diff --git a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
index e91e329f3bc650719b77e96fc2e35dcd9cff145b..9618bfd14fc819fda71bd0d85376015cc2530d67 100644
--- a/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+++ b/1-enrich-with-datacite/all_datacite_clients_for_uga.csv
@@ -1,16 +1,16 @@
 client,count,name,year,url
-cern.zenodo,799,Zenodo,2013,https://zenodo.org/
+cern.zenodo,805,Zenodo,2013,https://zenodo.org/
 inist.sshade,496,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
 figshare.ars,361,figshare Academic Research System,2016,http://figshare.com/
 inist.osug,238,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
-dryad.dryad,163,DRYAD,2018,https://datadryad.org
+dryad.dryad,164,DRYAD,2018,https://datadryad.org
 inist.resif,93,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
 rdg.prod,68,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
 inist.humanum,65,NAKALA,2020,https://nakala.fr
-inist.persyval,61,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
+inist.persyval,62,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
 fmsh.prod,28,Fondation Maison des sciences de l'homme,2023,
 mcdy.dohrmi,12,dggv-e-publications,2020,https://www.dggv.de/publikationen/dggv-e-publikationen.html
-pangaea.repository,7,PANGAEA,2020,https://www.pangaea.de/
+pangaea.repository,8,PANGAEA,2020,https://www.pangaea.de/
 figshare.sage,6,figshare SAGE Publications,2018,
 iris.iris,5,Incorporated Research Institutions for Seismology,2018,http://www.iris.edu/hq/
 tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
diff --git a/1-enrich-with-datacite/nb-dois.txt b/1-enrich-with-datacite/nb-dois.txt
index 5827dc196f8e5daeae9235daf7b283dd4c360c15..4baaea8c600d4cf7fd711e19db8327140dbdb86d 100644
--- a/1-enrich-with-datacite/nb-dois.txt
+++ b/1-enrich-with-datacite/nb-dois.txt
@@ -1 +1 @@
-2438
\ No newline at end of file
+2447
\ No newline at end of file
diff --git a/2-produce-graph/hist-evol-datasets-per-repo.png b/2-produce-graph/hist-evol-datasets-per-repo.png
index fb8d1115400610615cddeeb01cb4388b5b11fcd2..33a8be1e22c8d09a79ae3308bb585752f94fc911 100644
Binary files a/2-produce-graph/hist-evol-datasets-per-repo.png and b/2-produce-graph/hist-evol-datasets-per-repo.png differ
diff --git a/2-produce-graph/hist-last-datasets-by-client.png b/2-produce-graph/hist-last-datasets-by-client.png
index 76a4d6e5c7f10b5c247fec339d9812cce42d4613..8ecc1d1dc542af52de0d500fa4a30c34abab9737 100644
Binary files a/2-produce-graph/hist-last-datasets-by-client.png and b/2-produce-graph/hist-last-datasets-by-client.png differ
diff --git a/2-produce-graph/hist-quantity-year-type.png b/2-produce-graph/hist-quantity-year-type.png
index 6819ae377c13a3ae5e1d72449c6deaa0468c1272..b4272a3cb6bf46e78e88961f0f48e27e39d4f157 100644
Binary files a/2-produce-graph/hist-quantity-year-type.png and b/2-produce-graph/hist-quantity-year-type.png differ
diff --git a/2-produce-graph/pie--datacite-client.png b/2-produce-graph/pie--datacite-client.png
index c1b86f7b8bcff8536ce6643d5621fcbee33362a2..a2b4b5e1adb9355aeffd450bac21966fa2a3580b 100644
Binary files a/2-produce-graph/pie--datacite-client.png and b/2-produce-graph/pie--datacite-client.png differ
diff --git a/2-produce-graph/pie--datacite-type.png b/2-produce-graph/pie--datacite-type.png
index 308a6a8e1e4383c1b82c3049508492cdcc0efc9a..c371fc8a3bc22b928f89a4bb27a30915b74ee8a5 100644
Binary files a/2-produce-graph/pie--datacite-type.png and b/2-produce-graph/pie--datacite-type.png differ
diff --git a/dois-uga--last-500.csv b/dois-uga--last-500.csv
index dc18ae313cb6503e5f5349c3a609fc7e383c1bb9..aa642abc818362cc35545040df208b4e77dd7f34 100644
--- a/dois-uga--last-500.csv
+++ b/dois-uga--last-500.csv
@@ -1,4 +1,13 @@
 doi,client,resourceTypeGeneral,created,publisher,rights,sizes
