Skip to content
Snippets Groups Projects
Commit 9191a400 authored by Elias Chetouane's avatar Elias Chetouane
Browse files

Execution du pipeline. Actualisation des dois et des graphes.

parent b32b4007
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 106 additions and 5 deletions
1-enrich-with-datacite/all_datacite_clients_for_uga.csv
+ 4
− 4
View file @ 9191a400
client,count,name,year,url
cern.zenodo,725,Zenodo,2013,https://zenodo.org/
inist.sshade,470,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
cern.zenodo,730,Zenodo,2013,https://zenodo.org/
inist.sshade,471,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
inist.osug,238,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
figshare.ars,232,figshare Academic Research System,2016,http://figshare.com/
dryad.dryad,156,DRYAD,2018,https://datadryad.org
inist.resif,78,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
inist.resif,79,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
inist.persyval,55,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
rdg.prod,43,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
fmsh.prod,28,Fondation Maison des sciences de l'homme,2023,
......@@ -13,10 +13,10 @@ figshare.sage,14,figshare SAGE Publications,2018,
mcdy.dohrmi,12,dggv-e-publications,2020,https://www.dggv.de/publikationen/dggv-e-publikationen.html
tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
vqpf.dris,3,Direction des ressources et de l'information scientifique,2021,
tib.repod,3,RepOD,2015,
iris.iris,3,Incorporated Research Institutions for Seismology,2018,http://www.iris.edu/hq/
ugraz.unipub,2,unipub,2019,http://unipub.uni-graz.at
bl.nerc,2,NERC Environmental Data Service,2011,https://eds.ukri.org
tib.repod,2,RepOD,2015,
crui.ingv,1,Istituto Nazionale di Geofisica e Vulcanologia (INGV),2013,http://data.ingv.it/
estdoi.ttu,1,TalTech,2019,https://digikogu.taltech.ee
ardcx.nci,1,National Computational Infrastructure,2020,
......
1-enrich-with-datacite/nb-dois.txt
+ 1
− 1
View file @ 9191a400
2109
\ No newline at end of file
2117
\ No newline at end of file
2-produce-graph/hist-evol-datasets-per-repo.png
+ 0
− 0
View replaced file @ b32b4007 View file @ 9191a400
2-produce-graph/hist-evol-datasets-per-repo.png

74.9 KiB | W: | H:

2-produce-graph/hist-evol-datasets-per-repo.png

74.5 KiB | W: | H:

2-produce-graph/hist-evol-datasets-per-repo.png
2-produce-graph/hist-evol-datasets-per-repo.png
2-produce-graph/hist-evol-datasets-per-repo.png
2-produce-graph/hist-evol-datasets-per-repo.png
  • 2-up
  • Swipe
  • Onion skin
2-produce-graph/hist-last-datasets-by-client.png
+ 0
− 0
View replaced file @ b32b4007 View file @ 9191a400
2-produce-graph/hist-last-datasets-by-client.png

47.6 KiB | W: | H:

2-produce-graph/hist-last-datasets-by-client.png

48.5 KiB | W: | H:

2-produce-graph/hist-last-datasets-by-client.png
2-produce-graph/hist-last-datasets-by-client.png
2-produce-graph/hist-last-datasets-by-client.png
2-produce-graph/hist-last-datasets-by-client.png
  • 2-up
  • Swipe
  • Onion skin
2-produce-graph/hist-quantity-year-type.png
+ 0
− 0
View replaced file @ b32b4007 View file @ 9191a400
2-produce-graph/hist-quantity-year-type.png

55.1 KiB | W: | H:

2-produce-graph/hist-quantity-year-type.png

54.8 KiB | W: | H:

2-produce-graph/hist-quantity-year-type.png
2-produce-graph/hist-quantity-year-type.png
2-produce-graph/hist-quantity-year-type.png
2-produce-graph/hist-quantity-year-type.png
  • 2-up
  • Swipe
  • Onion skin
2-produce-graph/pie--datacite-client.png
+ 0
− 0
View replaced file @ b32b4007 View file @ 9191a400
2-produce-graph/pie--datacite-client.png

62.2 KiB | W: | H:

2-produce-graph/pie--datacite-client.png

62 KiB | W: | H:

2-produce-graph/pie--datacite-client.png
2-produce-graph/pie--datacite-client.png
2-produce-graph/pie--datacite-client.png
2-produce-graph/pie--datacite-client.png
  • 2-up
  • Swipe
  • Onion skin
2-produce-graph/pie--datacite-type.png
+ 0
− 0
View replaced file @ b32b4007 View file @ 9191a400
2-produce-graph/pie--datacite-type.png

