --git a/2-produce-graph/pie-data-type.py b/2-produce-graph/pie-data-type.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c893393af0eb8a1354640886b99ac48ae088a28
--- /dev/null
+++ b/2-produce-graph/pie-data-type.py
@@ -0,0 +1,34 @@
+import pandas as pd, matplotlib, matplotlib.pyplot  as plt
+import z_my_functions as my_fct
+import seaborn as sns
+import random
+
+df = my_fct.load_and_treat_csv()
+print(df.columns)
+
+df_type = df["resourceTypeGeneral"].value_counts()
+# print(df_type_raw)
+
+# ## regroup small values in "other"
+# treshold = 20
+# df_type = df_type_raw[df_type_raw > treshold]
+# df_type["other"] = df_type[df_type <= treshold].sum()
+
+
+
+#define Seaborn color palette to use
+colors = sns.color_palette('pastel')[0:len(df_type)]
+random.shuffle(colors) ## so that blue is not more the first item
+
+
+plt.pie(df_type, colors = colors, autopct=lambda p: '{:.0f}%'.format(round(p)) if p > 1 else '', startangle = 160)
+## auto pct only if value > 1
+
+plt.legend(df_type.index, loc = (0.7, -0.1) )
+
+plt.title(f"Type of datasets", fontsize = 20, x = 0.5, y = 1.03, alpha = 0.6)
+plt.suptitle(f"n = {len(df)}", fontsize = 11, x = 0.5, y = 0.9, alpha = 0.6)
+plt.savefig("pie--datacite-type.png")
+
+
+# print(len(df))
\ No newline at end of file
diff --git a/2-produce-graph/pie-datacite-client.py b/2-produce-graph/pie-datacite-client.py
index 257a6a4eec23a5c01818a2633341e57da39451b2..d88394da1b6f122f4b93d7d495af06c69a9da337 100644
--- a/2-produce-graph/pie-datacite-client.py
+++ b/2-produce-graph/pie-datacite-client.py
@@ -17,7 +17,7 @@ clients_name = []
 for item in df_client.index :
 	short_name = item[: item.find(".")]
 	if short_name not in ["inist", "jbru"] : 
-		clients_name.append( short_name)
+		clients_name.append( short_name.capitalize())
 	else : 
 		clients_name.append(item)
 
@@ -32,6 +32,7 @@ colors = sns.color_palette('pastel')[0:len(df_client)]
 
 plt.pie(df_client, labels = clients_name, colors = colors, autopct='%.0f%%')
 plt.title(f"Distribution of datasets by DataCite client", fontsize = 20, x = 0.5, y = 1.03, alpha = 0.6)
+plt.suptitle(f"n = {len(df)}", fontsize = 11, x = 0.5, y = 0.90, alpha = 0.6)
 plt.savefig("pie--datacite-client.png")
 
 
diff --git a/2-produce-graph/z_my_functions.py b/2-produce-graph/z_my_functions.py
index bac1d1fd03b661ca00e177364c5effe5e4c579a5..e683d1c0757565e601f6ae9576ec72b86fd8baf9 100644
--- a/2-produce-graph/z_my_functions.py
+++ b/2-produce-graph/z_my_functions.py
@@ -6,7 +6,7 @@ def load_and_treat_csv() :
 	df_raw = pd.read_csv("../dois-uga.csv", index_col=False)
 	
 	## remove datacite type that are not "research data"
-	type_to_explude = ["Book", "ConferencePaper", "JournalArticle", "BookChapter", "Service", "Preprint"]
+	type_to_explude = ["Book", "ConferencePaper", "ConferenceProceeding", "JournalArticle", "BookChapter", "Service", "Preprint"]
 	df = df_raw[ ~df_raw["resourceTypeGeneral"].isin(type_to_explude) ].copy()
 	
 	return df
diff --git a/dois-uga.csv b/dois-uga.csv
index 2891642247a9ad84d3aa4347dd7d7fc50ad6f40a..655c2f0126f4623ae2961fa1365a28ce25b653b2 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -1,5 +1,4 @@
 doi,title,publisher,publicationYear,language,resourceTypeGeneral,rights,description,source,isActive,state,viewCount,downloadCount,referenceCount,citationCount,versionCount,created,registered,client,provider,subject,subject_raw,sizes,formats
-10.57757/iugg23-4563,Constraining earthquake depth at teleseismic distance: Picking depth phases with deep learning,GFZ German Research Centre for Geosciences,2023,en,ConferencePaper,Creative Commons Attribution 4.0 International,"<!--!introduction!--><b></b><p>Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. However, while constraining earthquake epicenters in an automated fashion is an established technique, constraining event depth is substantially more difficult, especially in the absence of nearby stations. One solution to this challenge are teleseismic depth phases but these can currently not be identified by automatic detection methods.&nbsp; Here we propose two deep learning models, DepthPhaseNet and DepthPhaseTEAM to detect depth phases. The first model closely follows the PhaseNet architectures with minor modifications; the latter allows joint analysis of multiple stations by adding a transformer to this basic architecture.&nbsp; For training the models, we create a dataset based on the ISC EHB bulletin, a high-quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axes yields excellent estimates of earthquake depth. The models achieve mean absolute errors below 10 km. Furthermore, we demonstrate that the multi-station model, DepthPhaseTEAM, leads to better and more consistent predictions than the single-station model DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library for machine learning in seismology.</p>",fabricaForm,True,findable,0,0,0,0,0,2023-07-03T19:58:09.000Z,2023-07-10T20:46:27.000Z,gfz.iugg2023,gfz,,,,
 10.5281/zenodo.4767088,Atlas.TI deductive coding of normative assumptions in SE scholarship,Zenodo,2021,en,Dataset,"Creative Commons Attribution 4.0 International,Embargoed Access",Deductive content analysis of a sample of 100 influential publications in the field of social entrepreneurship (SE) to identify the normative assumptions in SE scholarship. Eight contemporary schools of thought in political philosophy are used as a template for analysis.,mds,True,findable,0,0,0,0,0,2021-05-17T08:55:20.000Z,2021-05-17T08:55:21.000Z,cern.zenodo,cern,"social entrepreneurship,political philosophy","[{'subject': 'social entrepreneurship'}, {'subject': 'political philosophy'}]",,
 10.57745/3d4dfw,"Stream concentrations of micropollutants in the Claduègne, Ardèche",Recherche Data Gouv,2023,,Dataset,,"Stream concentrations of different organic micropollutants at different times and locations throughout the Claduègne catchment in Ardèche. The data include mainly human and veterinary pharmaceuticals. The analyzed molecules are: Atenolol, Bisoprolol, Caffeine, Carbamazepine, Cetirizine, Diclofenac, Fenbendazole, Irbesartan, Iopromide, Ivermectin, Lidocaine, Metformin, Mebendazole, Nicotinamide, Sulfamethoxazole, Trimethoprim, Telmisartan. The samples were taken in 2019 &amp; 2020. An article describing and interpreting the data will be linked once published.",mds,True,findable,189,2,0,0,0,2023-04-20T13:24:04.000Z,2023-06-20T12:31:16.000Z,rdg.prod,rdg,,,,
 10.5281/zenodo.4761299,"Fig. 10 in Two New Species Of Dictyogenus Klapálek, 1904 (Plecoptera: Perlodidae) From The Jura Mountains Of France And Switzerland, And From The French Vercors And Chartreuse Massifs",Zenodo,2019,,Image,"Creative Commons Attribution 4.0 International,Open Access","Fig. 10. Dictyogenus jurassicum sp. n., larva. Spring of River Doubs, Mouthe, Doubs dpt, France. Photo A. Ruffoni.",mds,True,findable,0,0,4,0,0,2021-05-14T07:44:25.000Z,2021-05-14T07:44:26.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Perlodidae,Dictyogenus","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Perlodidae'}, {'subject': 'Dictyogenus'}]",,
@@ -37,7 +36,6 @@ Related Publication: Engilberge et al. (2019)",mds,True,findable,0,0,0,0,0,2019-
 10.5281/zenodo.4314872,"Amory et al. (2021), Geoscientific Model Development : data, model outputs and source code",Zenodo,2020,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","<strong>Data and model outputs for the replication of the analysis made in:</strong><br> (see the published version of this article in Geoscientific Model Development, 2021 - please cite this version if you use these data)<br> C. Amory, C. Kittel, L. Le Toumelin, C. Agosta, A. Delhasse, V. Favier, and X. Fettweis: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica, Geoscientific Model Development, accepted, 2021. See README.txt for a full description of the dataset content Please contact me at amory.charles@live.fr if you need other half-hourly outputs or for more details on the dataset",mds,True,findable,0,0,0,0,0,2020-12-10T14:32:28.000Z,2020-12-10T14:32:29.000Z,cern.zenodo,cern,,,,
 10.6084/m9.figshare.23822166.v1,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,0,0,0,0,0,2023-08-02T11:18:30.000Z,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'],
 10.5281/zenodo.56682,Ncomm-Goldnp-2016: Release 3.0,Zenodo,2016,,Dataset,"Creative Commons Attribution 4.0,Open Access",Supporting x-ray scattering data for Nature Communications Article on Polymorphism in Gold 144 clusters (DOI: 10.1038/ncomms11859),,True,findable,0,0,1,0,0,2016-06-29T16:40:36.000Z,2016-06-29T16:40:37.000Z,cern.zenodo,cern,"pair distribution function,gold cluster,nanoparticle,x-ray diffraction,polymorphism","[{'subject': 'pair distribution function'}, {'subject': 'gold cluster'}, {'subject': 'nanoparticle'}, {'subject': 'x-ray diffraction'}, {'subject': 'polymorphism'}]",,
-10.34616/wse.2019.13.61.80,"Anti-Unism in a landscape of Unism : a revival of Avant-Garde in Dong Yue's work ""The Looming Storm""","Wydział Prawa, Administracji i Ekonomii Uniwersytetu Wrocławskiego",2019,,JournalArticle,,,fabricaForm,True,findable,0,0,0,0,0,2021-09-20T11:37:56.000Z,2021-09-20T12:08:39.000Z,psnc.uwr,dxmj,,,,
 10.5281/zenodo.6913393,Evidence of dual Shapiro steps in a Josephson junction array,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","QCodes type databases containing raw data associated with the paper ""Evidence of dual Shapiro steps in a Josephson junctions array"" by N. Crescini, S. Cailleaux et al. acquired in the Institut Neel, CNRS, Grenoble, France between January 2022 and June 2022. There are two databases: one contains the characterization of the sample without microwave pump and the other one contains the study of the sample under microwave irradiation. Two Jupyter notebooks (python 3.8.11) are provided to analyze the datasets contained in the databases and reproduce the results of the article. For any additional information please contact: nicolo.crescini@neel.cnrs.fr or samuel.cailleaux@neel.cnrs.fr",mds,True,findable,0,0,0,1,0,2022-07-27T12:45:12.000Z,2022-07-27T12:45:13.000Z,cern.zenodo,cern,"Josephson junction,Superconductivity,Circuit QED,Metrology,Quantum physics","[{'subject': 'Josephson junction'}, {'subject': 'Superconductivity'}, {'subject': 'Circuit QED'}, {'subject': 'Metrology'}, {'subject': 'Quantum physics'}]",,
 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.5913981,artefact for ESOP2022 paper The Trusted Computing Base of the CompCert Verified Compiler.,Zenodo,2022,en,Software,Open Access,Coq source code demonstrating the examples in our ESOP 2022 paper <em>The Trusted Computing Base of the CompCert Verified Compiler</em>. Comes with a Docker container with a complete Coq installation.,mds,True,findable,0,0,0,0,0,2022-01-28T14:02:09.000Z,2022-01-28T14:02:10.000Z,cern.zenodo,cern,,,,
@@ -113,7 +111,6 @@ Update history for v3.0.1 by @jzuhone in https://github.com/yt-project/unyt/pull
 Full Changelog: https://github.com/yt-project/unyt/compare/v3.0.0...v3.0.1",api,True,findable,0,0,0,0,0,2023-11-02T17:02:12.000Z,2023-11-02T17:02:12.000Z,cern.zenodo,cern,,,,
 10.34847/nkl.9bd4vqc6,"Figure 10 : Extraits vidéos ""Différences de cadrage""",NAKALA - https://nakala.fr (Huma-Num - CNRS),2023,fr,Audiovisual,,"La captation de cette vidéo a eu lieu dans le cadre du projet FOCUS(E) financé par l'IDEX de l'Université Grenoble Alpes.
 Les participants (et les détenteurs de l'autorité parentale) ont consenti à la diffusion de leurs images dans le cadre exclusif du projet.",api,True,findable,0,0,0,0,0,2023-10-13T12:59:45.000Z,2023-10-13T12:59:45.000Z,inist.humanum,jbru,"enfant,méthode","[{'subject': 'enfant'}, {'subject': 'méthode'}]",['215322977 Bytes'],['video/quicktime']
-10.34847/nkl.bc2b1071,Bulletin franco-italien 1912 n°3 mai - juin,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/05 (A4,N3)-1912/06.",api,True,findable,0,0,0,0,0,2022-07-12T10:40:43.000Z,2022-07-12T10:40:43.000Z,inist.humanum,jbru,"Etudes Italiennes,Etudes italiennes","[{'subject': 'Etudes Italiennes'}, {'subject': 'Etudes italiennes'}]","['6464874 Bytes', '21194044 Bytes', '20981389 Bytes', '21201160 Bytes', '21170605 Bytes', '21009112 Bytes', '21218584 Bytes', '21088960 Bytes', '21271426 Bytes', '21317947 Bytes', '21327454 Bytes', '21287920 Bytes', '21089296 Bytes', '21296776 Bytes', '21203305 Bytes', '21091444 Bytes', '21347104 Bytes']","['application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
 10.5281/zenodo.4498331,Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century,Zenodo,2021,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This archive provides the ice sheet model outputs produced as part of the publication ""Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets"", published in GRL Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com <br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa). “We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."" You should also refer to and cite the following papers: For Greenland datasets Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319 Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020. Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020. For Antarctica datasets Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019. Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020. <br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.",mds,True,findable,0,0,0,0,0,2021-02-03T16:01:49.000Z,2021-02-03T16:01:49.000Z,cern.zenodo,cern,"ISMIP6, Greenland, Antarctica, CMIP","[{'subject': 'ISMIP6, Greenland, Antarctica, CMIP'}]",,
 10.5281/zenodo.7495559,Acetaldehyde binding energies: a coupled experimental and theoretical study,Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access",CRYSTAL17 output files of the atomic structures used in the related publication,mds,True,findable,0,0,0,0,0,2022-12-30T20:34:37.000Z,2022-12-30T20:34:37.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.10037888,Pooch v1.8.0: A friend to fetch your data files,Zenodo,2023,,Software,"BSD 3-Clause ""New"" or ""Revised"" License","Does your Python package include sample datasets? Are you shipping them with the code? Are they getting too big?
@@ -179,7 +176,6 @@ Abstract: Microarchitecture research relies on performance models with various
 10.5281/zenodo.6405782,Automatic affective reactions to physical effort,Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Dataset for the study titled ""automatic affective responses during physical effort: a virtual reality study"". This dataset includes: <strong>1) A codebook (including the name of the main variables)</strong> --&gt; ""code_book_affect_effort.xlsx"" <strong>2) Behavioral data (raw)</strong> --&gt; in the folder ""data_ps_VR"". The raw data are added for transparency, but are not necessary to run the models. <strong>3) Self-reported data (raw)</strong> --&gt; ""20220112_VR_expe.xlsx"" --&gt; ""20220112_VR_pilot.xlsx"" The raw data are added for transparency, but are not necessary to run the models. <strong>4) clean data ready to used for the statistical analyses</strong> --&gt; ""data_VR_all_clean.csv"". This clean data are produced by the R script. These data included the self-reported and the behavioral measures. <strong>5) R script for the data management (i.e., from the raw data to data ready to be analyzed)</strong> --&gt; ""data_management_effort.R"" to create the dataset (return the file: ""data_VR_all_clean.RData"") <strong>6) R script for the models tested</strong> --&gt; ""Data_mixed_effects_models.R"" for the models tested in the paper <strong>7) The video of the experiment</strong>",mds,True,findable,0,0,0,0,0,2022-04-01T11:17:14.000Z,2022-04-01T11:17:15.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.4302473,Alternative architecture of the E. coli chemosensory array.,Zenodo,2020,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","We present molecular models for extended patches of the <em>E. coli</em> chemosensory array. The models display either the canonical p6-symmetric architecture or an alternative p2-symmetric architecture recently observed in <em>E. coli</em> minicells. For more information, please see the associated BioRxiv preprint. Questions regarding the coordinates may be directed to Keith Cassidy (keith.cassidy@bioch.ox.ac.uk).",mds,True,findable,0,0,0,0,0,2020-12-06T13:52:06.000Z,2020-12-06T13:52:07.000Z,cern.zenodo,cern,"bacterial chemotaxis,chemosensory array","[{'subject': 'bacterial chemotaxis'}, {'subject': 'chemosensory array'}]",,
 10.7280/d1b114,"Dataset for: Fast retreat of Pope, Smith, and Kohler glaciers in West Antarctica observed by satellite interferometry",Dryad,2021,en,Dataset,Creative Commons Zero v1.0 Universal,"Pope, Smith, and Kohler glaciers, in the Amundsen Sea Embayment of West Antarctica, have experienced enhanced ocean-induced ice-shelf melt, glacier acceleration, ice thinning, and grounding line retreat in the past thirty years, in a glaciological setting with retrograde bedrock slopes conducive to marine ice sheet instability. Here we present observations of the grounding line retreat of these glaciers since 2014 using a constellation of interferometric radar satellites with a short revisit cycle combined with precision surface elevation data. We find that the glacier grounding lines develop spatially-variable, km-sized, tidally-induced migration zones. After correction for tidal effects, we detect a sustained pattern of retreat coincident with high melt rates of un-grounded ice, marked by episodes of more rapid retreat. In 2017, Pope Glacier retreated 3.5 km in 3.6 months, or 11.7 km/yr. In 2016-2018, Smith West retreated at 2 km/yr and Kohler at 1.3 km/yr. While the retreat slowed down in 2018-2020, these retreat rates are faster than anticipated by numerical models on yearly time scales. We hypothesize that the rapid retreat is caused by un-represented, vigorous ice-ocean interactions acting within newly-formed cavities at the ice-ocean boundary.",mds,True,findable,993,97,0,0,0,2021-11-01T23:46:08.000Z,2021-11-01T23:46:09.000Z,dryad.dryad,dryad,"FOS: Earth and related environmental sciences,FOS: Earth and related environmental sciences","[{'subject': 'FOS: Earth and related environmental sciences', 'subjectScheme': 'fos'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['2646545672 bytes'],
-10.57726/9782919732890,Les remontées mécaniques et le droit,Presses Universitaires Savoie Mont Blanc,2019,fr,Book,,,fabricaForm,True,findable,0,0,0,0,0,2022-03-04T15:01:42.000Z,2022-03-04T15:02:03.000Z,pusmb.prod,pusmb,FOS: Law,"[{'subject': 'FOS: Law', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['496 pages'],
 10.5061/dryad.6t1g1jx0m,Adaptive potential of Coffea canephora from Uganda in response to climate change,Dryad,2022,en,Dataset,Creative Commons Zero v1.0 Universal,"Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programs. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defense pathways in coffee. Seventy-one SNPs were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB-like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, like the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex-situ conservation strategies are discussed.",mds,True,findable,322,27,0,1,0,2022-01-31T07:37:46.000Z,2022-01-31T07:37:47.000Z,dryad.dryad,dryad,"Climate change,Conservation genetics,Landscape genetics,Agriculture","[{'subject': 'Climate change', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}, {'subject': 'Conservation genetics', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}, {'subject': 'Landscape genetics'}, {'subject': 'Agriculture', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}]",['2563202 bytes'],
 10.5281/zenodo.6303309,Structural Defects Improve the Memristive Characteristics of Epitaxial La0.8Sr0.2MnO3-based Devices,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Data files (.txt) of the data presented in the publication 10.1002/admi.202200498 File names are referred to as the reference of the Figure and number in the main manuscript, followed by the sample (LSM/STO or LSM/LAO) and keywords of the figure. Each file corresponds only to the data of one sample. Regarding the series in a Figure, in the case there is very little data, the series are in a single file, arranged in columns. Otherwise, the series with large amounts of data have been split into single files named according to the series (legend). All the files include two headers (Name and Units) for each column. The files containing data from multiple series contain an extra header to precise the data series.",mds,True,findable,0,0,0,0,0,2022-08-24T15:16:09.000Z,2022-08-24T15:16:10.000Z,cern.zenodo,cern,"epitaxial thin films,manganites,memristive devices,metal-organic chemical vapor deposition (MOCVD),resistive switching,structural defects,valence change memories (VCMs)","[{'subject': 'epitaxial thin films'}, {'subject': 'manganites'}, {'subject': 'memristive devices'}, {'subject': 'metal-organic chemical vapor deposition (MOCVD)'}, {'subject': 'resistive switching'}, {'subject': 'structural defects'}, {'subject': 'valence change memories (VCMs)'}]",,
 10.5281/zenodo.3135479,Fast and Faithful Performance Prediction of MPI applications: the HPL use case study (experiment artifact),Zenodo,2019,en,Other,"MIT License,Open Access",Artifact repository for the article Fast and Faithful Performance Prediction of MPI applications: the HPL use case study.,mds,True,findable,0,0,0,0,0,2019-05-22T08:58:00.000Z,2019-05-22T08:58:01.000Z,cern.zenodo,cern,,,,
@@ -326,8 +322,6 @@ Les multiples récits collectés au cours des arpentages et des rencontres réal
 Cette carte est la version finale d'octobre 2021 faite sur Illustrator (Adobe). Il s'agit de l'actualisation de la carte intermédiaire qui prend en compte les réactions et retours des habitants suite à la présentation lors de la journée ""Au fil de l'eau 2"" en juin 2021. Elle a été présentée lors de la journée d'études Les Ondes de l’Eau « Traduction cartographique des mémoires sensibles de la Romanche » en octobre 2021 pour être mise en discussion.",api,True,findable,0,0,0,0,0,2022-06-27T12:34:23.000Z,2022-06-27T12:34:23.000Z,inist.humanum,jbru,"enquêtes de terrain (ethnologie),Désindustrialisation,Patrimoine industriel,Pollution de l'air,Montagnes – aménagement,Énergie hydraulique,Rives – aménagement,Cartographie critique,Représentation graphique,Romanche, Vallée de la (France),Keller, Charles Albert (1874-1940,Ingénieur A&amp;M),Histoires de vie,Cartographie sensible,Mémoires des lieux,histoire orale,patrimoine immatériel,Sens et sensations,Perception de l'espace,Récit personnel,carte sensible","[{'lang': 'fr', 'subject': 'enquêtes de terrain (ethnologie)'}, {'lang': 'fr', 'subject': 'Désindustrialisation'}, {'lang': 'fr', 'subject': 'Patrimoine industriel'}, {'lang': 'fr', 'subject': ""Pollution de l'air""}, {'lang': 'fr', 'subject': 'Montagnes – aménagement'}, {'lang': 'fr', 'subject': 'Énergie hydraulique'}, {'lang': 'fr', 'subject': 'Rives – aménagement'}, {'lang': 'fr', 'subject': 'Cartographie critique'}, {'lang': 'fr', 'subject': 'Représentation graphique'}, {'lang': 'fr', 'subject': 'Romanche, Vallée de la (France)'}, {'lang': 'fr', 'subject': 'Keller, Charles Albert (1874-1940'}, {'lang': 'fr', 'subject': 'Ingénieur A&amp;M)'}, {'lang': 'fr', 'subject': 'Histoires de vie'}, {'lang': 'fr', 'subject': 'Cartographie sensible'}, {'lang': 'fr', 'subject': 'Mémoires des lieux'}, {'lang': 'fr', 'subject': 'histoire orale'}, {'lang': 'fr', 'subject': 'patrimoine immatériel'}, {'lang': 'fr', 'subject': 'Sens et sensations'}, {'lang': 'fr', 'subject': ""Perception de l'espace""}, {'lang': 'fr', 'subject': 'Récit personnel'}, {'lang': 'fr', 'subject': 'carte sensible'}]","['16654505 Bytes', '70369012 Bytes']","['image/jpeg', 'application/pdf']"
 10.5281/zenodo.6806404,pete-d-akers/scadi-d15N-SMB: SCADI nitrate and surface mass balance analysis,Zenodo,2022,en,Software,Open Access,Release v1.1. This version contains the complete R code for the SCADI project and associated publications as of 07 July 2022. Changes from v1.0 is removal of one plotted supplemental figure that was removed from linked publication.,mds,True,findable,0,0,0,1,0,2022-07-07T10:41:49.000Z,2022-07-07T10:41:49.000Z,cern.zenodo,cern,,,,
 10.7280/d1mm37,Annual Ice Velocity of the Greenland Ice Sheet (1972-1990),Dryad,2019,en,Dataset,Creative Commons Attribution 4.0 International,"We derive surface ice velocity using data from 16 satellite sensors deployed by 6 different space agencies. The list of sensors and the year that they were used are listed in the following (Table S1). The SAR data are processed from raw to single look complex using the GAMMA processor (www.gamma-rs.ch). All measurements rely on consecutive images where the ice displacement is estimated from tracking or interferometry (Joughin et al. 1998, Michel and Rignot 1999, Mouginot et al. 2012). Surface ice motion is detected using a speckle tracking algorithm for SAR instruments and feature tracking for Landsat. The cross-correlation program for both SAR and optical images is ampcor from the JPL/Caltech repeat orbit interferometry package (ROI_PAC). We assembled a composite ice velocity mosaic at 150 m posting using our entire speed database as described in Mouginot et al. 2017 (Fig. 1A). The ice velocity maps are also mosaicked in annual maps at 150 m posting, covering July, 1st to June, 30th of the following year, i.e. centered on January, 1st (12) because a majority of historic data were acquired in winter season, hence spanning two calendar years. We use Landsat-1&amp;2/MSS images between 1972 and 1976 and combine image pairs up to 1 years apart to measure the displacement of surface features between images as described in Dehecq et al., 2015 or Mouginot et al. 2017. We use the 1978 2-m orthorectified aerial images to correct the geolocation of Landsat-1 and -2 images (Korsgaard et al., 2016). Between 1984 and 1991, we processed Landsat-4&amp;5/TM image pairs acquired up to 1-year apart. Only few Landsat-4 and -5 images (~3%) needed geocoding refinement using the same 1978 reference as used previously. Between 1991 and 1998, we process radar images from the European ERS-1/2, with a repeat cycle varying from 3 to 36 days depending on the mission phase. Between 1999 and 2013, we use Landsat-7, ASTER, RADARSAT-1/2, ALOS/PALSAR, ENVISAT/ASAR to determine surface velocity (Joughin et al., 2010; Howat, I. 2017; Rignot &amp; Mouginot, 2012). After 2013, we use Landsat-8, Sentinel-1a/b and RADARSAT-2 (Mouginot et al., 2017). All synthetic aperture radar (SAR) datasets are processed assuming surface parallel flow using the digital elevation model (DEM) from the Greenland Mapping Project (GIMP; Howat et al., 2014) and calibrated as described in Mouginot et al., 2012, 2017. Data were provided by the European Space Agency (ESA) the EU Copernicus program (through ESA), the Canadian Space Agency (CSA), the Japan Aerospace Exploration Agency (JAXA), the Agenzia Spaziale Italiana (ASI), the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS). SAR data acquisition were coordinated by the Polar Space Task Group (PSTG). References: Dehecq, A, Gourmelen, N, Trouve, E (2015). Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir-Karakoram-Himalaya. Remote Sensing of Environment, 162, 55–66. Howat IM, Negrete A, Smith BE (2014) The greenland ice mapping project (gimp) land classification and surface elevation data sets. The Cryosphere 8(4):1509–1518. Howat, I (2017). MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from Optical Images, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. Joughin, I., B. Smith, I. Howat, T. Scambos, and T. Moon. (2010). Greenland Flow Variability from Ice-Sheet-Wide Velocity Mapping, J. of Glac.. 56. 415-430. Joughin IR, Kwok R, Fahnestock MA (1998) Interferometric estimation of three dimensional ice-flow using ascending and descending passes. IEEE Trans. Geosci. Remote Sens. 36(1):25–37. Joughin, I, Smith S, Howat I, and Scambos T (2015). MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. Michel R, Rignot E (1999) Flow of Glaciar Moreno, Argentina, from repeat-pass Shuttle Imaging Radar images: comparison of the phase correlation method with radar interferometry. J. Glaciol. 45(149):93–100. Mouginot J, Scheuchl B, Rignot E (2012) Mapping of ice motion in Antarctica using synthetic-aperture radar data. Remote Sens. 4(12):2753–2767. Mouginot J, Rignot E, Scheuchl B, Millan R (2017) Comprehensive annual ice sheet velocity mapping using landsat-8, sentinel-1, and radarsat-2 data. Remote Sensing 9(4). Rignot E, Mouginot J (2012) Ice flow in Greenland for the International Polar Year 2008- 2009. Geophys. Res. Lett. 39, L11501:1–7.",mds,True,findable,1196,207,0,3,0,2018-12-14T09:39:45.000Z,2018-12-14T09:39:46.000Z,dryad.dryad,dryad,,,['7913047164 bytes'],
-10.34746/cahierscostech48,Université Virtuelle Africaine (UVA) et universités partenaires en Afrique : Entretien commenté,Cahiers Costech,2018,fr,JournalArticle,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"Cette publication qui n’a pas pour ambition de lancer un nouveau type d’entretien, se présente sous une forme originale structurée en deux parties. La première donne à voir la version initiale d’une proposition de publication dont l’objectif était de rendre compte d’un travail de terrain constitué par la transcription d’un entretien et de son analyse au regard du sujet de thèse. La seconde partie présente les améliorations apportées à la version initiale suite aux commentaires formulés par les deux enseignants-chercheurs responsables de la rubrique « Education et numérique » des Cahiers Costech sollicités pour la publication. L’intérêt de cette démarche est de mettre en évidence le travail de conscientisation du doctorant résultant des directives pédagogiques des relecteurs.