+10.5281/zenodo.14170819,cern.zenodo,Dataset,2024-11-15,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14163781,cern.zenodo,Audiovisual,2024-11-14,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14151075,cern.zenodo,Dataset,2024-11-13,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14147172,cern.zenodo,Software,2024-11-13,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14106550,cern.zenodo,Text,2024-11-12,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.14100474,cern.zenodo,Text,2024-11-12,Zenodo,Creative Commons Attribution 4.0 International,
+10.18709/perscido.2024.11.ds415,inist.persyval,Dataset,2024-11-12,PerSCiDO,,['10 Mo']
+10.1594/pangaea.971917,pangaea.repository,Collection,2024-11-12,PANGAEA,Creative Commons Attribution 4.0 International,['9 datasets']
+10.5061/dryad.ht76hdrqg,dryad.dryad,Dataset,2024-11-11,Dryad,Creative Commons Zero v1.0 Universal,['2124418312 bytes']
 10.5281/zenodo.14051167,cern.zenodo,Other,2024-11-07,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.14045604,cern.zenodo,Dataset,2024-11-07,Zenodo,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.c.7528784,figshare.ars,Collection,2024-11-07,figshare,Creative Commons Attribution 4.0 International,
@@ -69,8 +78,8 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.6084/m9.figshare.27012151,figshare.ars,Text,2024-09-13,figshare,Creative Commons Attribution 4.0 International,['450818 Bytes']
 10.6084/m9.figshare.c.7447186,figshare.ars,Collection,2024-09-13,figshare,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13748961,cern.zenodo,Text,2024-09-11,Zenodo,"Creative Commons Attribution 4.0 International,Creative Commons Attribution Share Alike 4.0 International",
-10.6084/m9.figshare.26985715,figshare.ars,Text,2024-09-11,figshare,Creative Commons Attribution 4.0 International,['25284 Bytes']
 10.6084/m9.figshare.c.6585842,figshare.ars,Collection,2024-09-11,figshare,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.26985715,figshare.ars,Text,2024-09-11,figshare,Creative Commons Attribution 4.0 International,['25284 Bytes']
 10.5281/zenodo.13745070,cern.zenodo,Dataset,2024-09-11,Zenodo,Creative Commons Attribution 4.0 International,
 10.26302/sshade/experiment_zed_20240701_01,inist.sshade,Dataset,2024-09-11,SSHADE/DAYSY (OSUG Data Center),"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.",['14 spectra']
 10.5281/zenodo.11108225,cern.zenodo,Dataset,2024-09-08,Zenodo,Creative Commons Attribution 4.0 International,
@@ -113,8 +122,8 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.57760/sciencedb.11705,cnic.sciencedb,Dataset,2024-08-15,Science Data Bank,Creative Commons Attribution Non Commercial Share Alike 4.0 International,"['99398887984 bytes', '14 files']"
 10.6084/m9.figshare.26722614,figshare.ars,Text,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['12341 Bytes']
 10.6084/m9.figshare.25854698,figshare.ars,Image,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['464136 Bytes']
-10.6084/m9.figshare.c.7204785,figshare.ars,Collection,2024-08-15,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26713777,figshare.ars,Dataset,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['553168 Bytes']
+10.6084/m9.figshare.c.7204785,figshare.ars,Collection,2024-08-15,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.25711209,figshare.ars,Text,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['33449 Bytes']
 10.6084/m9.figshare.c.7116481,figshare.ars,Collection,2024-08-15,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.26691925,figshare.ars,Text,2024-08-15,figshare,Creative Commons Attribution 4.0 International,['2061125 Bytes']
@@ -205,8 +214,8 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.5281/zenodo.13234729,cern.zenodo,Text,2024-08-06,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13194009,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13194007,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.13189234,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13189238,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
+10.5281/zenodo.13189234,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5281/zenodo.13189236,cern.zenodo,Image,2024-08-03,Zenodo,Creative Commons Attribution 4.0 International,
 10.5061/dryad.wdbrv15xr,dryad.dryad,Dataset,2024-08-02,Dryad,Creative Commons Zero v1.0 Universal,['482590 bytes']
 10.5281/zenodo.13164857,cern.zenodo,Dataset,2024-08-02,Zenodo,Creative Commons Attribution 4.0 International,
@@ -413,8 +422,8 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.60527/fpa9-1718,fmsh.prod,Other,2024-03-06,"Univ. Grenoble Alpes, GRESEC",,
 10.60527/zxn9-6b90,fmsh.prod,Audiovisual,2024-03-06,"Univ. Grenoble Alpes, GRESEC",Droit commun de la propriété intellectuelle,
 10.5281/zenodo.10788911,cern.zenodo,Dataset,2024-03-06,Zenodo,Creative Commons Attribution 4.0 International,
-10.6084/m9.figshare.25341247,figshare.ars,Dataset,2024-03-05,figshare,Creative Commons Attribution 4.0 International,['29389796 Bytes']
 10.6084/m9.figshare.c.7105606,figshare.ars,Collection,2024-03-05,figshare,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.25341247,figshare.ars,Dataset,2024-03-05,figshare,Creative Commons Attribution 4.0 International,['29389796 Bytes']
 10.6084/m9.figshare.25329673,figshare.ars,Text,2024-03-02,figshare,Creative Commons Attribution 4.0 International,['15066 Bytes']
 10.6084/m9.figshare.c.7097182,figshare.ars,Collection,2024-02-29,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.25309966,figshare.ars,Text,2024-02-29,figshare,Creative Commons Attribution 4.0 International,['584702 Bytes']
@@ -483,19 +492,10 @@ doi,client,resourceTypeGeneral,created,publisher,rights,sizes
 10.5281/zenodo.10469399,cern.zenodo,Dataset,2024-01-08,Zenodo,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.24953169,figshare.ars,Text,2024-01-06,figshare,Creative Commons Attribution 4.0 International,['48627 Bytes']
 10.18150/vbwcr1,tib.repod,Dataset,2024-01-05,RepOD,,
-10.6084/m9.figshare.24946445,figshare.ars,Text,2024-01-05,figshare,Creative Commons Attribution 4.0 International,['20755 Bytes']
 10.6084/m9.figshare.c.7009820,figshare.ars,Collection,2024-01-05,figshare,Creative Commons Attribution 4.0 International,
+10.6084/m9.figshare.24946445,figshare.ars,Text,2024-01-05,figshare,Creative Commons Attribution 4.0 International,['20755 Bytes']
 10.6084/m9.figshare.c.7007130,figshare.ars,Collection,2024-01-04,figshare,Creative Commons Attribution 4.0 International,
 10.6084/m9.figshare.24940485,figshare.ars,Text,2024-01-04,figshare,Creative Commons Attribution 4.0 International,['399440 Bytes']
 10.5281/zenodo.10440363,cern.zenodo,Software,2024-01-04,Zenodo,,
 10.5281/zenodo.10441453,cern.zenodo,Dataset,2023-12-29,Zenodo,Creative Commons Attribution 4.0 International,
 10.26302/sshade/bandlist_raman_alstonite,inist.sshade,Dataset,2023-12-27,SSHADE/BANDLIST (OSUG Data Center),"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.",
-10.26302/sshade/bandlist_raman_barytocalcite,inist.sshade,Dataset,2023-12-26,SSHADE/BANDLIST (OSUG Data Center),"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.",
-10.5061/dryad.xksn02vn4,dryad.dryad,Dataset,2023-12-26,Dryad,Creative Commons Zero v1.0 Universal,['144079 bytes']
-10.26302/sshade/bandlist_raman_huntite,inist.sshade,Dataset,2023-12-25,SSHADE/BANDLIST (OSUG Data Center),"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.",
-10.26302/sshade/bandlist_raman_vaterite,inist.sshade,Dataset,2023-12-24,SSHADE/BANDLIST (OSUG Data Center),"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.",
-10.5281/zenodo.10412997,cern.zenodo,Dataset,2023-12-23,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.10419096,cern.zenodo,Dataset,2023-12-22,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.10410540,cern.zenodo,Dataset,2023-12-22,Zenodo,Creative Commons Attribution 4.0 International,