-",fabricaForm,True,findable,0,0,0,0,0,2022-07-06T12:43:45.000Z,2022-07-06T12:43:46.000Z,inist.utc,vcob,Education et technologie - Méthodologie de recherche - Simondon,[{'subject': 'Education et technologie - Méthodologie de recherche - Simondon'}],,
 10.5281/zenodo.4769825,"PrISM satellite rainfall product (2010-2020) based on SMOS soil moisture measurements in Africa (3h, 0.25°)",Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","The PrISM product is a satellite rainfall product initially designed for Africa over a regular grid at 0.25° (about 25x25 km²) and every 3 hours. It is obtained from the synergy of SMOS satellite soil moisture measurements and CMORPH-raw precipitation product through the PrIMS algorithm (<em>Pellarin et al., 2009, 2013, 2020, Louvet et al., 2015, </em>Román-Cascón et al. 2017).",mds,True,findable,0,0,0,0,0,2021-05-18T12:58:13.000Z,2021-05-18T12:58:14.000Z,cern.zenodo,cern,Rainfall product (mm/3h) in Africa (2010-2020),[{'subject': 'Rainfall product (mm/3h) in Africa (2010-2020)'}],,
 10.5281/zenodo.3405119,Proteomic characterization of human exhaled breath condensate.,Zenodo,2018,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","datasets from 3 studies, for In-depth proteomics characterization of exhaled breath condensate (EBC). 1) Lacombe M. et al, 2018 2) Muccilli V. et al, 2015 3) Bredberg A. et al, 2012",mds,True,findable,367,0,0,0,0,2019-09-11T12:00:51.000Z,2019-09-11T12:00:51.000Z,cern.zenodo,cern,"proteomics, exhaled breath condensate","[{'subject': 'proteomics, exhaled breath condensate'}]",,
 10.5281/zenodo.3528068,DeepPredSpeech: computational models of predictive speech coding based on deep learning,Zenodo,2018,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This dataset contains all data, source code, pre-trained computational predictive models and experimental results related to: Hueber T., Tatulli E., Girin L., Schwatz, J-L ""How predictive can be predictions in the neurocognitive processing of auditory and audiovisual speech? A deep learning study."" (biorXiv preprint https://doi.org/10.1101/471581). Raw data are extracted from the publicly available database NTCD-TIMIT (10.5281/zenodo.260228). Audio recordings are available in the audio_clean/ directory Post-processed lip image sequences are available in the lips_roi/ directory (67x67 pixels, 8bits, obtained by lossless inverse DCT-2D transform from the DCT feature available in the original repository of NTCD-TIMIT) Phonetic segmentation (extracted from NTCD-TIMIT original zenodo repository) is available in the HTK MLF file volunteer_labelfiles.mlf Audio features (MFCC-spectrogram and log-spectrogram) are available in the mfcc_16k/ and fft_16k/ directories. Models (audio-only, video-only and audiovisual, based on deep feed-forward neural networks and/or convolutional neural network, in .h5 format, trained with Keras 2.0 toolkit) and data normalization parameters (in .dat scikit-learn format) are available in models_mfcc/ and models_logspectro/ directories Predicted and target (ground truth) MFCC-spectro (resp. log-spectro) for the test databases (1909 sentences), and for the different values of \(\tau_p\) or \(\tau_f\) are available in pred_testdb_mfccspectro/ (resp. pred_testdb_logspectro/) directory Source code for extracting audio features, training and evaluating the models is available on GitHub https://github.com/thueber/DeepPredSpeech/ All directories have been zipped before upload. Feel free to contact me for more details. Thomas Hueber, Ph. D., CNRS research fellow, GIPSA-lab, Grenoble, France, thomas.hueber@gipsa-lab.fr",mds,True,findable,0,0,0,0,0,2019-11-04T14:03:16.000Z,2019-11-04T14:03:17.000Z,cern.zenodo,cern,"deep learning, computational model, multimodal, audiovisual, speech, predictive coding","[{'subject': 'deep learning, computational model, multimodal, audiovisual, speech, predictive coding'}]",,
@@ -359,7 +353,6 @@ Deux enfants racontent leurs déplacements quotidiens entre l’école et le dom
 10.5281/zenodo.4776977,PACT-1D model version v1 for the CALNEX case study - output files,Zenodo,2021,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","We provide model output files for the PACT-1D CalNex study discussed in Tuite et al., 2021 (DOI to follow). The PACT-1D source code used for this study are presented at: https://zenodo.org/record/4776419#.YKepky0Rpqs.",mds,True,findable,0,0,0,0,0,2021-05-21T00:47:18.000Z,2021-05-21T00:47:19.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.3981252,Ultra-wideband SAR Tomography on asteroids : FDBP and Compressive Sensing datasets,Zenodo,2020,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Our knowledge of the internal structure of asteroids is currently indirect and relies on inferences from remote sensing observations of surfaces. However, it is fundamental for understanding small bodies’ history and for planetary defense missions. Radar observation of asteroids is the most mature technique available to characterize their inner structure, and Synthetic Aperture Radar Tomography (TomoSAR) allows 3D imaging by extending the synthetic aperture principle in the elevation direction. However, as the geometry of observation of small asteroids is complex, and TomoSAR studies have always been performed in the Earth observation geometry, TomoSAR results in a small body geometry must be simulated to assess the methods’ performances. Different tomography algorithms can be adopted, depending on the characteristics of the problem. While the Frequency Domain Back Projection (FDBP) is based on the correction of the Fourier transform of the received signal by an <em>ad-hoc</em> function built from the geometry of study, it can only retrieve the true position of the scatterers when applied along with ray-tracing methods, which are unreliable in the case of rough asteroid surfaces. Meanwhile, the Compressive Sensing (CS) is based on the compressive sampling theory, which relies on the hypothesis that few scatterers lie in the same direction from the subsurface. The CS can be used to retrieve the position of the scatterers, but its application in the small body geometry is questioned. Thus, both performances of the FDBP and the CS in a small body geometry are demonstrated, and the quality of the reconstruction is analyzed.",mds,True,findable,0,0,0,0,0,2020-08-12T15:57:06.000Z,2020-08-12T15:57:07.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.4916120,Quantum Information Scrambling in a Trapped-Ion Quantum Simulator with Tunable Range Interactions,Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","The data repository folder, "" OTOCPaperData.zip"" contains the data used in our manuscript <strong><em>Joshi et al., Phys. Rev. Lett. 124, 240505 (2020)</em></strong> and also available at arXiv:2001.02176. The folder contains further subfolders with self-explanatory names and description files to aid in reusing the data for any future purposes.",mds,True,findable,0,0,0,0,0,2021-06-09T14:58:59.000Z,2021-06-09T14:59:01.000Z,cern.zenodo,cern,,,,
-10.57726/nt1t-n566,Voyager dans les États autoritaires et totalitaires de l'Europe de l'entre-deux-guerres,Presses Universitaires Savoie Mont Blanc,2017,fr,Book,,,fabricaForm,True,findable,0,0,0,0,0,2022-03-14T09:55:00.000Z,2022-03-14T09:55:01.000Z,pusmb.prod,pusmb,FOS: Humanities,"[{'subject': 'FOS: Humanities', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['251 pages'],
 10.6084/m9.figshare.22735503,Additional file 1 of Healthcare students’ prevention training in a sanitary service: analysis of health education interventions in schools of the Grenoble academy,figshare,2023,,Text,Creative Commons Attribution 4.0 International,Supplementary Material 1,mds,True,findable,0,0,0,0,0,2023-05-03T03:20:26.000Z,2023-05-03T03:20:27.000Z,figshare.ars,otjm,"Medicine,Biotechnology,69999 Biological Sciences not elsewhere classified,FOS: Biological sciences,Science Policy","[{'subject': 'Medicine'}, {'subject': 'Biotechnology'}, {'subject': '69999 Biological Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Science Policy'}]",['19715 Bytes'],
 10.5281/zenodo.4067946,SGS scalar transport - homogeneous isotropic turbulence,Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This dataset contains filtered data (spectral cut) from three DNS simulations (train, tests, decay) of 3-dimensional homogeneous isotropic turbulence in a \(512^3\) periodic domain. More precisely, the following fields are available: Filtered velocities Filtered transported (passive) scalar Divergence of the SGS term from the transport equation obtained from DNS SGS fluxes (in the three directions) from the transport equation obtained from DNS The scalar is forced on the high spectral wavenumbers, such that filtered data is not impacted in train and tests, while the scalar forcing is removed in the decay simulation. Note that all three simulations are forced on the velocities with an Alvelius-type scheme. The dataset also provide with three different filter sizes: 8, 16 and 32 times from the initial DNS resolution, which give domain sizes of \(64^3, 32^3, 16^3\) respectively. This dataset has been used to train NN models available : https://github.com/hrkz/SubgridTransportNN.",mds,True,findable,0,0,0,0,0,2020-10-12T08:04:21.000Z,2020-10-12T08:04:22.000Z,cern.zenodo,cern,"turbulence,fluid dynamics,machine learning,subgrid-scale","[{'subject': 'turbulence'}, {'subject': 'fluid dynamics'}, {'subject': 'machine learning'}, {'subject': 'subgrid-scale'}]",,
 10.5281/zenodo.5243352,Russian DBnary archive in original Lemon format,Zenodo,2021,ru,Dataset,"Creative Commons Attribution Share Alike 4.0 International,Open Access","The DBnary dataset is an extract of Wiktionary data from many language editions in RDF Format. Until July 1st 2017, the lexical data extracted from Wiktionary was modeled using the lemon vocabulary. This dataset contains the full archive of all DBnary dumps in Lemon format containing lexical information from Russian language edition, ranging from 3rd March 2013 to 1st July 2017. After July 2017, DBnary data has been modeled using the ontolex model and will be available in another Zenodo entry.<br>",mds,True,findable,0,0,0,0,0,2021-08-24T11:44:17.000Z,2021-08-24T11:44:18.000Z,cern.zenodo,cern,"Wiktionary,Lemon,Lexical Data,RDF","[{'subject': 'Wiktionary'}, {'subject': 'Lemon'}, {'subject': 'Lexical Data'}, {'subject': 'RDF'}]",,
@@ -389,7 +382,6 @@ Après plusieurs annulations suite aux confinements, le RDV est enfin fixé. En
 10.5281/zenodo.10201545,"Dataset for the paper ""Largest aftershock nucleation driven by afterslip during the 2014 Iquique sequence""",Zenodo,2023,en,Dataset,Creative Commons Attribution 4.0 International,"Dataset for the following paper 
 Yuji Itoh, Anne Socquet, and Mathilde Radiguet, Largest aftershock nucleation driven by afterslip during the 2014 Iquique sequence, Geophysical Research Letters, 2023GL104852
 See readme.txt files for details about the files and the contact informaton. Uncompressing produces files with ~ 500 MB in total.",api,True,findable,0,0,0,0,0,2023-12-05T12:30:45.000Z,2023-12-05T12:30:45.000Z,cern.zenodo,cern,"Afterslip,GPS,High-rate GPS,Iquique,Chile,Earthquake,subduction zone","[{'subject': 'Afterslip'}, {'subject': 'GPS'}, {'subject': 'High-rate GPS'}, {'subject': 'Iquique'}, {'subject': 'Chile'}, {'subject': 'Earthquake'}, {'subject': 'subduction zone'}]",,
-10.60662/7nek-jq08,"Favoriser l’innovation par le lean product development : le comportement humain, un indicateur pertinent ?",CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-12T15:19:12.000Z,2023-09-12T15:19:13.000Z,uqtr.mesxqq,uqtr,,,,
 10.5281/zenodo.7137482,Regional-Modeling-LATMOS-IGE/WRF-Chem-Polar: WRF-Chem 4.3.3 including mercury chemistry,Zenodo,2022,,Software,Open Access,"This is a development of the WRF-Chem 4.3.3 model which includes mercury gas-phase and heterogeneous chemistry, as well as mercury re-emission from snow and sea ice. This version was extended from the previously developed WRF-Chem version (published in Marelle et al., JAMES, 2021) which included halogen chemistry and bromine surface snow and blowing snow emissions. NOTE: This is an experimental version of the release and may not be compatible with particular model options.",mds,True,findable,0,0,0,1,0,2022-10-03T08:12:37.000Z,2022-10-03T08:12:37.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.6498344,Dataset for 'Unveiling the charge distribution of a GaAs-based nanoelectronic device',Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","*********************************************************<br> This repository contains the raw experimental data associated with the manuscript<br> ""Unveiling the charge distribution of a GaAs-based nanoelectronic device: A large experimental data-set approach""<br> by Eleni Chatzikyriakou, Junliang Wang et al.<br> See Arxiv:2205.00846 for more details. <br> ********************************************************* ***************************************<br> Content of the different data files<br> *************************************** The data are stored in 5 different files in the csv format. * data_1D_4K.csv: current versus gate voltage data for all the samples at 4K. <br> The same gate voltage is applied on the Top and bottom gates. For sample X1Y3, X2Y3, X5Y3 and X6Y3 some additional measurements have been realised : * data_1D_mk.csv : top and bottom gates of each QPC have been swept at the same time at 50 mK temperature. * data_2D_4K_TB.csv : top gate has been swept for different values of the bottom gate at 4K.<br> * data_2D_mK_TB.csv : top gate has been swept for different values of the bottom gate at 50 mK temperature.<br> * data_2D_mK_BT.csv : bottom gate has been swept for different values of the top gate at 50 mK temperature. ***********************************<br> Format of the csv files<br> *********************************** --- All the data files are in the following format. * A given curve ""current versus gate voltage"" is stored in two consecutive raws. The first one contains the value of the measured current, the second one contains the values of the applied<br> gate voltages. * A 2D measurement ""current versus top gate and bottom gate"" is stored in three consecutive raws. The first one contains the value of the measured current, the second one contains the list of values of voltage applied on one of the gate. The third third raw contains the list of values of voltage applied on the other gate. --- Each row of a csv file has the following format: * The first column identifies the quantum point contact and the quantity. The format is Xx_Yy_QpcNb_Meas.<br> - Xx is the column on the chip (from X1 to X6).<br> - Yy is the row on the chip (Y1, Y2 or Y3).<br> - QpcNb is the number of the qpc (from 1 to 8 or from 9 to 16).<br> - Meas is the reported quantity, either the (measured) ""current"" or the (applied) ""voltage""<br> or the ""TopVoltage"" or ""BotVoltage"" for 2D scans. * The second column is the unit (A or V). * The third column is the number of sweeps (1, 2 or 3) performed. <br> For some measurements, the same sweep has been done multiple times. * The fourth column is the design of the quantum point contact (A, B, C, D or E). * All the following columns contain the measured value. For 2D scans the different values of the gate corresponding to the third raw are placed one after the other. *******************************<br> Python scripts for data analysis<br> ******************************* For convenience, we provide an example python scripts that can be used to load the data and plot them. extract.py : Extracts the data into a dictionary and plots the I-V characteristics<br> extract.ipynb : jupyter notebook using the different functions of extract.py type -h for help",mds,True,findable,0,0,0,2,0,2022-05-03T17:46:12.000Z,2022-05-03T17:46:14.000Z,cern.zenodo,cern,"quantum point contacts,measurements,semiconducting quantum technologies,GaAs,nanoelectronics","[{'subject': 'quantum point contacts'}, {'subject': 'measurements'}, {'subject': 'semiconducting quantum technologies'}, {'subject': 'GaAs'}, {'subject': 'nanoelectronics'}]",,
 10.5281/zenodo.7941512,cicwi/PyCorrectedEmissionCT: Release version 0.8.1,Zenodo,2023,,Software,Open Access,Hotfix release adding the following features and fixing the following bugs: Added Power spectrum calculation function. ### Fixed Pypi package creation.,mds,True,findable,0,0,0,0,0,2023-05-16T14:26:31.000Z,2023-05-16T14:26:31.000Z,cern.zenodo,cern,,,,
@@ -399,7 +391,6 @@ See readme.txt files for details about the files and the contact informaton. Unc
 10.25384/sage.c.6567921.v1,Impact of a telerehabilitation programme combined with continuous positive airway pressure on symptoms and cardiometabolic risk factors in obstructive sleep apnea patients,SAGE Journals,2023,,Collection,Creative Commons Attribution 4.0 International,"BackgroundObstructive sleep apnea syndrome is a common sleep-breathing disorder associated with adverse health outcomes including excessive daytime sleepiness, impaired quality of life and is well-established as a cardiovascular risk factor. Continuous positive airway pressure is the reference treatment, but its cardiovascular and metabolic benefits are still debated. Combined interventions aiming at improving patient's lifestyle behaviours are recommended in guidelines management of obstructive sleep apnea syndrome but adherence decreases over time and access to rehabilitation programmes is limited. Telerehabilitation is a promising approach to address these issues, but data are scarce on obstructive sleep apnea syndrome.MethodsThe aim of this study is to assess the potential benefits of a telerehabilitation programme implemented at continuous positive airway pressure initiation, compared to continuous positive airway pressure alone and usual care, on symptoms and cardiometabolic risk factors of obstructive sleep apnea syndrome. This study is a 6-months multicentre randomized, parallel controlled trial during which 180 obese patients with severe obstructive sleep apnea syndrome will be included. We will use a sequential hierarchical criterion for major endpoints including sleepiness, quality of life, nocturnal systolic blood pressure and inflammation biological parameters.Discussionm-Rehab obstructive sleep apnea syndrome is the first multicentre randomized controlled trial to examine the effectiveness of a telerehabilitation lifestyle programme in obstructive sleep apnea syndrome. We hypothesize that a telerehabilitation lifestyle intervention associated with continuous positive airway pressure for 6 months will be more efficient than continuous positive airway pressure alone on symptoms, quality of life and cardiometabolic risk profile. Main secondary outcomes include continuous positive airway pressure adherence, usability and satisfaction with the telerehabilitation platform and medico-economic evaluation.Trial registrationClinicaltrials.gov Identifier: NCT05049928. Registration data: 20 September 2021",mds,True,findable,0,0,0,0,0,2023-04-07T00:07:22.000Z,2023-04-07T00:07:23.000Z,figshare.sage,sage,"111708 Health and Community Services,FOS: Health sciences,Cardiology,110306 Endocrinology,FOS: Clinical medicine,110308 Geriatrics and Gerontology,111099 Nursing not elsewhere classified,111299 Oncology and Carcinogenesis not elsewhere classified,111702 Aged Health Care,111799 Public Health and Health Services not elsewhere classified,99999 Engineering not elsewhere classified,FOS: Other engineering and technologies,Anthropology,FOS: Sociology,200299 Cultural Studies not elsewhere classified,FOS: Other humanities,89999 Information and Computing Sciences not elsewhere classified,FOS: Computer and information sciences,150310 Organisation and Management Theory,FOS: Economics and business,Science Policy,160512 Social Policy,FOS: Political science,Sociology","[{'subject': '111708 Health and Community Services', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Health sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Cardiology'}, {'subject': '110306 Endocrinology', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '110308 Geriatrics and Gerontology', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': '111099 Nursing not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': '111299 Oncology and Carcinogenesis not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': '111702 Aged Health Care', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': '111799 Public Health and Health Services not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': '99999 Engineering not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Other engineering and technologies', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Anthropology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '200299 Cultural Studies not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Other humanities', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '89999 Information and Computing Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Computer and information sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '150310 Organisation and Management Theory', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Economics and business', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Science Policy'}, {'subject': '160512 Social Policy', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Political science', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Sociology'}]",,
 10.5281/zenodo.7030984,Local structure and density of liquid Fe-C-S alloys at Moon's core conditions,Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access",This file includes the raw CAESAR and absorption data measured in each P-T condition and the Python codes for diffraction and absorption data analysis. Matlab codes for P-T calibration are also enclosed.,mds,True,findable,0,0,0,1,0,2022-08-30T14:33:46.000Z,2022-08-30T14:33:47.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.4759491,"Figs. 7-15 in Contribution To The Knowledge Of The Protonemura Corsicana Species Group, With A Revision Of The North African Species Of The P. Talboti Subgroup (Plecoptera: Nemouridae)",Zenodo,2009,,Image,"Creative Commons Attribution 4.0 International,Open Access","Figs. 7-15. Epiprocts of the North African species of the Protonemura talboti subgroup. 7-8: Protonemura dakkii sp. n.; 9-10: P. talboti (Nav{s, 1929); 11-12: P. berberica Vinçon &amp; S{nchez-Ortega, 1999; 13-14: P. algirica algirica Aubert, 1956; 15: P. algirica bejaiana ssp. n. (7, 9, 11, 13: lateral view; 8, 10, 12, 14-15: dorsal view; scale 0.5 mm).",mds,True,findable,0,0,10,0,0,2021-05-14T02:23:41.000Z,2021-05-14T02:23:42.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Nemouridae,Protonemura","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Nemouridae'}, {'subject': 'Protonemura'}]",,
-10.48390/0005-gz84,Les circuits courts distants approvisionnant Paris,"UMR CNRS 6266 IDEES, Université de Rouen Normandie",2021,fr,BookChapter,Creative Commons Attribution 4.0 International,,fabricaForm,True,findable,0,0,0,0,0,2021-09-20T12:59:19.000Z,2021-09-20T12:59:19.000Z,jbru.idees,jbru,,,,"['PDF', 'HTML']"
 10.5281/zenodo.5373490,"FIG. 3 in Le gisement paléontologique villafranchien terminal de Peyrolles (Issoire, Puy-de-Dôme, France): résultats de nouvelles prospections",Zenodo,2006,,Image,"Creative Commons Zero v1.0 Universal,Open Access","FIG. 3. — Position et représentation schématique des fossiles collectés durant les sondages de 1996 sur le site de Peyrolles près d'Issoire (Puy-de-Dôme, France).",mds,True,findable,0,0,0,0,0,2021-09-02T04:40:31.000Z,2021-09-02T04:40:32.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}]",,
 10.5281/zenodo.5769631,Magnetic resonance imaging of the octopus nervous system,Zenodo,2021,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access",Anatomical magnetic-resonance imaging of an octopus vulgaris with 0.5mm slice thickness and 0.25 by 0.25 mm in-plane resolution. The companion data paper details the acquisition procedure.,mds,True,findable,0,0,0,0,0,2021-12-17T17:15:46.000Z,2021-12-17T17:15:47.000Z,cern.zenodo,cern,"octopus vulgaris,MRI,cephalopod,https://species.wikimedia.org/wiki/Octopus_vulgaris","[{'subject': 'octopus vulgaris'}, {'subject': 'MRI'}, {'subject': 'cephalopod'}, {'subject': 'https://species.wikimedia.org/wiki/Octopus_vulgaris', 'subjectScheme': 'url'}]",,
 10.5281/zenodo.854619,Daily Gridded Datasets Of Snow Depth And Snow Water Equivalent For The Iberian Peninsula From 1980 To 2014,Zenodo,2017,,Dataset,"Creative Commons Attribution 4.0,Open Access","We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 ×10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands  for each 10 × 10 km grid cell in the Iberian Peninsula.  The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management .",,True,findable,0,0,0,0,0,2017-09-11T09:01:12.000Z,2017-09-11T09:01:13.000Z,cern.zenodo,cern,"SWE,Snow depth,WRF,FSM,Iberian Peninsula","[{'subject': 'SWE'}, {'subject': 'Snow depth'}, {'subject': 'WRF'}, {'subject': 'FSM'}, {'subject': 'Iberian Peninsula'}]",,
@@ -545,7 +536,6 @@ If you use this open data in your work (research or other), please cite in your
 10.5281/zenodo.7693381,Long term mean Potential Evapotranspiration (PET) and Actual Evapotranspiration (EAT) estimates using World-Wide HYPE and different PET-formula,Zenodo,2023,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Data of the article ""<strong>Which Potential Evapotranspiration Formula to Use in Hydrological Modelling World-wide? </strong>"" (Pimentel et al. 2023, <em>Water Resources Research, </em>https://doi.org/10.1029/2022WR033447)",mds,True,findable,0,0,0,0,0,2023-03-02T20:14:03.000Z,2023-03-02T20:14:03.000Z,cern.zenodo,cern,"Evapotranspiration, Global Hydrological Model, PET-formula","[{'subject': 'Evapotranspiration, Global Hydrological Model, PET-formula'}]",,
 10.6084/m9.figshare.c.6712342.v1,Decoupling of arsenic and iron release from ferrihydrite suspension under reducing conditions: a biogeochemical model,figshare,2023,,Collection,Creative Commons Attribution 4.0 International,"Abstract High levels of arsenic in groundwater and drinking water are a major health problem. Although the processes controlling the release of As are still not well known, the reductive dissolution of As-rich Fe oxyhydroxides has so far been a favorite hypothesis. Decoupling between arsenic and iron redox transformations has been experimentally demonstrated, but not quantitatively interpreted. Here, we report on incubation batch experiments run with As(V) sorbed on, or co-precipitated with, 2-line ferrihydrite. The biotic and abiotic processes of As release were investigated by using wet chemistry, X-ray diffraction, X-ray absorption and genomic techniques. The incubation experiments were carried out with a phosphate-rich growth medium and a community of Fe(III)-reducing bacteria under strict anoxic conditions for two months. During the first month, the release of Fe(II) in the aqueous phase amounted to only 3% to 10% of the total initial solid Fe concentration, whilst the total aqueous As remained almost constant after an initial exchange with phosphate ions. During the second month, the aqueous Fe(II) concentration remained constant, or even decreased, whereas the total quantity of As released to the solution accounted for 14% to 45% of the total initial solid As concentration. At the end of the incubation, the aqueous-phase arsenic was present predominately as As(III) whilst X-ray absorption spectroscopy indicated that more than 70% of the solid-phase arsenic was present as As(V). X-ray diffraction revealed vivianite Fe(II)3(PO4)2.8H2O in some of the experiments. A biogeochemical model was then developed to simulate these aqueous- and solid-phase results. The two main conclusions drawn from the model are that (1) As(V) is not reduced during the first incubation month with high Eh values, but rather re-adsorbed onto the ferrihydrite surface, and this state remains until arsenic reduction is energetically more favorable than iron reduction, and (2) the release of As during the second month is due to its reduction to the more weakly adsorbed As(III) which cannot compete against carbonate ions for sorption onto ferrihydrite. The model was also successfully applied to recent experimental results on the release of arsenic from Bengal delta sediments.",mds,True,findable,0,0,0,0,0,2023-06-25T03:12:09.000Z,2023-06-25T03:12:10.000Z,figshare.ars,otjm,"59999 Environmental Sciences not elsewhere classified,FOS: Earth and related environmental sciences,39999 Chemical Sciences not elsewhere classified,FOS: Chemical sciences,Ecology,FOS: Biological sciences,69999 Biological Sciences not elsewhere classified,Cancer","[{'subject': '59999 Environmental Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '39999 Chemical Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Chemical sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Ecology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '69999 Biological Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'Cancer'}]",,
 10.5281/zenodo.5142591,Bayesian Archetypes: Energy signature inference from national data for statistical definition of buildings archetypes.,Zenodo,2021,en,Software,"Apache License 2.0,Open Access",Energy signature inference from national data for statistical definition of buildings archetypes. This project contains the code and data used for the paper <strong>Bayesian inference of dwellings energy signature at national scale: case of the French residential stock</strong>. It aims to infer energy signature of dwelling categories from national census and consumption data.,mds,True,findable,0,0,0,0,0,2021-09-07T16:49:50.000Z,2021-09-07T16:49:50.000Z,cern.zenodo,cern,"Bayesian Inference,Energy Signature,Urban Energy Modeling,Uncertainties,Open Data","[{'subject': 'Bayesian Inference'}, {'subject': 'Energy Signature'}, {'subject': 'Urban Energy Modeling'}, {'subject': 'Uncertainties'}, {'subject': 'Open Data'}]",,
-10.34847/nkl.aacad5y8,Bulletin franco-italien 1912 n°1 janvier - février,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/01 (A4,N1)-1912/02.",api,True,findable,0,0,0,0,0,2022-06-29T10:15:11.000Z,2022-06-29T10:15:12.000Z,inist.humanum,jbru,Etudes italiennes,[{'subject': 'Etudes italiennes'}],"['21511939 Bytes', '20908048 Bytes', '21034528 Bytes', '21036982 Bytes', '20976655 Bytes', '20863780 Bytes', '20901724 Bytes', '21007228 Bytes', '20950432 Bytes', '21051706 Bytes', '20663584 Bytes', '21281992 Bytes', '20952853 Bytes', '20689258 Bytes', '20872804 Bytes', '21019279 Bytes', '20934214 Bytes', '6436585 Bytes', '21037318 Bytes']","['image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'application/pdf', 'image/tiff']"
 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'}]",,
@@ -616,7 +606,6 @@ Chaque entité a été renseignée selon les attributs suivant :
 10.5281/zenodo.8010223,Simulation-based Validation for Autonomous Driving Systems,Zenodo,2023,,Software,"Creative Commons Attribution 4.0 International,Open Access","The artifact for the paper ""Simulation-based Validation for Autonomous Driving Systems"" published on ISSTA 2023.",mds,True,findable,0,0,0,0,0,2023-06-06T12:30:44.000Z,2023-06-06T12:30:45.000Z,cern.zenodo,cern,,,,
 10.34847/nkl.ae94a74k,"Taciti et C. Velleii Paterculi scripta quae exstant; recognita, emaculata. Additique commentarii copiosissimi et notae non antea editae Paris e typographia Petri Chevalier, in monte diui Hilarii - II-0484",NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Image,,,api,True,findable,0,0,0,0,0,2023-01-16T15:46:03.000Z,2023-01-16T15:46:03.000Z,inist.humanum,jbru,,,['52170234 Bytes'],['image/tiff']
 10.26302/sshade/experiment_op_20200212_001,Vis-NIR bidirectional reflection spectra of several ammonium salts mixed with graphite powder at 296 K,SSHADE/GhoSST (OSUG Data Center),2020,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.","Mixtures of graphite powder (&lt;20 µm) and ammonium (NH4+) salts (chloride, sulfate, formate) were prepared by mixing manually these constituents in a mortar. Reflectance spectra (from 0.4 to 4 µm) of these mixtures were measured at 296 K under ambient air.",mds,True,findable,0,0,0,0,0,2020-02-12T11:12:11.000Z,2020-02-12T11:12:11.000Z,inist.sshade,mgeg,"mineral,commercial,elemental solid,Graphite,sulfate,Ammonium sulfate,organic salt,Ammonium formate,chloride,Ammonium chloride,laboratory measurement,bidirectional reflection,macroscopic,Vis,Visible,NIR,Near-Infrared,reflectance factor","[{'subject': 'mineral'}, {'subject': 'commercial'}, {'subject': 'elemental solid'}, {'subject': 'Graphite'}, {'subject': 'sulfate'}, {'subject': 'Ammonium sulfate'}, {'subject': 'organic salt'}, {'subject': 'Ammonium formate'}, {'subject': 'chloride'}, {'subject': 'Ammonium chloride'}, {'subject': 'laboratory measurement'}, {'subject': 'bidirectional reflection'}, {'subject': 'macroscopic'}, {'subject': 'Vis'}, {'subject': 'Visible'}, {'subject': 'NIR'}, {'subject': 'Near-Infrared'}, {'subject': 'reflectance factor'}]",['4 spectra'],['ASCII']
-10.34847/nkl.5bb02187,Bulletin franco-italien 1912 n°6 novembre - décembre,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/11 (A4,N6)-1912/12.",api,True,findable,0,0,0,0,0,2022-06-29T10:46:35.000Z,2022-06-29T10:46:35.000Z,inist.humanum,jbru,"Etudes italiennes,Etudes italiennes","[{'lang': 'fr', 'subject': 'Etudes italiennes'}, {'subject': 'Etudes italiennes'}]","['6036554 Bytes', '21797230 Bytes', '21498754 Bytes', '21658336 Bytes', '21954568 Bytes', '21816616 Bytes', '21709840 Bytes', '21664144 Bytes', '21657022 Bytes', '21577279 Bytes', '21667990 Bytes', '21719686 Bytes', '21847000 Bytes', '21846484 Bytes', '21619864 Bytes', '21711109 Bytes', '21842131 Bytes']","['application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
 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.3613951,Winter snow depths for initializing a glacio-hydrological model in high mountain Chile,Zenodo,2020,,Dataset,Creative Commons Attribution 4.0 International,"The following dataset consists of the forcings, initial conditions, model grids and parameters used to run the TOPKAPI-ETH model (Finger et al., 2011; Ragettli and Pellicciotti, 2012) for the Rio Yeso catchment of central Chile (33.44°S, 69.93°W - Burger et al., 2018). The data and model grids were used to investigate the importance of accurate snow depth maps for initialising the physically-oriented model in a high elevation catchment - For a manuscript submitted to Water Resources Research (WRR) - January 2020. 
 
@@ -775,7 +764,6 @@ Ragettli, S., & Pellicciotti, F. (2012). Calibration of a physically based, spat
 10.5281/zenodo.3550192,Rockfall localization routine,Zenodo,2019,,Software,Open Access,Algorithm for the localization of rockfalls by means of recorded seismic signals using simulated impulse responses.,mds,True,findable,2,0,0,0,0,2019-11-21T21:08:04.000Z,2019-11-21T21:08:04.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.1289969,Data Sets For The Simulated Ampi (Sampi) Load Balancing Simulation Workflow And Ondes3D Performance Analysis (Companion To Ccpe Paper),Zenodo,2018,en,Dataset,"Creative Commons Attribution Share-Alike 4.0,Open Access","This package contains data sets and scripts (in an Org-mode file) related to our submission to the  journal ""Concurrency and Computation: Practice and Experience"", under the title <em>""Performance Modeling of a Geophysics Application to Accelerate the Tuning of Over-decomposition Parameters through Simulation""</em>.",,True,findable,3,0,0,0,0,2018-06-14T22:08:54.000Z,2018-06-14T22:08:55.000Z,cern.zenodo,cern,"Simulation,Load Balancing,Performance Analysis,Finite-Differences Method,Simgrid,MPI,Ondes3d,Iterative parallel application","[{'subject': 'Simulation'}, {'subject': 'Load Balancing'}, {'subject': 'Performance Analysis'}, {'subject': 'Finite-Differences Method'}, {'subject': 'Simgrid'}, {'subject': 'MPI'}, {'subject': 'Ondes3d'}, {'subject': 'Iterative parallel application'}]",,
 10.5281/zenodo.10020953,robertxa/Lutiniere: Final data,Zenodo,2023,,Software,Creative Commons Attribution 4.0 International,Données topographiques du système d'Huretières et de la grotte de la Lutinière,api,True,findable,0,0,0,0,0,2023-10-19T08:39:08.000Z,2023-10-19T08:39:08.000Z,cern.zenodo,cern,,,,
-10.48550/arxiv.2310.14831,Formation of interstellar complex organic molecules on water-rich ices triggered by atomic carbon freezing,arXiv,2023,,Preprint,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"The reactivity of interstellar carbon atoms (C) on the water-dominated ices is one of the possible ways to form interstellar complex organic molecules (iCOMs). In this work, we report a quantum chemical study of the coupling reaction of C ($^3$P) with an icy water molecule, alongside possible subsequent reactions with the most abundant closed shell frozen species (NH$_3$, CO, CO$_2$ and H$_2$), atoms (H, N and O), and molecular radicals (OH, NH$_2$ and CH$_3$). We found that C spontaneously reacts with the water molecule, resulting in the formation of $^3$C-OH$_2$, a highly reactive species due to its triplet electronic state. While reactions with the closed-shell species do not show any reactivity, reactions with N and O form CN and CO, respectively, the latter ending up into methanol upon subsequent hydrogenation. The reactions with OH, CH$_3$ and NH$_2$ form methanediol, ethanol and methanimine, respectively, upon subsequent hydrogenation. We also propose an explanation for methane formation, observed in experiments through H additions to C in the presence of ices. The astrochemical implications of this work are: i) atomic C on water ice is locked into $^3$C-OH$_2$, making difficult the reactivity of bare C atoms on the icy surfaces, contrary to what is assumed in astrochemical current models; and ii) the extraordinary reactivity of $^3$C-OH$_2$ provides new routes towards the formation of iCOMs in a non-energetic way, in particular ethanol, mother of other iCOMs once in the gas-phase.",mds,True,findable,0,0,0,0,0,2023-10-24T03:25:43.000Z,2023-10-24T03:25:44.000Z,arxiv.content,arxiv,"Astrophysics of Galaxies (astro-ph.GA),Chemical Physics (physics.chem-ph),FOS: Physical sciences,FOS: Physical sciences","[{'lang': 'en', 'subject': 'Astrophysics of Galaxies (astro-ph.GA)', 'subjectScheme': 'arXiv'}, {'lang': 'en', 'subject': 'Chemical Physics (physics.chem-ph)', 'subjectScheme': 'arXiv'}, {'subject': 'FOS: Physical sciences', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'FOS: Physical sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",,
 10.6084/m9.figshare.23575360,Additional file 1 of Decoupling of arsenic and iron release from ferrihydrite suspension under reducing conditions: a biogeochemical model,figshare,2023,,Text,Creative Commons Attribution 4.0 International,Additional file 1: Composition of growth media and phylogenetic characterization. The data provided describe the both growth media and the phylogenetic affiliation of the pure strains which were isolated from the FR bacterial community. (DOC 76 KB),mds,True,findable,0,0,0,0,0,2023-06-25T03:11:45.000Z,2023-06-25T03:11:45.000Z,figshare.ars,otjm,"59999 Environmental Sciences not elsewhere classified,FOS: Earth and related environmental sciences,39999 Chemical Sciences not elsewhere classified,FOS: Chemical sciences,Ecology,FOS: Biological sciences,69999 Biological Sciences not elsewhere classified,Cancer","[{'subject': '59999 Environmental Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '39999 Chemical Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Chemical sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Ecology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '69999 Biological Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'Cancer'}]",['77312 Bytes'],
 10.5281/zenodo.5508669,Effects of population density on static allometry between horn length and body mass in mountain ungulates,Zenodo,2021,,Software,"MIT License,Open Access","Little is known about the effects of environmental variation on allometric relationships of condition-dependent traits, especially in wild populations. We estimated sex-specific static allometry between horn length and body mass in four populations of mountain ungulates that experienced periods of contrasting density over the course of the study. These species displayed contrasting sexual dimorphism in horn size; high dimorphism in <i>Capra ibex</i> and <i>Ovis canadensis</i> and low dimorphism in <i>Rupicapra rupicapra</i> and <i>Oreamnos americanus</i>. The effects of density on static allometric slopes were weak and inconsistent while allometric intercepts were generally lower at high density, especially in males from species with high sexual dimorphism in horn length. These results confirm that static allometric slopes are more canalized than allometric intercepts against environmental variation induced by changes in population density, particularly when traits appear more costly to produce and maintain.",mds,True,findable,0,0,0,0,0,2021-09-15T18:15:53.000Z,2021-09-15T18:15:54.000Z,cern.zenodo,cern,"Density dependence,Alpine ungulates,horn","[{'subject': 'Density dependence'}, {'subject': 'Alpine ungulates'}, {'subject': 'horn'}]",,
 10.6084/m9.figshare.23575375.v1,Additional file 6 of Decoupling of arsenic and iron release from ferrihydrite suspension under reducing conditions: a biogeochemical model,figshare,2023,,Text,Creative Commons Attribution 4.0 International,Authors’ original file for figure 5,mds,True,findable,0,0,0,0,0,2023-06-25T03:11:53.000Z,2023-06-25T03:11:53.000Z,figshare.ars,otjm,"59999 Environmental Sciences not elsewhere classified,FOS: Earth and related environmental sciences,39999 Chemical Sciences not elsewhere classified,FOS: Chemical sciences,Ecology,FOS: Biological sciences,69999 Biological Sciences not elsewhere classified,Cancer","[{'subject': '59999 Environmental Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '39999 Chemical Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Chemical sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Ecology'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '69999 Biological Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'Cancer'}]",['67584 Bytes'],
@@ -799,7 +787,6 @@ https://soundcloud.com/user-145016407/sets/la-mecanique-des-roches""",api,True,f
 10.5281/zenodo.8083133,"MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall",Zenodo,2023,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","<strong>Dataset overview</strong> This dataset provides data and images of snowflakes in free fall collected with a Multi-Angle Snowflake Camera (MASC) The dataset includes, for each recorded snowflakes: A triplet of gray-scale images corresponding to the three cameras of the MASC A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement. The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example: Textural and geometrical descriptors as in <em>Praz et al 2017</em> Hydrometeor classification, riming degree estimation, melting identification, as in <em>Praz et al 2017</em> Blowing snow identification, as in <em>Schaer et al 2020 </em> Mass, volume, gyration estimation<em>, as in Leinonen et al 2021</em> <strong>Data format and structure</strong> The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available here. <strong>Supporting code</strong> A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on GitHub. See also the code documentation on ReadTheDocs. <strong>Download notes</strong> All files available here for download should be stored in the same folder, if the python-based API is used <em>MASCdb.zarr.zip</em> must be unzipped after download <strong>Field campaigns</strong> A list of campaigns included in the dataset, with a minimal description is given in the following table <strong>Campaign_name</strong> <strong>Information</strong> <strong>Shielded / Not shielded</strong> <em>DFIR = Double Fence Intercomparison Reference</em> <em>APRES3-2016 &amp; APRES3-2017</em> Installed in Antarctica in the context of the APRES3 project. See for example Genthon et al, 2018 or Grazioli et al 2017 Not shielded <em>Davos-2015</em> Installed in the Swiss Alps within the context of SPICE (Solid Precipitation InterComparison Experiment) Shielded (DFIR) <em>Davos-2019</em> Installed in the Swiss Alps within the context of RACLETS (<em>Role of Aerosols and CLouds Enhanced by Topography on Snow</em>) Not shielded <em>ICEGENESIS-2021</em> Installed in the Swiss Jura in a MeteoSwiss ground measurement site, within the context of ICE-GENESIS. See for example Billault-Roux et al, 2023 Not shielded <em>ICEPOP-2018</em> Installed in Korea, in the context of ICEPOP. See for example Gehring et al 2021. Shielded (DFIR) <em>Jura-2019 &amp; Jura-2023</em> Installed in the Swiss Jura within a MeteoSwiss measurement site Not shielded <em>Norway-2016</em> Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example Cooper et al, 2022. Not shielded <em>PLATO-2019</em> Installed in the ""Davis"" Antarctic base during the PLATO field campaign Not shielded <em>POPE-2020</em> Installed in the ""Princess Elizabeth Antarctica"" base during the POPE campaign. See for example Ferrone et al, 2023. Not shielded <em>Remoray-2022</em> Installed in the French Jura. Not shielded <em>Valais-2016</em> Installed in the Swiss Alps in a ski resort. Not shielded ISLAS-2022 Installed in Norway during the ISLAS campaign Not shielded Norway-2023 Installed in Norway during the MC2-ICEPACKS campaign Not shielded <strong>Version</strong> 1.1 - Two new campaigns (""ISLAS-2022"", ""Norway-2023"") added. 1.0 - Two new campaigns (""Jura-2023"", ""Norway-2016"") added. Added references and list of campaigns. 0.3 - a new campaign is added to the dataset (""Remoray-2022"") 0.2 - rename of variables. Variable precision (digits) standardized 0.1 - first upload",mds,True,findable,0,0,6,0,0,2023-07-05T09:42:40.000Z,2023-07-05T09:42:41.000Z,cern.zenodo,cern,"Snowfall,ice crystals,snow images,snowflakes,multi angle snowflake camera (MASC),image classification,meteorology","[{'subject': 'Snowfall'}, {'subject': 'ice crystals'}, {'subject': 'snow images'}, {'subject': 'snowflakes'}, {'subject': 'multi angle snowflake camera (MASC)'}, {'subject': 'image classification'}, {'subject': 'meteorology'}]",,