-10.5281/zenodo.10392425,cern.zenodo,Dataset,2023-12-18,Zenodo,Creative Commons Attribution 4.0 International,
-10.25577/m0dc-n549,inist.eost,Dataset,2023-12-18,"EOST UAR830, Université de Strasbourg, CNRS",Creative Commons Attribution 4.0 International,
diff --git a/dois-uga.csv b/dois-uga.csv
index a35c007cb6254ab8f07653f8dc11fac865ab1c6f..28cba18824008429a4ed59ca30ca0ff261974423 100644
--- a/dois-uga.csv
+++ b/dois-uga.csv
@@ -11532,3 +11532,101 @@ As a result, despite its clinical importance, spinal cord MRI is currently under
 10.5281/zenodo.14036125,Triple AMR Parser,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,"This is the code implementation to train a sequence-to-sequence AMR parser. In this work, we represent an AMR graph as a set of triples for linearization. We train the parser with triple AMRs  and evaluate the weakness/strength of the linearization method.",api,True,findable,0,0,0,0,0,2024-11-04T17:18:05.000Z,2024-11-04T17:18:05.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14036125']]"
 10.57745/rvc6wq,Simulated data for searches for electroweakino dark matter in the monojet channel,Recherche Data Gouv,2024,,Dataset,,"Dataset associated with the ""Machine Learning Electroweakino Production"" publication (https://doi.org/10.48550/arXiv.2411.00093). In this study, we explore the possibility of enhancing searches for supersymmetric dark matter particles at the LHC in the monojet channel, by using Graph Neural Networks (GNNs). We train an ensemble of 10 networks for Wino- and Higgsino-like neutralinos, and we use it on Bino, Wino, and Higgsino test samples in order to derive the sensitivity achievable at the end of Run-3 and High Luminosity phases of the LHC. The dataset contains 5 folders: 1) wino_train, 2) wino_val, 3) higgsino_train, 4) higgsino_val, 5) test. Each ""train"" folder contains 10 files (archives) corresponding to an ensemble of 10 networks, for either Wino- or Higgsino-like neutralino. ""Val"" folders contain validation data for the ensemble, 10 files per each neutralino type. Validation and training data are all for the same mass point: neutralino mass 300 GeV and squark mass 2.2 TeV. The ""Test"" folder contains test data for SM, Binos, Higgsinos, and Winos. For neutralino test data, the archives contain all 30 mass points. For the test set, masses of neutralinos vary between 200 GeV and 1100 GeV, while the masses of squarks vary between 2.0 TeV and 3.0 TeV. Data was produced using Monte Carlo simulation methods, with MadGraph5, Pythia, and Delphes. The published data was subject to preselection, described in the associated article. All files are in the awkd0 format. Example code demonstrating how to read the files and use them for NN training can be found in the official repository of the project: https://github.com/Rav2/monojet",mds,True,findable,7,0,0,0,0,2024-10-30T13:09:16.000Z,2024-11-07T09:32:54.000Z,rdg.prod,rdg,,,,,,,"['HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart', 'HasPart']",
 10.5281/zenodo.14035431,The Little Prince AMR Corpus (Expanded with Korean and Croatian),Zenodo,2024,ko,Dataset,Creative Commons Attribution 4.0 International,"We expanded The Little Prince AMR Corpus (https://amr.isi.edu/). The original data is available in English and Chinese. By manually aligning Korean and Croatian texts to English, we obtained multilingual The Little Corpus AMR Corpus. ",api,True,findable,0,0,0,0,0,2024-11-04T14:19:01.000Z,2024-11-04T14:19:02.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14035431']]"
+10.5281/zenodo.14147172,SPECTROview : A Tool for Spectroscopic Data Processing and Visualization,Zenodo,2024,en,Software,Creative Commons Attribution 4.0 International,"Introduction.
+
+Spectroscopy techniques such as Raman Spectroscopy and Photoluminescence (PL) are widely used in various fields, including materials science, chemistry, biology, and geology. In recent years, these techniques have increasingly found their place in cleanroom environments, particularly within the microelectronics industry, where they serve as critical metrology tools for wafer-scale measurements. The data collected from these in-line measurements (wafer data) require specific processing, but existing software solutions are often not optimized for this type of data and typically lack advanced plotting and visualization capabilities. Additionally, the licensing requirements of these software solutions can restrict access for a broader community of users.
+
+SPECTROview addresses these gap by offering free, open-source software that is compataible with both in-line data (wafer-map) as well as standard spectroscopic data (discret spectra, 2D maps). It also features a built-in visualization tool, enabling users to streamline both data processing and visualization in a single application, making the workflow more efficient.
+
+Features:
+
+-          Cross-platform compatibility (Windows, macOS, Linux).
+
+-          Optimized user inferface for easy and quick inspection and comparison of spectra.
+
+-          Supports processing of spectral data (1D) and hyperspectral data (2D maps or wafer maps)*.
+
+-          Ability to fit multiple spectra or 2Dmaps using predefined models or by creating custom fit models*.
+
+-          Collect all best-fit results with one click.
+
+-          Dedicated module for effortless, fast, and easy data visualization.
+
+*Fitting features are powered by the fitspy and LMfit open-source packages.
+
+Acknowledgements:
+
+This work, carried out on the CEA - Platform for Nanocharacterisation (PFNC), was supported by the “Recherche Technologique de Base” program of the French National Research Agency (ANR).",api,True,findable,0,0,0,0,0,2024-11-13T18:24:43.000Z,2024-11-13T18:24:43.000Z,cern.zenodo,cern,"Spectroscopy,Data processing,Raman Spectroscopy,Photoluminescence,SPECTROview,Data processing,Wafer-map,Hyperspectral,Spectroscopic data,Fitting","[{'subject': 'Spectroscopy', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Data processing', 'subjectScheme': 'EuroSciVoc'}, {'subject': 'Raman Spectroscopy'}, {'subject': 'Photoluminescence'}, {'subject': 'SPECTROview'}, {'subject': 'Data processing'}, {'subject': 'Wafer-map'}, {'subject': 'Hyperspectral'}, {'subject': 'Spectroscopic data'}, {'subject': 'Fitting'}]",,,,,"['HasVersion', 'HasVersion']","[['IsVersionOf', '10.5281/zenodo.14147172']]"
+10.5281/zenodo.14170819,Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"This repository contains the data and results associated to the publication submitted entitled ""Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations"".
+
+The results and data contain:
+
+
+
+Raw and processed ASTER DEM time series data stored in netcdf format (Hala_surges_aster**.nc): 
+
+
+
+
+Raw DEM stack composed of 56 ASTER DEM.
+
+Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.
+
+
+
+
+Multi-temporal elevation change maps stored in geotiff format:
+
+
+
+
+multi-temporal elevation change results calculated from different DEMs during different period  (Hala_surges_[sensor]_[period]_dh_final.tif).
+
+Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference (  strm-c_x_n37_39_e96_e98_pentration_dh_final.tif)
+
+
+
+
+Flow velocity time-series result processed by TICOI package stored in netcdf format:
+
+
+
+
+Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements (    Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc)
+
+Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results (    Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc)",api,True,findable,0,0,0,0,0,2024-11-15T19:37:54.000Z,2024-11-15T19:37:54.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14170819']]"
+10.5281/zenodo.14106550,Revisiting rotationally excited CH at radio wavelengths: A case study towards W51,Zenodo,2024,en,Text,Creative Commons Attribution 4.0 International,"Appendicies A and B of the paper: ""Revisiting rotationally excited CH at radio wavelengths: A case study towards W51""",api,True,findable,0,0,0,0,0,2024-11-12T19:10:44.000Z,2024-11-12T19:10:44.000Z,cern.zenodo,cern,,,,,,,"['HasVersion', 'IsPartOf']","[['IsVersionOf', '10.5281/zenodo.14106550']]"
+10.5281/zenodo.14163781,Arctic leads and their contribution to summertime sea salt aerosol,Zenodo,2024,,Audiovisual,Creative Commons Attribution 4.0 International,"Sea ice fraction and associated sea spray emissions from leads as implemented in the WRF-Chem atmospheric model, within the framework of the H2020 project CRiceS. The animation shows the relative contribution of emissions from sea ice leads in summer 2018 to sea salt aerosol surface concentration.