 10.57745/ktfzqd,Fichiers QGIS et Excel des cas d'étude d'application du protocole d'aide à la décision pour le traitement des embâcles (protocole de Wohl et al. 2019 adapté par Benaksas et Piton 2022),Recherche Data Gouv,2023,,Dataset,,"Chaque archive se rapporte à un cas d'étude du rapport de Benaksas &amp; Piton (2022), à savoir: Le Bresson (Isère) Torrent de montagne et Alloix (Isère) Ruisseau de montagne, Le Lagamas (Hérault) Ruisseau méditerranéen, La Brague (Alpes-Maritimes) Rivière méditerranéenne, La Clamoux (Aude) Rivière torrentielle méditerranéenne. Chaque archive contient trois documents maîtres et les fichiers sources associés permettant de faciliter l'application du protocole de Wohl et al. (2019) tel que adapté par Benaksas &amp; Piton (2022): Le fichier export_csv_vers_shp.qgz est un projet QGIS qui a facilité la transformation en données SIG des fichiers textes (format .csv) importés de l'application Epicollect 5 tel que décrit dans l'Annexe C du rapport de Benaksas et Piton (2023) , L'autre fichier "".gqz"" est un projet QGIS qui facilite l'affichage et l'interprétation des données SIG compilées préalablement aux missions de terrains et sur le terrain via l'application Epicollect 5 tel que décrit dans les Annexe C et D u rapport de Benaksas et Piton (2023) , ResultatsProtocole_epicollecte.xlsx est un tableur Excel qui a facilité la notation des indicateurs de l'approche multicritère, qui permet la modification éventuelle des pondérations entre sous-critères, et qui a mené le calcul des scores pondérés fournis dans le rapport et a préparé les sorties graphiques fournies dans le rapport. Nota: Le cas d'étude du Bréda (Isère) rivière de montagne affluent de l'Isère ayant servi à caler le protocole, les données associées à ce cas d'étude ne sont pas homogènes et ne sont ainsi pas mis librement à disposition. Elles peuvent éventuellement être mises à disposition sur demande directe à Guillaume Piton.",mds,True,findable,48,2,0,0,0,2023-03-06T13:05:38.000Z,2023-03-06T14:03:27.000Z,rdg.prod,rdg,,,,
 10.5281/zenodo.8131976,Prior information differentially affects discrimination decisions and subjective confidence reports,Zenodo,2023,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Experimental data from a series of three human experiments described in the submitted article ""Prior information differentially affects discrimination decisions and subjective confidence reports"" by Marika Constant, Michael Pereira, Nathan Faivre, and Elisa Filevich (2023). This dataset includes all of the data needed to run the analyses in the article. Please consult the article for a full description of the data.",mds,True,findable,0,0,0,0,0,2023-07-10T15:37:26.000Z,2023-07-10T15:37:26.000Z,cern.zenodo,cern,,,,
-10.60662/x25q-yv27,Vers une approche générique du raisonnement par cas : application à la gestion énergétique dans le bâtiment,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-11T15:17:26.000Z,2023-09-11T15:17:26.000Z,uqtr.mesxqq,uqtr,,,,
 10.5281/zenodo.8289247,Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction,Zenodo,2023,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This Zip file contains the cartesian coordinates of optimized stationary points of the OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> potential energy surface published in our article “OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (P<em>hys. Chem. A</em> 2023, 127, 21, 4609–4623), that can be found in https://doi.org/10.1021/acs.jpca.2c08662. All calculations have been performed with Gaussian 09, Revision D.01. All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.",mds,True,findable,0,0,0,1,0,2023-08-29T13:59:34.000Z,2023-08-29T13:59:34.000Z,cern.zenodo,cern,"Alcohols,Chemical calculations,Energy,Kinetics,Vinyl","[{'subject': 'Alcohols'}, {'subject': 'Chemical calculations'}, {'subject': 'Energy'}, {'subject': 'Kinetics'}, {'subject': 'Vinyl'}]",,
 10.5281/zenodo.5336853,Canopy and understory tree guilds respond differently to the environment in an Indian rainforest,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Questions. Changes in the functional composition of tree communities along resource availability gradients have received attention, but it is unclear whether or not understory and canopy guilds respond similarly to different light, biomechanical, and hydraulic constraints. Location. An anthropically-undisturbed, old-growth wet evergreen Dipterocarp forest plot located in Karnataka State, India. Methods. We measured leaf and wood traits of 89 tree species representing 99% of all individuals in a 10 ha permanent plot with varying topographic and canopy conditions inferred from LiDAR data. We assigned tree species to guilds of canopy and understory species and assessed the variation of the guild weighted means of functional trait values with canopy height and topography. Results. The functional trait space did not differ between canopy and understory tree species. However, environmental filtering led to significantly different functional composition of canopy and understory guild assemblages. Furthermore, they responded differently along environmental gradients related to water, nutrients, light, and wind exposure. For example, the canopy guild responded to wind exposure while the understory guild did not. Conclusions. The pools of understory and canopy species are functionally similar. However, fine-scale environmental heterogeneity impacts differently on these two guilds, generating striking differences in functional composition between understory and canopy guild assemblages. Accounting for vertical guilds improves our understanding of forest communities’ assembly processes.",mds,True,findable,0,0,0,0,0,2021-10-11T12:59:38.000Z,2021-10-11T12:59:39.000Z,cern.zenodo,cern,"Rainforest,Western Ghats,Leaf economics spectrum,Environmental filtering,Vertical strata,Wood economics spectrum","[{'subject': 'Rainforest'}, {'subject': 'Western Ghats'}, {'subject': 'Leaf economics spectrum'}, {'subject': 'Environmental filtering'}, {'subject': 'Vertical strata'}, {'subject': 'Wood economics spectrum'}]",,
 10.5281/zenodo.7269139,Catalog of P-wave secondary microseisms events,Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access",Catalog of P-wave secondary microseisms events,mds,True,findable,0,0,0,0,0,2022-11-01T00:26:15.000Z,2022-11-01T00:26:16.000Z,cern.zenodo,cern,,,,
@@ -811,7 +798,6 @@ https://soundcloud.com/user-145016407/sets/la-mecanique-des-roches""",api,True,f
 10.6084/m9.figshare.21717750.v1,Neuroblast Differentiation-Associated Protein Derived Polypeptides: AHNAK(5758-5775) Induces Inflammation by Activating Mast Cells via ST2,Taylor & Francis,2022,,Text,Creative Commons Attribution 4.0 International,"Psoriasis is a chronic inflammatory skin disease. Mast cells are significantly increased and activated in psoriatic lesions and are involved in psoriatic inflammation. Some endogenous substances can interact with the surface receptors of mast cells and initiate the release of downstream cytokines that participate in inflammatory reactions. Neuroblast differentiation-associated protein (AHNAK) is mainly expressed in the skin, esophagus, kidney, and other organs and participates in various biological processes in the human body. AHNAK and its derived peptides have been reported to be involved in the activation of mast cells and other immune processes. This study aimed to investigate whether AHNAK (5758–5775), a neuroblast differentiation-associated protein-derived polypeptide, could be considered a new endogenous substance in psoriasis patients, which activates mast cells and induces the skin inflammatory response contributing to psoriasis. Wild-type mice were treated with AHNAK(5758–5775) to observe the infiltration of inflammatory cells in the skin and cytokine release in vivo. The release of inflammatory mediators by mouse primary mast cells and the laboratory of allergic disease 2 (LAD2) human mast cells was measured in vitro. Molecular docking analysis, molecular dynamics simulation, and siRNA transfection were used to identify the receptor of AHNAK(5758–5775). AHNAK(5758–5775) could cause skin inflammation and cytokine release in wild-type mice and activated mast cells in vitro. Moreover, suppression of tumorigenicity 2 (ST2) might be a key receptor mediating AHNAK(5758–5775)’s effect on mast cells and cytokine release. We propose a novel polypeptide, AHNAK(5758–5775), which induces an inflammatory reaction and participates in the occurrence and development of psoriasis by activating mast cells.",mds,True,findable,0,0,0,0,0,2022-12-13T16:00:06.000Z,2022-12-13T16:00:06.000Z,figshare.ars,otjm,"Biochemistry,Medicine,Microbiology,FOS: Biological sciences,Cell Biology,Genetics,Physiology,39999 Chemical Sciences not elsewhere classified,FOS: Chemical sciences,Immunology,FOS: Clinical medicine,69999 Biological Sciences not elsewhere classified,Developmental Biology,Cancer,111714 Mental Health,FOS: Health sciences,Computational Biology","[{'subject': 'Biochemistry'}, {'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': 'Cell Biology'}, {'subject': 'Genetics'}, {'subject': 'Physiology'}, {'subject': '39999 Chemical Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Chemical sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '69999 Biological Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'Developmental Biology'}, {'subject': 'Cancer'}, {'subject': '111714 Mental Health', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Health sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Computational Biology'}]",['264022 Bytes'],
 10.5281/zenodo.3407127,Experiments on Grain Size Segregation in Bedload Transport on a steep Slope,Zenodo,2019,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This dataset is the basis of the publication: Frey, P., Lafaye de Micheaux, H., Bel, C., Maurin, R., Rorsman, K., Martin, T., Ducottet, C., 2020. Experiments on grain size segregation in bedload transport on a steep slope. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2019.103478. Experiments consisted in bedload of two-size spherical glass beads transported at equilibrium by a turbulent supercritical free surface water flow over a mobile bed. Two runs, one with a low rate of large black beads (S6), the other with a higher rate (S20), are considered. This dataset consists in: - temporal sequences of uncompressed tif images corresponding to figure 5 showing small particle concentration : 9 sequences for run S6 (BillesBaumerMicro) and 9 sequences for run S20 (BillesbaumerAmontSequence) - two ‘.mat’ file corresponding to runs S6 (trackData_Micro_S6.mat) and S20 (trackData_Amont_S20.mat) giving all the trajectories of all beads. Trajectories were obtained with a tracking algorithm developed by H. Lafaye de Micheaux et al. (2016,2018) building on Hergault et al. (2010). The code implementing the tracking algorithm is available on https://github.com/hugolafaye/BeadTracking. The files contain the variable 'trackData' being a cell array of tracking matrices. There is one tracking matrix for each image of the sequence giving in particular the coordinate and velocity of each bead. Complete information on data format is given in the file readme.txt in the github BeadTracking package. Parameter files necessary to replicate our results from the images are also available on the BeadTracking package as well as on https://doi.org/10.5281/zenodo.3454628 where a 1000-image ground truth is stored. Important note: Experimental images were grabbed with the flow from right to the left implying for instance negative values for the x-coordinate of velocities. To comply with a traditional convention, images and associated results in the publication are shown from left to the right.",mds,True,findable,5,0,0,0,0,2019-10-14T14:33:25.000Z,2019-10-14T14:33:25.000Z,cern.zenodo,cern,"Sediment transport, bedload, experimental, segregation, two-phase flow, granular flow, particle tracking","[{'subject': 'Sediment transport, bedload, experimental, segregation, two-phase flow, granular flow, particle tracking'}]",,
 10.5061/dryad.1s7v5,Data from: Evaluation of redundancy analysis to identify signatures of local adaptation,Dryad,2018,en,Dataset,Creative Commons Zero v1.0 Universal,"Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This paper aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. Additionally, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., 2013). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the northwestern American coast.",mds,True,findable,636,324,1,1,0,2018-06-15T04:58:08.000Z,2018-06-15T04:58:15.000Z,dryad.dryad,dryad,"RDA,Genome scans","[{'subject': 'RDA'}, {'subject': 'Genome scans', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}]",['1307365306 bytes'],
-10.57726/j3mg-e206,Les Inscriptions latines de l'Ain (ILAIN),Presses Universitaires Savoie Mont Blanc,2005,fr,Book,,,fabricaForm,True,findable,0,0,0,0,0,2022-03-14T14:28:43.000Z,2022-03-14T14:28:43.000Z,pusmb.prod,pusmb,FOS: Humanities,"[{'subject': 'FOS: Humanities', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['300 pages'],
 10.5061/dryad.qrfj6q5h6,Designing industry 4.0 implementation from the initial background and context of companies,Dryad,2021,en,Dataset,Creative Commons Zero v1.0 Universal,"Industry 4.0 is a promising concept that allows industries to meet customers’ demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Next, other dimensions have been identified such as organisation, strategy, and human resources as being key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then, built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company’s background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation’s success or failure depends on the combination of two variables related to a company’s background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects’ members. The interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation models have been identified. Industry practitioners could design their digital transformation project organisation and strategy according to the right model.",mds,True,findable,139,15,0,0,0,2021-11-03T18:22:27.000Z,2021-11-03T18:22:28.000Z,dryad.dryad,dryad,Industrial engineering,"[{'subject': 'Industrial engineering', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}]",['985101 bytes'],
 10.5281/zenodo.4022283,Topological Weaire-Thorpe models of amorphous matter,Zenodo,2020,,Software,"BSD 2-Clause ""Simplified"" License,Open Access","<strong>Abstract</strong> Amorphous solids remain outside of the classification and systematic discovery of new topological materials, partially due to the lack of realistic models that are analytically tractable. Here we introduce the topological Weaire-Thorpe class of models, which are defined on amorphous lattices with fixed coordination number, a realistic feature of covalently bonded amorphous solids. Their short-range properties allow us to analytically predict spectral gaps. Their symmetry under permutation of orbitals allows us to compute analytically topological phase diagrams, which determine quantized observables like circular dichroism, by introducing symmetry indicators for the first time in amorphous systems. These models and our procedures to define invariants are generalizable to higher coordination number and dimensions, opening a route towards a complete classification of amorphous topological states in real space using quasilocal properties. <strong>Contents</strong> Code to generate all data and figures in the manuscript: Plots.ipynb most plots, no calculations Figure2.ipynb figure 2 c) and d) including calculations kpm_weaire_thorpe.ipynb heavy calculations and some supplementary plots fourfold_model_plots.ipynb calculations and figures for fourfold coordinated model Data produced by the longer calculations. <strong>Requirements</strong> kwant &gt;= 1.4",mds,True,findable,0,0,1,0,0,2020-09-10T10:56:45.000Z,2020-09-10T10:56:46.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.5835995,Ultrafast imaging recordings from the axon initial segment of neocortical layer-5 pyramidal neurons.,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This dataset contains imaging and whole-cell electrophysiological recordings from neocortical layer-5 pyramidal neuron from brain slices of the mouse. Electrophysiological recordings (at 20 kHz) are from the soma. Imaging data (10 kHz) are from lines along the axon initial segment (distal&gt;proximal) with 500 nm pixel resolution. These correspond to: Sodium imaging (Figures 1 and S6). Voltage imaging (Figures 2,4,5,S4,S7) Calcium imaging (Figures 3,S3,S8). This dataset is used in the paper available online: Filipis L, Blömer LA, Montnach J, De Waard M, Canepari M. Nav1.2 and BK channels interaction shapes the action potential in the axon initial segment. bioRxiv, 2022. doi: 10.1101/2022.04.12.488116.",mds,True,findable,0,0,0,0,0,2022-10-20T13:43:28.000Z,2022-10-20T13:43:29.000Z,cern.zenodo,cern,"Nav1.2 channel,BK Ca2+-activated K+ channel,axon initial segment,action potential,neocortical layer-5 pyramidal neuron,calcium","[{'subject': 'Nav1.2 channel'}, {'subject': 'BK Ca2+-activated K+ channel'}, {'subject': 'axon initial segment'}, {'subject': 'action potential'}, {'subject': 'neocortical layer-5 pyramidal neuron'}, {'subject': 'calcium'}]",,
@@ -831,7 +817,6 @@ https://soundcloud.com/user-145016407/sets/la-mecanique-des-roches""",api,True,f
 10.5281/zenodo.6787572,2D honeycomb transformation into dodecagonal quasicrystals driven by electrostatic forces,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This repository contains the SXRD data measured for the 48-18-6 approximant in Sr-Ti-O on Pt(111). The data has been acquired at the SixS beamline of the Synchrotron SOLEIL in France on 19.09.2019. Monochromatic x-rays with photon energy of 11 keV were used to avoid Pt fluorescence (at 11.1 and 13 keV) and consequently reduced the background signal. The diffraction experiment was performed under grazing incidence at an angle of 0.2 °. The orientation matrix was defined relative to the Pt substrate lattice with its lattice constants of a = 392 pm. Additionally, the 3D voxel map generated from the raw data for further use in the binoculars software is provided as well as the input and output of SHELXL, which has been used for structure relaxation.",mds,True,findable,0,0,0,0,0,2022-07-22T09:10:33.000Z,2022-07-22T09:10:34.000Z,cern.zenodo,cern,"Oxide quasicrystals, 2D ternary oxide, quasicrystal approximant, SXRD","[{'subject': 'Oxide quasicrystals, 2D ternary oxide, quasicrystal approximant, SXRD'}]",,
 10.5281/zenodo.8314927,"Simulations and scripts for ""Glacier surges controlled by the close interplay between subglacial friction and drainage"".",Zenodo,2023,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This repository contains the model and scripts to reproduce the results presented in ""Glacier surges controlled by the close interplay between subglacial friction and drainage"" and submitted to the Journal of Geophysical Research - Earth Surface. It provides the running model files associated with each result figure of the manuscript as well as the Python script to generate them from the simulation output. The model is also described and updated at: https://github.com/kjetilthogersen/pyGlacier.",mds,True,findable,0,0,0,0,0,2023-09-04T10:53:19.000Z,2023-09-04T10:53:20.000Z,cern.zenodo,cern,,,,
 10.5061/dryad.3r2280ggb,Lags in phenological acclimation of mountain grasslands after recent warming,Dryad,2021,en,Dataset,Creative Commons Zero v1.0 Universal,"1. In the current biodiversity crisis, one of the crucial questions is how quickly plant communities can acclimate to climate warming and longer growing seasons to buffer the impairment of community functioning. Answering this question is pivotal especially for mountain grasslands that experience harsh conditions but provide important ecosystem services to people. 2. We conducted a reciprocal transplant experiment along an elevation gradient (1920 m vs. 2450 m) in the French Alps to test the ability of plant species and communities to acclimate to warming and cooling. For three years, we measured weekly the timing of phenological events (e.g. start of flowering or greening) and the length of phenological stages linked to demographic performance (e.g. lengths of flowering or greening periods). 3. We found that warming (and cooling) changed the timing of phenological events strongly enough to result in complete acclimation for graminoids, for communities in early and mid-season, but not at all for forbs. For example, warming resulted in later greening of communities and delayed all phenophases of graminoids. Lengths of phenological stages did not respond strongly enough to climate change to acclimate completely, except for graminoids. For example, warming led to an acclimation lag in the community’s yearly productivity and had a strong negative impact on flowering of forbs. Overall, when there was an acclimation failure, responses to cooling were mostly symmetric and confirmed slow acclimation in mountain grasslands. 4. Synthesis. Our study highlights that phenological plasticity cannot prevent impairment of community functioning under climate warming in the short-term. The failures to acclimate after three years of warming signals that species and communities underperform and are probably at high risk of being replaced by locally better-adapted plants.",mds,True,findable,134,7,0,1,0,2021-06-22T17:02:43.000Z,2021-06-22T17:02:44.000Z,dryad.dryad,dryad,"reciprocal transplant,warming experiment,transient dynamics,mountain grasslands,Climate change","[{'subject': 'reciprocal transplant'}, {'subject': 'warming experiment'}, {'subject': 'transient dynamics'}, {'subject': 'mountain grasslands'}, {'subject': 'Climate change', 'schemeUri': 'https://github.com/PLOS/plos-thesaurus', 'subjectScheme': 'PLOS Subject Area Thesaurus'}]",['11714487 bytes'],
-10.60662/pyvs-cj63,Intégration de connaissances du domaine et de l’apprentissage automatique pour l’estimation des paramètres de fabrication,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-11T17:19:29.000Z,2023-09-11T17:19:29.000Z,uqtr.mesxqq,uqtr,,,,
 10.5281/zenodo.4761307,"Fig. 27 in Two New Species Of Dictyogenus Klapálek, 1904 (Plecoptera: Perlodidae) From The Jura Mountains Of France And Switzerland, And From The French Vercors And Chartreuse Massifs",Zenodo,2019,,Image,"Creative Commons Attribution 4.0 International,Open Access","Fig. 27. Dictyogenus muranyii sp. n., adult female habitus. Karstic spring of Bruyant, Isère dpt, France. Photo G. Vinçon.",mds,True,findable,0,0,2,0,0,2021-05-14T07:45:23.000Z,2021-05-14T07:45:24.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Perlodidae,Dictyogenus","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Perlodidae'}, {'subject': 'Dictyogenus'}]",,
 10.6084/m9.figshare.24202750,Additional file 2 of Obstructive sleep apnea: a major risk factor for COVID-19 encephalopathy?,figshare,2023,,Text,Creative Commons Attribution 4.0 International,Additional file 2: Supplemental Table 2. Comparison of patient characteristics at the time of COVID-19 onset and COVID-19 acute encephalopathy between definite OSA group and No OSA group.,mds,True,findable,0,0,0,0,0,2023-09-27T03:26:09.000Z,2023-09-27T03:26:10.000Z,figshare.ars,otjm,"Biophysics,Medicine,Cell Biology,Neuroscience,Physiology,FOS: Biological sciences,Pharmacology,Biotechnology,Sociology,FOS: Sociology,Immunology,FOS: Clinical medicine,Cancer,Mental Health,Virology","[{'subject': 'Biophysics'}, {'subject': 'Medicine'}, {'subject': 'Cell Biology'}, {'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': 'Pharmacology'}, {'subject': 'Biotechnology'}, {'subject': 'Sociology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Cancer'}, {'subject': 'Mental Health'}, {'subject': 'Virology'}]",['28280 Bytes'],
 10.5281/zenodo.3899776,COP21: Results and Implications for Pathways and Policies for Low Emissions European Societies,Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This database contains national and global level modelling scenario results produced under the COP21:RIPPLES project https://www.cop21ripples.eu/ The data is also hosted in the IIASA RIPPLES Scenario Explorer https://data.ene.iiasa.ac.at/cop21ripples/#/login The National Determined Contributions (NDCs) provide important indications regarding the future GHG emissions and related policies, in relation to the international energy market, technological, economic, trade and financial context. This key information on the development trajectories of major economies is essential for EU policy development as it will determine the global context in which EU policies will evolve. Nevertheless, the NDCs adopt a medium-term horizon and do not provide all the required information to fully characterize the detailed energy system pathways that meet the Paris Agreement (PA) goals. NDCs therefore fall short of characterizing the global trajectories at a sufficiently granular and long-term perspective for informing EU policies. To close this knowledge gap, COP21:RIPPLES aims at analysing the underlying transformations required in the different sectors of the economy to meet the PA mitigation targets. To this purpose, COP21:RIPPLES uses existing scenarios as well as a number of new national and global scenarios. These new scenarios are not conceived themselves as an output of the project but rather as methodological tool to answer specific questions across different Work Packages. For description of the models and scenarios included in each Excel file, see the documentation ""RIPPLES_ScenarioExplorer_Doc_v4.docx"". For more information on these models and scenarios, see the COP21:RIPPLES Deliverables D2.6, D3.2 and D3.5.",mds,True,findable,0,0,0,0,0,2020-06-18T08:20:59.000Z,2020-06-18T08:21:00.000Z,cern.zenodo,cern,RIPPLES,[{'subject': 'RIPPLES'}],,
@@ -1225,7 +1210,6 @@ Lastly, the ground truth was obtained from homogeneous images for pre/post event
 10.5061/dryad.9w0vt4bd1,Power and limitations of environmental DNA metabarcoding for surveying leaf litter eukaryotic communities,Dryad,2020,en,Dataset,Creative Commons Zero v1.0 Universal,"Leaf litter habitats shelter a great variety of organisms, which play an important role in ecosystem dynamics. However, monitoring species in leaf litter is challenging, especially in highly diverse environments such as tropical forests, because individuals may easily camouflage themselves or hide in the litter layer. Identifying species based on environmental DNA (eDNA) would allow us to assess biodiversity in this microhabitat, without the need for direct observation of individuals. We applied eDNA metabarcoding to analyze large amounts of leaf litter (1 kg per sample) collected in the Brazilian Atlantic forest. We compared two DNA extraction methods, one total and one extracellular, and amplified a fragment of the mitochondrial 18S rRNA gene common to all eukaryotes, to assess the performance of eDNA from leaf litter samples in identifying different eukaryotic taxonomic groups. We also amplified two fragments of the mitochondrial 12S rRNA gene to specifically test the power of this approach for monitoring vertebrate species, with a focus on anurans. Most of the eukaryote sequence reads obtained were classified as Fungi, followed by Metazoa, and Viridiplantae. Most vertebrate sequences were assigned to Homo sapiens; only two sequences assigned to the genus Phyllomedusa and the species Euparkerella brasiliensis can be considered true detections of anurans in our eDNA samples. The detection of taxa varied depending on the DNA extraction method applied. Our results demonstrate that the analysis of eDNA from leaf litter samples has low power for monitoring vertebrate species, and should be preferentially applied to describe active and abundant taxa in terrestrial communities, such as Fungi and invertebrates.",mds,True,findable,97,3,0,0,0,2021-03-23T17:40:28.000Z,2021-03-23T17:40:29.000Z,dryad.dryad,dryad,"FOS: Biological sciences,FOS: Biological sciences","[{'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)'}]",['4618934791 bytes'],
 10.5281/zenodo.6653187,316L L-PBF fatigue dataset,Zenodo,2022,fr,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This file contains 316L Laser Powder Bed Fusion fatigue tests dataset. Experiments were carried on a MTS Landmark 100 kN servohydraulic fatigue test machine. This experimental campaign took place in the context of a PhD grant from the French region Pays de la Loire (see https://pastel.archives-ouvertes.fr/tel-03688021 for the thesis manuscript). Fatigue tests were carried : - in air or in salt-spray - on different batches (polished, pre-corroded, with artificial defects,...) - at R=-1 and R=0.1",mds,True,findable,0,0,0,0,0,2022-06-16T15:13:18.000Z,2022-06-16T15:13:19.000Z,cern.zenodo,cern,"Fatigue,316L,L-PBF,Additive Manufacturing,Defects,Salt-Spray,Corrosion","[{'subject': 'Fatigue'}, {'subject': '316L'}, {'subject': 'L-PBF'}, {'subject': 'Additive Manufacturing'}, {'subject': 'Defects'}, {'subject': 'Salt-Spray'}, {'subject': 'Corrosion'}]",,
 10.5281/zenodo.7225366,Videos of scenario executions on carla,Zenodo,2022,,Audiovisual,"Creative Commons Attribution 4.0 International,Open Access",Videos of the executions on the simulator Carla of driving scenarios.,mds,True,findable,0,0,0,0,0,2022-10-19T13:03:58.000Z,2022-10-19T13:03:58.000Z,cern.zenodo,cern,,,,
-10.34847/nkl.3dbc2mtb,Bulletin franco-italien 1912 n°4-5 juillet - octobre,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/11 (A4,N6)-1912/12.",api,True,findable,0,0,0,0,0,2022-07-12T13:48:55.000Z,2022-07-12T13:48:55.000Z,inist.humanum,jbru,Etudes italiennes,[{'subject': 'Etudes italiennes'}],"['10539495 Bytes', '21558100 Bytes', '21840529 Bytes', '21062686 Bytes', '21714700 Bytes', '21445744 Bytes', '21836002 Bytes', '21825382 Bytes', '21214213 Bytes', '21303211 Bytes', '21876550 Bytes', '21499546 Bytes', '21614056 Bytes', '21780892 Bytes', '21669382 Bytes', '21654679 Bytes', '21598168 Bytes', '21779833 Bytes', '21611542 Bytes', '21629782 Bytes', '21183421 Bytes', '21241312 Bytes', '21237016 Bytes', '21224224 Bytes', '21229654 Bytes', '21175816 Bytes', '21237016 Bytes', '21138790 Bytes', '21129388 Bytes']","['application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
 10.5281/zenodo.4745568,robertxa/Topographies-Samoens_Folly: Zenodo DOI,Zenodo,2021,,Software,Open Access,"Base de données topographiques du massif du Folly (Samoëns, France)",mds,True,findable,0,0,0,0,0,2021-05-10T10:27:06.000Z,2021-05-10T10:27:07.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.5243257,Dutch DBnary archive in original Lemon format,Zenodo,2021,nl,Dataset,"Creative Commons Attribution Share Alike 4.0 International,Open Access","The DBnary dataset is an extract of Wiktionary data from many language editions in RDF Format. Until July 1st 2017, the lexical data extracted from Wiktionary was modeled using the lemon vocabulary. This dataset contains the full archive of all DBnary dumps in Lemon format containing lexical information from Dutch language edition, ranging from 25th April 2015 to 1st July 2017. After July 2017, DBnary data has been modeled using the ontolex model and will be available in another Zenodo entry.",mds,True,findable,0,0,0,0,0,2021-08-24T10:51:42.000Z,2021-08-24T10:51:43.000Z,cern.zenodo,cern,"Wiktionary,Lemon,Lexical Data,RDF","[{'subject': 'Wiktionary'}, {'subject': 'Lemon'}, {'subject': 'Lexical Data'}, {'subject': 'RDF'}]",,
 10.6084/m9.figshare.21430971,Additional file 1 of Digitally-supported patient-centered asynchronous outpatient follow-up in rheumatoid arthritis - an explorative qualitative study,figshare,2022,,Text,Creative Commons Attribution 4.0 International,Supplementary Material 1,mds,True,findable,0,0,0,0,0,2022-10-29T03:17:12.000Z,2022-10-29T03:17:13.000Z,figshare.ars,otjm,"Medicine,Immunology,FOS: Clinical medicine,69999 Biological Sciences not elsewhere classified,FOS: Biological sciences,Science Policy,111714 Mental Health,FOS: Health sciences","[{'subject': 'Medicine'}, {'subject': 'Immunology'}, {'subject': 'FOS: Clinical medicine', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': '69999 Biological Sciences not elsewhere classified', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Science Policy'}, {'subject': '111714 Mental Health', 'schemeUri': 'http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E', 'subjectScheme': 'FOR'}, {'subject': 'FOS: Health sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['24235 Bytes'],
@@ -1324,7 +1308,6 @@ tion (GPL, BSD, etc).
 The Debsources Dataset comes as a set of tarballs containing deduplicated unique source code files organized by their SHA1 checksums (the source code), plus a portable PostgreSQL database dump (the metadata).
 
 The Debsources Dataset is described in full in the paper The Debsources Dataset: Two Decades of Free and Open Source Software, published on the Empirical Software Engineering journal with DOI 10.1007/s10664-016-9461-5 . A preprint of the paper is available at https://upsilon.cc/~zack/research/publications/debsources-ese-2016.pdf .",,True,findable,1,0,0,0,0,2016-08-29T13:52:40.000Z,2016-08-29T13:52:40.000Z,cern.zenodo,cern,"debian,open source,free software,source code,software evolution","[{'subject': 'debian'}, {'subject': 'open source'}, {'subject': 'free software'}, {'subject': 'source code'}, {'subject': 'software evolution'}]",,
-10.57726/cbag-z376,Édifier l'État: politique et culture en Savoie au temps de Christine de France,Presses Universitaires Savoie Mont Blanc,2014,fr,Book,,,fabricaForm,True,findable,0,0,0,0,0,2022-03-14T08:40:42.000Z,2022-03-14T08:40:43.000Z,pusmb.prod,pusmb,FOS: Humanities,"[{'subject': 'FOS: Humanities', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['266 pages'],
 10.5281/zenodo.4761287,"Fig. 1 in Two New Species Of Dictyogenus Klapálek, 1904 (Plecoptera: Perlodidae) From The Jura Mountains Of France And Switzerland, And From The French Vercors And Chartreuse Massifs",Zenodo,2019,,Image,"Creative Commons Attribution 4.0 International,Open Access","Fig. 1. Dictyogenus jurassicum sp. n., adult female habitus. Spring of River Doubs, Mouthe, Doubs dpt, France. Photo A. Ruffoni.",mds,True,findable,0,0,2,0,0,2021-05-14T07:42:49.000Z,2021-05-14T07:42:50.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Perlodidae,Dictyogenus","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Perlodidae'}, {'subject': 'Dictyogenus'}]",,
 10.5281/zenodo.4804629,FIGURES 11–14 in Review and contribution to the stonefly (Insecta: Plecoptera) fauna of Azerbaijan,Zenodo,2021,,Image,Open Access,"FIGURES 11–14. Mermithid infected Protonemura sp. (aculeata?) male from the Greater Caucasus—11: terminalia, ventral view; 12: same, lateral view; 13: same, dorsal view; 14: same, dorsocaudal view—scale 1 mm.",mds,True,findable,0,0,2,0,0,2021-05-26T07:55:03.000Z,2021-05-26T07:55:04.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Nemouridae,Protonemura","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Nemouridae'}, {'subject': 'Protonemura'}]",,
 10.5285/634ee206-258f-4b47-9237-efff4ef9eedd,"Polarimetric ApRES data on a profile across Dome C, East Antarctica, 2013-2014",NERC EDS UK Polar Data Centre,2021,en,Dataset,Open Government Licence V3.0,"The radar data collected in 2013-2014 at Dome C, East Antarctica, aims to understand bulk preferred crystal orientation fabric near a dome. We measure changes in englacial birefringence and anisotropic scattering in 21 sites along a 36 km long profile across Dome C. These optical properties are obtained by analysing radar returns for different antenna orientations. More details can be found in Ershadi et al, 2021. Funding was provided by BAS National Capability and IPEV core funding.",mds,True,findable,0,0,0,1,0,2021-09-16T11:17:15.000Z,2021-09-16T11:19:24.000Z,bl.nerc,rckq,"""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""GLACIER MOTION/ICE SHEET MOTION"",""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""ICE SHEETS"",ApRES,Dome C,fabric,polarimetric radar","[{'subject': '""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""GLACIER MOTION/ICE SHEET MOTION""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': '""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""ICE SHEETS""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': 'ApRES'}, {'subject': 'Dome C'}, {'subject': 'fabric'}, {'subject': 'polarimetric radar'}]","['81 files', '148.8 MB']","['text/plain', 'text/csv', 'application/x-hdf', 'application/netcdf']"
@@ -1446,7 +1429,6 @@ Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet ret
 10.34847/nkl.ef903o6v,"Taciti et C. Velleii Paterculi scripta quae exstant; recognita, emaculata. Additique commentarii copiosissimi et notae non antea editae Paris e typographia Petri Chevalier, in monte diui Hilarii - II-0491",NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Image,,,api,True,findable,0,0,0,0,0,2023-02-05T15:02:14.000Z,2023-02-05T15:02:14.000Z,inist.humanum,jbru,,,['52597290 Bytes'],['image/tiff']
 10.5281/zenodo.4761319,"Fig. 36 in Two New Species Of Dictyogenus Klapálek, 1904 (Plecoptera: Perlodidae) From The Jura Mountains Of France And Switzerland, And From The French Vercors And Chartreuse Massifs",Zenodo,2019,,Image,"Creative Commons Attribution 4.0 International,Open Access","Fig. 36. Dictyogenus muranyii sp. n., female, subgenital plate. Karstic spring of Brudour, Drôme dpt, France. Photo B. Launay.",mds,True,findable,0,0,4,0,0,2021-05-14T07:47:09.000Z,2021-05-14T07:47:10.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Perlodidae,Dictyogenus","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Perlodidae'}, {'subject': 'Dictyogenus'}]",,
 10.57745/id1ls6,"Ice texture data from ice core, NEEM, Greenland, 2007-2012",Recherche Data Gouv,2023,,Dataset,,"NEEM (North Greenland Eemian Ice Drilling) was an international ice core research project in Greenland. As other projects like GRIP and NGRIP, this ice core had the goal to extract informations and data about the last interglacial period. The project was directed and organized by the Danish former Centre for Ice and Climate at the Niels Bohr Institute and US NSF, Office of Polar Programs. It was supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO), Canada (NRCan/GSC), China(CAS), Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR), South Korea (KOPRI), the Netherlands (NWO/ ALW), Sweden (VR), Switzerland (SNF), the United Kingdom (NERC) and the USA (US NSF, Office of Polar Programs) and the EU Seventh Framework programmes Past4Future and WaterundertheIce The coring site was located in North West Greenland (camp position 77.45°N 51.06°W). The drilling took place between 2007 and 2012. For more information about the project: https://neem.dk/, NEEM community members (doi:https://doi.org/10.1038/nature11789 ). The data provided here is published in Montagnat et al., (2014) (doi:https://doi.org/10.5194/tc-8-1129-2014) The dataset contains texture data (crystallographic orientations) measured on thin sections of ice extracted along the 2540 m depth ice core. The ice core has been subdivided and stored into core sections (also called “bags”) of 0.55 m long.",mds,True,findable,34,2,0,0,0,2023-03-27T12:10:05.000Z,2023-11-03T15:28:56.000Z,rdg.prod,rdg,,,,
-10.57726/pw2d-az70,Spolier et confisquer dans les mondes grec et romain,Presses Universitaires Savoie Mont Blanc,2013,fr,Book,,,fabricaForm,True,findable,0,0,0,0,0,2022-03-11T09:46:56.000Z,2022-03-11T09:46:56.000Z,pusmb.prod,pusmb,FOS: Humanities,"[{'subject': 'FOS: Humanities', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['511 pages'],
 10.5281/zenodo.5838249,ACAM - Apposed Cortex Adhesion Model,Zenodo,2022,,Software,"GNU General Public License v2.0 or later,Open Access","<strong>ACAM: an Apposed-Cortex Adhesion Model of an epithelial tissue.</strong> The <code>ACAM</code> library is an implementation of a mechanical model of an active epithelial tissue. See bioRxiv 10.1101/2021.04.11.439313 for the modelling and results. <strong>How is the cell cortex represented?</strong> Each cell cortex in ACAM is represented as an active, continuum morphoelastic rod with resistance to bending and extension. By explicitly considering both cortices along bicellular junctions, the model is able to replicate important cell behaviours that are not captured in many existing models e.g. cell-cell shearing and material flow around cell vertices. <strong>How are adhesions represented?</strong> Adhesions are modelled as simple springs, explicitly coupling neighbouring cell cortices. Adhesion molecules are given a characteristic timescale, representing the average time between binding and unbinding, which modules tissue dynamics.",mds,True,findable,0,0,0,0,0,2022-01-11T15:53:38.000Z,2022-01-11T15:53:39.000Z,cern.zenodo,cern,"mechanics,biophysics,living tissue","[{'subject': 'mechanics'}, {'subject': 'biophysics'}, {'subject': 'living tissue'}]",,
 10.5061/dryad.ksn02v75q,A multicentre study on spontaneous in-cage activity and micro-environmental conditions of IVC housed C57BL/6J mice during consecutive cycles of bi-weekly cage change,Dryad,2021,en,Dataset,Creative Commons Zero v1.0 Universal,"Mice respond to a cage change (CC) with altered activity, disrupted sleep and increased anxiety. A bi-weekly cage change is, therefore, preferred over a shorter CC interval and is currently the prevailing routine for Individually ventilated cages (IVCs). However, the build-up of ammonia (NH3) during this period is a potential threat to the animal health and the literature holds conflicting reports leaving this issue unresolved. We have therefor examined longitudinally in-cage activity, animal health and the build-up of ammonia across the cage floor with female and male C57BL/6 mice housed four per IVC changed every other week. We used a multicentre design with a standardised husbandry enabling us to tease-out features that replicated across sites from those that were site-specific. CC induce a marked increase in activity, especially during daytime (~50%) when the animals rest. A reduction in density from four to two mice did not alter this response. This burst was followed by a gradual decrease till the next cage change. Female but not male mice preferred to have the latrine in the front of the cage. Male mice allocate more of the activity to the latrine free part of the cage floor already the day after a CC. A behaviour that progressed through the CC cycle but was not impacted by the type of bedding used. Reducing housing density to two mice abolished this behaviour. Female mice used the entire cage floor the first week while during the second week activity in the latrine area decreased. Measurement of NH3 ppm across the cage floor revealed x3 higher values for the latrine area compared with the opposite area. NH3 ppm increases from 0-1 ppm to reach ≤25 ppm in the latrine free area and 50-100 ppm in the latrine area at the end of a cycle. As expected in-cage bacterial load covaried with in-cage NH3 ppm. Histopathological analysis revealed no changes to the upper airways covarying with recorded NH3 ppm or bacterial load. We conclude that housing of four (or equivalent biomass) C57BL/6J mice for 10 weeks under the described conditions does not cause any overt discomfort to the animals.",mds,True,findable,169,4,0,0,0,2022-05-02T17:25:52.000Z,2022-05-02T17:25:53.000Z,dryad.dryad,dryad,"FOS: Animal and dairy science,FOS: Animal and dairy science","[{'subject': 'FOS: Animal and dairy science', 'subjectScheme': 'fos'}, {'subject': 'FOS: Animal and dairy science', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['80979400 bytes'],
 10.5281/zenodo.8379418,Synthetic along-track altimetry data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project,Zenodo,2023,en,Dataset,Creative Commons Attribution 4.0 International,"""Synthetic observations"" of along-track SSH have been extracted online during the  production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and locations where a true SLA observation exists in the AVISO database for the along-track altimetry from the TOPEX, Jason-1, Jason-2 and Jason-3 satellite continuous series over the period 1993-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal)  based on the ISBA hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica.This synthetic along-track SSH dataset from the model is available over the altimetry period (1993-2018). It is provided there along with a time-mean model SSH (gridded model field) over the same period that can be used as a proxy for mean dynamic topography (""MDT"").