+
+Reference publication: Lapere, R., Marelle, L., Rampal, P., Brodeau, L., Melsheimer, C., Spreen, G., and Thomas, J. L.: Modeling the contribution of leads to sea spray aerosol in the high Arctic, Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, 2024.",api,True,findable,0,0,0,0,1,2024-11-14T15:36:45.000Z,2024-11-14T15:36:45.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],
+10.5281/zenodo.14100474,Bad practices in open peer review: lessons learned from mining MDPI open reports,Zenodo,2024,en,Text,Creative Commons Attribution 4.0 International,"The reviewing process is a key step in the publication of scientific papers. Unreliable scientific papers exist, some leading to retractions. The publication of such flawed papers reveals flaws in the peer review process, which is generally not made public. However, some rare publishers such as MDPI provide access to some of the reviewers’ reports on articles accepted in its journals. This talk presents various examples of questionable practices in the review process, revealed by applying text mining techniques to a corpus of open review reports of MDPI journals from 2011 to 2022. These range from extremely short reports to identical reports, including the reuse of text passages in several reports. These findings about disclosed reports cannot but raise questions about the trustworthiness of the vast majority of reports that are not made public.",api,True,findable,0,0,0,0,0,2024-11-12T15:39:43.000Z,2024-11-12T15:39:44.000Z,cern.zenodo,cern,"research integrity,open peer review","[{'subject': 'research integrity'}, {'subject': 'open peer review'}]",,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14100474']]"
+10.5281/zenodo.14151075,Dataset for Characterizing Distributed Machine Learning Workloads on Apache Spark,Zenodo,2024,,Dataset,Creative Commons Attribution 4.0 International,"YasmineDjebrouni,IsabellyRocha,SaraBouchenak,LydiaChen,PascalFelber,Vania Marangozova, and Valerio Schiavoni. 2023. Characterizing Distributed Machine Learning Workloads on Apache Spark. In Proceedings of the 24th International Middleware Conference (Middleware ’23). Association for Computing Machinery, New York, NY, USA, 151–164.",api,True,findable,0,0,0,0,0,2024-11-13T20:45:29.000Z,2024-11-13T20:45:29.000Z,cern.zenodo,cern,,,,,,,['HasVersion'],"[['IsVersionOf', '10.5281/zenodo.14151075']]"
+10.1594/pangaea.971917,"Age model, elemental geochemistry and magnetic data of cores MD03-2673, MD03-2679 and MD03-2685",PANGAEA,2024,,Collection,Creative Commons Attribution 4.0 International,"These datasets contain the tie-points used to construct the chronologies and the continuous and high resolution elemental and magnetic data of three couples of cores: MD03-2673-74Cq, MD03-2679-80Cq and MD03-2685-84Cq. These cores were retrieved South of Iceland, on Gardar and Björn drifts (56 to 61°N). These data are used to investigate the past changes in intensity of the Iceland Scotland Overflow Water (ISOW) over the last 400 ka (1ka = 1000 years) and include basaltic-derived concentrations proxies and grain-size proxies, both recording the past ISOW intensity. The chronologies were align to the ice-core age scale AICC2012 (Bazin et al., 2013; Veres et al., 2013), but for convenience, we also propose a revised AICC2023 chronology (Bouchet et al., 2023).",mds,True,findable,0,0,4,0,0,2024-11-12T10:52:54.000Z,2024-11-12T10:52:55.000Z,pangaea.repository,pangaea,"Deep current intensity,magnetism,North Atlantic circulation,XRF","[{'subject': 'Deep current intensity'}, {'subject': 'magnetism'}, {'subject': 'North Atlantic circulation'}, {'subject': 'XRF'}]",['9 datasets'],['application/zip'],,,"['References', 'References', 'References', 'References']",
+10.18709/perscido.2024.11.ds415,Timeseries of shortwave and longwave radiation measurements at Dome C in Antarctica (2018-2020),PerSCiDO,2024,,Dataset,,"The dataset contains a timeseries of CNR4 radiometer measurements at 10 min timestep. The records include measurements from the up- and downward looking shortwave and longwave sensors. The data were acquired at Dome C, near the Concordia station (75°S, 123°E). The sensor height is about 1.6 m and the surface in the footprint of the sensors is not guaranteed to be flat at this scale due to dunes and stratugi, although the overall area is extremely flat. This influences the measured (apparent) albedo and must be corrected with adequate algorithm before comparison with large scale measurements or modeling results. The files contains one year of data each and the data format is CSV with 5 columns: TIMESTAMP in UTC time, and the measurements in W/m2: Lwdn,Lwup,SWdn,SWup. Note that dn (resp up) refers to the downward (resp upward) looking sensor, which measure the upwelling (resp. downwelling) radiation. ",api,True,findable,0,0,0,0,0,2024-11-12T11:51:46.000Z,2024-11-12T11:51:47.000Z,inist.persyval,vcob,"glaciology,climate","[{'subject': 'glaciology', 'subjectScheme': 'https://perscido.univ-grenoble-alpes.fr/glaciology'}, {'subject': 'climate', 'subjectScheme': 'https://perscido.univ-grenoble-alpes.fr/climate'}]",['10 Mo'],,,,,
+10.5061/dryad.ht76hdrqg,Data from: Multiplex vs. singleplex assay for the simultaneous identification of the three components of avian malaria vector-borne disease by DNA metabarcoding,Dryad,2024,en,Dataset,Creative Commons Zero v1.0 Universal,"Accurate detection and identification of vector-host-parasite systems are
+ key to understanding their evolutionary dynamics and to design effective
+ disease prevention strategies. Traditionally, microscopical and
+ serological techniques were employed to analyse arthropod blood meals for
+ host/parasite detection, but these were limited in taxonomic resolution
+ and only to pre-selected taxa. In recent years, molecular techniques have
+ emerged as a promising alternative, offering enhanced resolution and
+ taxonomic range. While singleplex PCR assays were used at first to
+ identify host, vector and parasite components in separate reactions, today
+ multiple primer pairs can be combined in a single reaction, i.e.
+ multiplex, offering substantial time and cost savings. Nonetheless,
+ despite the potential benefits of multiplex PCR, studies quantifying its
+ efficacy compared to singleplex reactions are scarce. In this study, we
+ used partially digested mosquito blood meals within an avian malaria
+ framework to jointly identify the host, vector and parasite using
+ multiplex DNA metabarcoding, and to compare it with separate singleplex
+ PCRs. We aimed to compare the detection probabilities and taxonomic
+ assignments between both approaches. We found both to have similar
+ performances in terms of detection for the host and the vector, but
+ singleplex performed better than multiplex for the parasite component. We
+ suggest adjusting the relative concentrations of the PCR primers used in
+ the multiplex assay can increase the efficiency in detecting all the
+ components of the studied multi-species system. Overall, the results show
+ that DNA metabarcoding is an effective approach that could be applied to
+ any vector-borne interaction involving blood-feeding arthropods. Our
+ insights will not only refine laboratory procedures, but also enhance
+ research efforts and medical diagnosis of vector-borne diseases.",mds,True,findable,0,0,0,0,0,2024-11-11T09:15:32.000Z,2024-11-11T09:15:33.000Z,dryad.dryad,dryad,"FOS: Biological sciences,FOS: Biological sciences,DNA metabarcoding,host,vector,parasite,mosquito blood meals,Plasmodium infection,vector-borne disease","[{'subject': 'FOS: Biological sciences', 'subjectScheme': 'fos'}, {'subject': 'FOS: Biological sciences', 'schemeUri': 'http://www.oecd.org/science/inno/38235147.pdf', 'subjectScheme': 'Fields of Science and Technology (FOS)'}, {'subject': 'DNA metabarcoding'}, {'subject': 'host'}, {'subject': 'vector'}, {'subject': 'parasite'}, {'subject': 'mosquito blood meals'}, {'subject': 'Plasmodium infection'}, {'subject': 'vector-borne disease'}]",['2124418312 bytes'],,,,,