@@ -1635,10 +1617,8 @@ Cette carte est la version intermédiaire de juin 2021 faite sur Illustrator (Ad
 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.3635402,Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics,Zenodo,2020,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","<strong>Data set of measurements related to the following:</strong> <strong>Purpose: </strong>Development and validation of an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera which provides simultaneously high definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity in order to calculate the absolute retinal blood flow. <strong>Methods: </strong>This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes©,Orsay, France) outside its optical wavefront correction path. A 4 seconds recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides the vessel diameter close to the probing beam and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. Combination of those values gives an average of the absolute retinal blood flow. An in vitro experiment consisting of latex microspheres flowing in water through a glass-capillary to simulate a blood vessel and in vivo measurements on six healthy human retinal venous junctions were done to assess the device. <strong>Results: </strong>In the in vitro experiment, the calculated flow varied between 1.75 μl/min and 25.9 μl/min and was highly correlated (r2 = 0.995) with the imposed flow by a syringe pump. In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between −25% and 17% (mean±sd −2 ± 17%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 1.3 μL/min and 28.7 μL/min <strong>Conclusion: </strong>This adaptive optics LDV prototype (aoLDV) allows the measurement of absolute retinal blood flow derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.",mds,True,findable,0,0,0,0,0,2020-02-06T10:39:05.000Z,2020-02-06T10:39:06.000Z,cern.zenodo,cern,"laser Doppler velocimetry, ocular blood flow","[{'subject': 'laser Doppler velocimetry, ocular blood flow'}]",,
 10.5281/zenodo.7092357,Code for PhD thesis: Numerical Analysis for the reconciliation in space and time of the discretizations of the air-sea exchanges and their parameterization,Zenodo,2022,en,Software,"Creative Commons Attribution 4.0 International,Open Access","Python3 code used to generate the Figures of the PhD thesis ""Numerical Analysis for the reconciliation in space and time of the discretizations of the air-sea exchanges and their parameterization"". Each chapter has a Jupyter Notebook associated to it. Some scientific packages for python3 are necessary to run the code (progressbar, scipy, numba, matplotlib).",mds,True,findable,0,0,0,0,0,2022-09-26T08:29:49.000Z,2022-09-26T08:29:49.000Z,cern.zenodo,cern,"Schwarz methods,Waveform relaxation,Semi-discrete,Finite Volume methods","[{'subject': 'Schwarz methods'}, {'subject': 'Waveform relaxation'}, {'subject': 'Semi-discrete'}, {'subject': 'Finite Volume methods'}]",,
-10.60662/n581-qq67,A Systemic approach for Material Handling System Design,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T18:16:51.000Z,2023-09-01T18:16:51.000Z,uqtr.mesxqq,uqtr,,,,
 10.5281/zenodo.4603535,Atomic coordinates of the periodic amorphous ice grain model,Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access",This dataset contains the atomic coordinates in the crystallographic interchange format (CIF) of the periodic model of an amorphous water icy grain surface as resulted from the geometry optimization at HF-3c level with the CRYSTAL17 computer code. The crystallographic unit cell has the c-axis arbitrary defined to be 60 Ã… to simulate the void upper/lower the icy surface.,mds,True,findable,0,0,0,0,0,2021-03-14T08:50:39.000Z,2021-03-14T08:50:39.000Z,cern.zenodo,cern,"Amorphous ice,HF-3c,CRYSTAL17,DFT","[{'subject': 'Amorphous ice'}, {'subject': 'HF-3c'}, {'subject': 'CRYSTAL17'}, {'subject': 'DFT'}]",,
 10.5281/zenodo.5535624,"Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow",Zenodo,2021,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 . Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with:<br> * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a)<br> * base - basal topography altitude (m)<br> * lithk - ice thickness (m)<br> * orog - surface altitude (m)<br> * strbasemag - magnitude of basal friction tb (MPa)<br> * xvelbase, yvelbase, zvelbase - 3D basal velocity (m/yr)<br> * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr)<br> * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr)<br> * n - effective pressure (MPa) The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the <em>As</em> Weertman sliding coeffitient. The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).",mds,True,findable,0,0,1,0,0,2021-10-05T09:56:20.000Z,2021-10-05T09:56:21.000Z,cern.zenodo,cern,"ice flow modelling, seasonal, ELMER-Ice, Greenland, Russell","[{'subject': 'ice flow modelling, seasonal, ELMER-Ice, Greenland, Russell'}]",,
-10.60662/ydh4-8904,Approche intégrée basée sur l’intelligence artificielle pour la reconfiguration automatique des systèmes de production,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T19:53:39.000Z,2023-09-01T19:53:39.000Z,uqtr.mesxqq,uqtr,,,,
 10.5281/zenodo.4442416,An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results,Zenodo,2021,,Audiovisual,"Creative Commons Attribution 4.0 International,Open Access","A video illustrating the results presented in the paper: <em>""Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online.""</em>",mds,True,findable,0,0,0,0,0,2021-01-15T12:04:42.000Z,2021-01-15T12:04:42.000Z,cern.zenodo,cern,"Vehicle-Pedestrian Interaction,Trajectory Prediction,Multi-Agent Simulation,Human behavior,Social Force Model","[{'subject': 'Vehicle-Pedestrian Interaction'}, {'subject': 'Trajectory Prediction'}, {'subject': 'Multi-Agent Simulation'}, {'subject': 'Human behavior'}, {'subject': 'Social Force Model'}]",,
 10.25647/liepp.pb.11,"Les effets de la réglementation du cumul des mandats de 2001 : enseignements pour la nouvelle loi de 2014 (Policy Brief, n°11)",Sciences Po - LIEPP,2014,fr,Other,,"Le texte évalue les conséquences du changement de la réglementation française de 2001en ce qui concerne le cumul des mandats, qui a limité la possibilité de tenir simultanément plusieurs mandats électifs. La comparaison avant et après la mise en œuvre de la nouvelle loi permet de conclure que (i) les candidats aux élections législatives se sont adaptés aux nouvelles règles en réduisant les mandats locaux détenus; (ii) les candidats ont également montré une tendance à changer la nature des mandats exercés. Ces résultats mettent en lumière les modalités d'application de la loi qui lui donneront toute son efficacité.",fabricaForm,True,findable,0,0,0,0,0,2022-01-04T15:00:53.000Z,2022-01-05T13:25:39.000Z,vqpf.dris,vqpf,FOS: Social sciences,"[{'subject': 'FOS: Social sciences', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",,
 10.5281/zenodo.4305929,DEM simulations of size-segregation during bedload transport,Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This depository contains the data of all DEM simulations used in the publication Chassagne, R., Maurin, R., Chauchat, J., Gray, J., &amp; Frey, P. (2020). Discrete and continuum modelling of grain size segregation during bedload transport. <em>Journal of Fluid Mechanics,</em> <em>895</em>, A30. doi:10.1017/jfm.2020.274, as well as post processing scripts to use the data. The simulations are located in two folders, fine/ (simulations for which the amount of fine particles is varied) and sizeRatio (simulations for which the size ratio between large and small particles is varied). The data of each simulations are contained in separate subfolders named after the simulation. For example, Fine2R1.5/ corresponds to a simulation with 2 layers of small particles and a size ratio of 1.5. For each simulation, the time data are saved in data.hdf5 and averaged data in average.hdf5. A GeomParam.txt file is also in each folder. It contains information of the simulation that the post processing programm will read. The python script used to initiate the YADE-DEM simulation is also given for information (it contains all parameters of the simulation). The post-processing program has been coded in python2.7 with an oriented-object procedure. The h5py package is necessary to read the .hdf5 files. The scripts do not work in python3, but can be very easily adapted if necessary (you only have to modify the ""print"" functions). The scripts are available in ScriptsPP/ and are organized as follow. A mother class in SegregationPP and two child classes SegFull (to load the full time data set) and SegMean (to load only average data). A script examplePP.py is proposed and shows how to manipulate theses classes and the data.",mds,True,findable,0,0,0,1,0,2020-12-04T14:22:25.000Z,2020-12-04T14:22:25.000Z,cern.zenodo,cern,"Granular flow,Sediment transport,Size-segregation,Coupled Fluid-DEM simulations","[{'subject': 'Granular flow'}, {'subject': 'Sediment transport'}, {'subject': 'Size-segregation'}, {'subject': 'Coupled Fluid-DEM simulations'}]",,
@@ -1726,7 +1706,6 @@ Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro
 10.5281/zenodo.5189179,MICCAI 2016 challenge dataset demographics data,Zenodo,2021,,Dataset,"Creative Commons Attribution 4.0 International,Open Access",This dataset contains supplementary material for the 2016 MS segmentation challenge data article. It contains the full demographic data for the datasets opened to the public.,mds,True,findable,0,0,0,0,0,2021-08-12T15:52:54.000Z,2021-08-12T15:52:55.000Z,cern.zenodo,cern,,,,
 10.57745/gzkuzs,Ventricular-fold dynamics in human phonation,Recherche Data Gouv,2022,,Dataset,,"This database of images, audio samples and highspeed videos have been established as a supplementary material to the paper : “Ventricular-fold dynamics in human phonation” Bailly L., Henrich Bernardoni N., Müller F., Rohlfs A-K., Hess M., JSLHR, Vol. 57 pp. 1219–1242, August 2014 (https://hal.archives-ouvertes.fr/hal-00998464). It completes Figures B1 and B2 in Appendix B. It includes 58 videos (avi format, without sound for highspeed videos), the 2*58 audio files in wav, ""short"" and ""long"" version (with context), 58 images of folds (png format) and 58 associated kymographic images (bmp format). The whole database is 1.66 GB. The largest files are the videos, from 6 to 102 MB.",mds,True,findable,119,7,0,0,0,2022-06-23T12:01:02.000Z,2022-07-08T08:35:24.000Z,rdg.prod,rdg,,,,
 10.5281/zenodo.2540773,Yam genomics supports West Africa as a major cradle of crop domestication,Zenodo,2019,,Dataset,"Creative Commons Attribution Non Commercial Share Alike 4.0 International,Open Access","167 yam samples were fully resequenced (WGS, Illumina HiSeq) and mapped (BWA) to the Dioscorea rotundata genome (BDMI01000001.1 to BDMI01000021.1). Calling_ALL_Rotundata_Allc05.vcf.gz  is the result of the SNP calling (GATK). See associated publication for details;",mds,True,findable,2,0,0,0,0,2019-01-15T14:21:21.000Z,2019-01-15T14:21:22.000Z,cern.zenodo,cern,,,,
-10.34847/nkl.81dcdekj,Histoire de la Société d'études italiennes,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,,api,True,findable,0,0,0,0,0,2022-06-28T14:09:19.000Z,2022-06-28T14:09:19.000Z,inist.humanum,jbru,Etudes italiennes,"[{'lang': 'fr', 'subject': 'Etudes italiennes'}]","['9097465 Bytes', '7416683 Bytes', '9282460 Bytes', '8907544 Bytes', '8799070 Bytes', '8751142 Bytes', '8883043 Bytes', '8854078 Bytes', '9254668 Bytes', '8599864 Bytes', '8792680 Bytes', '8913379 Bytes', '9142621 Bytes', '9078376 Bytes', '8741896 Bytes', '8768425 Bytes', '9371872 Bytes', '10155217 Bytes', '8874292 Bytes', '8991184 Bytes', '9151126 Bytes', '9029416 Bytes', '8833840 Bytes', '8645749 Bytes', '8984503 Bytes', '9104779 Bytes', '9083704 Bytes', '9129640 Bytes', '8991916 Bytes', '8870686 Bytes', '8942368 Bytes', '8877484 Bytes', '8990458 Bytes', '8738002 Bytes', '9089236 Bytes', '8899528 Bytes', '9166045 Bytes', '8746906 Bytes', '8934232 Bytes', '9108013 Bytes', '8750344 Bytes', '8841088 Bytes', '9063529 Bytes', '8678512 Bytes', '9126250 Bytes', '8955814 Bytes', '9153598 Bytes', '9105520 Bytes', '8982352 Bytes', '9005464 Bytes', '9093784 Bytes', '8923579 Bytes', '8910184 Bytes', '8986132 Bytes', '8956915 Bytes', '8643076 Bytes', '8739199 Bytes', '8970193 Bytes', '8731336 Bytes', '8730004 Bytes', '8585428 Bytes', '8853844 Bytes', '9099775 Bytes', '8922001 Bytes', '9018292 Bytes', '8726236 Bytes', '8906944 Bytes', '8817862 Bytes', '8782159 Bytes', '8980324 Bytes', '9100354 Bytes', '8920492 Bytes', '8933611 Bytes', '9053359 Bytes', '8990599 Bytes', '8861440 Bytes', '9090760 Bytes', '9004756 Bytes', '9013276 Bytes', '9112240 Bytes', '9062704 Bytes', '9096484 Bytes', '9042016 Bytes', '9109333 Bytes', '8979859 Bytes', '9068872 Bytes', '9027544 Bytes', '8715541 Bytes', '8659984 Bytes', '8917960 Bytes', '8823898 Bytes', '8741800 Bytes', '8765539 Bytes', '9058498 Bytes', '8832010 Bytes', '9170812 Bytes', '9279169 Bytes', '9430825 Bytes', '10287784 Bytes', '75295 Bytes', '75304 Bytes']","['image/tiff', 'application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'application/json', 'application/json']"
 10.57745/tor3sf,Caractérisation d’un poste de soudure Cold Métal Transfer pour le pilotage du procédé Wire Arc Additive Manufacturing,Recherche Data Gouv,2023,,Dataset,,Les données présentées ici font partie de l'article : Pilotage d’un poste de soudure Cold Métal Transfert pour le Wire Arc Additive Manufacturing Les données permettent produire les graphiques en 3 dimensions ainsi que la diffusion des résultats produits. Le jeu de données contient les fichiers suivant : - data.csv : ensemble des données utilisé dans cet article - graph.py : fonctions pour générer les graphiques de l'article en 3 Dimentions. - main.py : fichier principal pour lancer le code. Il permet de changer la valeur exemple de wfs_v consigne ainsi que les points aberrants.,mds,True,findable,89,10,0,0,0,2023-01-10T14:45:48.000Z,2023-01-26T13:29:24.000Z,rdg.prod,rdg,,,,
 10.5281/zenodo.1156633,Sheet Flow Data,Zenodo,2018,en,Dataset,"Creative Commons Attribution 4.0,Open Access","The netCDF files ""data_expe_mb1.nc"" and ""data_expe_mb2.nc"" contains the experimental results of intense sediment transport experiments (sheet flow) carried out in the LEGI tilting flume. Synchronised and colocated concentration and veclocity (wall-normal and streamwise components) measurements have been obtained by using the Acoustic Concentration and Velocity Profiler (ACVP - Hurther et al., 2011). Details about the experimental protocol can be found in Revil-Baudard et al. (2015) and Revil-Baudard et al. (2016). As there is no sediment recirculation in the flume, the same run has been repeated several times to perform ensemble averages. The results of two-phase flow numerical simulations performed using SedFOAM-2.0 are disseminated in two formats (i) the complete openFOAM case directories can be found in the repository ""data_num"" (ii) NetCDF files containing the concentration, velocity, shear stress and Turbulent Kinetic Energy profiles. The repository contains different combination of intergranular stress and turbulence models: the mu(I) rheology or the kinetic theory of granular flows and mixing length or k-epsilon turbulence models. All the details concerning the numerical results and the configurations can be found in Chauchat et al. (2017a) The SedFOAM-2.0 source code is distributed under a GNU General Public License v2.0 (GNU GPL v2.0) and is available at https://github.com/SedFoam/sedfoam/releases/tag/v2.0 or on Zenodo at https://zenodo.org/record/836643#.Wc47Yoo690s with the following DOI https://doi.org/10.5281/zenodo.836643 (Chauchat et al.,2017b).",,True,findable,0,0,0,0,0,2018-01-22T09:43:53.000Z,2018-01-22T09:43:53.000Z,cern.zenodo,cern,"Sediment transport,Sheet flow,Experiments,Acoustic measurements,Two-phase flow model,turbulence,numerical simulation","[{'subject': 'Sediment transport'}, {'subject': 'Sheet flow'}, {'subject': 'Experiments'}, {'subject': 'Acoustic measurements'}, {'subject': 'Two-phase flow model'}, {'subject': 'turbulence'}, {'subject': 'numerical simulation'}]",,
 10.48380/061t-ae68,Pressure anomaly of the ATP hydrolysis rate facilitates life of extremophiles,Deutsche Geologische Gesellschaft - Geologische Vereinigung e.V. (DGGV),2022,en,Text,,"<p>Life is prevalent on Earth even in extreme environments, e.g., near black smokers. This biological community has to face temperatures of up to 120 °C and pressures of 40 MPa. To maintain vital reactions, extremophiles have developed varies mechanisms to survive. The stability of the energy-storing molecules adenosine triphosphate (ATP) and adenosine diphosphate (ADP) are of essential importance because reactions involving these phosphates constrain the range of life. ATP is limited by the non-enzymatic hydrolysis, which is kinetically enhanced at high temperatures. If this abiotic process is too rapid, metabolism as we know won’t be possible anymore. The effect of elevated temperatures on the hydrolysis rate constants of ATP is widely known and is best described by an Arrhenius relationship. In contrast to previous studies, our first findings showed a decelerating effect from 0 – 60 MPa with a minimum in the reaction rate at 20 – 40 MPa at 100 °C. The rate constants of the non-enzymatic hydrolysis of ATP are decreasing from 5.8 x 10-4 s-1 at 0.1 MPa to 4.2 x 10-4s-1 at 20 MPa at 100 °C. The corresponding half-lives are 1195 s and 20 MPa. This observation is extremely fascinating as Takai et al. (2008) have seen a similar pressure anomaly at extreme temperatures for Methanopyrus Kandleri.</p>
@@ -1762,7 +1741,6 @@ Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro
 10.5281/zenodo.7762439,"FIGURE 2. Bulbophyllum anderosonii. A. Flattened flowering plant. B. Leaf apex. C in Bulbophyllum sondangii (Orchidaceae), a new species from Da Lat Plateau, southern Vietnam",Zenodo,2023,,Image,Open Access,"FIGURE 2. Bulbophyllum anderosonii. A. Flattened flowering plant. B. Leaf apex. C. Apical portion of inflorescence, view from above and from below. D. Floral bract, adaxial and abaxial side. E. Flower, view from above, from below, and side view. F. Median sepal, abaxial and adaxial side. G. Apex of median sepal. H. Lateral sepal, view from above and from below. I. Petal, abaxial and adaxial side. J. Petal margin. K. Lip, views from different sides. L. Pedicel, ovary and column, with petal and with petals removed, side view. M, N. Apex of column, side and frontal view. O. Anther cap, view from above and from below. P. Pollinia. All photos by Truong Ba Vuong, made from the specimens BV 1671, photo correction and design by L. Averyanov and T. Maisak.",mds,True,findable,0,0,0,3,0,2023-03-23T07:56:22.000Z,2023-03-23T07:56:23.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Plantae,Tracheophyta,Liliopsida,Asparagales,Orchidaceae,Bulbophyllum","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Plantae'}, {'subject': 'Tracheophyta'}, {'subject': 'Liliopsida'}, {'subject': 'Asparagales'}, {'subject': 'Orchidaceae'}, {'subject': 'Bulbophyllum'}]",,
 10.5281/zenodo.5642126,"FIGURE 1 in Notes on the genus Chamaeanthus (Orchidaceae, Epidendroideae, Vandeae, Aeridinae) with a new species from Vietnam",Zenodo,2021,,Image,Open Access,"FIGURE 1. Chamaeanthus averyanovii Vuong, Kumar, V.H. Bui, V.S.Dang: A &amp; B. Plant habit; C. Leaf apex; D. Leaf sheath; E. Inflorescence; F. Flower; G. Floral bract; H. Dorsal sepal; I. Lateral sepals; J. Petals; K. Lip; L. Column and column foot; M. Stigma; N. Anher cap; O. Pollinia. All photo by Truong Ba Vuong from specimen BV 1194.",mds,True,findable,0,0,0,0,0,2021-11-03T06:44:17.000Z,2021-11-03T06:44:18.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Plantae,Tracheophyta,Liliopsida,Asparagales,Orchidaceae,Chamaeanthus","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Plantae'}, {'subject': 'Tracheophyta'}, {'subject': 'Liliopsida'}, {'subject': 'Asparagales'}, {'subject': 'Orchidaceae'}, {'subject': 'Chamaeanthus'}]",,
 10.5061/dryad.rjdfn2z7p,Data from: Landscape does matter: disentangling founder effects from natural and human-aided post-introduction dispersal during an ongoing biological invasion,Dryad,2020,en,Dataset,Creative Commons Zero v1.0 Universal,"Environmental features impacting the spread of invasive species after introduction can be assessed using population genetic structure as a quantitative estimation of effective dispersal at the landscape scale. However, in the case of an ongoing biological invasion, deciphering whether genetic structure represents landscape connectivity or founder effects is particularly challenging. We examined the modes of dispersal (natural and human-aided) and the factors (landscape or founders history) shaping genetic structure in range edge invasive populations of the Asian tiger mosquito, Aedes albopictus, in the region of Grenoble (Southeast France). Based on detailed occupancy-detection data and environmental variables (climatic, topographic, land-cover), we modelled A. albopictus potential suitable area and its expansion history since first introduction. The relative role of dispersal modes was estimated using biological dispersal capabilities and landscape genetics approaches using genome-wide SNP dataset. We demonstrate that both natural and human-aided dispersal have promoted the expansion of populations. Populations in diffuse urban areas, representing highly suitable habitat for A. albopictus, tend to disperse less, while roads facilitate long-distance dispersal. Yet demographic bottlenecks during introduction played a major role in shaping the genetic variability of these range edge populations. The present study is one of the few investigating the role of founder effects and ongoing expansion processes in shaping spatial patterns of genetic variation in an invasive species at the landscape scale. The combination of several dispersal modes and large proportions of continuous suitable habitats for A. albopictus promoted range filling of almost its entire potential distribution in the region of Grenoble only few years after introduction.",mds,True,findable,127,5,0,0,0,2020-07-06T23:26:39.000Z,2020-07-06T23:26:41.000Z,dryad.dryad,dryad,,,['4074602 bytes'],
-10.15454/l7qn45,Soil Microbial Metagenomics Facility,INRAE,2018,,Service,,,fabricaForm,True,findable,43,0,0,0,0,2018-10-03T11:07:03.000Z,2018-10-03T11:07:03.000Z,rdg.prod,rdg,,,,
 10.5281/zenodo.7113144,Geothermal and structural features of La Palma island (Canary Islands) imaged by ambient noise tomography,Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access",These folders contain all the results obtained in the ambient noise tomography of La Palma for geothermal exploration.,mds,True,findable,0,0,0,0,0,2022-09-26T11:44:00.000Z,2022-09-26T11:44:01.000Z,cern.zenodo,cern,,,,
 10.5061/dryad.g72v731,Data from: Plant DNA metabarcoding of lake sediments: how does it represent the contemporary vegetation,Dryad,2019,en,Dataset,Creative Commons Zero v1.0 Universal,"Metabarcoding of lake sediments have been shown to reveal current and past biodiversity, but little is known about the degree to which taxa growing in the vegetation are represented in environmental DNA (eDNA) records. We analysed composition of lake and catchment vegetation and vascular plant eDNA at 11 lakes in northern Norway. Out of 489 records of taxa growing within 2 m from the lake shore, 17-49% (mean 31%) of the identifiable taxa recorded were detected with eDNA. Of the 217 eDNA records of 47 plant taxa in the 11 lakes, 73% and 12% matched taxa recorded in vegetation surveys within 2 m and up to about 50 m away from the lakeshore, respectively, whereas 16% were not recorded in the vegetation surveys of the same lake. The latter include taxa likely overlooked in the vegetation surveys or growing outside the survey area. The percentages detected were 61, 47, 25, and 15 for dominant, common, scattered, and rare taxa, respectively. Similar numbers for aquatic plants were 88, 88, 33 and 62%, respectively. Detection rate and taxonomic resolution varied among plant families and functional groups with good detection of e.g. Ericaceae, Roseaceae, deciduous trees, ferns, club mosses and aquatics. The representation of terrestrial taxa in eDNA depends on both their distance from the sampling site and their abundance and is sufficient for recording vegetation types. For aquatic vegetation, eDNA may be comparable with, or even superior to, in-lake vegetation surveys and may therefore be used as an tool for biomonitoring. For reconstruction of terrestrial vegetation, technical improvements and more intensive sampling is needed to detect a higher proportion of rare taxa although DNA of some taxa may never reach the lake sediments due to taphonomical constrains. Nevertheless, eDNA performs similar to conventional methods of pollen and macrofossil analyses and may therefore be an important tool for reconstruction of past vegetation.",mds,True,findable,382,54,1,1,0,2018-03-23T21:54:37.000Z,2018-03-23T21:54:38.000Z,dryad.dryad,dryad,"biomonitoring,palaeobotany,Holocene","[{'subject': 'biomonitoring'}, {'subject': 'palaeobotany'}, {'subject': 'Holocene'}]",['3664863480 bytes'],
 10.5281/zenodo.10207869,"Link to data for the paper ""Diode effect in Josephson junctions with a single magnetic atom""",Zenodo,2023,,Other,Creative Commons Attribution 4.0 International,"The refubium repository contains the experimental data and the code used in the paper ""Diode effect in Josephson junctions with a single magnetic atom"" published in Nature 618, 625 (2023).",api,True,findable,0,0,0,0,0,2023-11-26T19:52:29.000Z,2023-11-26T19:52:29.000Z,cern.zenodo,cern,,,,
@@ -1771,7 +1749,6 @@ Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro
 10.5281/zenodo.8421859,"Codes of the article ""evolutionary dynamics of plasting foraging and its ecological consequences: a resource-consumer model""",Zenodo,2023,,Software,Open Access,"The ""Codes"" folder contains the MATLAB codes used for all the simulations. In order to run some of these codes, you will need to download the simulation output (.mat format) from the ""Data"" folder and from the link in the README for this folder. The ""Figures"" folder contains all the figures in the paper and appendices. Finally, the appendices to the paper are in the Appendix.pdf file.",mds,True,findable,0,0,0,0,0,2023-10-09T13:51:19.000Z,2023-10-09T13:51:20.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.6786833,Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier,Zenodo,2023,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This Zenodo repository contains all datasets (IPSL CMIP6 data, glaciological data, SAFRAN data), ElmerIce codes and Python Jupyter Notebook used in the study reported in the article <strong><em>""Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier""</em></strong>",mds,True,findable,0,0,0,0,0,2023-02-17T14:34:31.000Z,2023-02-17T14:34:31.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.8247362,Solar-powered Shape-changing Origami Microfliers,Zenodo,2023,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Datasets used in the paper: ""Solar-powered Shape-changing Origami Microfliers"". This deposit contains the <strong>entirety</strong> of the raw datasets collected from real world experiments used to generate all of the figures and supplementary materials in the aforementioned paper. These also include the MCU's source code and PCB design files used to create the leaf-out origami robot prototypes. Please see the Readme for more information.",mds,True,findable,0,0,0,0,0,2023-09-13T18:09:22.000Z,2023-09-13T18:09:22.000Z,cern.zenodo,cern,"Origami Microfliers,Battery-Free Robotics,Wireless Sensor Networks","[{'subject': 'Origami Microfliers'}, {'subject': 'Battery-Free Robotics'}, {'subject': 'Wireless Sensor Networks'}]",,
-10.57726/grx4-f695,"Say It Again, Please",Presses Universitaires Savoie Mont Blanc,2015,en,Book,,,fabricaForm,True,findable,0,0,0,0,0,2022-03-14T14:54:06.000Z,2022-03-14T14:54:06.000Z,pusmb.prod,pusmb,FOS: Humanities,"[{'subject': 'FOS: Humanities', 'valueUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'schemeUri': 'http://www.oecd.org/science/inno', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['149 pages'],
 10.5281/zenodo.4964225,"FIGURE 40 in Two new species of Protonemura Kempny, 1898 (Plecoptera: Nemouridae) from the Italian Alps",Zenodo,2021,,Image,Open Access,FIGURE 40. Biotope of Protonemura bispina sp. n. (Sella Ciampigotto Sella di Razzo),mds,True,findable,0,0,3,0,0,2021-06-16T08:25:50.000Z,2021-06-16T08:25:51.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy,Animalia,Arthropoda,Insecta,Plecoptera,Nemouridae,Protonemura","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}, {'subject': 'Animalia'}, {'subject': 'Arthropoda'}, {'subject': 'Insecta'}, {'subject': 'Plecoptera'}, {'subject': 'Nemouridae'}, {'subject': 'Protonemura'}]",,
 10.5285/3ea504d8-41c2-40dc-86dc-284c341badaa,"Ice radar data from Little Dome C, Antarctica, 2016-2018",NERC EDS UK Polar Data Centre,2022,en,Dataset,Open Government Licence V3.0,"The dataset consists of 14 selected lines of radar data, collected from the Little Dome C region close to Concordia Station in East Antarctica. The data were collected in austral field seasons 2016-17, and 2017-18, from within the search region for the planned European project Beyond EPICA - Oldest Ice, an EU-funded 10-nation consortium project to drill an ice core that spans up to 1.5 million years of climate and atmospheric history. Radar lines were recorded using the BAS DELORES sledge-borne, over-snow, ice radar system and geolocated with a precise GPS system. This data was generated within the project Beyond EPICA - Oldest Ice (BE-OI). The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 730258 (BE-OI CSA). It has received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 16.0144. It is further supported by national partners and funding agencies in Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland, the Netherlands and the UK. Logistic support is mainly provided by AWI, BAS, ENEA and IPEV. Collection of this data also benefited from support by the joint French-Italian Concordia Programme, which established and runs the permanent station Concordia at Dome C. We particularly acknowledge those who collected the data in the field, and assisted with the processing: Robert Mulvaney, Massimo Frezzotti, Marie Cavitte, Ed King, Carlos Martin, Catherine Ritz, Julius Rix.",mds,True,findable,0,0,0,0,0,2022-03-04T09:26:18.000Z,2022-03-04T09:29:51.000Z,bl.nerc,rckq,"""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""GLACIER THICKNESS/ICE SHEET THICKNESS"",""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""GLACIER TOPOGRAPHY/ICE SHEET TOPOGRAPHY"",""EARTH SCIENCE"",""CRYOSPHERE"",""SNOW/ICE"",""SNOW STRATIGRAPHY"",""EARTH SCIENCE"",""SPECTRAL/ENGINEERING"",""RADAR"",""RADAR REFLECTIVITY"",""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""EARTH SCIENCE"",""SPECTRAL/ENGINEERING"",""RADAR"",DELORES,EPICA,Little Dome C,oldest ice,radar","[{'subject': '""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""GLACIER THICKNESS/ICE SHEET THICKNESS""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': '""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS"",""GLACIER TOPOGRAPHY/ICE SHEET TOPOGRAPHY""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': '""EARTH SCIENCE"",""CRYOSPHERE"",""SNOW/ICE"",""SNOW STRATIGRAPHY""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': '""EARTH SCIENCE"",""SPECTRAL/ENGINEERING"",""RADAR"",""RADAR REFLECTIVITY""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': '""EARTH SCIENCE"",""CRYOSPHERE"",""GLACIERS/ICE SHEETS""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': '""EARTH SCIENCE"",""SPECTRAL/ENGINEERING"",""RADAR""', 'schemeUri': 'http://gcmdservices.gsfc.nasa.gov/kms/concepts/concept_scheme/sciencekeywords/?format=xml', 'subjectScheme': 'GCMD'}, {'subject': 'DELORES'}, {'subject': 'EPICA'}, {'subject': 'Little Dome C'}, {'subject': 'oldest ice'}, {'subject': 'radar'}]","['16 files', '300.2 MB']","['text/x-fortranapplication/octet-stream', 'image/png', 'text/plain', 'text/csv', 'SEG-Y']"
 10.5281/zenodo.6035534,"Consilience across multiple, independent genomic data sets reveals species in a complex with limited phenotypic variation",Zenodo,2023,,Software,"MIT License,Open Access","Species delimitation in the genomic era has focused predominantly on the application of multiple analytical methodologies to a single massive parallel sequencing (MPS) data set, rather than leveraging the unique but complementary insights provided by different classes of MPS data. In this study we demonstrate how the use of two independent MPS data sets, a sequence capture data set and a single nucleotide polymorphism (SNP) data set generated via genotyping-by-sequencing, enables the resolution of species in three complexes belonging to the grass genus <em>Ehrharta, </em>whose strong population structure and subtle morphological variation limit the effectiveness of traditional species delimitation approaches. Sequence capture data are used to construct a comprehensive phylogenetic tree of <em>Ehrharta </em>and to resolve population relationships within the focal clades, while SNP data are used to detect patterns of gene pool sharing across populations, using a novel approach that visualises multiple values of K. Given that the two genomic data sets are fully independent, the strong congruence in the clusters they resolve provides powerful ratification of species boundaries in all three complexes studied. Our approach is also able to resolve a number of single-population species and a probable hybrid species, both which would be difficult to detect and characterize using a single MPS data set. Overall, the data reveal the existence of 11 and five species in the <em>E. setacea</em> and <em>E. rehmannii </em>complexes, with the <em>E. ramosa</em> complex requiring further sampling before species limits are finalized. Despite phenotypic differentiation being generally subtle, true crypsis is limited to just a few species pairs and triplets. We conclude that, in the absence of strong morphological differentiation, the use of multiple, independent genomic data sets is necessary in order to provide the cross-data set corroboration that is foundational to an integrative taxonomic approach.",mds,True,findable,0,0,0,0,0,2023-02-14T19:26:17.000Z,2023-02-14T19:26:17.000Z,cern.zenodo,cern,,,,
@@ -1878,7 +1855,6 @@ Cette fiction s’inspire de l’enquête menée auprès des enfants ayant réal
 
 Deux enfants racontent leurs déplacements quotidiens entre l’école et le domicile. A travers leurs échanges, nous découvrons la ville à hauteur d’enfants au travers des espaces parcourus, seul.e ou accompagné.e. Ils évoquent ce qu’ils aiment faire ou non sur ce trajet, ce dont ils ont peur, ce qui les attire ou les repousse. Apparaissent aussi les recommandations et autres conseils ou avertissements parentaux. Par le rythme de leurs déplacements, leurs choix de cheminements, le besoin d’être et de jouer avec les copains/copines, leurs descriptions des environnements traversés, se dessinent les expériences urbaines enfantines.",api,True,findable,0,0,0,0,1,2023-03-10T14:31:23.000Z,2023-03-10T14:31:23.000Z,inist.humanum,jbru,"mobilité quotidienne,enfant,autonomie,récit personnel,amitié--chez l'enfant,sens et sensations,perception du risque,Villes -- Sons, environnement sonore,mobilité spatiale,itinéraire,matériaux de terrain éditorialisés,récit-fiction","[{'lang': 'fr', 'subject': 'mobilité quotidienne'}, {'lang': 'fr', 'subject': 'enfant'}, {'lang': 'fr', 'subject': 'autonomie'}, {'lang': 'fr', 'subject': 'récit personnel'}, {'lang': 'fr', 'subject': ""amitié--chez l'enfant""}, {'lang': 'fr', 'subject': 'sens et sensations'}, {'lang': 'fr', 'subject': 'perception du risque'}, {'lang': 'fr', 'subject': 'Villes -- Sons, environnement sonore'}, {'lang': 'fr', 'subject': 'mobilité spatiale'}, {'lang': 'fr', 'subject': 'itinéraire'}, {'lang': 'fr', 'subject': 'matériaux de terrain éditorialisés'}, {'lang': 'fr', 'subject': 'récit-fiction'}]",['6347927 Bytes'],['application/pdf']
 10.5281/zenodo.5578340,Salem simulator 2.0,Zenodo,2021,en,Software,"GNU Library General Public License v2.1 or later,Open Access","Installer for the Salem simulator 2.0, including the source code. This version corresponds to the release r17186 on the Capsis repository (http://capsis.cirad.fr/capsis/home). Java Runtime Environment 1.8.xxx is required to run the simulator. Salem predicts the dynamics of pure and mixed even-aged forest stands and makes it possible to simulate management operations. Its purpose is to be a decision support tool for forest managers and stakeholders as well as for policy makers. It is also designed to conduct virtual experiments and help answer research questions. Salem is essentially calibrated with French National Forest Inventory for 12 common tree species of Eupore. The mixture effect on species growth is assessed for 24 pairs of these species. Salem runs on Windows, Linux, or Mac. Its user-friendly Graphical User Interface makes it easy to use for non-modellers.",mds,True,findable,0,0,0,0,0,2021-10-19T12:00:16.000Z,2021-10-19T12:00:18.000Z,cern.zenodo,cern,"Forest,Simulator,Growth,Mixture effect,Foret management,Silviculture","[{'subject': 'Forest'}, {'subject': 'Simulator'}, {'subject': 'Growth'}, {'subject': 'Mixture effect'}, {'subject': 'Foret management'}, {'subject': 'Silviculture'}]",,
-10.60662/re4y-6g57,Application d’un réseau de neurones artificiels auto-encodeur pour la détection de bourrages sur tapis convoyeurs en centre de tri de déchets,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T19:18:09.000Z,2023-09-01T19:18:09.000Z,uqtr.mesxqq,uqtr,,,,
 10.5061/dryad.2j5s7,Data from: Are variations of direct and indirect plant interactions along a climatic gradient dependent on species' strategies? An experiment on tree seedlings,Dryad,2015,en,Dataset,Creative Commons Zero v1.0 Universal,"Investigating how interactions among plants depend on environmental conditions is key to understand and predict plant communities’ response to climate change. However, while many studies have shown how direct interactions change along climatic gradients, indirect interactions have received far less attention. In this study, we aim at contributing to a more complete understanding of how biotic interactions are modulated by climatic conditions. We investigated both direct and indirect effects of adult tree canopy and ground vegetation on seedling growth and survival in five tree species in the French Alps. To explore the effect of environmental conditions, the experiment was carried out at 10 sites along a climatic gradient closely related to temperature. While seedling growth was little affected by direct and indirect interactions, seedling survival showed significant patterns across multiple species. Ground vegetation had a strong direct competitive effect on seedling survival under warmer conditions. This effect decreased or shifted to facilitation at lower temperatures. While the confidence intervals were wider for the effect of adult canopy, it displayed the same pattern. The monitoring of micro-environmental conditions revealed that competition by ground vegetation in warmer sites could be related to reduced water availability; and weak facilitation by adult canopy in colder sites to protection against frost. For a cold-intolerant and shade-tolerant species (Fagus sylvatica), adult canopy indirectly facilitated seedling survival by suppressing ground vegetation at high temperature sites. The other more cold tolerant species did not show this indirect effect (Pinus uncinata, Larix decidua and Abies alba). Our results support the widely observed pattern of stronger direct competition in more productive climates. However, for shade tolerant species, the effect of direct competition may be buffered by tree canopies reducing the competition of ground vegetation, resulting in an opposite trend for indirect interactions across the climatic gradient.",mds,True,findable,303,37,1,1,0,2015-08-11T14:26:07.000Z,2015-08-11T14:26:10.000Z,dryad.dryad,dryad,"Abies alba,Larix decidua,Pinus uncinata,Quercus petraea,Fagus sylvatica","[{'subject': 'Abies alba'}, {'subject': 'Larix decidua'}, {'subject': 'Pinus uncinata'}, {'subject': 'Quercus petraea'}, {'subject': 'Fagus sylvatica'}]",['4698866 bytes'],
 10.5281/zenodo.10055461,Mont Blanc ice core data for NH3 source investigation in Europe,Zenodo,2023,,Dataset,Creative Commons Attribution 4.0 International,Dataset to interpret the δ15N(NH4+) in a Mont Blanc ice core,api,True,findable,0,0,0,0,0,2023-11-03T08:48:44.000Z,2023-11-03T08:48:44.000Z,cern.zenodo,cern,,,,
 10.57745/z3bg2u,Electrical measurement of the spin Hall effect isotropy in ferromagnets with strong spin-orbit interactions,Recherche Data Gouv,2023,,Dataset,,"Data set of the paper : Electrical measurement of the spin Hall effect isotropy in ferromagnets with strong spin-orbit interactions. This includes anisotropic magnetoresistance, spin signal and spin Hall effect, along with their temperature dependance, measured on NiCu and NiPd alloys.",mds,True,findable,47,0,0,0,0,2022-12-08T16:00:42.000Z,2023-03-14T13:34:43.000Z,rdg.prod,rdg,,,,
@@ -2077,7 +2053,6 @@ Mandatory: cite the reference article and the DOI of the observatory
 Optional: cite the DOI of each dataset used.
 
 Co-authorship: depending on the contribution of the data to the scientific results obtained, the authors should either propose co-authorship to the data providers or at least acknowledge their contribution.",Documentation of charge-discharge processes of the saprolith groundwater on the Donga catchment. Contibution to the water balance of the Donga catchment Electric conductivity is an integrative measurement of the groundwater chemical composition. This parameter is used to define the groundwater pole in hydrograph separations.,mds,True,findable,0,0,1,0,0,2018-03-16T15:37:09.000Z,2018-03-16T15:37:10.000Z,inist.osug,jbru,"Aquifer, recharge, groundwater,Sudanian climate,Water Table","[{'subject': 'Aquifer, recharge, groundwater', 'subjectScheme': 'main'}, {'subject': 'Sudanian climate', 'subjectScheme': 'main'}, {'subject': 'Water Table', 'subjectScheme': 'var'}]",,"['CSV', 'NETCDF', 'O&amp;M 1.0']"
-10.57757/iugg23-2595,A broader look at licensing and copyright issues for global seismological data and products from a data center perspective,GFZ German Research Centre for Geosciences,2023,en,ConferencePaper,Creative Commons Attribution 4.0 International,"<!--!introduction!--><b></b><p>Sharing data - arrival time readings, earthquake parameters, waveforms and further derived products - has for many decades been key to the scientific advancement of seismology and our understanding of the Earth. The establishment of data centers, from institutional to global, that receive, archive, curate and make accessible large volumes of seismological data, following community standards and best practices, was a logical consequence. IASPEI, with its commissions, evolved as a de-facto standards body for seismological data, governed by the community of data providers and users alike.</p><p>However, conditions of use for these shared data did not receive much attention by data providers, distributors, and groups working on the definition of standards of data and services. If mentioned at all, generic statements on allowed use were provided somewhere on websites that offered access, often declaring ‘only for scientific/academic purposes’ or ‘not for commercial purposes’. Driven by the desire or requirement to improve FAIRness of our data, better understand data usage and adapt to technological changes, and support open science, putting proper licenses on data and metadata has now become a significant topic.</p><p>In this presentation we look at current practices and evolving ideas regarding application of licenses to the holdings of seismological data centers, covering waveforms, earthquake parameters, and further derived products, also including views from other geoscience domains. The relation to (legal) copyright and intellectual property issues, local/national licensing regulations that may hinder a globally uniform approach, and downstream implications for citation, attribution and general re-use of data will also be addressed.</p>",fabricaForm,True,findable,0,0,0,0,0,2023-06-12T10:12:32.000Z,2023-06-16T10:01:50.000Z,gfz.iugg2023,gfz,,,,
 10.5281/zenodo.6941739,Dataset of publication: Deposit-feeding of Nonionellina labradorica (foraminifera) from an Arctic methane seep site and possible association with a methanotroph,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This file contains all TEM (Transmission Electron Microscopy) images of the foraminifera <em>N. labradorica </em>(foraminifera)<em> </em>used in a feeding experiment for the publication DOI: https://doi.org/10.5194/bg-2021-284 Samples were collected at Gas Hydrate Pingo 3 (GHP3), app. 50 km south of Svalbard at 382m water depth at the mouth of Storfjordrenna, Barents Sea. Blade corer (BLC18) used for sampling was taken at following location 76°6'23.7""N 15°58'1.7""E. After sampling a feeding experiment was performed using the marine methanothroph<em> Methyloprofundus sedimenti</em>. More details can be fount in the methods paper. The file contains",mds,True,findable,0,0,0,0,0,2022-08-12T18:51:39.000Z,2022-08-12T18:51:40.000Z,cern.zenodo,cern,"TEM, Transmission electron microscopy, feeding, foraminifera","[{'subject': 'TEM, Transmission electron microscopy, feeding, foraminifera'}]",,
 10.5281/zenodo.8046630,Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses,Zenodo,2023,en,Software,"BSD 3-Clause Clear License,Open Access","Melissa is a file avoiding, fault tolerant and elastic framework, generalized to perform ensemble runs such as <em>large scale sensitivity analysis</em> and <em>large scale deep surrogate training</em> on supercomputers. Some of the largest Melissa studies so far employed up to 30k cores to execute 80 000 parallel simulations while avoiding up to 288 TB of intermediate data storage. These large-scale studies avoid intermediate file storage due to Melissa's ""online"" (also referred to as in-transit and on-the-fly) data handling approach. Melissa's architecture relies on three interacting components, the launcher, the server, and the client: Melissa client: the parallel numerical simulation code turned into a client. Each client sends its output to the server as soon as available. Clients are independent jobs. Melissa server: a parallelized process in charge of processing the data upon arrival from the distributed and parallelized clients (<em>e.g.</em> computing statistics or training a neural network). Melissa Launcher: the front-end Python script in charge of orchestrating the execution of the study. This piece of code interacts directly with <code>OpenMPI</code> or with the cluster scheduler (<em>e.g.</em> <code>slurm</code> or <code>OAR</code>) to submit and monitor the proper execution of all instances. The Melissa server component is designed to be specialized for various types of ensemble runs: Sensitivity Analysis (melissa-sa) Melissa's sensitivity analysis server is built around two key concepts: iterative (sometimes also called incremental) statistics algorithms and asynchronous client/server model for data transfer. Simulation outputs are never stored on disk. Instead, they are sent via NxM communication patterns from the simulations to a parallelized server. This method of data aggregation enables the calculation of rapid statistical fields in an iterative fashion, without storing any data to disk. Avoiding disk storage opens up the ability to compute oblivious statistical maps for all mesh elements, for every time step and on a full resolution study. Melissa comes with iterative algorithms for computing various statistical quantities (<em>e.g.</em> mean, variance, skewness, kurtosis and Sobol indices) and can easily be extended with new algorithms. Deep Surrogate Training (melissa-dl) Melissa's deep learning server adopts a similar philosophy. Clients communicate data in a round-robin fashion to the parallelized server. The multi-threaded server then puts and pulls data samples in and out of a buffer which is used for building training batches. Melissa can perform data distributed parallelism training on several GPUs, associating a buffer to each of them. To ensure a proper memory management during execution, samples are selected and evicted according to a predefined policy. This strategy enables the online training method shown in. Furthermore, the Melissa architecture is designed to accommodate popular deep learning libraries such as PyTorch or Tensorflow.",mds,True,findable,0,0,0,0,0,2023-06-16T09:40:23.000Z,2023-06-16T09:40:24.000Z,cern.zenodo,cern,"supercomputing,sensitivity analysis,deep learning,distributed systems,orchestration,ensemble runs","[{'subject': 'supercomputing'}, {'subject': 'sensitivity analysis'}, {'subject': 'deep learning'}, {'subject': 'distributed systems'}, {'subject': 'orchestration'}, {'subject': 'ensemble runs'}]",,
 10.17178/emaa_n2h-plus_hyperfine_e9d3c782,Hyperfine excitation of N2H+ by para-H2 collisions,"UGA, CNRS, CNRS-INSU, OSUG",2021,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
@@ -2223,7 +2198,6 @@ G. Guyomarc'h, H. bellot, V. Vionnet, F. Naaim-Bouvet, Y. Deliot, F. Fontaine, P
 ",api,True,findable,0,0,0,0,0,2023-12-11T19:41:41.000Z,2023-12-11T19:41:41.000Z,mcdy.dohrmi,mcdy,,,,
 10.5281/zenodo.7147022,CaliParticles: A Benchmark Standard for Experiments in Granular Materials,Zenodo,2022,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Granular materials are discrete particulate media that can flow like a liquid but also be rigid like a solid. This complex mechanical behavior originates in part from the particles shape. How particle shape affects mechanical behavior remains poorly understood. Understanding this micro-macro link would enable the rational design of potentially cheap, light weight or robust materials. To aid this development, we have produced a set of standard particle shapes that can be used as benchmarks for granular materials research. Here we describe the collection of benchmark shapes. Some part of the particles are modeled on superquadrics, others are custom designed. The particles used so far were made from polyoxymethylene (POM) whose specifications are also listed. The benchmark shapes are available as molds in a plastics manufacturing company, whose contact information is also included. The company is capable of making other molds as well, giving access to more particle shapes. The same particle shapes can thus also be made in different types of (colored) plastic, and in amounts of 50.000 particles or more, larger than conveniently be produced with a 3D printer. We also provide the associated .step and .stl files in the repository in which this document is included.",mds,True,findable,0,0,0,0,0,2022-10-17T08:50:34.000Z,2022-10-17T08:50:34.000Z,cern.zenodo,cern,"Particles,Macaroni,Ellipsoid,Tetrapod,Hexapod,Sphereotetrahedron,Caliper","[{'subject': 'Particles'}, {'subject': 'Macaroni'}, {'subject': 'Ellipsoid'}, {'subject': 'Tetrapod'}, {'subject': 'Hexapod'}, {'subject': 'Sphereotetrahedron'}, {'subject': 'Caliper'}]",,
 10.17178/ohmcv.dsd.mre.12-14.1,"DSD network, Mont-Redon",CNRS - OSUG - OREME,2011,en,Dataset,"Data access and use are ruled by the OHMCV data policy.,The following acknowledging sentence should appear in publications using OHMCV data and products: ""OHMCV is funded by the Institut National des Sciences de l’Univers (INSU/CNRS) and the Observatoire des Sciences de l’Univers de Grenoble (OSUG / Université Grenoble Alpes)”.",This dataset is part of the Cevennes‐Vivarais Mediterranean Hydrometeorological Observatory (OHMCV),mds,True,findable,0,0,1,1,0,2017-03-10T17:09:19.000Z,2017-03-10T17:09:20.000Z,inist.osug,jbru,"Atmospheric conditions,Precipitation,Precipitation Rate,Droplet Size,DISDROMETERS,Ground networks,Hydrometeorological sites,Fixed Observation Stations","[{'subject': 'Atmospheric conditions', 'subjectScheme': 'main'}, {'subject': 'Precipitation', 'subjectScheme': 'main'}, {'subject': 'Precipitation Rate', 'subjectScheme': 'main'}, {'subject': 'Droplet Size', 'subjectScheme': 'main'}, {'subject': 'DISDROMETERS', 'subjectScheme': 'main'}, {'subject': 'Ground networks', 'subjectScheme': 'main'}, {'subject': 'Hydrometeorological sites', 'subjectScheme': 'main'}, {'subject': 'Fixed Observation Stations', 'subjectScheme': 'main'}]",,['NETCDF']
-10.34847/nkl.adc04b9w,Bulletin franco-italien 1912 n°2 mars - avril,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/03 (A4,N2)-1912/04.",api,True,findable,0,0,0,0,0,2022-06-29T10:30:34.000Z,2022-06-29T10:30:34.000Z,inist.humanum,jbru,Etudes italiennes,[{'subject': 'Etudes italiennes'}],"['5911653 Bytes', '36330 Bytes', '20948809 Bytes', '21088168 Bytes', '20945074 Bytes', '20995618 Bytes', '21018802 Bytes', '21114103 Bytes', '21203224 Bytes', '21063487 Bytes', '21032884 Bytes', '21059062 Bytes', '20963617 Bytes', '20835679 Bytes', '21114352 Bytes', '20978806 Bytes']","['application/pdf', 'application/json', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
 10.17178/emaa_ortho-nh3_rotation_331d9739,"Rotation excitation of ortho-NH3 by H, ortho-H2 and para-H2 collisions","UGA, CNRS, CNRS-INSU, OSUG",2021,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
 This research has made use of spectroscopic and collisional data from the EMAA database (https://emaa.osug.fr). EMAA is supported by the Observatoire des Sciences de l'Univers de Grenoble (OSUG),Creative Commons Attribution 4.0 International,Data access and use are ruled by the EMAA data policy.",17 rotation energy levels / 25 radiative transitions / 136 collisional transitions for H (20 temperatures in the range 10-200K) / 136 collisional transitions for ortho-H2 (20 temperatures in the range 10-200K) / 136 collisional transitions for para-H2 (20 temperatures in the range 10-200K),mds,True,findable,0,0,0,0,0,2023-12-07T15:51:56.000Z,2023-12-07T15:51:57.000Z,inist.osug,jbru,"target ortho-NH3,excitationType Rotation,collisional excitation,collider.0 H,collider.1 ortho-H2,collider.2 para-H2,astrophysics,interstellar medium,comets,circumstellar medium,gas,microwave spectroscopy,infrared spectroscopy,rotational excitation,rovibrational excitation,vibrational excitation,electronic excitation,collisional rate coefficients,fine structure,hyperfine structure","[{'subject': 'target ortho-NH3', 'subjectScheme': 'main'}, {'subject': 'excitationType Rotation', 'subjectScheme': 'main'}, {'subject': 'collisional excitation', 'subjectScheme': 'main'}, {'subject': 'collider.0 H', 'subjectScheme': 'var'}, {'subject': 'collider.1 ortho-H2', 'subjectScheme': 'var'}, {'subject': 'collider.2 para-H2', 'subjectScheme': 'var'}, {'subject': 'astrophysics', 'subjectScheme': 'var'}, {'subject': 'interstellar medium', 'subjectScheme': 'var'}, {'subject': 'comets', 'subjectScheme': 'var'}, {'subject': 'circumstellar medium', 'subjectScheme': 'var'}, {'subject': 'gas', 'subjectScheme': 'var'}, {'subject': 'microwave spectroscopy', 'subjectScheme': 'var'}, {'subject': 'infrared spectroscopy', 'subjectScheme': 'var'}, {'subject': 'rotational excitation', 'subjectScheme': 'var'}, {'subject': 'rovibrational excitation', 'subjectScheme': 'var'}, {'subject': 'vibrational excitation', 'subjectScheme': 'var'}, {'subject': 'electronic excitation', 'subjectScheme': 'var'}, {'subject': 'collisional rate coefficients', 'subjectScheme': 'var'}, {'subject': 'fine structure', 'subjectScheme': 'var'}, {'subject': 'hyperfine structure', 'subjectScheme': 'var'}]",,['Radex']
 10.17178/draixbleone_gal_rob_ssc_0719,Suspended Sediment Concentration of the river Galabre at the Robine station of the Galabre watershed,IGE - CNRS - OSUG,2020,en,Dataset,"Always quote below citation to Navratil et al. (2011) when using these data. Navratil O., Esteves M., Legout C., Gratiot N., Némery J., Willmore S., Grangeon T. (2011).  Global uncertainty analysis of suspended sediment monitoring using turbidimeter in a small mountainous river catchment. Journal of Hydrology. 398: 246-259.,Creative Commons Attribution 4.0 International,The following acknowledging sentence should appear in publications using data and products from the Galabre watershed of the Draix Bleone Observatory: ""DRAIX BLEONE is funded by the Institut National des Sciences de l’Univers (INSU/CNRS) and the Observatoire des Sciences de l’Univers de Grenoble (OSUG / Université Grenoble Alpes)”.,Always quote below citation to Legout et al. (submitted) when using these data. Legout C., Freche G., Biron R., Esteves M., Nord G, Navratil O., Uber M., Grangeon T., Hachgenei N., Boudevillain B. Voiron C., Spadini L. A critical zone observatory dedicated to suspended sediment transport: the meso-scale Galabre catchment (southern French Alps), submitted to Hydrological Processes.",This suspended sediment concentration data set is part of the DRAIXBLEONE_GAL observatory.,mds,True,findable,0,0,0,0,0,2020-09-15T15:58:50.000Z,2020-09-15T15:58:52.000Z,inist.osug,jbru,"Mediterranean mountainous climate,Surface water,Sediments,Water quality / Water chemistry","[{'subject': 'Mediterranean mountainous climate', 'subjectScheme': 'main'}, {'subject': 'Surface water', 'subjectScheme': 'var'}, {'subject': 'Sediments', 'subjectScheme': 'var'}, {'subject': 'Water quality / Water chemistry', 'subjectScheme': 'var'}]",,['CSV']
@@ -2930,12 +2904,6 @@ Co-authorship: depending on the contribution of the data to the scientific resul
 10.5281/zenodo.5913708,Supplementary data for the publication of Characterization of Emissions in Fab Labs: an Additive Manu-facturing Environment Issue,Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Datasets for the publication of the article ""Characterization of Emissions in Fab Labs: an Additive Manufacturing Environment Issue"": - Ultrafine Particles: UFP per Zone and mode; - VOC emissions: VOC per Zone and mode.",mds,True,findable,0,0,0,0,0,2022-01-28T13:07:09.000Z,2022-01-28T13:07:10.000Z,cern.zenodo,cern,,,,
 10.18709/perscido.2018.10.ds133,Micro-seismic-monitoring of a floating ice plate to monitor its deformation: Catalog,PerSciDo,2018,en,Dataset,Creative Commons Attribution Non Commercial Share Alike 4.0 International,This dataset corresponds to the detected fractures characteristics related to the microseismic monitoring of a floating ice plate.,fabrica,True,findable,0,0,0,0,0,2018-12-06T13:54:50.000Z,2018-12-06T13:54:51.000Z,inist.persyval,vcob,"Glaciology,Materials Science,Geology,FOS: Earth and related environmental sciences,FOS: Earth and related environmental sciences,Physics","[{'lang': 'en', 'subject': 'Glaciology'}, {'lang': 'en', 'subject': 'Materials Science'}, {'lang': 'en', 'subject': 'Geology'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'FOS: Earth and related environmental sciences', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'lang': 'en', 'subject': 'Physics'}]",['10 MB'],['txt']
 10.5281/zenodo.5500364,Data and scripts from: A new westward migration route in an Asian passerine bird,Zenodo,2021,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","Contains relevant datasets and code for niche modeling analyses performed in ""A new westward migration route in an Asian passerine bird"" by Dufour P<sup>*</sup>, de Franceschi<sup> </sup>C, Doniol-Valcroze P, Jiguet F, Maya Guéguen M, Renaud J, Lavergne<sup>† </sup>S, Crochet<sup>†</sup> PA (<sup>†</sup>co-senior authors)",mds,True,findable,0,0,0,0,0,2021-10-22T15:02:48.000Z,2021-10-22T15:02:49.000Z,cern.zenodo,cern,,,,
-10.34745/numerev_1937,La norme et la variation dans le cadre du Traitement Automatique du Langage,"CJC-Praxiling, (actes n°2022)",2023,fr,JournalArticle,Creative Commons Attribution Non Commercial No Derivatives 4.0 International,"R&eacute;sum&eacute; :&nbsp;Cet article pose la probl&eacute;matique du statut de la norme et de la variation en TAL en proposant des exemples tir&eacute;s des recherches pr&eacute;c&eacute;dentes concernant des mod&egrave;les informatiques employ&eacute;s pour repr&eacute;senter l&rsquo;acquisition de langue fran&ccedil;aise. Deux cas d&rsquo;&eacute;tude exemplifient le choix&nbsp;autour de l&rsquo;axe norme-variation : le calcul automatique d&rsquo;une distribution de fr&eacute;quence et la reconnaissance de motifs s&eacute;quentiels. Que le niveau d&rsquo;analyse soit le mot (premier exemple) ou le phon&egrave;me (deuxi&egrave;me exemple), des obstacles et compromis reviennent d&rsquo;une mani&egrave;re analogue. Le choix &ndash; souvent difficile et contraint - entre la pr&eacute;cision de la description du langage et la n&eacute;cessit&eacute; d&rsquo;avoir des donn&eacute;es uniformes pour que la machine puisse les traiter ais&eacute;ment. Les biais &eacute;vitables et in&eacute;vitables, les pr&eacute;cautions &agrave; prendre en amont, ainsi que les avantages et les inconv&eacute;nients de ce type de mod&egrave;les seront discut&eacute;s. L&rsquo;article se termine en dessinant les contours des futures compl&eacute;mentarit&eacute;s possibles entre m&eacute;thodes qualitatives et quantitatives.
-
-Abstract : This article deals with the problem of the status of norm and variation in NLP by proposing examples drawn from previous research concerning computer models used to represent French language acquisition. Two case studies illustrate the choice around the norm-variation axis: the automatic computation of a frequency distribution and the recognition of sequential patterns in words containing specific syllable sequences that are hard to learn due to their inner phonetic difficulty. Whether the level of analysis is the word (first example) or the phoneme (second example), obstacles and trade-offs come up in a similar way. The choice - often difficult and constrained - between the accuracy of the language description and the need to have uniform data for the machine to be easily handled. The avoidable and unavoidable biases, the precautions to be taken beforehand, as well as the advantages and disadvantages of these types of NLP models will be discussed. The article ends by outlining the possible future complementarities between qualitative and quantitative methods in current linguistics.
-
-Keywords : first language acquisition; NLP, French; variation; norm
-",api,True,findable,0,0,0,0,0,2023-11-29T08:55:52.000Z,2023-11-29T08:56:00.000Z,inist.mshsud,jbru,"variation,TAL,Acquisition du langage,français L1,norme","[{'subject': 'variation'}, {'subject': 'TAL'}, {'subject': 'Acquisition du langage'}, {'subject': 'français L1'}, {'subject': 'norme'}]",,
 10.48537/hal-03220348,The Tangible Presence of Human Labor in Architecture,Reseau International Ambiances,2020,en,Text,Creative Commons Attribution Non Commercial No Derivatives 2.0 Generic,"This essay aims to show that in many of the theories that fundament material culture and architectural experience, labor is implied in the constitution of material and, although seldom directly addressed, it is a determining dimension of materiality. From the Vitruvian and Renaissance treatises and Gottfried Semper to John Ruskin and the Art and Crafts Movement, the underlying presence of labor can be seen intertwined with materials whenever they are called into architectural discussion as sensorial arguments. Just like the physical qualities of materials, labor, skills and techniques are imprinted in the built environment and contribute to the creation of particular atmospheres.",mds,True,findable,0,0,0,0,0,2021-06-17T20:55:03.000Z,2021-06-17T20:55:05.000Z,jbru.aau,jbru,"Architectural Experience,Sensuous Perception,Material Culture,Labor","[{'lang': 'eng', 'subject': 'Architectural Experience'}, {'lang': 'eng', 'subject': 'Sensuous Perception'}, {'lang': 'eng', 'subject': 'Material Culture'}, {'lang': 'eng', 'subject': 'Labor'}]",['6 pages'],['application/pdf']
 10.17178/amma-catch.ce.sw_nc,"Soil dataset (soil moisture and temperature profiles), within the Fakara site (2 000 km2), Niger","IRD, CNRS-INSU, OSUG, OMP, OREME",2004,en,Dataset,"Creative Commons Attribution 4.0 International,Data access and use are ruled by the AMMA-CATCH data policy.,For any publication using AMMA-CATCH data, authors are asked to:
 
@@ -2949,7 +2917,6 @@ Optional: cite the DOI of each dataset used.
 Co-authorship: depending on the contribution of the data to the scientific results obtained, the authors should either propose co-authorship to the data providers or at least acknowledge their contribution.",Documentation of soil water content and of soil hydrodynamic behaviour from surface to 2.5 m deep. Knowledge of main infiltration areas and of the speed of water front progression.,mds,True,findable,0,0,1,0,0,2018-03-16T15:37:04.000Z,2018-03-16T15:37:05.000Z,inist.osug,jbru,"Soil temperature, soil moisture content,Sahelian climate,Soil Moisture/CS616 Period at depth 5 cm,Soil Moisture/CS616 Period -0.4 to -0.7 m,Soil Moisture/CS616 Period at depth 5 cm (2),Soil Moisture/CS616 Period at depth 28 cm,Soil Water/Watermark Conductance at depth 11 cm,Soil Water/Watermark Conductance at height 1.19 m,Soil Water/Watermark Conductance at depth 2.3 m,Soil Temperature at depth 70 cm,Soil Temperature at depth 50 cm,Soil Moisture/CS616 Period at depth 15 cm,Soil Temperature at depth 1.15 m,Soil Temperature at depth 15 cm,Soil Water/Watermark Conductance at depth 2 m,Soil Water/Watermark Conductance at depth 2.47 m,Soil Moisture/CS615 Period -0.7 to -1 m,Soil Temperature at depth 30 cm,Soil Water/Watermark Conductance at depth 1.97 m,Soil Water/Watermark Conductance at depth 47 cm,Soil Water/Watermark Conductance at depth 2.27 m,Soil Moisture/CS616 Period -1.3 to -1.6 m,Soil Water/Watermark Conductance at depth 1.5 m,Soil Temperature at depth 1.5 m,Soil Water/Watermark Conductance at height 86 cm,Soil Water/Watermark Conductance at depth 2.5 m,Soil Moisture/CS616 Period at height 28 cm,Soil Temperature at depth 85 cm,Soil Water/Watermark Conductance at depth 25 cm,Soil Water/Watermark Conductance at depth 85 cm,Soil Water/Watermark Conductance at depth 30 cm,Soil Temperature at depth 25 cm,Soil Temperature at depth 55 cm,Soil Temperature at depth 1 m,Soil Moisture/CS616 Period at depth 30 cm,Soil Water/Watermark Conductance at height 43 cm,Soil Water/Watermark Conductance at height 38 cm,Soil Moisture/CS616 Period -1.4 to -1.7 m,Soil Moisture/CS616 Period -0.1 to -0.4 m,Soil Moisture/CS616 Period -0.7 to -1 m,Soil Moisture/CS616 Period -1.05 to -1.35 m,Soil Water/Watermark Conductance at depth 1.15 m,Soil Moisture/CS615 Period -1.05 to -1.35 m,Soil Water/Watermark Conductance at depth 1.33 m,Soil Water/Watermark Conductance at depth 29 cm,Soil Water/Watermark Conductance at depth 55 cm,Soil Water/Watermark Conductance at depth 1.47 m","[{'subject': 'Soil temperature, soil moisture content', 'subjectScheme': 'main'}, {'subject': 'Sahelian climate', 'subjectScheme': 'main'}, {'subject': 'Soil Moisture/CS616 Period at depth 5 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period -0.4 to -0.7 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 5 cm (2)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 28 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 11 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at height 1.19 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 2.3 m', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 70 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 50 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 15 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1.15 m', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 15 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 2 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 2.47 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS615 Period -0.7 to -1 m', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 30 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 1.97 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 47 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 2.27 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period -1.3 to -1.6 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 1.5 m', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1.5 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at height 86 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 2.5 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at height 28 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 85 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 25 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 85 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 30 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 25 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 55 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 30 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at height 43 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at height 38 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period -1.4 to -1.7 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period -0.1 to -0.4 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period -0.7 to -1 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period -1.05 to -1.35 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 1.15 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS615 Period -1.05 to -1.35 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 1.33 m', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 29 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 55 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Water/Watermark Conductance at depth 1.47 m', 'subjectScheme': 'var'}]",,"['CSV', 'NETCDF', 'O&amp;M 1.0']"
 10.48537/10.48537/hal-03220247,Atmosphere and the Anthropogenic Metapolis,Reseau International Ambiances,2020,en,Text,Creative Commons Attribution Non Commercial No Derivatives 2.0 Generic,"This article explores connections between the concepts of atmosphere, Anthropocene and contemporary urbanity. First, contemporary urbanity is specified as Metapolis composed of different assemblies of density and heterogeneity. Second, the aisthetic atmospheres of the European Metapolis are portrayed as intensified in the historical centres and pluralized throughout the Metapolis. Third, the Metapolis is connected to the concept of the Anthropocene identified as the Great Acceleration. Fourth, the atmospheric and the anthropogenic aspects are assembled under the headings of the weather, atmospheric atten- tiveness to the Anthropocene and atmospheric aspects of Metapolitan climate politics.",fabrica,True,findable,0,0,0,0,0,2021-06-16T16:24:43.000Z,2021-06-16T16:24:43.000Z,jbru.aau,jbru,"Air,Anthropocene,Atmosphere,Great Acceleration,Metapolis","[{'lang': 'en', 'subject': 'Air'}, {'lang': 'en', 'subject': 'Anthropocene'}, {'lang': 'en', 'subject': 'Atmosphere'}, {'lang': 'en', 'subject': 'Great Acceleration'}, {'lang': 'en', 'subject': 'Metapolis'}]",['6 pages'],['application/pdf']
 10.5281/zenodo.7015277,"Supplemental information data from: ""Evidence for amorphous sulfates as the main carrier of soil hydration in Gale crater, Mars""",Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This dataset includes the target name and chemical composition of each ChemCam sequence used in the above-mentioned article. The quantification for each ChemCam spectrum in the soils of the Bradbury, Rocknest and Yellowknife Bay area are in percentage mass fractions (wt.%) for most major oxides (i.e., SiO2, TiO2, Al2O3, FeOT , MgO, CaO, Na2O, K2O). Sulfur and hydrogen abundances are expressed respectively in peak area (normalized) and ICA H scores.",mds,True,findable,0,0,0,0,0,2022-08-22T16:01:22.000Z,2022-08-22T16:01:22.000Z,cern.zenodo,cern,"Mars,ChemCam,Laser-Induced Breakdown Spectroscopy,Soils","[{'subject': 'Mars'}, {'subject': 'ChemCam'}, {'subject': 'Laser-Induced Breakdown Spectroscopy'}, {'subject': 'Soils'}]",,
-10.60662/0yc3-e898,Vers un service générique d’aide à la décision pour gérer un logement basé sur des techniques d’apprentissage interactif et coopératif,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T19:58:37.000Z,2023-09-01T19:58:37.000Z,uqtr.mesxqq,uqtr,,,,
 10.17178/emaa_ortho-h2o_rotation_0d2ed16f,Rotation excitation of ortho-H2O by H and electron collisions,"UGA, CNRS, CNRS-INSU, OSUG",2022,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
 This research has made use of spectroscopic and collisional data from the EMAA database (https://emaa.osug.fr). EMAA is supported by the Observatoire des Sciences de l'Univers de Grenoble (OSUG),Creative Commons Attribution 4.0 International,Data access and use are ruled by the EMAA data policy.",45 rotation energy levels / 124 radiative transitions / 990 collisional transitions for H (14 temperatures in the range 5-1500K) / 91 collisional transitions for electron (9 temperatures in the range 10-500K),mds,True,findable,0,0,0,0,0,2022-02-07T11:25:37.000Z,2022-02-07T11:25:38.000Z,inist.osug,jbru,"target ortho-H2O,excitationType Rotation,collisional excitation,collider.0 H,collider.1 electron,astrophysics,interstellar medium,comets,circumstellar medium,gas,microwave spectroscopy,infrared spectroscopy,rotational excitation,rovibrational excitation,vibrational excitation,electronic excitation,collisional rate coefficients,fine structure,hyperfine structure","[{'subject': 'target ortho-H2O', 'subjectScheme': 'main'}, {'subject': 'excitationType Rotation', 'subjectScheme': 'main'}, {'subject': 'collisional excitation', 'subjectScheme': 'main'}, {'subject': 'collider.0 H', 'subjectScheme': 'var'}, {'subject': 'collider.1 electron', 'subjectScheme': 'var'}, {'subject': 'astrophysics', 'subjectScheme': 'var'}, {'subject': 'interstellar medium', 'subjectScheme': 'var'}, {'subject': 'comets', 'subjectScheme': 'var'}, {'subject': 'circumstellar medium', 'subjectScheme': 'var'}, {'subject': 'gas', 'subjectScheme': 'var'}, {'subject': 'microwave spectroscopy', 'subjectScheme': 'var'}, {'subject': 'infrared spectroscopy', 'subjectScheme': 'var'}, {'subject': 'rotational excitation', 'subjectScheme': 'var'}, {'subject': 'rovibrational excitation', 'subjectScheme': 'var'}, {'subject': 'vibrational excitation', 'subjectScheme': 'var'}, {'subject': 'electronic excitation', 'subjectScheme': 'var'}, {'subject': 'collisional rate coefficients', 'subjectScheme': 'var'}, {'subject': 'fine structure', 'subjectScheme': 'var'}, {'subject': 'hyperfine structure', 'subjectScheme': 'var'}]",,['Radex']
 10.17178/emaa_c(15n)_hyperfine_a1b197e3,Hyperfine excitation of C[15N] by para-H2 collisions,"UGA, CNRS, CNRS-INSU, OSUG",2021,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
@@ -3495,7 +3462,6 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.5281/zenodo.7937759,Quantum mechanical modeling of the on-grain formation of acetaldehyde on H2O:CO dirty ice surfaces,Zenodo,2023,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This Supporting Material contains: Cartesian coordinates of HF-3c optimized minima and transition state for the reaction in gas phase, in .xyz format, computed using Gaussian16 code; Fractional coordinates of HF-3c optimized minima and trasition state structures for crystalline periodic models in .mol format, editable with MOLDRAW, computed using CRYSTAL17 computer code.",mds,True,findable,0,0,0,0,0,2023-08-12T07:23:25.000Z,2023-08-12T07:23:26.000Z,cern.zenodo,cern,,,,
 10.5281/zenodo.3696502,Pretrained parsing model for french with FlauBERT,Zenodo,2020,,Other,"Creative Commons Attribution 4.0 International,Open Access",Pretrained parsing models for French to use with https://github.com/mcoavoux/self-attentive-parser fork of https://github.com/nikitakit/self-attentive-parser parser. These are retrained models (results are slightly different from those reported in Flaubert paper https://arxiv.org/abs/1912.05372).,mds,True,findable,0,0,0,0,0,2020-03-04T09:38:15.000Z,2020-03-04T09:38:15.000Z,cern.zenodo,cern,,,,
 10.17178/ohmcv.dsd.val.12-16.1,"DSD network, Valescure",CNRS - OSUG - OREME,2012,en,Dataset,"Data access and use are ruled by the OHMCV data policy.,The following acknowledging sentence should appear in publications using OHMCV data and products: ""OHMCV is funded by the Institut National des Sciences de l’Univers (INSU/CNRS) and the Observatoire des Sciences de l’Univers de Grenoble (OSUG / Université Grenoble Alpes)”.",This dataset is part of the Cevennes‐Vivarais Mediterranean Hydrometeorological Observatory (OHMCV),mds,True,findable,0,0,1,0,0,2017-10-17T13:24:22.000Z,2017-10-17T13:24:22.000Z,inist.osug,jbru,"Atmospheric conditions,Precipitation Rate,Droplet Size,DISDROMETERS,Ground networks,Hydrometeorological sites,Fixed Observation Stations","[{'subject': 'Atmospheric conditions', 'subjectScheme': 'main'}, {'subject': 'Precipitation Rate', 'subjectScheme': 'main'}, {'subject': 'Droplet Size', 'subjectScheme': 'main'}, {'subject': 'DISDROMETERS', 'subjectScheme': 'main'}, {'subject': 'Ground networks', 'subjectScheme': 'main'}, {'subject': 'Hydrometeorological sites', 'subjectScheme': 'main'}, {'subject': 'Fixed Observation Stations', 'subjectScheme': 'main'}]",,['NETCDF']
-10.48649/asdc.1201,Caen vu par les médias. L'exemple de Ouest-France.,Atlas Social de Caen - e-ISSN : 2779-654X,2023,fr,JournalArticle,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"Comment l'agglomération de Caen est-elle représentée dans les médias ? Pourquoi certains lieux font-ils l'actualité et pas d'autres ? Quels sont les lieux qui ne sont jamais évoqués ? Quelle géographie des sujets médiatiques se dessine et quel en est le sens ? Pour répondre à ces questions, nous avons dépouillé tous les numéros du journal quotidien Ouest-France pour l'année 2019 puis réalisé une cartographie thématique ?",fabrica,True,findable,0,0,0,0,0,2023-06-23T12:32:59.000Z,2023-06-23T12:32:59.000Z,jbru.eso,jbru,"médias,conflit,aménagement,actualité","[{'subject': 'médias'}, {'subject': 'conflit'}, {'subject': 'aménagement'}, {'subject': 'actualité'}]",,
 10.17178/emaa_c(17o)_hyperfine_0ecc62b5,Hyperfine excitation of C[17O] by ortho-H2 and para-H2 collisions,"UGA, CNRS, CNRS-INSU, OSUG",2023,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
 This research has made use of spectroscopic and collisional data from the EMAA database (https://emaa.osug.fr). EMAA is supported by the Observatoire des Sciences de l'Univers de Grenoble (OSUG),Creative Commons Attribution 4.0 International,Data access and use are ruled by the EMAA data policy.",75 hyperfine energy levels / 176 radiative transitions / 2775 collisional transitions for para-H2 (11 temperatures in the range 5-400K) / 2775 collisional transitions for ortho-H2 (11 temperatures in the range 5-400K),mds,True,findable,0,0,0,0,0,2023-12-07T15:50:37.000Z,2023-12-07T15:50:37.000Z,inist.osug,jbru,"target C[17O],excitationType Hyperfine,collisional excitation,collider.0 para-H2,collider.1 ortho-H2,astrophysics,interstellar medium,comets,circumstellar medium,gas,microwave spectroscopy,infrared spectroscopy,rotational excitation,rovibrational excitation,vibrational excitation,electronic excitation,collisional rate coefficients,fine structure,hyperfine structure","[{'subject': 'target C[17O]', 'subjectScheme': 'main'}, {'subject': 'excitationType Hyperfine', 'subjectScheme': 'main'}, {'subject': 'collisional excitation', 'subjectScheme': 'main'}, {'subject': 'collider.0 para-H2', 'subjectScheme': 'var'}, {'subject': 'collider.1 ortho-H2', 'subjectScheme': 'var'}, {'subject': 'astrophysics', 'subjectScheme': 'var'}, {'subject': 'interstellar medium', 'subjectScheme': 'var'}, {'subject': 'comets', 'subjectScheme': 'var'}, {'subject': 'circumstellar medium', 'subjectScheme': 'var'}, {'subject': 'gas', 'subjectScheme': 'var'}, {'subject': 'microwave spectroscopy', 'subjectScheme': 'var'}, {'subject': 'infrared spectroscopy', 'subjectScheme': 'var'}, {'subject': 'rotational excitation', 'subjectScheme': 'var'}, {'subject': 'rovibrational excitation', 'subjectScheme': 'var'}, {'subject': 'vibrational excitation', 'subjectScheme': 'var'}, {'subject': 'electronic excitation', 'subjectScheme': 'var'}, {'subject': 'collisional rate coefficients', 'subjectScheme': 'var'}, {'subject': 'fine structure', 'subjectScheme': 'var'}, {'subject': 'hyperfine structure', 'subjectScheme': 'var'}]",,['Radex']
 10.17178/ohmcv.dsd.sou.12-16.1,"DSD network, La Souche",CNRS - OSUG - OREME,2012,en,Dataset,"Data access and use are ruled by the OHMCV data policy.,The following acknowledging sentence should appear in publications using OHMCV data and products: ""OHMCV is funded by the Institut National des Sciences de l’Univers (INSU/CNRS) and the Observatoire des Sciences de l’Univers de Grenoble (OSUG / Université Grenoble Alpes)”.",This dataset is part of the Cevennes‐Vivarais Mediterranean Hydrometeorological Observatory (OHMCV),mds,True,findable,0,0,1,0,0,2017-10-17T13:24:20.000Z,2017-10-17T13:24:20.000Z,inist.osug,jbru,"Atmospheric conditions,Precipitation,Precipitation Rate,Droplet Size,DISDROMETERS,Ground networks,Hydrometeorological sites,Fixed Observation Stations","[{'subject': 'Atmospheric conditions', 'subjectScheme': 'main'}, {'subject': 'Precipitation', 'subjectScheme': 'main'}, {'subject': 'Precipitation Rate', 'subjectScheme': 'main'}, {'subject': 'Droplet Size', 'subjectScheme': 'main'}, {'subject': 'DISDROMETERS', 'subjectScheme': 'main'}, {'subject': 'Ground networks', 'subjectScheme': 'main'}, {'subject': 'Hydrometeorological sites', 'subjectScheme': 'main'}, {'subject': 'Fixed Observation Stations', 'subjectScheme': 'main'}]",,['NETCDF']
@@ -3675,7 +3641,6 @@ Mandatory: cite the reference article and the DOI of the observatory
 Optional: cite the DOI of each dataset used.
 
 Co-authorship: depending on the contribution of the data to the scientific results obtained, the authors should either propose co-authorship to the data providers or at least acknowledge their contribution.","The northernmost site of the AMMA-CATCH observatory, the Gourma meso-scale site in Mali, is located between 14.5°N and 17.5°N in the Sahelian zone sensu stricto stretching mainly from the loop of the Niger River southward down to the border region with Burkina-Faso. It also reaches the Saharo-Sahelian transition zone, north of the Niger River. The climate is semi-arid, daytime air temperatures are always high and annual rainfall amounts (from about 100 mm in the northern part to about 450 mm in the southern part of the site) exhibit strong inter-annual and seasonal variations. The region is mainly pastoral and agriculture fields cover less than 1% of the Gourma. Measurements sites are organized along the north–south rainfall transect on two main types of soil surfaces and hydrologic systems which reveal sharp gradients in soil moisture, vegetation cover and energy budget: a) sandy soils with high water infiltration rates and limited run-off, that support an open tree savannah; b) shallow soils characterized by a poor water infiltration and a sparse vegetation, with more concentrated run-off that ends in pools or low lands within structured endorheic watersheds. Seasonally inundated lowlands are covered by open Acacia forests. Since 2010, due to security issues, field measurements are restricted within the Hombori super-site.",mds,True,findable,0,0,1,0,0,2018-03-16T15:37:19.000Z,2018-03-16T15:37:19.000Z,inist.osug,jbru,"Sahelian climate,Precipitation,Surface water,Meteo,Flux,Radiation,Vegetation,Soils,Water quality / Water chemistry","[{'subject': 'Sahelian climate', 'subjectScheme': 'main'}, {'subject': 'Precipitation', 'subjectScheme': 'var'}, {'subject': 'Surface water', 'subjectScheme': 'var'}, {'subject': 'Meteo', 'subjectScheme': 'var'}, {'subject': 'Flux', 'subjectScheme': 'var'}, {'subject': 'Radiation', 'subjectScheme': 'var'}, {'subject': 'Vegetation', 'subjectScheme': 'var'}, {'subject': 'Soils', 'subjectScheme': 'var'}, {'subject': 'Water quality / Water chemistry', 'subjectScheme': 'var'}]",,"['CSV', 'NETCDF', 'O&amp;M 1.0']"
-10.5281/zenodo.10205580,Proceedings of the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7).[on line],Université Grenoble-Alpes,2023,en,ConferenceProceeding,Creative Commons Attribution 4.0 International,"This is the online, compiled proceedings from the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) which was held May 18–20, 2022 at Université Grenoble-Alpes, France. It includes 23 double-blind, peer-reviewed chapters written by authors from several countries, an introduction and a thematic index, and is licensed under the Creative Commons Attribution 4.0 International License. (To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.)",api,True,findable,0,0,0,0,0,2023-11-25T08:33:50.000Z,2023-11-25T08:33:50.000Z,cern.zenodo,cern,"English pronunciation,second language pronunciation,language learning,language teaching,second language acquisition,phonetics,phonology,English pronunciation research","[{'subject': 'English pronunciation'}, {'subject': 'second language pronunciation'}, {'subject': 'language learning'}, {'subject': 'language teaching'}, {'subject': 'second language acquisition'}, {'subject': 'phonetics'}, {'subject': 'phonology'}, {'subject': 'English pronunciation research'}]",,
 10.5281/zenodo.10400476,Supplementary Data to journal publication on 'The Foundations of the Patagonian Icefields',Zenodo,2023,en,Dataset,Creative Commons Attribution 4.0 International,Partitioning and comparison of ice discharge estimates from the the Patagonian Icefields comprising associated uncertainties. For further details please refer to the notes in the individual files and/or consult the associated publication entitled 'The Foundations of the Patagonian Icefields' published in Communications Earth & Environment.,api,True,findable,0,0,0,0,0,2023-12-18T09:11:12.000Z,2023-12-18T09:11:13.000Z,cern.zenodo,cern,"Patagonia,icefield,discharge,thickness","[{'subject': 'Patagonia'}, {'subject': 'icefield'}, {'subject': 'discharge'}, {'subject': 'thickness'}]",,
 10.6084/m9.figshare.c.6950873.v1,Effects of a physical activity and endometriosis-based education program delivered by videoconference on endometriosis symptoms: the CRESCENDO program (inCRease physical Exercise and Sport to Combat ENDOmetriosis) protocol study,figshare,2023,,Collection,Creative Commons Attribution 4.0 International,"Abstract Background Endometriosis is a chronic disease characterized by growth of endometrial tissue outside the uterine cavity which could affect 200 million women (The term “woman” is used for convenience. Individuals gendered as man or as nonbinary can also suffer from this disease) worldwide. One of the most common symptoms of endometriosis is pelvic chronic pain associated with fatigue. This pain can cause psychological distress and interpersonal difficulties. As for several chronic diseases, adapted physical activity could help to manage the physical and psychological symptoms. The present study will investigate the effects of a videoconference-based adapted physical activity combined with endometriosis-based education program on quality of life, pain, fatigue, and other psychological symptoms and on physical activity. Methods This multicentric randomized-controlled trial will propose to 200 patients with endometriosis to be part of a trial which includes a 6-month program with 45 min to more than 120 min a week of adapted physical activity and/or 12 sessions of endometriosis-based education program. Effects of the program will be compared to a control group in which patients will be placed on a waiting list. All participants will be followed up 3 and 6 months after the intervention. None of the participants will be blind to the allocated trial arm. The primary outcome measure will be quality of life. Secondary outcomes will include endometriosis-related perceived pain, fatigue, physical activity, and also self-image, stereotypes, motivational variables, perceived support, kinesiophobia, basic psychological need related to physical activity, and physical activity barriers. General linear models and multilevel models will be performed. Predictor, moderator, and mediator variables will be investigated. Discussion This study is one of the first trials to test the effects of a combined adapted physical activity and education program for improving endometriosis symptoms and physical activity. The results will help to improve care for patients with endometriosis. Trial registration ClinicalTrials.gov, NCT05831735 . Date of registration: April 25, 2023",mds,True,findable,0,0,0,0,0,2023-11-28T04:40:38.000Z,2023-11-28T04:40:38.000Z,figshare.ars,otjm,"Medicine,Genetics,FOS: Biological sciences,Physiology,Science Policy,Sociology,FOS: Sociology,Biological Sciences not elsewhere classified","[{'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': 'Physiology'}, {'subject': 'Science Policy'}, {'subject': 'Sociology'}, {'subject': 'FOS: Sociology', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'Biological Sciences not elsewhere classified'}]",,
 10.18709/perscido.2020.06.ds299,GreEn-ER - Dataset of electricity consumption,PerSciDo,2020,en,Dataset,,Dataset of electricity consumption of the GreEn-ER Building Located in Grenoble,fabrica,True,findable,0,0,0,0,0,2020-06-18T11:27:55.000Z,2020-06-18T11:27:55.000Z,inist.persyval,vcob,"Computer Science,Engineering","[{'lang': 'en', 'subject': 'Computer Science'}, {'lang': 'en', 'subject': 'Engineering'}]",['62.42 MB'],['csv']
@@ -3721,7 +3686,6 @@ Co-authorship: depending on the contribution of the data to the scientific resul
 10.17178/emaa_(13c)o_rotation_922483ff,"Rotation excitation of [13C]O by CO, ortho-H2, ortho-H2O, para-H2 and para-H2O collisions","UGA, CNRS, CNRS-INSU, OSUG",2023,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
 This research has made use of spectroscopic and collisional data from the EMAA database (https://emaa.osug.fr). EMAA is supported by the Observatoire des Sciences de l'Univers de Grenoble (OSUG),Creative Commons Attribution 4.0 International,Data access and use are ruled by the EMAA data policy.",14 rotation energy levels / 13 radiative transitions / 91 collisional transitions for para-H2 (11 temperatures in the range 5-400K) / 91 collisional transitions for ortho-H2 (11 temperatures in the range 5-400K) / 55 collisional transitions for para-H2O (20 temperatures in the range 5-100K) / 55 collisional transitions for ortho-H2O (20 temperatures in the range 5-100K) / 55 collisional transitions for CO (15 temperatures in the range 10-150K),mds,True,findable,0,0,0,0,0,2023-12-07T15:50:25.000Z,2023-12-07T15:50:25.000Z,inist.osug,jbru,"target [13C]O,excitationType Rotation,collisional excitation,collider.0 para-H2,collider.1 ortho-H2,collider.2 para-H2O,collider.3 ortho-H2O,collider.4 CO,astrophysics,interstellar medium,comets,circumstellar medium,gas,microwave spectroscopy,infrared spectroscopy,rotational excitation,rovibrational excitation,vibrational excitation,electronic excitation,collisional rate coefficients,fine structure,hyperfine structure","[{'subject': 'target [13C]O', 'subjectScheme': 'main'}, {'subject': 'excitationType Rotation', 'subjectScheme': 'main'}, {'subject': 'collisional excitation', 'subjectScheme': 'main'}, {'subject': 'collider.0 para-H2', 'subjectScheme': 'var'}, {'subject': 'collider.1 ortho-H2', 'subjectScheme': 'var'}, {'subject': 'collider.2 para-H2O', 'subjectScheme': 'var'}, {'subject': 'collider.3 ortho-H2O', 'subjectScheme': 'var'}, {'subject': 'collider.4 CO', 'subjectScheme': 'var'}, {'subject': 'astrophysics', 'subjectScheme': 'var'}, {'subject': 'interstellar medium', 'subjectScheme': 'var'}, {'subject': 'comets', 'subjectScheme': 'var'}, {'subject': 'circumstellar medium', 'subjectScheme': 'var'}, {'subject': 'gas', 'subjectScheme': 'var'}, {'subject': 'microwave spectroscopy', 'subjectScheme': 'var'}, {'subject': 'infrared spectroscopy', 'subjectScheme': 'var'}, {'subject': 'rotational excitation', 'subjectScheme': 'var'}, {'subject': 'rovibrational excitation', 'subjectScheme': 'var'}, {'subject': 'vibrational excitation', 'subjectScheme': 'var'}, {'subject': 'electronic excitation', 'subjectScheme': 'var'}, {'subject': 'collisional rate coefficients', 'subjectScheme': 'var'}, {'subject': 'fine structure', 'subjectScheme': 'var'}, {'subject': 'hyperfine structure', 'subjectScheme': 'var'}]",,['Radex']
 10.48537/hal-03220313,"Fragile entities at work in ambiances, Understanding ambiances from the cultures and practices of the invisible",Reseau International Ambiances,2020,en,Text,Creative Commons Attribution Non Commercial No Derivatives 2.0 Generic,"Through her investigation of fragile entities, philosopher of science Vinciane Despret sheds new light on our relationship to the deceased. Through a relation of obligations, the dead put the living to work. The living take care of their deceased by choosing to respond to the call. Practices of memory modify, densify and colour our under-standing of our living environment and create specific ambiances. Focused on the singular link of a Georgian family with their deceased, Nino Kirtadze’s film, Tell my friends that I’m dead, enlightens the full scope of these fragile entities at work on the existing. Through this documentary, we identify the invisible chains that generate ambiances above ground and outside time. Furthermore, transgenerational psychoanalyst Christine Ulivucci conducted critical analysis showing that ambiance memories, sensitive impressions are rooted in our personal history. The encounter with familiar places or objects activates reminiscence and produces what we call: fragile ambiances.",mds,True,findable,0,0,0,0,0,2021-06-17T09:44:22.000Z,2021-06-17T09:44:22.000Z,jbru.aau,jbru,"Fragile Entities,Memory,Dead,House,Cult of the Dead,Transgenerational Psychoanalysis,Fragile Ambiances","[{'lang': 'eng', 'subject': 'Fragile Entities'}, {'lang': 'eng', 'subject': 'Memory'}, {'lang': 'eng', 'subject': 'Dead'}, {'lang': 'eng', 'subject': 'House'}, {'lang': 'eng', 'subject': 'Cult of the Dead'}, {'lang': 'eng', 'subject': 'Transgenerational Psychoanalysis'}, {'lang': 'eng', 'subject': 'Fragile Ambiances'}]",['6 pages'],['application/pdf']
-10.5281/zenodo.10205579,Proceedings of the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7).[on line],Université Grenoble-Alpes,2023,en,ConferenceProceeding,Creative Commons Attribution 4.0 International,"This is the online, compiled proceedings from the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) which was held May 18–20, 2022 at Université Grenoble-Alpes, France. It includes 23 double-blind, peer-reviewed chapters written by authors from several countries, an introduction and a thematic index, and is licensed under the Creative Commons Attribution 4.0 International License. (To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.)",api,True,findable,0,0,0,0,0,2023-11-25T08:33:51.000Z,2023-11-25T08:33:51.000Z,cern.zenodo,cern,"English pronunciation,second language pronunciation,language learning,language teaching,second language acquisition,phonetics,phonology,English pronunciation research","[{'subject': 'English pronunciation'}, {'subject': 'second language pronunciation'}, {'subject': 'language learning'}, {'subject': 'language teaching'}, {'subject': 'second language acquisition'}, {'subject': 'phonetics'}, {'subject': 'phonology'}, {'subject': 'English pronunciation research'}]",,
 10.48537/hal-03220306,"The Way of Ambiances: Scientific Practices, Artistic Practices, Session 15 – Introduction",Reseau International Ambiances,2020,en,Text,Creative Commons Attribution Non Commercial No Derivatives 2.0 Generic,,mds,True,findable,0,0,0,0,0,2021-06-17T20:45:54.000Z,2021-06-17T20:45:55.000Z,jbru.aau,jbru,,,['1 pages'],['application/pdf']
 10.17178/emaa_ortho-(13c)c2h2_rotation_a4239764,Rotation excitation of ortho-c-[13C]C2H2 by He and para-H2 collisions,"UGA, CNRS, CNRS-INSU, OSUG",2023,en,Dataset,"Please acknowledge the use of EMAA Database by citing the original articles in which the data were published (BibTeX format available), and adding the following sentence in your publication: 
 This research has made use of spectroscopic and collisional data from the EMAA database (https://emaa.osug.fr). EMAA is supported by the Observatoire des Sciences de l'Univers de Grenoble (OSUG),Creative Commons Attribution 4.0 International,Data access and use are ruled by the EMAA data policy.",49 rotation energy levels / 109 radiative transitions / 1176 collisional transitions for para-H2 (9 temperatures in the range 5-120K) / 1176 collisional transitions for He (9 temperatures in the range 5-120K),mds,True,findable,0,0,0,0,0,2023-12-07T15:51:34.000Z,2023-12-07T15:51:35.000Z,inist.osug,jbru,"target ortho-c-[13C]C2H2,excitationType Rotation,collisional excitation,collider.0 para-H2,collider.1 He,astrophysics,interstellar medium,comets,circumstellar medium,gas,microwave spectroscopy,infrared spectroscopy,rotational excitation,rovibrational excitation,vibrational excitation,electronic excitation,collisional rate coefficients,fine structure,hyperfine structure","[{'subject': 'target ortho-c-[13C]C2H2', 'subjectScheme': 'main'}, {'subject': 'excitationType Rotation', 'subjectScheme': 'main'}, {'subject': 'collisional excitation', 'subjectScheme': 'main'}, {'subject': 'collider.0 para-H2', 'subjectScheme': 'var'}, {'subject': 'collider.1 He', 'subjectScheme': 'var'}, {'subject': 'astrophysics', 'subjectScheme': 'var'}, {'subject': 'interstellar medium', 'subjectScheme': 'var'}, {'subject': 'comets', 'subjectScheme': 'var'}, {'subject': 'circumstellar medium', 'subjectScheme': 'var'}, {'subject': 'gas', 'subjectScheme': 'var'}, {'subject': 'microwave spectroscopy', 'subjectScheme': 'var'}, {'subject': 'infrared spectroscopy', 'subjectScheme': 'var'}, {'subject': 'rotational excitation', 'subjectScheme': 'var'}, {'subject': 'rovibrational excitation', 'subjectScheme': 'var'}, {'subject': 'vibrational excitation', 'subjectScheme': 'var'}, {'subject': 'electronic excitation', 'subjectScheme': 'var'}, {'subject': 'collisional rate coefficients', 'subjectScheme': 'var'}, {'subject': 'fine structure', 'subjectScheme': 'var'}, {'subject': 'hyperfine structure', 'subjectScheme': 'var'}]",,['Radex']
@@ -3737,7 +3701,6 @@ This research has made use of spectroscopic and collisional data from the EMAA d
 10.18709/perscido.2023.12.ds403,Data repository of the paper by Schwartz et al. in Communications earth & environment,PerSCiDO,2023,,Dataset,,"This data repository provides the seismic data used in the paper by Schwartz et al. ""Role of mantle indentation in collisional deformation evidenced by deep geophysical imaging of Western Alps"". ",api,True,findable,0,0,0,0,0,2023-12-11T10:01:56.000Z,2023-12-11T10:01:56.000Z,inist.persyval,vcob,"Geology,FOS: Earth and related environmental sciences","[{'subject': 'Geology', 'subjectScheme': 'http://www.radar-projekt.org/display/Geological_Sciences'}, {'subject': 'FOS: Earth and related environmental sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",['10 Mo'],
 10.5281/zenodo.7054555,"Dataset of ""PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing""",Zenodo,2022,,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This is the underlying dataset of ""PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing""",mds,True,findable,0,0,0,0,0,2022-10-18T16:12:00.000Z,2022-10-18T16:12:01.000Z,cern.zenodo,cern,"Polymer Electrolyte Membrane Fuel Cell,Dynamic load cycle,Local degradation,Automotive,Catalyst layer durability,Degradation mechanism","[{'subject': 'Polymer Electrolyte Membrane Fuel Cell'}, {'subject': 'Dynamic load cycle'}, {'subject': 'Local degradation'}, {'subject': 'Automotive'}, {'subject': 'Catalyst layer durability'}, {'subject': 'Degradation mechanism'}]",,
 10.5281/zenodo.3606016,Modifications of the plant-pollinator network structure and species' roles along a gradient of urbanization,Zenodo,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Open Access","This file includes data and codes used in the article titled: "" Modifications of the plant-pollinator network structure and species’ roles along a gradient of urbanization"". Data include plant-pollinator interactions sampled in each site (1-12) at each sampling event (6 events) in the three urbanization classes (low, medium, high). Each row is a single insect pollinator X plant interaction. Full species names and abbreviations used in figures in the Supplementary Information are reported.<br> The data file is .txt with tab-separated values.",mds,True,findable,1,0,0,0,0,2020-01-13T10:19:33.000Z,2020-01-13T10:19:34.000Z,cern.zenodo,cern,"bees, beta-diversity, conservation biology, global changes, hoverflies, interaction diversity, land-use change, motifs, mutualistic networks, pollinators, plant-pollinator interactions, urbanization","[{'subject': 'bees, beta-diversity, conservation biology, global changes, hoverflies, interaction diversity, land-use change, motifs, mutualistic networks, pollinators, plant-pollinator interactions, urbanization'}]",,
-10.57757/iugg23-4534,Deep-learning-based phase picking in SeisComP using SeisBench,GFZ German Research Centre for Geosciences,2023,en,ConferencePaper,Creative Commons Attribution 4.0 International,"<!--!introduction!--><b></b><p>The open-source, seismological software package SeisComP is widely used for seismic monitoring world-wide.&nbsp; Its automatic phase picking module consists of an STA/LTA-based P-wave detector augmented by an AIC onset picker.&nbsp; With proper configuration, it allows detection and accurate onset picking for a wide range of seismic signals. However, it cannot match the performance of experienced analysts.&nbsp; Especially broadband data are often challenging for phase pickers due to the enormous variety of the signals of interest. <br><br>In order to optimize quality and number of automatic picks and reduce time-consuming manual revision, we chose to develop a machine-learning repicker module for SeisComP based on the SeisBench framework. SeisBench supports several deep-learning pickers and comes pre-trained for different benchmark datasets, one of which was contributed by GFZ Potsdam.<br><br>The repicking module consists of several submodules that interact with both SeisComP and SeisBench via their Python interfaces. The current workflow is based on existing locations generated with a classical SeisComP setup. Shortly after event detection and preliminary location, our repicker (1) starts to repick previously picked onsets and (2) attempts to generate additional picks.<br><br>Preliminary results are encouraging. The deep-learning repicker substantially improves pick quality over a large frequency range. The number of picks available per event has approximately doubled and the publication delay is often reduced, especially for small events. The total number of published events has increased by about 20 per cent.</p>",fabricaForm,True,findable,0,0,0,0,0,2023-07-03T19:58:01.000Z,2023-07-11T19:19:09.000Z,gfz.iugg2023,gfz,,,,
 10.6084/m9.figshare.23822154.v1,Dataset for the main 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,Data for the main experiment in CSV format.,mds,True,findable,0,0,0,0,0,2023-08-02T11:18:26.000Z,2023-08-02T11:18:26.000Z,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'}]",['29026 Bytes'],
 10.5281/zenodo.4964221,"FIGURES 35–38. Protonemura risi, 35 in Two new species of Protonemura Kempny, 1898 (Plecoptera: Nemouridae) from the Italian Alps",Zenodo,2021,,Image,Open Access,"FIGURES 35–38. Protonemura risi, 35. male, epiproct, lateral view. 36. male, paraproct median lobe and outer lobe with bifurcated sclerite. 37. female, ventral view (Jura Mountains). 38. female, ventral view (Massif central, northern flank)",mds,True,findable,0,0,0,0,0,2021-06-16T08:25:42.000Z,2021-06-16T08:25:43.000Z,cern.zenodo,cern,"Biodiversity,Taxonomy","[{'subject': 'Biodiversity'}, {'subject': 'Taxonomy'}]",,
 10.17178/erosion_model.2020,Modeled contributions of sediment sources to total suspended sediment flux in two mesoscale catchments,UGA,2020,en,Dataset,"Creative Commons Attribution 4.0 International,Data access and use are ruled by the CC-BY 4.0 license.","The application enables to show the contribution of erosion zones that act as sediment sources to total suspended sediment load in percent simulated with the IBER soil erosion model. Model output can be visualized for two mesoscale Mediterranean catchments in southeastern France, the 42 km2 Claduègne catchment and the 20 km2 Galabre catchment and for different sets of scenarios: (i) CDA threshold: The threshold of contributing drainage area (CDA) defines the length of the river network. Values were varied from 15 ha to 500 ha. (ii) Manning's n: river: Manning's roughness parameter in the river network. Values were varied from 0.025 to 0.1. (iii) Manning's n: hillslopes: Manning's roughness parameter on the hillslopes. Values were varied from 0.2 to 0.8. (iv) Source classification: Source classification based on connectivity, i.e. sediment sources were subdivided based on their distance to the outlet and their distance to the river. In addition to the modeled source contributions the time series of rainfall intensity, liquid and solid discharge can be displayed.",mds,True,findable,0,0,0,0,0,2020-07-22T14:26:36.000Z,2020-07-22T14:26:37.000Z,inist.osug,jbru,erosion model,"[{'subject': 'erosion model', 'subjectScheme': 'main'}]",,"['.rep', '.R']"
@@ -3931,3 +3894,145 @@ Mandatory: cite the reference article and the DOI of the observatory
 Optional: cite the DOI of each dataset used.
 
 Co-authorship: depending on the contribution of the data to the scientific results obtained, the authors should either propose co-authorship to the data providers or at least acknowledge their contribution.",Measure components of the local-scale energy budget. Obtain forcing and validation data for modeling of soil-vegetation-atmosphere exchanges in Sahel. Contribute to the flux station network over the AMMA regional transect.,mds,True,findable,0,0,1,0,0,2018-03-16T15:36:51.000Z,2018-03-16T15:36:51.000Z,inist.osug,jbru,"Land surface exchange, water budget, energy budget, sahelian vegetation, evapo-transpiration, sahelian hydrology,Sahelian climate,Wind Speed,Soil Moisture/CS650 Period at depth 10 cm (loc. b),Soil Temperature at depth 10 cm,Standard Deviation of Wind Direction (2),Soil Moisture/CS616 Period at depth 50 cm (loc. b),Soil Moisture/CS616 Period at depth 1 m (loc. b),Sensible Heat Flux,Wind Speed (2),Precipitation Amount (previous 30 minutes),Soil Heat Flux at depth 5 cm,Soil Moisture/CS616 Period at depth 1.5 m (loc. b),Soil Moisture/Water Content at depth 10 cm (loc. b),Wind Direction (2),Soil Moisture/CS616 Period at depth 2.5 m,Precipitation Rate,Soil Temperature at depth 50 cm,Precipitation Amount (since January 1),Soil Moisture/CS616 Period at depth 2 m (loc. b),Soil Moisture/CS616 Period at depth 2.5 m (loc. b),Relative Humidity,Soil Temperature at depth 2.5 m,Soil Moisture/Water Content at depth 1 m (loc. b),Soil Moisture/CS650 Period at depth 2 m (loc. b),Soil Moisture/CS616 Period at depth 1 m,Soil Moisture/CS616 Period at depth 10 cm,Outgoing Longwave Radiation,Carbon Dioxide Flux,Soil Moisture/CS650 Period at depth 2.5 m (loc. b),Soil Temperature at depth 1.5 m,Carbon Dioxide Mean Concentration,Incoming Shortwave Radiation,Soil Heat Flux at depth 5 cm (3),Soil Temperature at depth 50 cm (loc. b),Soil Moisture/Water Content at depth 1.5 m (loc. b),Soil Moisture/Water Content at depth 2.5 m (loc. b),Soil Temperature at depth 1 m (loc. b),Wind Direction,Soil Moisture/Water Content at depth 50 cm (loc. b),Soil Moisture/CS650 Period at depth 1.5 m (loc. b),Soil Temperature at depth 1.5 m (loc. b),Latent Heat Flux,Soil Moisture/CS616 Period at depth 10 cm (loc. b),Soil Temperature at depth 2 m,Air Temperature,Soil Moisture/CS616 Period at depth 1.5 m,Soil Moisture/CS616 Period at depth 2 m,Soil Temperature at depth 2.5 m (loc. b),Soil Temperature at depth 1 m,Soil Temperature at depth 2 m (loc. b),Standard Deviation of Wind Direction,Soil Moisture/CS650 Period at depth 50 cm (loc. b),Soil Moisture/CS616 Period at depth 50 cm,Soil Moisture/Water Content at depth 2 m (loc. b),Soil Temperature at depth 10 cm (loc. b),Outgoing Shortwave Radiation,Precipitation Amount (since last tip),Soil Moisture/CS650 Period at depth 1 m (loc. b),Soil Heat Flux at depth 5 cm (2),Incoming Longwave Radiation","[{'subject': 'Land surface exchange, water budget, energy budget, sahelian vegetation, evapo-transpiration, sahelian hydrology', 'subjectScheme': 'main'}, {'subject': 'Sahelian climate', 'subjectScheme': 'main'}, {'subject': 'Wind Speed', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS650 Period at depth 10 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 10 cm', 'subjectScheme': 'var'}, {'subject': 'Standard Deviation of Wind Direction (2)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 50 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 1 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Sensible Heat Flux', 'subjectScheme': 'var'}, {'subject': 'Wind Speed (2)', 'subjectScheme': 'var'}, {'subject': 'Precipitation Amount (previous 30 minutes)', 'subjectScheme': 'var'}, {'subject': 'Soil Heat Flux at depth 5 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 1.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/Water Content at depth 10 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Wind Direction (2)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 2.5 m', 'subjectScheme': 'var'}, {'subject': 'Precipitation Rate', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 50 cm', 'subjectScheme': 'var'}, {'subject': 'Precipitation Amount (since January 1)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 2 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 2.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Relative Humidity', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 2.5 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/Water Content at depth 1 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS650 Period at depth 2 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 1 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 10 cm', 'subjectScheme': 'var'}, {'subject': 'Outgoing Longwave Radiation', 'subjectScheme': 'var'}, {'subject': 'Carbon Dioxide Flux', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS650 Period at depth 2.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1.5 m', 'subjectScheme': 'var'}, {'subject': 'Carbon Dioxide Mean Concentration', 'subjectScheme': 'var'}, {'subject': 'Incoming Shortwave Radiation', 'subjectScheme': 'var'}, {'subject': 'Soil Heat Flux at depth 5 cm (3)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 50 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/Water Content at depth 1.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/Water Content at depth 2.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Wind Direction', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/Water Content at depth 50 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS650 Period at depth 1.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Latent Heat Flux', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 10 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 2 m', 'subjectScheme': 'var'}, {'subject': 'Air Temperature', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 1.5 m', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 2 m', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 2.5 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 1 m', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 2 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Standard Deviation of Wind Direction', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS650 Period at depth 50 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS616 Period at depth 50 cm', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/Water Content at depth 2 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Temperature at depth 10 cm (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Outgoing Shortwave Radiation', 'subjectScheme': 'var'}, {'subject': 'Precipitation Amount (since last tip)', 'subjectScheme': 'var'}, {'subject': 'Soil Moisture/CS650 Period at depth 1 m (loc. b)', 'subjectScheme': 'var'}, {'subject': 'Soil Heat Flux at depth 5 cm (2)', 'subjectScheme': 'var'}, {'subject': 'Incoming Longwave Radiation', 'subjectScheme': 'var'}]",,"['CSV', 'NETCDF', 'O&amp;M 1.0']"
+10.34616/wse.2019.13.61.80,"Anti-Unism in a landscape of Unism : a revival of Avant-Garde in Dong Yue's work ""The Looming Storm""","Wydział Prawa, Administracji i Ekonomii Uniwersytetu Wrocławskiego",2019,,JournalArticle,,,fabricaForm,True,findable,0,0,0,0,0,2021-09-20T11:37:56.000Z,2021-09-20T12:08:39.000Z,psnc.uwr,dxmj,,,,
+10.34847/nkl.b1cb3arm,"Taciti et C. Velleii Paterculi scripta quae exstant; recognita, emaculata. Additique commentarii copiosissimi et notae non antea editae Paris e typographia Petri Chevalier, in monte diui Hilarii - II-0689",NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Image,,,api,True,findable,0,0,0,0,0,2023-01-17T17:24:40.000Z,2023-01-17T17:24:41.000Z,inist.humanum,jbru,,,['52872938 Bytes'],['image/tiff']
+10.34847/nkl.345bf9i7,"Le Havre city centre: isovists, Min. ellipse, descriptors",NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Dataset,,,api,True,findable,0,0,0,0,0,2022-11-29T04:23:48.000Z,2022-11-29T04:23:48.000Z,inist.humanum,jbru,,,['1872762 Bytes'],['application/zip']
+10.34847/nkl.aacad5y8,Bulletin franco-italien 1912 n°1 janvier - février,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/01 (A4,N1)-1912/02.",api,True,findable,0,0,0,0,0,2022-06-29T10:15:11.000Z,2022-06-29T10:15:12.000Z,inist.humanum,jbru,Etudes italiennes,[{'subject': 'Etudes italiennes'}],"['21511939 Bytes', '20908048 Bytes', '21034528 Bytes', '21036982 Bytes', '20976655 Bytes', '20863780 Bytes', '20901724 Bytes', '21007228 Bytes', '20950432 Bytes', '21051706 Bytes', '20663584 Bytes', '21281992 Bytes', '20952853 Bytes', '20689258 Bytes', '20872804 Bytes', '21019279 Bytes', '20934214 Bytes', '6436585 Bytes', '21037318 Bytes']","['image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'application/pdf', 'image/tiff']"
+10.5281/zenodo.10392426,"Data for ""An autonomous quantum machine to measure the thermodynamic arrow of time""",Zenodo,2023,,Dataset,Creative Commons Attribution 4.0 International,"Numerical simulation data from the article ""An autonomous quantum machine to measure the thermodynamic arrow of time""
+
+J. Monsel, C. Elouard, A. Auffèves npj Quantum Inf 4, 59 (2018). https://doi.org/10.1038/s41534-018-0109-8
+
+See the jupyter notebook for the data analysis and figures.
+
+The code to perform the numerical simulations is given in the repository https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system.",api,True,findable,0,0,0,0,0,2023-12-18T17:28:47.000Z,2023-12-18T17:28:48.000Z,cern.zenodo,cern,"quantum thermodynamics,optomechanics,fluctuation theorems,open quantum systems","[{'subject': 'quantum thermodynamics'}, {'subject': 'optomechanics'}, {'subject': 'fluctuation theorems'}, {'subject': 'open quantum systems'}]",,
+10.5281/zenodo.10344112,palantiri,Zenodo,2023,,Image,Creative Commons Attribution 4.0 International,Olivier KLEIN,api,True,findable,0,0,0,0,0,2023-12-10T22:24:40.000Z,2023-12-10T22:24:41.000Z,cern.zenodo,cern,,,,
+10.34847/nkl.e1e41vdi,fichier0,NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Image,,blablablablblaa,api,True,findable,0,0,0,0,0,2023-02-03T19:38:11.000Z,2023-02-03T19:38:11.000Z,inist.humanum,jbru,,,['20608 Bytes'],['image/png']
+10.34847/nkl.81dcdekj,Histoire de la Société d'études italiennes,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,,api,True,findable,0,0,0,0,0,2022-06-28T14:09:19.000Z,2022-06-28T14:09:19.000Z,inist.humanum,jbru,Etudes italiennes,"[{'lang': 'fr', 'subject': 'Etudes italiennes'}]","['9097465 Bytes', '7416683 Bytes', '9282460 Bytes', '8907544 Bytes', '8799070 Bytes', '8751142 Bytes', '8883043 Bytes', '8854078 Bytes', '9254668 Bytes', '8599864 Bytes', '8792680 Bytes', '8913379 Bytes', '9142621 Bytes', '9078376 Bytes', '8741896 Bytes', '8768425 Bytes', '9371872 Bytes', '10155217 Bytes', '8874292 Bytes', '8991184 Bytes', '9151126 Bytes', '9029416 Bytes', '8833840 Bytes', '8645749 Bytes', '8984503 Bytes', '9104779 Bytes', '9083704 Bytes', '9129640 Bytes', '8991916 Bytes', '8870686 Bytes', '8942368 Bytes', '8877484 Bytes', '8990458 Bytes', '8738002 Bytes', '9089236 Bytes', '8899528 Bytes', '9166045 Bytes', '8746906 Bytes', '8934232 Bytes', '9108013 Bytes', '8750344 Bytes', '8841088 Bytes', '9063529 Bytes', '8678512 Bytes', '9126250 Bytes', '8955814 Bytes', '9153598 Bytes', '9105520 Bytes', '8982352 Bytes', '9005464 Bytes', '9093784 Bytes', '8923579 Bytes', '8910184 Bytes', '8986132 Bytes', '8956915 Bytes', '8643076 Bytes', '8739199 Bytes', '8970193 Bytes', '8731336 Bytes', '8730004 Bytes', '8585428 Bytes', '8853844 Bytes', '9099775 Bytes', '8922001 Bytes', '9018292 Bytes', '8726236 Bytes', '8906944 Bytes', '8817862 Bytes', '8782159 Bytes', '8980324 Bytes', '9100354 Bytes', '8920492 Bytes', '8933611 Bytes', '9053359 Bytes', '8990599 Bytes', '8861440 Bytes', '9090760 Bytes', '9004756 Bytes', '9013276 Bytes', '9112240 Bytes', '9062704 Bytes', '9096484 Bytes', '9042016 Bytes', '9109333 Bytes', '8979859 Bytes', '9068872 Bytes', '9027544 Bytes', '8715541 Bytes', '8659984 Bytes', '8917960 Bytes', '8823898 Bytes', '8741800 Bytes', '8765539 Bytes', '9058498 Bytes', '8832010 Bytes', '9170812 Bytes', '9279169 Bytes', '9430825 Bytes', '10287784 Bytes', '75295 Bytes', '75304 Bytes']","['image/tiff', 'application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'application/json', 'application/json']"
+10.5281/zenodo.10408866,NeoGeographyToolkit/StereoPipeline: 2023-12-19-daily-build,Zenodo,2023,,Software,Creative Commons Attribution 4.0 International,Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html,api,True,findable,0,0,0,0,0,2023-12-20T07:42:19.000Z,2023-12-20T07:42:19.000Z,cern.zenodo,cern,,,,
+10.60662/pyvs-cj63,Intégration de connaissances du domaine et de l’apprentissage automatique pour l’estimation des paramètres de fabrication,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-11T17:19:29.000Z,2023-09-11T17:19:29.000Z,uqtr.mesxqq,uqtr,,,,
+10.5281/zenodo.10277799,"Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE",Zenodo,2023,en,Dataset,Creative Commons Attribution 4.0 International,"We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.
+The complete database is also accessible with SEISBENCH: https://seisbench.readthedocs.ioSEISBENCH on github:https://github.com/seisbench
+Related paper:
+Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science).  PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. ",api,True,findable,0,0,0,0,0,2023-12-07T16:56:11.000Z,2023-12-07T16:56:11.000Z,cern.zenodo,cern,"seismology,Ocean Bottom Seismometer,phase picking,OBS seismicity database,P and S onsets,machine learning","[{'subject': 'seismology'}, {'subject': 'Ocean Bottom Seismometer'}, {'subject': 'phase picking'}, {'subject': 'OBS seismicity database'}, {'subject': 'P and S onsets'}, {'subject': 'machine learning'}]",,
+10.34745/numerev_1937,La norme et la variation dans le cadre du Traitement Automatique du Langage,"CJC-Praxiling, (actes n°2022)",2023,fr,JournalArticle,Creative Commons Attribution Non Commercial No Derivatives 4.0 International,"R&eacute;sum&eacute; :&nbsp;Cet article pose la probl&eacute;matique du statut de la norme et de la variation en TAL en proposant des exemples tir&eacute;s des recherches pr&eacute;c&eacute;dentes concernant des mod&egrave;les informatiques employ&eacute;s pour repr&eacute;senter l&rsquo;acquisition de langue fran&ccedil;aise. Deux cas d&rsquo;&eacute;tude exemplifient le choix&nbsp;autour de l&rsquo;axe norme-variation : le calcul automatique d&rsquo;une distribution de fr&eacute;quence et la reconnaissance de motifs s&eacute;quentiels. Que le niveau d&rsquo;analyse soit le mot (premier exemple) ou le phon&egrave;me (deuxi&egrave;me exemple), des obstacles et compromis reviennent d&rsquo;une mani&egrave;re analogue. Le choix &ndash; souvent difficile et contraint - entre la pr&eacute;cision de la description du langage et la n&eacute;cessit&eacute; d&rsquo;avoir des donn&eacute;es uniformes pour que la machine puisse les traiter ais&eacute;ment. Les biais &eacute;vitables et in&eacute;vitables, les pr&eacute;cautions &agrave; prendre en amont, ainsi que les avantages et les inconv&eacute;nients de ce type de mod&egrave;les seront discut&eacute;s. L&rsquo;article se termine en dessinant les contours des futures compl&eacute;mentarit&eacute;s possibles entre m&eacute;thodes qualitatives et quantitatives.
+
+Abstract : This article deals with the problem of the status of norm and variation in NLP by proposing examples drawn from previous research concerning computer models used to represent French language acquisition. Two case studies illustrate the choice around the norm-variation axis: the automatic computation of a frequency distribution and the recognition of sequential patterns in words containing specific syllable sequences that are hard to learn due to their inner phonetic difficulty. Whether the level of analysis is the word (first example) or the phoneme (second example), obstacles and trade-offs come up in a similar way. The choice - often difficult and constrained - between the accuracy of the language description and the need to have uniform data for the machine to be easily handled. The avoidable and unavoidable biases, the precautions to be taken beforehand, as well as the advantages and disadvantages of these types of NLP models will be discussed. The article ends by outlining the possible future complementarities between qualitative and quantitative methods in current linguistics.
+
+Keywords : first language acquisition; NLP, French; variation; norm
+",api,True,findable,0,0,0,0,0,2023-11-29T08:55:52.000Z,2023-11-29T08:56:00.000Z,inist.mshsud,jbru,"variation,TAL,Acquisition du langage,français L1,norme","[{'subject': 'variation'}, {'subject': 'TAL'}, {'subject': 'Acquisition du langage'}, {'subject': 'français L1'}, {'subject': 'norme'}]",,
+10.57757/iugg23-2595,A broader look at licensing and copyright issues for global seismological data and products from a data center perspective,GFZ German Research Centre for Geosciences,2023,en,ConferencePaper,Creative Commons Attribution 4.0 International,"<!--!introduction!--><b></b><p>Sharing data - arrival time readings, earthquake parameters, waveforms and further derived products - has for many decades been key to the scientific advancement of seismology and our understanding of the Earth. The establishment of data centers, from institutional to global, that receive, archive, curate and make accessible large volumes of seismological data, following community standards and best practices, was a logical consequence. IASPEI, with its commissions, evolved as a de-facto standards body for seismological data, governed by the community of data providers and users alike.</p><p>However, conditions of use for these shared data did not receive much attention by data providers, distributors, and groups working on the definition of standards of data and services. If mentioned at all, generic statements on allowed use were provided somewhere on websites that offered access, often declaring ‘only for scientific/academic purposes’ or ‘not for commercial purposes’. Driven by the desire or requirement to improve FAIRness of our data, better understand data usage and adapt to technological changes, and support open science, putting proper licenses on data and metadata has now become a significant topic.</p><p>In this presentation we look at current practices and evolving ideas regarding application of licenses to the holdings of seismological data centers, covering waveforms, earthquake parameters, and further derived products, also including views from other geoscience domains. The relation to (legal) copyright and intellectual property issues, local/national licensing regulations that may hinder a globally uniform approach, and downstream implications for citation, attribution and general re-use of data will also be addressed.</p>",fabricaForm,True,findable,0,0,0,0,0,2023-06-12T10:12:32.000Z,2023-06-16T10:01:50.000Z,gfz.iugg2023,gfz,,,,
+10.5281/zenodo.10394959,Data from 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations',Zenodo,2023,,Dataset,Creative Commons Attribution 4.0 International,"Datasets used in the manuscript 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'
+
+Format: CPLEX .dat data files",api,True,findable,0,0,0,0,0,2023-12-16T12:03:47.000Z,2023-12-16T12:03:47.000Z,cern.zenodo,cern,,,,
+10.34847/nkl.adc04b9w,Bulletin franco-italien 1912 n°2 mars - avril,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/03 (A4,N2)-1912/04.",api,True,findable,0,0,0,0,0,2022-06-29T10:30:34.000Z,2022-06-29T10:30:34.000Z,inist.humanum,jbru,Etudes italiennes,[{'subject': 'Etudes italiennes'}],"['5911653 Bytes', '36330 Bytes', '20948809 Bytes', '21088168 Bytes', '20945074 Bytes', '20995618 Bytes', '21018802 Bytes', '21114103 Bytes', '21203224 Bytes', '21063487 Bytes', '21032884 Bytes', '21059062 Bytes', '20963617 Bytes', '20835679 Bytes', '21114352 Bytes', '20978806 Bytes']","['application/pdf', 'application/json', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
+10.5281/zenodo.8390942,Dataset for: Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio,Zenodo,2023,en,Dataset,Creative Commons Attribution 4.0 International,"Repository of data plotted in figures for the journal publication ""Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio"" (doi: 10.1364/OE.500077 ).
+
+
+Please see metadata file for details on individual data files.",api,True,findable,0,0,0,0,0,2023-12-18T12:45:11.000Z,2023-12-18T12:45:12.000Z,cern.zenodo,cern,,,,
+10.48390/0005-gz84,Les circuits courts distants approvisionnant Paris,"UMR CNRS 6266 IDEES, Université de Rouen Normandie",2021,fr,BookChapter,Creative Commons Attribution 4.0 International,,fabricaForm,True,findable,0,0,0,0,0,2021-09-20T12:59:19.000Z,2021-09-20T12:59:19.000Z,jbru.idees,jbru,,,,"['PDF', 'HTML']"
+10.34847/nkl.bc2b1071,Bulletin franco-italien 1912 n°3 mai - juin,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/05 (A4,N3)-1912/06.",api,True,findable,0,0,0,0,0,2022-07-12T10:40:43.000Z,2022-07-12T10:40:43.000Z,inist.humanum,jbru,"Etudes Italiennes,Etudes italiennes","[{'subject': 'Etudes Italiennes'}, {'subject': 'Etudes italiennes'}]","['6464874 Bytes', '21194044 Bytes', '20981389 Bytes', '21201160 Bytes', '21170605 Bytes', '21009112 Bytes', '21218584 Bytes', '21088960 Bytes', '21271426 Bytes', '21317947 Bytes', '21327454 Bytes', '21287920 Bytes', '21089296 Bytes', '21296776 Bytes', '21203305 Bytes', '21091444 Bytes', '21347104 Bytes']","['application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
+10.60662/re4y-6g57,Application d’un réseau de neurones artificiels auto-encodeur pour la détection de bourrages sur tapis convoyeurs en centre de tri de déchets,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T19:18:09.000Z,2023-09-01T19:18:09.000Z,uqtr.mesxqq,uqtr,,,,
+10.60662/7nek-jq08,"Favoriser l’innovation par le lean product development : le comportement humain, un indicateur pertinent ?",CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-12T15:19:12.000Z,2023-09-12T15:19:13.000Z,uqtr.mesxqq,uqtr,,,,
+10.60662/ydh4-8904,Approche intégrée basée sur l’intelligence artificielle pour la reconfiguration automatique des systèmes de production,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T19:53:39.000Z,2023-09-01T19:53:39.000Z,uqtr.mesxqq,uqtr,,,,
+10.57757/iugg23-4534,Deep-learning-based phase picking in SeisComP using SeisBench,GFZ German Research Centre for Geosciences,2023,en,ConferencePaper,Creative Commons Attribution 4.0 International,"<!--!introduction!--><b></b><p>The open-source, seismological software package SeisComP is widely used for seismic monitoring world-wide.&nbsp; Its automatic phase picking module consists of an STA/LTA-based P-wave detector augmented by an AIC onset picker.&nbsp; With proper configuration, it allows detection and accurate onset picking for a wide range of seismic signals. However, it cannot match the performance of experienced analysts.&nbsp; Especially broadband data are often challenging for phase pickers due to the enormous variety of the signals of interest. <br><br>In order to optimize quality and number of automatic picks and reduce time-consuming manual revision, we chose to develop a machine-learning repicker module for SeisComP based on the SeisBench framework. SeisBench supports several deep-learning pickers and comes pre-trained for different benchmark datasets, one of which was contributed by GFZ Potsdam.<br><br>The repicking module consists of several submodules that interact with both SeisComP and SeisBench via their Python interfaces. The current workflow is based on existing locations generated with a classical SeisComP setup. Shortly after event detection and preliminary location, our repicker (1) starts to repick previously picked onsets and (2) attempts to generate additional picks.<br><br>Preliminary results are encouraging. The deep-learning repicker substantially improves pick quality over a large frequency range. The number of picks available per event has approximately doubled and the publication delay is often reduced, especially for small events. The total number of published events has increased by about 20 per cent.</p>",fabricaForm,True,findable,0,0,0,0,0,2023-07-03T19:58:01.000Z,2023-07-11T19:19:09.000Z,gfz.iugg2023,gfz,,,,
+10.34847/nkl.c9e92or4,"Taciti et C. Velleii Paterculi scripta quae exstant; recognita, emaculata. Additique commentarii copiosissimi et notae non antea editae Paris e typographia Petri Chevalier, in monte diui Hilarii - II-0705",NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Image,,,api,True,findable,0,0,0,0,0,2023-01-30T09:25:06.000Z,2023-01-30T09:25:06.000Z,inist.humanum,jbru,,,['53932108 Bytes'],['image/tiff']
+10.34847/nkl.deb655as,"Brouillons de ""La Réticence"" de Jean-Philippe Toussaint",NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Image,,"Il s'agit des brouillons de ""La Réticence"" de Jean-Philippe Toussaint, publié aux Éditions de minuit (1991). 
+
+Ces brouillons ont été confiés par leur auteur à l’Unité Mixte de Recherche Litt&Arts (UMR 5316 – Arts et pratiques du texte, de l’image, de l’écran et de la scène –  Université Grenoble Alpes / CNRS) sous la responsabilité scientifique de Brigitte Ferrato-Combe. Réunissant la totalité des documents préparatoires du roman, depuis les premières notes jusqu’aux épreuves et correspondances avec l’éditeur, ce fonds d’archives se révèle particulièrement intéressant pour les études littéraires, stylistiques ou génétiques.
+
+Les 2700 feuillets tapuscrits, comportant une abondante annotation manuscrite, ont été numérisés par le Service interuniversitaire de Documentation de l’Université Grenoble Alpes où ils sont momentanément conservés. Ils seront déposés en totalité sur la plateforme collaborative TACT ( https://tact.demarre-shs.fr/ ) pour faire l’objet d’une transcription, opération indispensable pour leur donner une pleine lisibilité et permettre les analyses et recherches automatiques sur le texte.",api,True,findable,0,0,0,0,0,2022-06-20T08:52:01.000Z,2022-06-20T08:52:01.000Z,inist.humanum,jbru,"Roman,Littérature,Numérisation,Brouillons d'écrivains","[{'lang': 'fr', 'subject': 'Roman'}, {'lang': 'fr', 'subject': 'Littérature'}, {'lang': 'fr', 'subject': 'Numérisation'}, {'lang': 'fr', 'subject': ""Brouillons d'écrivains""}]","['2046790 Bytes', '2241023 Bytes', '2325721 Bytes', '1989166 Bytes', '1932694 Bytes', '1893451 Bytes', '2118354 Bytes', '2147233 Bytes', '1983636 Bytes', '1958790 Bytes', '1894068 Bytes', '1918702 Bytes', '1937866 Bytes', '1867242 Bytes', '2062908 Bytes', '1941417 Bytes', '1868017 Bytes', '1769630 Bytes', '2143693 Bytes', '1804149 Bytes', '1970057 Bytes', '1996921 Bytes', '2042590 Bytes', 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'2143574 Bytes', '2268131 Bytes', '1988731 Bytes', '1681848 Bytes', '2026465 Bytes', '1811601 Bytes', '1727594 Bytes', '2349043 Bytes', '1827842 Bytes', '1941834 Bytes', '2041704 Bytes', '1894843 Bytes', '1947635 Bytes', '1885564 Bytes', '1852942 Bytes', '2022882 Bytes', '2086760 Bytes', '1767185 Bytes', '1903486 Bytes', '1632853 Bytes', '1775791 Bytes', '2048393 Bytes', '1751058 Bytes', '2033134 Bytes', '2473588 Bytes', '1908521 Bytes', '2331679 Bytes', '1471278 Bytes', '1782799 Bytes', '2421041 Bytes', '2128110 Bytes', '1693570 Bytes', '2287919 Bytes', '2406094 Bytes', '2163751 Bytes', '2034471 Bytes', '1706547 Bytes', '1745713 Bytes', '1810993 Bytes', '1841154 Bytes', '1696264 Bytes', '1649322 Bytes', '2096729 Bytes', '1727937 Bytes', '1668082 Bytes', '1866350 Bytes', '1817267 Bytes', '1757373 Bytes', '1925471 Bytes', '1846660 Bytes', '2065702 Bytes', '2033751 Bytes', '1854724 Bytes', '1682160 Bytes', '1691009 Bytes', '1675896 Bytes', '1986489 Bytes', '1815857 Bytes', '1909412 Bytes', '1710080 Bytes', '1896160 Bytes', '1693136 Bytes', '2437382 Bytes', '2138459 Bytes', '1609574 Bytes', '1848030 Bytes', '1789875 Bytes', '1841172 Bytes', '1777410 Bytes', '1832211 Bytes', '1662985 Bytes', '2088034 Bytes', '1758564 Bytes', '1825280 Bytes', '1688251 Bytes', '1895308 Bytes', '1726961 Bytes', '2135241 Bytes', '1759124 Bytes', '2063094 Bytes', '1980053 Bytes', '2139855 Bytes', '1676132 Bytes', '1705603 Bytes', '1575627 Bytes', '1870624 Bytes', '1917677 Bytes', '1661066 Bytes', '1664756 Bytes', '1688736 Bytes', '1559087 Bytes', '1709844 Bytes', '1724656 Bytes', '1764414 Bytes', '1927453 Bytes', '1507047 Bytes', '1723868 Bytes', '1771850 Bytes', '1969027 Bytes', '2044655 Bytes', '1777081 Bytes', '1755779 Bytes', '1802725 Bytes', '1643955 Bytes', '1831076 Bytes', '1574803 Bytes', '1605256 Bytes', '1628938 Bytes', '1722088 Bytes', '1663013 Bytes', '1682981 Bytes', '1497465 Bytes', '1734101 Bytes', '1802239 Bytes', '1601738 Bytes', '1700509 Bytes', '1622049 Bytes', '1521154 Bytes', '1976904 Bytes', '1500306 Bytes', '1611821 Bytes', '1610552 Bytes', '1894607 Bytes', '2114461 Bytes', '2070844 Bytes', '1021747 Bytes', '1794335 Bytes', '1703835 Bytes', '1957877 Bytes', '1549233 Bytes', '1678346 Bytes', '1522916 Bytes', '1613508 Bytes', '1527549 Bytes', '1593591 Bytes', '1472892 Bytes', '1601562 Bytes', '1553229 Bytes', '1524418 Bytes', '1747655 Bytes', '2029867 Bytes', '1714903 Bytes', '1844828 Bytes', '2064842 Bytes', '1731285 Bytes', '1711238 Bytes', '1540871 Bytes', '1897629 Bytes', '1678938 Bytes', '1806780 Bytes', '1173890 Bytes', '1627740 Bytes', '1815535 Bytes', '1543832 Bytes', '1790147 Bytes', '1579108 Bytes', '2043461 Bytes', '1714972 Bytes', '1759387 Bytes', '2041500 Bytes', '2125534 Bytes', '1474889 Bytes', '1760068 Bytes', '1630572 Bytes', '1854527 Bytes', '1437011 Bytes', '1547309 Bytes', '1781046 Bytes', '1769645 Bytes', '1792489 Bytes', '1738345 Bytes', '1677180 Bytes', '2046626 Bytes', '1637266 Bytes', '2088072 Bytes', '1746930 Bytes', '2087927 Bytes', '1661375 Bytes', '1446197 Bytes', '1702290 Bytes', '976902 Bytes', '1960056 Bytes', '1827073 Bytes', '1836037 Bytes', '1746252 Bytes', '1580401 Bytes', '1950579 Bytes', '1616133 Bytes', '1601262 Bytes', '1715225 Bytes', '1893446 Bytes', '1872644 Bytes', '1798686 Bytes', '2075028 Bytes', '1627621 Bytes', '1709548 Bytes', '1722529 Bytes', '1895018 Bytes', '2051436 Bytes', '2331337 Bytes', '2126514 Bytes', '2099493 Bytes', '2118618 Bytes', '2085376 Bytes', '1753091 Bytes', '1699380 Bytes', '2066595 Bytes', '1880827 Bytes', '1739458 Bytes', '2281053 Bytes', '2192294 Bytes', '1776444 Bytes', '1761032 Bytes', '1687024 Bytes', '1693600 Bytes', '1863489 Bytes', '1743857 Bytes', '2149500 Bytes', '2252495 Bytes', '2129518 Bytes', '2194697 Bytes', '2027763 Bytes', '2440103 Bytes', '2021899 Bytes', '2179525 Bytes', '2314393 Bytes', '1802912 Bytes', '1628157 Bytes', '2090121 Bytes', '2198730 Bytes', '1806564 Bytes', '1833244 Bytes', '1976503 Bytes', '2148571 Bytes', '1690199 Bytes', '1924626 Bytes', '2008224 Bytes', '1878743 Bytes', '1710346 Bytes', '2016879 Bytes', '1834974 Bytes', '2112013 Bytes', '1951034 Bytes', '1833315 Bytes', '1664569 Bytes', '1866326 Bytes', '2185677 Bytes', '2085171 Bytes', '1590839 Bytes', '1761774 Bytes', '2079905 Bytes', '2016868 Bytes', '2033769 Bytes', '1979712 Bytes', '2103003 Bytes', '1782611 Bytes', '2040817 Bytes', '1762120 Bytes', '1795570 Bytes', '2184077 Bytes', '2050455 Bytes', '1953665 Bytes', '1626622 Bytes', '2042294 Bytes', '2096432 Bytes', '1827594 Bytes', '1937622 Bytes', '2055231 Bytes', '1913707 Bytes', '1888277 Bytes', '1933865 Bytes', '1732182 Bytes', '1952222 Bytes', '1822250 Bytes', '1681685 Bytes', '2203870 Bytes', '2070931 Bytes', '2212024 Bytes', '1839907 Bytes', '1580446 Bytes', '1698881 Bytes', '1526527 Bytes', '1639603 Bytes', '1573102 Bytes', '1589747 Bytes', '1552761 Bytes', '2066574 Bytes', '2047901 Bytes', '1632714 Bytes', '1556051 Bytes', '1801738 Bytes', '1438779 Bytes', '1864635 Bytes', '1563637 Bytes', '1884698 Bytes', '1941215 Bytes', '1672749 Bytes', '1575780 Bytes', '2025024 Bytes', '2001434 Bytes', '1617762 Bytes', '1741405 Bytes', '1719299 Bytes', '1715410 Bytes', '1594328 Bytes', '1411367 Bytes', '1788390 Bytes', '1770562 Bytes', '1814725 Bytes', '1755213 Bytes', '1858606 Bytes', '1729107 Bytes', '2008239 Bytes', '1488010 Bytes', '1991399 Bytes', '1443415 Bytes', '1850153 Bytes', '1599408 Bytes', '2137303 Bytes', '1826302 Bytes', '1582294 Bytes', '1408842 Bytes', '1765896 Bytes', '1729909 Bytes', '1515194 Bytes', '2016864 Bytes', '2016825 Bytes', '1972837 Bytes', '1861974 Bytes', '1661996 Bytes', '1523572 Bytes', '1830906 Bytes', '1697072 Bytes', '1975833 Bytes', '3125560 Bytes', '3085202 Bytes', '2895008 Bytes', '2892347 Bytes', '3221909 Bytes', '2100853 Bytes', '2646238 Bytes', '2363135 Bytes', '2515024 Bytes', '2869902 Bytes', '2744315 Bytes', '2115508 Bytes', '1870487 Bytes', '1612673 Bytes', '1841513 Bytes', '1699393 Bytes', '1697118 Bytes', '1776058 Bytes', '1876316 Bytes', '1820593 Bytes', '1696869 Bytes', '1712089 Bytes', '1262634 Bytes', '1924340 Bytes', '1572168 Bytes', '1558239 Bytes', '1525185 Bytes', '1555916 Bytes', '1792510 Bytes', '1809032 Bytes', '1729199 Bytes', '2105116 Bytes', '1758920 Bytes', '2091937 Bytes', '2026724 Bytes', '1997545 Bytes', '1592083 Bytes', '2131258 Bytes', '1910829 Bytes', '1694731 Bytes', '2416413 Bytes', '1934205 Bytes', '1705647 Bytes', '1623314 Bytes', '1841426 Bytes', '1504649 Bytes', '1490037 Bytes', '1966668 Bytes', '1905051 Bytes', '2029406 Bytes', '681973 Bytes', '382078 Bytes', '1952470 Bytes', '1732533 Bytes', '1686362 Bytes', '1562653 Bytes', '1656672 Bytes', '2192302 Bytes', '1984140 Bytes', '1531261 Bytes', '2102029 Bytes', '2119806 Bytes', '1686097 Bytes', '1573684 Bytes', '1431988 Bytes', '1535855 Bytes', '1528695 Bytes', '1715044 Bytes', '1447363 Bytes', '1760294 Bytes', '1493862 Bytes', '1571480 Bytes', '1726620 Bytes', '1776059 Bytes', '1527294 Bytes', '1900102 Bytes', '1715476 Bytes', '1991283 Bytes', '1646909 Bytes', '1870411 Bytes', '1780012 Bytes', '1739578 Bytes', '1834596 Bytes', '1847464 Bytes', '1618557 Bytes', '1685656 Bytes', '1699231 Bytes', '1581885 Bytes', '1743086 Bytes', '2013783 Bytes', '1586986 Bytes', '1732037 Bytes', '1664762 Bytes', '2054210 Bytes', '1815538 Bytes', '1629428 Bytes', '1762578 Bytes', '2046199 Bytes', '2036641 Bytes', '1763100 Bytes', '2053864 Bytes', '1727638 Bytes', '1868199 Bytes', '1971112 Bytes', '1600558 Bytes', '2159634 Bytes', '2150797 Bytes', '2117772 Bytes', '1827661 Bytes', '1740110 Bytes', '2005835 Bytes', '1736410 Bytes', '1885368 Bytes', '1704013 Bytes', '1541971 Bytes', '1824347 Bytes', '2013493 Bytes', '1933163 Bytes', '1926061 Bytes', '1941556 Bytes', '1839237 Bytes', '1821007 Bytes', '2004594 Bytes', '1842571 Bytes', '2451681 Bytes', '2129561 Bytes', '1986310 Bytes', '1849867 Bytes', '2247863 Bytes', '1798126 Bytes', '1879345 Bytes', '2054432 Bytes', '1756620 Bytes', '2161360 Bytes', '2014631 Bytes', '1959917 Bytes', '1586235 Bytes', '1836284 Bytes', '2162541 Bytes', '2004892 Bytes', '1638590 Bytes', '1747337 Bytes', '1657686 Bytes', '1876017 Bytes', '2024747 Bytes', '2201657 Bytes', '1838466 Bytes', '1611609 Bytes', '1951722 Bytes', '2054629 Bytes', '1817804 Bytes', '1867395 Bytes', '1991361 Bytes', '1870016 Bytes', '1923355 Bytes', '1828749 Bytes', '1896127 Bytes', '1891161 Bytes', '1751642 Bytes', '2107863 Bytes', '1915835 Bytes', '1599067 Bytes', '1849512 Bytes', '1919067 Bytes', '1980913 Bytes', '2035092 Bytes', '2205667 Bytes', '1939124 Bytes', '1662455 Bytes', '1982641 Bytes', '2389708 Bytes', '1946483 Bytes', '1613085 Bytes', '1836984 Bytes', '2173365 Bytes', '1900846 Bytes', '1900075 Bytes', '1839007 Bytes', '1492009 Bytes', '1892468 Bytes', '1786029 Bytes', '1683248 Bytes', '1721430 Bytes', '2029909 Bytes', '2387156 Bytes', '1491331 Bytes', '2088929 Bytes', '1478358 Bytes', '1946702 Bytes', '1903983 Bytes', '1925672 Bytes', '1499939 Bytes', '1519125 Bytes', '1816264 Bytes', '1622847 Bytes', '1659561 Bytes', '1621029 Bytes', '2062708 Bytes', '1502081 Bytes', '1836767 Bytes', '1715280 Bytes', '1606123 Bytes', '1473048 Bytes', '2071173 Bytes', '1853737 Bytes', '1912101 Bytes', '2201690 Bytes', '1578699 Bytes', '2019695 Bytes', '1693021 Bytes', '2116934 Bytes', '1594179 Bytes', '1327646 Bytes', '1249908 Bytes', '1293649 Bytes', '1030360 Bytes', '1238549 Bytes', '1028833 Bytes', '1171974 Bytes', '1053795 Bytes', '1115343 Bytes', '1251617 Bytes', '1060086 Bytes', '1173681 Bytes', '1146448 Bytes', '1144210 Bytes', '1073385 Bytes', '1275064 Bytes', '1302821 Bytes', '1240983 Bytes', '1386290 Bytes', '1537519 Bytes', '1020294 Bytes', '1585774 Bytes', '1345125 Bytes', '1132322 Bytes', '1049230 Bytes', '1414832 Bytes', '1220863 Bytes', '1004496 Bytes', '1406718 Bytes', '1052547 Bytes', '1726837 Bytes', '1606761 Bytes', '1539854 Bytes', '1706071 Bytes', '1593572 Bytes', '2505156 Bytes', '1978982 Bytes', '1867467 Bytes', '1382159 Bytes', '1621699 Bytes', '1166911 Bytes', '1629582 Bytes', '1572715 Bytes', '1480141 Bytes', '1283602 Bytes', '1190476 Bytes', '1256715 Bytes', '1302102 Bytes', '1071770 Bytes', '1253452 Bytes', '1086879 Bytes', '1165558 Bytes', '1597822 Bytes', '1017803 Bytes', '1011656 Bytes', '1517654 Bytes', '1393052 Bytes', '1306654 Bytes', '1195504 Bytes', '1062981 Bytes', '1047098 Bytes', '1047041 Bytes', '1301155 Bytes', '1183511 Bytes', '1395150 Bytes', '1434858 Bytes', '1019918 Bytes', '1005020 Bytes', '1088176 Bytes', '1114292 Bytes', '1380463 Bytes', '1074184 Bytes', '1042809 Bytes', '1102996 Bytes', '1161161 Bytes', '1110261 Bytes', '1036279 Bytes', '1025304 Bytes', '1317626 Bytes', '1732431 Bytes', '1447886 Bytes', '1339309 Bytes', '1655934 Bytes', '1818990 Bytes', '1164239 Bytes', '1538785 Bytes', '1145565 Bytes', '1401702 Bytes', '1044823 Bytes', '1103903 Bytes', '1191771 Bytes', '1231248 Bytes', '922428 Bytes', '1014163 Bytes', '1223236 Bytes', '1345489 Bytes', '1511906 Bytes', '1277748 Bytes', '1152352 Bytes', '1392757 Bytes', '1472190 Bytes', '1221314 Bytes', '962039 Bytes', '1080147 Bytes', '1171505 Bytes', '1063148 Bytes', '1468956 Bytes', '1643700 Bytes', '1471242 Bytes', '1180890 Bytes', '1122160 Bytes', '1533903 Bytes', '1214791 Bytes', '1270105 Bytes', '1482849 Bytes', '1256130 Bytes', '1286401 Bytes', '1374215 Bytes', '1392387 Bytes', '1175160 Bytes', '1431047 Bytes', '1482706 Bytes', '1148813 Bytes', '1397407 Bytes', '1575569 Bytes', '1083983 Bytes', '1395852 Bytes', '1430139 Bytes', '1387949 Bytes', '1379720 Bytes', '1059206 Bytes', '1217163 Bytes', '1257352 Bytes', '1084111 Bytes', '1193276 Bytes', '1079489 Bytes', '1568435 Bytes', '1713862 Bytes', '1008842 Bytes', '1475227 Bytes', '1453712 Bytes', '1140057 Bytes', '1432008 Bytes', '1285372 Bytes', '1653506 Bytes', '1130473 Bytes', '1127118 Bytes', '1030087 Bytes', '1665977 Bytes', '1820166 Bytes', '1564376 Bytes', '1580413 Bytes', '1608357 Bytes', '1472321 Bytes', '1229037 Bytes', '1193268 Bytes', '1213923 Bytes', '1545127 Bytes', '1456061 Bytes', '1468608 Bytes', '1470090 Bytes', '1253685 Bytes', '1672554 Bytes', '1218213 Bytes', '1090125 Bytes', '1583306 Bytes', '1591922 Bytes', '1433694 Bytes', '1480368 Bytes', '1375529 Bytes', '1491659 Bytes', '1161317 Bytes', '1191376 Bytes', '1318030 Bytes', '1336596 Bytes', '1119405 Bytes', '1386836 Bytes', '1329339 Bytes', '1132372 Bytes', '1223525 Bytes', '953076 Bytes', '1254825 Bytes', '972621 Bytes', '1264793 Bytes', '1135097 Bytes', '1227192 Bytes', '1296706 Bytes', '1601819 Bytes', '1800675 Bytes', '1432307 Bytes', '1406266 Bytes', '1173540 Bytes', '1415931 Bytes', '1567375 Bytes', '1241617 Bytes', '1225827 Bytes', '1687241 Bytes', '1248728 Bytes', '1094298 Bytes', '1119367 Bytes', '1475790 Bytes', '1075897 Bytes', '1133577 Bytes', '1268069 Bytes', '1139110 Bytes', '1295656 Bytes', '1371951 Bytes', '1376546 Bytes', '1564577 Bytes', '1318116 Bytes', '1408495 Bytes', '1120908 Bytes', '1195368 Bytes', '1103299 Bytes', '1212074 Bytes', '1449997 Bytes', '1598569 Bytes', '1697011 Bytes', '1412556 Bytes', '1470842 Bytes', '1020112 Bytes', '1753468 Bytes', '1443055 Bytes', '1509285 Bytes', '1761535 Bytes', '1111595 Bytes', '1065503 Bytes', '1056480 Bytes', '1205226 Bytes', '1369702 Bytes', '1098901 Bytes', '1359746 Bytes', '1576546 Bytes', '1050952 Bytes', '1448909 Bytes', '1556820 Bytes', '1904514 Bytes', '1524327 Bytes', '1709706 Bytes', '1710823 Bytes', '1389532 Bytes', '1454569 Bytes', '1301412 Bytes', '1454766 Bytes', '1354924 Bytes', '1166240 Bytes', '1176360 Bytes', '1473685 Bytes', '1026682 Bytes', '1183647 Bytes', '1109640 Bytes', '1705167 Bytes', '1233539 Bytes', '1090679 Bytes', '1020092 Bytes', '1092763 Bytes', '1222935 Bytes', '1567068 Bytes', '1484333 Bytes', '1463160 Bytes', '1268116 Bytes', '1411822 Bytes', '1370655 Bytes', '1404055 Bytes', '1528046 Bytes', '1131547 Bytes', '1423719 Bytes', '1293818 Bytes', '1323340 Bytes', '1407156 Bytes', '1566226 Bytes', '1470789 Bytes', '1334265 Bytes', '1078859 Bytes', '1818953 Bytes', '1285791 Bytes', '1508102 Bytes', '1491082 Bytes', '1507563 Bytes', '1327297 Bytes', '1195901 Bytes', '1309626 Bytes', '1197169 Bytes', '1319836 Bytes', '1154687 Bytes', '1383038 Bytes', '1399347 Bytes', '1330807 Bytes', '1208520 Bytes', '1394352 Bytes', '1001275 Bytes', '1042124 Bytes', '1263225 Bytes', '1312877 Bytes', '503496 Bytes', '1234793 Bytes', '1673963 Bytes', '1405527 Bytes', '1561180 Bytes', '1261700 Bytes', '1038337 Bytes', '1151236 Bytes', '1105972 Bytes', '1079842 Bytes', '1297930 Bytes', '1098665 Bytes', '1422914 Bytes', '1242673 Bytes', '1330430 Bytes', '1399413 Bytes', '1905214 Bytes', '1389330 Bytes', '1317979 Bytes', '1495938 Bytes', '1418387 Bytes', '1561426 Bytes', '1452592 Bytes', '1403860 Bytes', '1488124 Bytes', '1192090 Bytes', '1132954 Bytes', '1096955 Bytes', '1570771 Bytes', '1092859 Bytes', '1458201 Bytes', '1666236 Bytes', '1289075 Bytes', '1206562 Bytes', '1118472 Bytes', '1224533 Bytes', '1370824 Bytes', '1229474 Bytes', '1137372 Bytes', '1530311 Bytes', '1179135 Bytes', '1229588 Bytes', '1152394 Bytes', '1131868 Bytes', '1092710 Bytes', '1121233 Bytes', '1219742 Bytes', '1431038 Bytes', '1231820 Bytes', '1332637 Bytes', '1043507 Bytes', '1080571 Bytes', '1427944 Bytes', '1164916 Bytes', '1112420 Bytes', '1115047 Bytes', '1184421 Bytes', '1284581 Bytes', '1364948 Bytes', '1472107 Bytes', '1521743 Bytes', '1593927 Bytes', '1497326 Bytes', '1356916 Bytes', '1436076 Bytes', '1058507 Bytes', '1537923 Bytes', '1477250 Bytes', '1400135 Bytes', '1463521 Bytes', '1432741 Bytes', '1541826 Bytes', '1735314 Bytes', '1298722 Bytes', '1390200 Bytes', '1402110 Bytes', '1180567 Bytes', '1550898 Bytes', '1466190 Bytes', '1442342 Bytes', '1139893 Bytes', '1332900 Bytes', '969318 Bytes', '1207475 Bytes', '1293302 Bytes', '1161357 Bytes', '1192843 Bytes', '1272523 Bytes', '1179222 Bytes', '1176928 Bytes', '1385108 Bytes', '1140842 Bytes', '1122202 Bytes', '1267938 Bytes', '1015967 Bytes', '1397182 Bytes', '1181378 Bytes', '1578903 Bytes', '1015173 Bytes', '1429557 Bytes', '1554003 Bytes', '1413205 Bytes', '1674927 Bytes', '1376894 Bytes', '1819028 Bytes', '1571827 Bytes', '1183086 Bytes', '1300717 Bytes', '1371771 Bytes', '1105754 Bytes', '1195216 Bytes', '1580798 Bytes', '1517637 Bytes', '1160034 Bytes', '1326416 Bytes', '1640463 Bytes', '1451033 Bytes', '1934640 Bytes', '1597070 Bytes', '1121697 Bytes', '1113604 Bytes', '1220704 Bytes', '1228251 Bytes', '1251599 Bytes', '1481499 Bytes', '1182060 Bytes', '1342331 Bytes', '1165019 Bytes', '1113023 Bytes', '1114779 Bytes', '1007324 Bytes', '1295818 Bytes', '916298 Bytes', '535347 Bytes', '530088 Bytes', '1056450 Bytes', '970804 Bytes', '1318062 Bytes', '1329961 Bytes', '1193468 Bytes', '1190634 Bytes', '1103297 Bytes', '1208685 Bytes', '1278305 Bytes', '1261196 Bytes', '1207766 Bytes', '1483261 Bytes', '1507307 Bytes', '1198630 Bytes', '1037734 Bytes', '1135562 Bytes', '1410485 Bytes', '1275551 Bytes', '1014551 Bytes', '1129178 Bytes', '1404079 Bytes', '1269125 Bytes', '1097930 Bytes', '1131256 Bytes', '1004280 Bytes', '1406104 Bytes', '1140340 Bytes', '1215680 Bytes', '1086279 Bytes', '1080466 Bytes', '1137972 Bytes', '1402316 Bytes', '1017855 Bytes', '1073028 Bytes', '1383122 Bytes', '1446286 Bytes', '1672618 Bytes', '1687324 Bytes', '1289565 Bytes', '1535984 Bytes', '1302081 Bytes', '1221172 Bytes', '1157247 Bytes', '1446731 Bytes', '1224484 Bytes', '1301446 Bytes', '1432391 Bytes', '1440310 Bytes', '1595141 Bytes', '1437310 Bytes', '1143892 Bytes', '1260715 Bytes', '1253738 Bytes', '1468352 Bytes', '1418113 Bytes', '2805052 Bytes', '2538817 Bytes', '2718738 Bytes', '2721176 Bytes', '2711180 Bytes', '2588229 Bytes', '2446541 Bytes', '2869245 Bytes', '2912578 Bytes', '2962126 Bytes', '2750849 Bytes', '2677189 Bytes', '2626471 Bytes', '3169847 Bytes', '2719093 Bytes', '2650212 Bytes', '2607494 Bytes', '2731540 Bytes', '2564968 Bytes', '2634723 Bytes', '2775495 Bytes', '2975426 Bytes', '2875638 Bytes', '2541412 Bytes', '2511056 Bytes', '3121340 Bytes', '3088115 Bytes', '2605023 Bytes', '2816897 Bytes', '2602092 Bytes', '3014658 Bytes', '3031034 Bytes', '2780240 Bytes', '3001306 Bytes', '2966599 Bytes', '2612497 Bytes', '2630569 Bytes', '2610493 Bytes', '2611524 Bytes', '2606725 Bytes', '2681016 Bytes', '2453377 Bytes', '2713542 Bytes', '2634541 Bytes', '2625192 Bytes', '3253832 Bytes', '2981181 Bytes', '2831843 Bytes', '2720321 Bytes', '2801076 Bytes', '2768865 Bytes', '2725552 Bytes', '2708977 Bytes', '2986861 Bytes', '2559948 Bytes', '2989000 Bytes', '2726479 Bytes', '2584025 Bytes', '2775579 Bytes', '2759588 Bytes', '3175764 Bytes', '2434412 Bytes', '2799647 Bytes', '2613367 Bytes', '2850218 Bytes', '2503168 Bytes', '2462009 Bytes', '2772398 Bytes', '2953844 Bytes', '2856554 Bytes', '2950840 Bytes', '2781750 Bytes', '2932272 Bytes', '2979137 Bytes', '2836253 Bytes', '2705438 Bytes', '2452353 Bytes', '2787467 Bytes', '2492712 Bytes', '2546799 Bytes', '2717887 Bytes', '2497617 Bytes', '2955338 Bytes', '2990703 Bytes', '2654572 Bytes', '2736051 Bytes', '2921106 Bytes', '2664178 Bytes', '2417547 Bytes', '2869821 Bytes', '2588268 Bytes', '2637976 Bytes', '2567736 Bytes', '2775295 Bytes', '2674575 Bytes', '2583895 Bytes', '2603107 Bytes', '2902833 Bytes', '2922572 Bytes', '2525835 Bytes', '2716912 Bytes', '2675806 Bytes', '2842816 Bytes', '2602605 Bytes', '2629958 Bytes', '2703569 Bytes', '2730110 Bytes', '2426943 Bytes', '2592442 Bytes', '2685789 Bytes', '2751159 Bytes', '2970780 Bytes', '2610255 Bytes', '2539535 Bytes', '2580598 Bytes', '2607270 Bytes', '2733107 Bytes', '2631179 Bytes', '2589775 Bytes', '2849330 Bytes', '2614054 Bytes', '2887029 Bytes', '2687443 Bytes', '2773964 Bytes', '2621486 Bytes', '2755103 Bytes', '2421430 Bytes', '2387056 Bytes', '2691476 Bytes', '2600870 Bytes', '2460752 Bytes', '2371283 Bytes', '2660800 Bytes', '2788816 Bytes', '2548841 Bytes', '2441949 Bytes', '2644707 Bytes', '2658611 Bytes', '2658538 Bytes', '2972628 Bytes', '2535822 Bytes', '2785933 Bytes', '2808645 Bytes', '2798068 Bytes', '2737163 Bytes', '2590913 Bytes', '2515295 Bytes', '2543171 Bytes', '2707723 Bytes', '2717392 Bytes', '2756127 Bytes', '2456269 Bytes', '2757051 Bytes', '2708772 Bytes', '2745731 Bytes', '2589453 Bytes', '2558592 Bytes', '2620198 Bytes', '2515547 Bytes', '2702406 Bytes', '2777940 Bytes', '2640664 Bytes', '2761493 Bytes', '2637813 Bytes', '2703403 Bytes', '2449958 Bytes', '2718916 Bytes', '2851091 Bytes', '2816960 Bytes', '2460249 Bytes', '2923678 Bytes', '2852850 Bytes', '2829658 Bytes', '2445762 Bytes', '2395321 Bytes', '2951999 Bytes', '2581819 Bytes', '2643161 Bytes', '2592827 Bytes', '2684380 Bytes', '2527410 Bytes', '2593300 Bytes', '2738597 Bytes', '2754976 Bytes', '2659569 Bytes', '2738294 Bytes', '2696005 Bytes', '2734023 Bytes', '2812650 Bytes', '2586873 Bytes', '2762385 Bytes', '2581594 Bytes', '2610706 Bytes', '2882784 Bytes', '2586512 Bytes', '2746325 Bytes', '2528155 Bytes', '2498341 Bytes', '2484423 Bytes', '2962313 Bytes', '2579944 Bytes', '2636608 Bytes', '2667947 Bytes', '2581690 Bytes', '2485311 Bytes', '2701522 Bytes', '2735536 Bytes', '2820172 Bytes', '2808391 Bytes', '2451365 Bytes', '2945901 Bytes', '2515369 Bytes', '2588931 Bytes', '2780658 Bytes', '2486136 Bytes', '2514827 Bytes', '2571651 Bytes', '2501637 Bytes', '2478317 Bytes', '2595035 Bytes', '2373079 Bytes', '2393248 Bytes', '2631896 Bytes', '2467321 Bytes', '2546077 Bytes', '2768202 Bytes', '2444301 Bytes', '2483351 Bytes', '2395838 Bytes', '2572644 Bytes', '2635153 Bytes', '2405012 Bytes', '2383766 Bytes', '2756968 Bytes', '2936854 Bytes', '2682028 Bytes', '2651122 Bytes', '2464676 Bytes', '2569441 Bytes', '2620070 Bytes', '2612335 Bytes', '2670334 Bytes', '3186086 Bytes', '3348548 Bytes', '3339399 Bytes', '2683402 Bytes', '2698316 Bytes', '2696870 Bytes', '2678497 Bytes', '2916001 Bytes', '2491271 Bytes', '2505190 Bytes', '2424830 Bytes', '2600316 Bytes', '2426631 Bytes', '2682563 Bytes', '2514053 Bytes', '2519780 Bytes', '2674698 Bytes', '2622153 Bytes', '2586410 Bytes', '2689017 Bytes', '2585696 Bytes', '2598073 Bytes', '2758742 Bytes', '2583577 Bytes', '2609807 Bytes', '2532506 Bytes', '2558555 Bytes', '2646103 Bytes', '2762782 Bytes', '2558615 Bytes', '2623530 Bytes', '2536780 Bytes', '2647967 Bytes', '2515102 Bytes', '2595339 Bytes', '2488089 Bytes', '2625234 Bytes', '2561152 Bytes', '2749246 Bytes', '2441201 Bytes', '2837357 Bytes', '2489640 Bytes', '2701520 Bytes', '2696763 Bytes', '2473350 Bytes', '2450865 Bytes', '2668654 Bytes', '2679760 Bytes', '2705201 Bytes', '2537435 Bytes', '2492535 Bytes', '2516262 Bytes', '2527639 Bytes', '2770602 Bytes', '2640890 Bytes', '2450271 Bytes', '2508388 Bytes', '2533687 Bytes', '2467644 Bytes', '2617191 Bytes', '2817534 Bytes', '2449137 Bytes', '2620601 Bytes', '2714090 Bytes', '2724924 Bytes', '2764645 Bytes', '2822120 Bytes', '2578900 Bytes', '2767872 Bytes', '2554025 Bytes', '2552775 Bytes', '2669528 Bytes', '2742974 Bytes', '2602985 Bytes', '2549398 Bytes', '2545093 Bytes', '2593404 Bytes', '2552074 Bytes', '2576583 Bytes', '2600382 Bytes', '2572707 Bytes', '2561068 Bytes', '2676489 Bytes', '2642117 Bytes', '2591588 Bytes', '2680159 Bytes', '2499034 Bytes', '2586481 Bytes', '2682304 Bytes', '2810175 Bytes', '2646033 Bytes', '2608938 Bytes', '2653087 Bytes', '2831487 Bytes', '2373466 Bytes', '2620422 Bytes', '2607258 Bytes', '2644266 Bytes', '2714689 Bytes', '2700460 Bytes', '2507121 Bytes', '2758350 Bytes', '2752108 Bytes', '2600062 Bytes', '2528479 Bytes', '2914819 Bytes', '2847219 Bytes', '2549637 Bytes', '2544014 Bytes', '2502909 Bytes', '2637219 Bytes', '2632286 Bytes', '2492947 Bytes', '2729874 Bytes', '2581005 Bytes', '2711226 Bytes', '2665985 Bytes', '2539300 Bytes', '2415689 Bytes', '2499477 Bytes', '2805610 Bytes', '2641863 Bytes', '2685073 Bytes', '2749731 Bytes', '2623599 Bytes', '2656527 Bytes', '2734612 Bytes', '2464525 Bytes', '2834934 Bytes', '2698853 Bytes', '2543818 Bytes', '2622461 Bytes', '2425647 Bytes', '2442746 Bytes', '2459962 Bytes', '2650686 Bytes', '2630062 Bytes', '2632078 Bytes', '2683712 Bytes', '2652282 Bytes', '2844642 Bytes', '2580494 Bytes', '2812337 Bytes', '2721134 Bytes', '2662502 Bytes', '3080071 Bytes', '2622756 Bytes', '2656095 Bytes', '2793056 Bytes', '2582074 Bytes', '2633070 Bytes', '2635778 Bytes', '2690154 Bytes', '2910419 Bytes', '2691029 Bytes', '2440181 Bytes', '2835796 Bytes', '2909551 Bytes', '2755645 Bytes', '2667826 Bytes', '2683634 Bytes', '2622070 Bytes', '2770946 Bytes', '2383770 Bytes', '2631926 Bytes', '2655362 Bytes', '2833991 Bytes', '2785892 Bytes', '2634254 Bytes', '2589384 Bytes', '2599140 Bytes', '2492696 Bytes', '2509480 Bytes', '2503103 Bytes', '2535554 Bytes', '2863528 Bytes', '2530774 Bytes', '2769773 Bytes', '2579778 Bytes', '2847113 Bytes', '2639682 Bytes', '2919900 Bytes', '2576672 Bytes', '2622207 Bytes', '2639125 Bytes', '2654375 Bytes', '2718034 Bytes', '3145265 Bytes', '2631278 Bytes', '2564563 Bytes', '2588752 Bytes', '2932705 Bytes', '2662489 Bytes', '2628450 Bytes', '2541037 Bytes', '2708489 Bytes', '2939728 Bytes', '2694713 Bytes', '2424627 Bytes', '2454343 Bytes', '2574850 Bytes', '2564554 Bytes', '3035739 Bytes', '2837971 Bytes', '2558366 Bytes', '2501252 Bytes', '2504030 Bytes', '2692977 Bytes', '2819167 Bytes', '2620651 Bytes', '2906034 Bytes', '2677734 Bytes', '2489206 Bytes', '2834125 Bytes', '2526841 Bytes', '2471041 Bytes', '2611950 Bytes', '2703429 Bytes', '2782746 Bytes', '2657699 Bytes', '2697085 Bytes', '2711260 Bytes', '2545299 Bytes', '2542195 Bytes', '2658718 Bytes', '2789393 Bytes', '2504694 Bytes', '2664577 Bytes', '2755839 Bytes', '2811303 Bytes', '2955213 Bytes', '2605334 Bytes', '2574914 Bytes', '2833226 Bytes', '2510194 Bytes', '2593031 Bytes', '2843633 Bytes', '2871636 Bytes', '2489537 Bytes', '2563445 Bytes', '2494629 Bytes', '2717684 Bytes', '2975288 Bytes', '2508127 Bytes', '2530365 Bytes', '2443985 Bytes', '2888286 Bytes', '2489045 Bytes', '2536125 Bytes', '2436992 Bytes', '2613138 Bytes', '2459472 Bytes', '2553235 Bytes', '2527662 Bytes', '2430570 Bytes', '2921708 Bytes', '2826366 Bytes', '3032231 Bytes', '3015348 Bytes', '2911062 Bytes', '2665849 Bytes', '2549586 Bytes', '2754448 Bytes', '2600024 Bytes', '2900525 Bytes', '2583275 Bytes', '2741637 Bytes', '2836875 Bytes', '2890580 Bytes', '3101977 Bytes', '2987647 Bytes', '2590328 Bytes', '2538126 Bytes', '2761057 Bytes', '2990933 Bytes', '2675932 Bytes', '2940298 Bytes', '3032093 Bytes', '2876847 Bytes', '2663918 Bytes', '2949053 Bytes', '2733648 Bytes', '2629650 Bytes', '2673694 Bytes', '2772991 Bytes', '2875062 Bytes', '2893654 Bytes', '2927552 Bytes', '2874495 Bytes', '2775042 Bytes', '3212947 Bytes', '2760885 Bytes', '3068395 Bytes', '2985322 Bytes', '3269218 Bytes', '2713499 Bytes', '2678763 Bytes', '2450371 Bytes', '2884576 Bytes', '2539176 Bytes', '2997377 Bytes', '3119597 Bytes', '2603005 Bytes', '2462695 Bytes', '2960818 Bytes', '2462170 Bytes', '2791215 Bytes', '2770194 Bytes', '2685803 Bytes', '3153311 Bytes', '2671365 Bytes', '2964545 Bytes', '2673480 Bytes', '2597876 Bytes', '2532957 Bytes', '2531298 Bytes', '2647304 Bytes', '3299785 Bytes', '2743868 Bytes', '2976017 Bytes', '2628686 Bytes', '2932961 Bytes', '2533383 Bytes', '2495382 Bytes', '2623743 Bytes', '2788843 Bytes', '2839505 Bytes', '2685565 Bytes', '2847978 Bytes', '2645771 Bytes', '2675237 Bytes', '2687029 Bytes', '2659416 Bytes', '2696440 Bytes', '2510607 Bytes', '2964712 Bytes', '2746728 Bytes', '2865632 Bytes', '2872111 Bytes', '2594869 Bytes', '2630218 Bytes', '2694207 Bytes', '2634032 Bytes', '2523977 Bytes', '2960003 Bytes', '2541976 Bytes', '1309402 Bytes', '1397046 Bytes', '1277137 Bytes', '1148418 Bytes', '1208381 Bytes', '1261843 Bytes', '1303741 Bytes', '1300214 Bytes', '904709 Bytes', '1371447 Bytes', '1255998 Bytes', '1478937 Bytes', '1266158 Bytes', '1260959 Bytes', '1520933 Bytes', '903554 Bytes', '1508839 Bytes', '1511108 Bytes', '1442555 Bytes', '1502241 Bytes', '1286243 Bytes', '1418422 Bytes', '1308865 Bytes', '1304823 Bytes', '1212531 Bytes', '1468711 Bytes', '907118 Bytes', '1278013 Bytes', '1299247 Bytes', '1219461 Bytes', '1371833 Bytes', '1350438 Bytes', '1577060 Bytes', '1416742 Bytes', '1728137 Bytes', '1485651 Bytes', '1420553 Bytes', '1268323 Bytes', '1558773 Bytes', '1370585 Bytes', '1431196 Bytes', '1353627 Bytes', '1369229 Bytes', '1343998 Bytes', '1281076 Bytes', '1331342 Bytes', '1323612 Bytes', '1500176 Bytes', '1439014 Bytes', '1487237 Bytes', '1718209 Bytes', '1635966 Bytes', '1290244 Bytes', '1528533 Bytes', '1327815 Bytes', '1435240 Bytes', '1600738 Bytes', '1354811 Bytes', '1326361 Bytes', '1373562 Bytes', '1279708 Bytes', '1396840 Bytes', '1331405 Bytes', '1489220 Bytes', '1529953 Bytes', '1404356 Bytes', '1275882 Bytes', '1490418 Bytes', '1664912 Bytes', '1441919 Bytes', '1302509 Bytes', '1424178 Bytes', '1290639 Bytes', '1344491 Bytes', '1511300 Bytes', '1293873 Bytes', '1246205 Bytes', '1468818 Bytes', '1329872 Bytes', '849624 Bytes', '1364565 Bytes', '1263704 Bytes', '1300811 Bytes', '1323679 Bytes', '1231534 Bytes', '1281809 Bytes', '1303689 Bytes', '1248063 Bytes', '867727 Bytes', '1125437 Bytes', '1335795 Bytes', '921961 Bytes', '1043574 Bytes', '1123266 Bytes', '1348224 Bytes', '874082 Bytes', '844115 Bytes', '842327 Bytes', '1032833 Bytes', '1081495 Bytes', '1269013 Bytes', '1071522 Bytes', '526993 Bytes', '450221 Bytes', '961277 Bytes', '1091512 Bytes', '1312710 Bytes', '1351228 Bytes', '1359951 Bytes', '1082578 Bytes', '1200320 Bytes', '1296469 Bytes', '1301956 Bytes', '1300319 Bytes', '1272103 Bytes', '896713 Bytes', '1261780 Bytes', '928341 Bytes', '1200135 Bytes', '1147959 Bytes', '971284 Bytes', '930025 Bytes', '1166140 Bytes', '1126129 Bytes', '1186727 Bytes', '1076533 Bytes', '1092697 Bytes', '972475 Bytes', '894468 Bytes', '935594 Bytes', '1273356 Bytes', '1398731 Bytes', '1384958 Bytes', '1353586 Bytes', '1314461 Bytes', '1408040 Bytes', '1553682 Bytes', '1397980 Bytes', '1287515 Bytes', '1442245 Bytes', '1417894 Bytes', '1493869 Bytes', '1364812 Bytes', '1319035 Bytes', '1309768 Bytes', '1162903 Bytes', '1235775 Bytes', '1133102 Bytes', '941052 Bytes', '1106709 Bytes', '987714 Bytes', '889958 Bytes', '1055517 Bytes', '938385 Bytes', '1010360 Bytes', '935931 Bytes', '1042921 Bytes', '1103159 Bytes', '1086637 Bytes', '882423 Bytes', '948265 Bytes', '1048500 Bytes', '872022 Bytes', '1118028 Bytes', '1090533 Bytes', '1214739 Bytes', '1310226 Bytes', '1035959 Bytes', '929168 Bytes', '1215080 Bytes', '1164468 Bytes', '1188333 Bytes', '1116934 Bytes', '1061550 Bytes', '989079 Bytes', '902622 Bytes', '887494 Bytes', '882373 Bytes', '1116301 Bytes', '942528 Bytes', '1275031 Bytes', '497449 Bytes', '1196181 Bytes', '905752 Bytes', '910194 Bytes', '939872 Bytes', '1115951 Bytes', '837638 Bytes', '937392 Bytes', '1258943 Bytes', '1345345 Bytes', '980776 Bytes', '1210110 Bytes', '1199185 Bytes', '947611 Bytes', '987811 Bytes', '1055418 Bytes', '931582 Bytes', '943925 Bytes', '1194849 Bytes', '1577554 Bytes', '1334085 Bytes', '1276833 Bytes', '1359111 Bytes', '1272477 Bytes', '1017166 Bytes', '1215072 Bytes', '1215513 Bytes', '1242076 Bytes', '1388185 Bytes', '1357961 Bytes', '1263381 Bytes', '1314988 Bytes', '1214378 Bytes', '1173316 Bytes', '1330105 Bytes', '952797 Bytes', '1335490 Bytes', '1275836 Bytes', '1236031 Bytes', '1162391 Bytes', '1160624 Bytes', '1181550 Bytes', '1158795 Bytes', '1144627 Bytes', '1155867 Bytes', '1160416 Bytes', '1178197 Bytes', '1190934 Bytes', '1131194 Bytes', '1189805 Bytes', '1231437 Bytes', '1377025 Bytes', '1098496 Bytes', '1112366 Bytes', '1397233 Bytes', '1303486 Bytes', '1427961 Bytes', '1424572 Bytes', '1361284 Bytes', '1416960 Bytes', '1585059 Bytes', '1129222 Bytes', '1373109 Bytes', '1354757 Bytes', '1154659 Bytes', '1307080 Bytes', '1300137 Bytes', '1193109 Bytes', '1139579 Bytes', '1286724 Bytes', '1461767 Bytes', '1247701 Bytes', '1231476 Bytes', '1449369 Bytes', '1499068 Bytes', '1404180 Bytes', '1156453 Bytes', '1151212 Bytes', '1416264 Bytes', '1173708 Bytes', '1166889 Bytes', '1128558 Bytes', '1194222 Bytes', '1211912 Bytes', '1211778 Bytes', '1340019 Bytes', '1342307 Bytes', '1189100 Bytes', '1200181 Bytes', '1266817 Bytes', '1222728 Bytes', '1379301 Bytes', '1315169 Bytes', '1078532 Bytes', '1083980 Bytes', '1153531 Bytes', '1159972 Bytes', '1104981 Bytes', '1275454 Bytes', '1255275 Bytes', '1276800 Bytes', '1190769 Bytes', '1120859 Bytes', '1191091 Bytes', '1157678 Bytes', '1204229 Bytes', '1162613 Bytes', '1184743 Bytes', '1188880 Bytes', '1190866 Bytes', '1141220 Bytes', '1357748 Bytes', '1333857 Bytes', '1152843 Bytes', '1261255 Bytes', '1305875 Bytes', '1297548 Bytes', '1316205 Bytes', '1292587 Bytes', '1313309 Bytes', '1253483 Bytes', '1165264 Bytes', '1474882 Bytes', '1344475 Bytes', '1325559 Bytes', '1358102 Bytes', '1295319 Bytes', '1412759 Bytes', '1306127 Bytes', '1338045 Bytes', '940205 Bytes', '1614798 Bytes', '1510077 Bytes', '1558187 Bytes', '1588758 Bytes', '1403888 Bytes', '1309911 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'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg', 'image/jpeg']"
+10.60662/0yc3-e898,Vers un service générique d’aide à la décision pour gérer un logement basé sur des techniques d’apprentissage interactif et coopératif,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T19:58:37.000Z,2023-09-01T19:58:37.000Z,uqtr.mesxqq,uqtr,,,,
+10.25577/m0dc-n549,Intensités macrosismiques du séisme de La Laigne du 16 juin 2023,"EOST UAR830, Université de Strasbourg, CNRS",2023,fr,Dataset,Creative Commons Attribution 4.0 International,Ce jeu de données présente les intensités macrosismiques du séisme de La Laigne survenu le 16 juin 2023 à 16h38 (TU). Elles ont été établies par le BCSF-Rénass à partir des règles de l'échelle d'intensité macrosismique européenne EMS-98.,fabrica,True,findable,0,0,0,0,0,2023-12-18T14:52:02.000Z,2023-12-20T15:26:57.000Z,inist.eost,jbru,"macrosismique,intensité,EMS-98,séisme,tremblement de terre,La Laigne","[{'subject': 'macrosismique'}, {'subject': 'intensité'}, {'subject': 'EMS-98'}, {'subject': 'séisme'}, {'subject': 'tremblement de terre'}, {'subject': 'La Laigne'}]",,['text/csv']
+10.15454/l7qn45,Soil Microbial Metagenomics Facility,INRAE,2018,,Service,,,fabricaForm,True,findable,44,0,0,0,0,2018-10-03T11:07:03.000Z,2018-10-03T11:07:03.000Z,rdg.prod,rdg,,,,
+10.5281/zenodo.10277631,Real space grids for def2-TZVP/def2-TZVP-RI basis sets resolution of the identity,Zenodo,2023,,Dataset,Creative Commons Attribution 4.0 International,"Real space grids (in Bohr) for def2-TZVP/def2-TZVP-RI basis sets resolution of the identity, as per described in the following works:
+1) ""Separable resolution-of-the-identity with all-electron Gaussian bases: Application to cubic-scaling RPA"",  J. Chem. Phys. 150, 174120 (2019); https://doi.org/10.1063/1.5090605.
+2) ""Cubic-scaling all-electron GW calculations with a separable density-fitting space-time approach"",  J. Chem. Theory Comput. 2021, 17, 2383−2393; https://doi.org/10.1021/acs.jctc.1c00101.",api,True,findable,0,0,0,0,0,2023-12-06T16:22:21.000Z,2023-12-06T16:22:21.000Z,cern.zenodo,cern,,,,
+10.34847/nkl.9f85iol5,Analysis of the relevance of the deployment of ICARE systems with regard to the photovoltaic potential of the roofs of the La Madeleine district in Nantes (France),NAKALA - https://nakala.fr (Huma-Num - CNRS),2021,en,ComputationalNotebook,,"This notebook has been produced in the context of the AVIDON research project which associates the two laboratories AAU and LIRMM. It aims at analysing the potentialities of the IRIS ""La Madeleine"", a district of the city centre of Nantes (France), in the perspective of a massive deployment of the ICARE device*. The more precise objective of this very preliminary work is to evaluate the photovoltaic potential of the neighbourhood's roofs on the one hand and, on the other hand, to estimate the energy demand of households to operate digital devices. AVIDON is a follow-up of the ISORE PEPS project funded by CNRS, associating the same partner laboratories.
+
+* G. Sassatelli, A. Gamatié and M. Robert, « Système de traitement de données avec transfert d’énergie », patent n° 1653238, 2016, https://patents.google.com/patent/WO2017178571A1.",api,True,findable,0,0,0,0,0,2021-10-21T09:52:22.000Z,2021-10-21T09:52:22.000Z,inist.humanum,jbru,"Photovoltaic potential of roofs,Energy demand for household IT equipment,Spatial analysis (Statistics)","[{'lang': 'en', 'subject': 'Photovoltaic potential of roofs'}, {'lang': 'en', 'subject': 'Energy demand for household IT equipment'}, {'lang': 'en', 'subject': 'Spatial analysis (Statistics)'}]","['1070287 Bytes', '793780 Bytes']","['text/plain', 'application/pdf']"
+10.34847/nkl.5bb02187,Bulletin franco-italien 1912 n°6 novembre - décembre,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/11 (A4,N6)-1912/12.",api,True,findable,0,0,0,0,0,2022-06-29T10:46:35.000Z,2022-06-29T10:46:35.000Z,inist.humanum,jbru,"Etudes italiennes,Etudes italiennes","[{'lang': 'fr', 'subject': 'Etudes italiennes'}, {'subject': 'Etudes italiennes'}]","['6036554 Bytes', '21797230 Bytes', '21498754 Bytes', '21658336 Bytes', '21954568 Bytes', '21816616 Bytes', '21709840 Bytes', '21664144 Bytes', '21657022 Bytes', '21577279 Bytes', '21667990 Bytes', '21719686 Bytes', '21847000 Bytes', '21846484 Bytes', '21619864 Bytes', '21711109 Bytes', '21842131 Bytes']","['application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
+10.60662/x25q-yv27,Vers une approche générique du raisonnement par cas : application à la gestion énergétique dans le bâtiment,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-11T15:17:26.000Z,2023-09-11T15:17:26.000Z,uqtr.mesxqq,uqtr,,,,
+10.57757/iugg23-4563,Constraining earthquake depth at teleseismic distance: Picking depth phases with deep learning,GFZ German Research Centre for Geosciences,2023,en,ConferencePaper,Creative Commons Attribution 4.0 International,"<!--!introduction!--><b></b><p>Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. However, while constraining earthquake epicenters in an automated fashion is an established technique, constraining event depth is substantially more difficult, especially in the absence of nearby stations. One solution to this challenge are teleseismic depth phases but these can currently not be identified by automatic detection methods.&nbsp; Here we propose two deep learning models, DepthPhaseNet and DepthPhaseTEAM to detect depth phases. The first model closely follows the PhaseNet architectures with minor modifications; the latter allows joint analysis of multiple stations by adding a transformer to this basic architecture.&nbsp; For training the models, we create a dataset based on the ISC EHB bulletin, a high-quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axes yields excellent estimates of earthquake depth. The models achieve mean absolute errors below 10 km. Furthermore, we demonstrate that the multi-station model, DepthPhaseTEAM, leads to better and more consistent predictions than the single-station model DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library for machine learning in seismology.</p>",fabricaForm,True,findable,0,0,0,0,0,2023-07-03T19:58:09.000Z,2023-07-10T20:46:27.000Z,gfz.iugg2023,gfz,,,,
+10.48649/asdc.1201,Caen vu par les médias. L'exemple de Ouest-France.,Atlas Social de Caen - e-ISSN : 2779-654X,2023,fr,JournalArticle,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"Comment l'agglomération de Caen est-elle représentée dans les médias ? Pourquoi certains lieux font-ils l'actualité et pas d'autres ? Quels sont les lieux qui ne sont jamais évoqués ? Quelle géographie des sujets médiatiques se dessine et quel en est le sens ? Pour répondre à ces questions, nous avons dépouillé tous les numéros du journal quotidien Ouest-France pour l'année 2019 puis réalisé une cartographie thématique ?",fabrica,True,findable,0,0,0,0,0,2023-06-23T12:32:59.000Z,2023-06-23T12:32:59.000Z,jbru.eso,jbru,"médias,conflit,aménagement,actualité","[{'subject': 'médias'}, {'subject': 'conflit'}, {'subject': 'aménagement'}, {'subject': 'actualité'}]",,
+10.34847/nkl.9cd8hi4k,"Paris IX borough: isovists, Min. ellipse, descriptors",NAKALA - https://nakala.fr (Huma-Num - CNRS),2020,,Dataset,,,api,True,findable,0,0,0,0,0,2022-12-16T09:53:17.000Z,2022-12-16T09:53:17.000Z,inist.humanum,jbru,,,['1782223 Bytes'],['application/zip']
+10.48550/arxiv.2310.14831,Formation of interstellar complex organic molecules on water-rich ices triggered by atomic carbon freezing,arXiv,2023,,Preprint,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"The reactivity of interstellar carbon atoms (C) on the water-dominated ices is one of the possible ways to form interstellar complex organic molecules (iCOMs). In this work, we report a quantum chemical study of the coupling reaction of C ($^3$P) with an icy water molecule, alongside possible subsequent reactions with the most abundant closed shell frozen species (NH$_3$, CO, CO$_2$ and H$_2$), atoms (H, N and O), and molecular radicals (OH, NH$_2$ and CH$_3$). We found that C spontaneously reacts with the water molecule, resulting in the formation of $^3$C-OH$_2$, a highly reactive species due to its triplet electronic state. While reactions with the closed-shell species do not show any reactivity, reactions with N and O form CN and CO, respectively, the latter ending up into methanol upon subsequent hydrogenation. The reactions with OH, CH$_3$ and NH$_2$ form methanediol, ethanol and methanimine, respectively, upon subsequent hydrogenation. We also propose an explanation for methane formation, observed in experiments through H additions to C in the presence of ices. The astrochemical implications of this work are: i) atomic C on water ice is locked into $^3$C-OH$_2$, making difficult the reactivity of bare C atoms on the icy surfaces, contrary to what is assumed in astrochemical current models; and ii) the extraordinary reactivity of $^3$C-OH$_2$ provides new routes towards the formation of iCOMs in a non-energetic way, in particular ethanol, mother of other iCOMs once in the gas-phase.",mds,True,findable,0,0,0,0,0,2023-10-24T03:25:43.000Z,2023-10-24T03:25:44.000Z,arxiv.content,arxiv,"Astrophysics of Galaxies (astro-ph.GA),Chemical Physics (physics.chem-ph),FOS: Physical sciences,FOS: Physical sciences","[{'lang': 'en', 'subject': 'Astrophysics of Galaxies (astro-ph.GA)', 'subjectScheme': 'arXiv'}, {'lang': 'en', 'subject': 'Chemical Physics (physics.chem-ph)', 'subjectScheme': 'arXiv'}, {'subject': 'FOS: Physical sciences', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'FOS: Physical sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}]",,
+10.34847/nkl.3dbc2mtb,Bulletin franco-italien 1912 n°4-5 juillet - octobre,NAKALA - https://nakala.fr (Huma-Num - CNRS),2022,fr,Book,,"1912/11 (A4,N6)-1912/12.",api,True,findable,0,0,0,0,0,2022-07-12T13:48:55.000Z,2022-07-12T13:48:55.000Z,inist.humanum,jbru,Etudes italiennes,[{'subject': 'Etudes italiennes'}],"['10539495 Bytes', '21558100 Bytes', '21840529 Bytes', '21062686 Bytes', '21714700 Bytes', '21445744 Bytes', '21836002 Bytes', '21825382 Bytes', '21214213 Bytes', '21303211 Bytes', '21876550 Bytes', '21499546 Bytes', '21614056 Bytes', '21780892 Bytes', '21669382 Bytes', '21654679 Bytes', '21598168 Bytes', '21779833 Bytes', '21611542 Bytes', '21629782 Bytes', '21183421 Bytes', '21241312 Bytes', '21237016 Bytes', '21224224 Bytes', '21229654 Bytes', '21175816 Bytes', '21237016 Bytes', '21138790 Bytes', '21129388 Bytes']","['application/pdf', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff', 'image/tiff']"
+10.5281/zenodo.10205579,Proceedings of the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7).[on line],Université Grenoble-Alpes,2023,en,ConferenceProceeding,Creative Commons Attribution 4.0 International,"This is the online, compiled proceedings from the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) which was held May 18–20, 2022 at Université Grenoble-Alpes, France. It includes 23 double-blind, peer-reviewed chapters written by authors from several countries, an introduction and a thematic index, and is licensed under the Creative Commons Attribution 4.0 International License. (To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.)",api,True,findable,0,0,0,0,0,2023-11-25T08:33:51.000Z,2023-11-25T08:33:51.000Z,cern.zenodo,cern,"English pronunciation,second language pronunciation,language learning,language teaching,second language acquisition,phonetics,phonology,English pronunciation research","[{'subject': 'English pronunciation'}, {'subject': 'second language pronunciation'}, {'subject': 'language learning'}, {'subject': 'language teaching'}, {'subject': 'second language acquisition'}, {'subject': 'phonetics'}, {'subject': 'phonology'}, {'subject': 'English pronunciation research'}]",,
+10.60662/n581-qq67,A Systemic approach for Material Handling System Design,CIGI QUALITA MOSIM 2023,2023,,ConferencePaper,,,fabricaForm,True,findable,0,0,0,0,0,2023-09-01T18:16:51.000Z,2023-09-01T18:16:51.000Z,uqtr.mesxqq,uqtr,,,,
+10.5281/zenodo.10213989,Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model,Zenodo,2023,,Dataset,Creative Commons Attribution 4.0 International,"=========================================================================Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model=========================================================================
+-----------------------INTRODUCTION-----------------------
+This dataset contains the data and scripts required to reproduce the figures and tables presented in the study:""Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model"" in The Cryosphere.
+We perform an ensemble of simulations of the Antarctic ice sheet between 1950 and 3014, forced by a panel of CMIP6 climate models, starting from present-day geometry with the Kori-ULB ice-sheet model v0.9. We calibrate our ensemble in a Bayesian framework to produce observationally-calibrated Antarctic projections used to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the surface mass balance on the other. All simulations are performed at a spatial resolution of 16 km.
+Hindcasts of the behaviour of the AIS over the period 1950-2014 CE are reproduced using changes in oceanic and atmospheric boundary conditions derived from the CMIP5 climate model NorESM1-M. As of the year 2015 CE, climate projections derived from a subset of CMIP6 climate models (MRI-ESM2-0, IPSL-CM6A-LR, CESM2-WACCM and UKESM1-0-LL) are used as forcing until the year 2300 CE. Afterwards, no climate trend is applied. The forcing applied is derived from both the Shared Socioeconomic Pathways (SSP) 5-8.5 and 1-2.6 scenarios. 
+------------------------------PROVIDED SCRIPTS: ------------------------------
+ - 'KoriModelAll.m' and 'KoriInputParams.m': Kori-ULB ice flow model (more info at https://github.com/FrankPat/Kori-ULB) - 'Compute_Bayesian_Weight.m': calculation of the ensemble likelihood weights used in the Bayesian calibration. - 'Plot_parameter_space_distributions.m': calculation and plots of prior and posterior parameter  probability distributions. - 'Plot_sea_level_distributions.m': calculation and plots of prior and posterior sea-level distributions. - 'Plot_mass_balance_components_distributions.m': calculation and plots of mass balance components distributions. - 'Plot_mean_thickness_change.m': calculation and plots of calibrated mean thickness change. - 'Plot_ungrounded_probability.m': calculation and plots of the marginal probability of being ungrounded. - 'Plot_SMB_sensitivity.m': Calculation and plots of surface mass balance sensitivity. - 'run_MISMIPplus.m' and 'MISMIPplus.m': run and compare MISMIP+ experiment
+-------------------------PROVIDED DATA: -------------------------
+
+'LHSensemble.mat': 100x9 matrices containing the values of the 100-member ensemble sampled (using maximin Latin Hypercube) within the parameter space in Table 1.
+
+1rst column ((:,1)) contains values of atmospheric present-day climatology (CLIMatm): MARv3.11 (1) - RACMOv2.3p2 (2)
+2nd column ((:,2)) contains values of oceanic present-day climatology (CLIMocn): Jourdain2020 (1) - Schmidtko2014 (2)
+3rd column ((:,3)) contains values of the atmospheric lapse rate (°C/km)
+4th column ((:,4)) contains values of the thickness of the thermally-active layer influencing surface refreezing (m)
+5th column ((:,5)) contains values of the contains values of the Degree day factor for the melting of ice (mm/PDD)
+6th column ((:,6)) contains values of the contains values of the Degree day factor for the melting of snow (mm/PDD)
+7th column ((:,7)) contains values of the applied Sub-shelf melt parameterisation: Quadratic-local Antarctic slope parameterisation (1) - PICO model (2) - Plume model (3) - ISMIP6 Nonlocal quadratic parameterisation (4) - ISMIP6 Nonlocal quadratic parameterisation including dependency on local slope (5)
+8th column ((:,8)) contains values of the effective ice-ocean heat flux: [0.1 x 10^-5 - 10 x 10^-5] m/s for gammaT* in PICO - [1 x 10^-4 - 10 x 10^-4] for Cd^1/2Gamma_TS in Plume -  [1 x 10^-4 - 10 x 10^-4] for K in Quadratic-local Antarctic slope parameterisation - [1 x 10^4 - 4 x 10^4] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation - [1 x 10^6 - 4 x 10^6] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation with slope dependency
+9th column ((:,9)) contains values of the CMIP6 climate model applied for climate forcing: MRI-ESM2-0 (1) - UKESM1-0-LL (2) - CESM2-WACCM (3) - IPSL-CM6A-LR (4)'LHval' and 'LHS' contain the absolute values and the values of the parameters scaled linearly between 0 and 1 (0: minimum value, 1:maximum value) of the nine parameters, respectively. 
+'HIST_ENSEMBLE_DATA.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014).
+
+H_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with ice thickness field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid.
+MASK_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with grounded mask field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid. It distinguishes grounded ice (1: grounded) from ocean or floating ice (0: ocean/floating).
+mbcomp_ensemble: 3D matrix of dimension [time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr). The components mbcomp include the following ice-sheet aggregated and grounded ice sheet components:                    (1) Ice-sheet aggregated surface mass balance                    (2) Ice-sheet aggregated accumulation                    (3) Ice-sheet aggregated surface melt                    (4) Ice-sheet aggregated runoff                    (5) Ice-sheet aggregated rain                    (6) sub-shelf melt                    (7) dynamic ice loss (calving)                    (8) surface mass balance over the grounded ice sheet                    (9) accumulation over the grounded ice sheet                    (10) surface melt over the grounded ice sheet                    (11) runoff over the grounded ice sheet                    (12) rain over the grounded ice sheet                                (13) Net mass balance (rate of HAF change)
+SLC_ensemble: 2D matrix of dimension [ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m)  
+'HIST_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback. 
+'HIST_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015). 
+'CONTROL_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 when considering constant present-day conditions as of the year 2015. 
+'SSP126_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario. 
+'SSP585_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario. It also contains the variable Runoff_ensemble, a 4D matrix of dimension [X, Y, snap_time, ensemble member] with surface runoff field (in m/yr i.e.) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid, as used in Fig. 7. 
+'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback. 
+'SSP585_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015). 
+'SSP585_ENSEMBLE_DATA_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015. 
+'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015. 
+'SSP585_ENSEMBLE_DATA_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015. 
+'HIST_ENSEMBLE_DATA_BASIN.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) integrated over 27 drainage basins (http://imbie.org/imbie-2016/drainage-basins/).
+
+SLC_ensemble_basin: 3D matrix of dimension [basin, ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) by basin
+mbcomp_ensemble_basin: 4D matrix of dimension [basin, time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr) by basin. The components mbcomp include the same ice-sheet aggregated and grounded ice-sheet components as in 'HIST_ENSEMBLE_DATA.mat'. 
+'HIST_ENSEMBLE_DATA_BASIN_NO_ELEVATION_DATA.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback. 
+'HIST_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015). 
+'SSP126_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA°BASIN') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario. 
+'SSP585_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario. 
+'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario when neglecting the melt-elevation feedback. 
+'SSP585_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015). 
+'SSP585_ENSEMBLE_DATA_BASIN_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015. 
+'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015. 
+'SSP585_ENSEMBLE_DATA_BASIN_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015. 
+'GCM_SSPXXX_mean_aTs.mat': Timeseries of the regionally-averaged (between 90–60°S) annual near-surface (2-m) air temperature anomaly (°C) projected by the climate model 'GCM' from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) between 2015 and 2300 under the SSPXXX emission scenario, compared to the 1995-2014 reference period. SSPXXX may be 'SSP126' and 'SSP585' and GCM may be 'MRI-ESM2-0', 'CESM2-WACCM', 'IPSL-CM6A-LR', or 'UKESM1-0-LL'. 
+'CALIBRATION DATA.mat': values ('val'), uncertainty ('sigma'), beginning ('year1') and end ('year2') of the average time period of the 12 regionally and temporally aggregated IMBIE data used in the Bayesian calibration (Table 2 in this study, coming from Table 2 from Otosaka et al., 2023) 
+'INIT_MAR_aNorESM1-M_1950.mat' and 'INIT_RACMO_aNorESM1-M_1950.mat': Ice-sheet initial states at year 1950 obtained with the 1995-2014 atmospheric climatology from MARv3.11(Kittel eta l.,2021) or RACMOv2.3p2 (van Wessem et al., 2018), respectively, adjusted with a 1945-1955 anomaly from NorESM1-M. H is the ice thickness (in meters), B is the bedrock topography (in meters), and u is the surface velocity (in m/yr). These files were provided as input files to Kori-ULB to produce the projections. More info on the input files and their variables can be found here: https://github.com/FrankPat/Kori-ULB.
+----------------------------------------------------------MATLAB FUNCTIONS USED IN SCRIPTS: ----------------------------------------------------------
+- imagescn: imagesc with transparent NaNs, by Chad Greene (2023), downloaded from MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/61293-imagescn), - brewermap: provides all ColorBrewer colorschemes for MATLAB, by Stephen23. Downloaded from https://github.com/DrosteEffect/BrewerMap.- crameri: returns perceptually-uniform scientific colormaps created by Fabio Crameri (requires CrameriColourMaps8.0.mat)
+----------------------------------------------------------------------------------EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY:----------------------------------------------------------------------------------
+- BedMachine data used for the present-day grounding lines in Figures 2 and 7: It is BedMachine v2 (Morlighem et al., 2020) and can be found here: https://nsidc.org/data/nsidc-0756/versions/2.- The delineation of the 27 Zwally Basins used to identify and separate the West and East Antarctic ice sheets and the Antarctic Peninsula can be found at http://imbie.org/imbie-2016/drainage-basins/- Outputs from MAR(CNRM-CM6-1) and MAR(CESM2) used in Figures 7 and S10. The data can be downloaded at 10.5281/zenodo.4529004 and 10.5281/zenodo.4529002, respectively. It was then interpolated to the 16-km grid used by Kori-ULB.- CESM2-WACCM outputs used in Figure 7 were downloaded from the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/) and interpolated to the 16-km grid used by Kori-ULB.- The CMIP6 forcing data used in this study (and plotted in Figures S6 and S7) are accessible through the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/). They have been interpolated to the interpolated to the 16-km grid used by Kori-ULB.
+---------------------REFERENCES: ---------------------
+Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021.
+Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nat. Geosci. 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8
+Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.
+Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure, Earth and Planetary Science Letters, 412, 112–121, https://doi.org/10.1016/j.epsl.2014.12.035, 2015. van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.",api,True,findable,0,0,0,0,0,2023-12-12T08:04:54.000Z,2023-12-12T08:04:54.000Z,cern.zenodo,cern,"Ice-sheet modelling, Antarctic ice sheet, Sea-level projections","[{'subject': 'Ice-sheet modelling, Antarctic ice sheet, Sea-level projections'}]",,
+10.34847/nkl.bf5f263z,Atelier SAGEO 2021 : Traitements spatiaux avec le plugin Python t4gpd dans le contexte d’un Jupyter Notebook,NAKALA - https://nakala.fr (Huma-Num - CNRS),2021,fr,ComputationalNotebook,,"Cet atelier s'inscrit dans le contexte de l'édition 2021 de la conférence SAGEO (http://sageo2021.univ-lr.fr/). Il a pour objectif d’initier les participants au traitement de données spatiales via le plugin Python3 t4gpd. Il alternera des mises en contexte introductives avec des travaux pratiques en environnement Jupyter Notebook. L’outil t4gpd permet d’analyser les formes d’espace construit dans différents registres, de l’analyse bioclimatique (orientation héliothermique, vue du ciel, etc.), à l’analyse de tracés (orientations, distances sur un graphe, etc.), en passant par des analyses à connotations paysagères (visibilités, études d’alignements d’arbres, etc.) ou des analyses de composantes des tissus urbains (identification de rues canyons, typologie d’intersections, etc.). Développé au sein de AAU-CRENAU (https://aau.archi.fr/), il bénéficie autant d’un ensemble de travaux conduits depuis plusieurs décennies à l’école nationale supérieure d’architecture de Nantes, que des développements récents autour de bibliothèques telles que GeoPandas ou Shapely.
+
+Plan de l’atelier :
+
+- Introduction : Opérations géométriques élémentaires et initiation à Shapely. GeoPandas comme cartouche spatiale pour Pandas. Production de cartes élémentaires via Matplotlib. Exercice pratique : désagrégation de données carroyées.
+
+- Proximité et zone de confinement : De la distance à vol d’oiseau à la distance parcourue au sol sur un graphe – identification d’un plus court chemin. Retour sur une polémique : le périmètre d’un kilomètre. Exercices pratiques : périmètre de confinement de 1km, associer chaque bâtiment d’une zone d’étude à l’espace de coworking le plus proche.
+
+- Forme de l’espace environnant et ressenti de densité : Indices de forme, champs d'isovists, analyse des vues du ciel. Exercices pratiques : convexité, rectangularité, circularité, ellipticité ; analyse de l’évolution du facteur de vues du ciel au cours d’un cheminement piéton.
+
+- Où trouver l’ombre en ville ? Course solaire et ombre au sol. Cumuls d’ombre et partitionnement de l’espace. Exercice pratique : délimiter les zones du Cours Cambronne (Nantes) à l’ombre pendant plus de 4h le 21 juin.
+
+Note : les fichiers d'extension ipynb peuvent être lus en ligne à l'aide de l'outil https://nbviewer.jupyter.org/.",api,True,findable,0,0,0,0,0,2021-05-20T15:40:24.000Z,2021-05-20T15:40:24.000Z,inist.humanum,jbru,"t4gpd,Python,geopandas,shapely,Jupyter Notebook","[{'lang': 'fr', 'subject': 't4gpd'}, {'lang': 'fr', 'subject': 'Python'}, {'lang': 'fr', 'subject': 'geopandas'}, {'lang': 'fr', 'subject': 'shapely'}, {'lang': 'fr', 'subject': 'Jupyter Notebook'}]","['170361 Bytes', '175080 Bytes', '1047624 Bytes', '1028496 Bytes', '980582 Bytes', '692178 Bytes', '949943 Bytes', '819001 Bytes', '913679 Bytes']","['text/plain', 'application/pdf', 'text/plain', 'application/pdf', 'text/plain', 'application/pdf', 'text/plain', 'application/pdf', 'application/pdf']"
+10.5281/zenodo.10205580,Proceedings of the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7).[on line],Université Grenoble-Alpes,2023,en,ConferenceProceeding,Creative Commons Attribution 4.0 International,"This is the online, compiled proceedings from the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) which was held May 18–20, 2022 at Université Grenoble-Alpes, France. It includes 23 double-blind, peer-reviewed chapters written by authors from several countries, an introduction and a thematic index, and is licensed under the Creative Commons Attribution 4.0 International License. (To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.)",api,True,findable,0,0,0,0,0,2023-11-25T08:33:50.000Z,2023-11-25T08:33:50.000Z,cern.zenodo,cern,"English pronunciation,second language pronunciation,language learning,language teaching,second language acquisition,phonetics,phonology,English pronunciation research","[{'subject': 'English pronunciation'}, {'subject': 'second language pronunciation'}, {'subject': 'language learning'}, {'subject': 'language teaching'}, {'subject': 'second language acquisition'}, {'subject': 'phonetics'}, {'subject': 'phonology'}, {'subject': 'English pronunciation research'}]",,
+10.34746/cahierscostech48,Université Virtuelle Africaine (UVA) et universités partenaires en Afrique : Entretien commenté,Cahiers Costech,2018,fr,JournalArticle,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"Cette publication qui n’a pas pour ambition de lancer un nouveau type d’entretien, se présente sous une forme originale structurée en deux parties. La première donne à voir la version initiale d’une proposition de publication dont l’objectif était de rendre compte d’un travail de terrain constitué par la transcription d’un entretien et de son analyse au regard du sujet de thèse. La seconde partie présente les améliorations apportées à la version initiale suite aux commentaires formulés par les deux enseignants-chercheurs responsables de la rubrique « Education et numérique » des Cahiers Costech sollicités pour la publication. L’intérêt de cette démarche est de mettre en évidence le travail de conscientisation du doctorant résultant des directives pédagogiques des relecteurs.
+",fabricaForm,True,findable,0,0,0,0,0,2022-07-06T12:43:45.000Z,2022-07-06T12:43:46.000Z,inist.utc,vcob,Education et technologie - Méthodologie de recherche - Simondon,[{'subject': 'Education et technologie - Méthodologie de recherche - Simondon'}],,
diff --git a/run-all-codes.py b/run-all-codes.py
index c53b525200ff971213f4b4d8d0ac874e2af7b040..851e8e9da50c35aa18750d19dc84cbd75b60699f 100644
--- a/run-all-codes.py
+++ b/run-all-codes.py
@@ -19,4 +19,9 @@ file_names = [
 	"rdg.py"
 ]
 
-execute_python_file(file_names[1])
+for file in file_names : 
+	execute_python_file(file)
+
+
+
+# execute_python_file(file_names[1])