56.9 KiB | W: | H:

2-produce-graph/pie--datacite-type.png

55.3 KiB | W: | H:

2-produce-graph/pie--datacite-type.png
2-produce-graph/pie--datacite-type.png
2-produce-graph/pie--datacite-type.png
2-produce-graph/pie--datacite-type.png
  • 2-up
  • Swipe
  • Onion skin
dois-uga.csv
+ 101
− 0
View file @ 9191a400
......@@ -6029,3 +6029,104 @@ The calculations were carried out using t4gpd (https://pypi.org/project/t4gpd/).
10.5281/zenodo.10801510,NeoGeographyToolkit/StereoPipeline: 2024-03-10-daily-build,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html,api,True,findable,0,0,0,1,0,2024-03-10T09:19:59.000Z,2024-03-10T09:19:59.000Z,cern.zenodo,cern,,,,
10.5281/zenodo.10803259,NeoGeographyToolkit/StereoPipeline: 2024-03-11-daily-build,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html,api,True,findable,0,0,0,1,0,2024-03-11T08:22:07.000Z,2024-03-11T08:22:07.000Z,cern.zenodo,cern,,,,
10.26302/sshade/experiment_jf_20201104_001,"Visible near infrared spectra of salt crusts in Andean Salars, Chile",SSHADE/Mirabelle (OSUG Data Center),2021,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.","Visible near infrared spectra of sulfates and chlorides salt crusts in Andean Salars, Chile",mds,True,findable,0,0,2,0,0,2024-03-11T18:37:50.000Z,2024-03-11T18:37:50.000Z,inist.sshade,mgeg,"field measurement,biconical reflection,macroscopic,Vis,Visible,NIR,Near-Infrared,reflectance factor,Halite,Albite,Anhydrite,Ankerite,Gypsum,Sylvite,Aragonite,Pinnoite,Quartz,Dolomite,Ulexite,Thenardite,Nobleite,Glauberite,Anorthite,Bassanite,Calcite,Quartz_alpha,Blodite,Natron,Mirabilite,mineral,natural terrestrial,halide,tektosilicate,sulfate,carbonate,borate","[{'subject': 'field measurement', 'subjectScheme': 'main'}, {'subject': 'biconical reflection', 'subjectScheme': 'main'}, {'subject': 'macroscopic', 'subjectScheme': 'main'}, {'subject': 'Vis', 'subjectScheme': 'variables'}, {'subject': 'Visible', 'subjectScheme': 'variables'}, {'subject': 'NIR', 'subjectScheme': 'variables'}, {'subject': 'Near-Infrared', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'Halite', 'subjectScheme': 'name'}, {'subject': 'Albite', 'subjectScheme': 'name'}, {'subject': 'Anhydrite', 'subjectScheme': 'name'}, {'subject': 'Ankerite', 'subjectScheme': 'name'}, {'subject': 'Gypsum', 'subjectScheme': 'name'}, {'subject': 'Sylvite', 'subjectScheme': 'name'}, {'subject': 'Aragonite', 'subjectScheme': 'name'}, {'subject': 'Pinnoite', 'subjectScheme': 'name'}, {'subject': 'Quartz', 'subjectScheme': 'name'}, {'subject': 'Dolomite', 'subjectScheme': 'name'}, {'subject': 'Ulexite', 'subjectScheme': 'name'}, {'subject': 'Thenardite', 'subjectScheme': 'name'}, {'subject': 'Nobleite', 'subjectScheme': 'name'}, {'subject': 'Glauberite', 'subjectScheme': 'name'}, {'subject': 'Anorthite', 'subjectScheme': 'name'}, {'subject': 'Bassanite', 'subjectScheme': 'name'}, {'subject': 'Calcite', 'subjectScheme': 'name'}, {'subject': 'Quartz_alpha', 'subjectScheme': 'name'}, {'subject': 'Blodite', 'subjectScheme': 'name'}, {'subject': 'Natron', 'subjectScheme': 'name'}, {'subject': 'Mirabilite', 'subjectScheme': 'name'}, {'subject': 'mineral', 'subjectScheme': 'family'}, {'subject': 'natural terrestrial', 'subjectScheme': 'origin'}, {'subject': 'halide', 'subjectScheme': 'compound type'}, {'subject': 'tektosilicate', 'subjectScheme': 'compound type'}, {'subject': 'sulfate', 'subjectScheme': 'compound type'}, {'subject': 'carbonate', 'subjectScheme': 'compound type'}, {'subject': 'borate', 'subjectScheme': 'compound type'}]",['18 spectra'],['ASCII']
10.5281/zenodo.10807437,Screening rural data sources,GRANULAR,2023,en,Text,Creative Commons Attribution 4.0 International,"This document presents an initial screening of datasets that are relevant to capture rural diversity and to create novel indicators for rural areas. Following a semi-structured format of discovery and evaluation, we have documented 90 different datasets to date which are either already used to characterise rural areas, or could underpin novel indicators. In addition to identifying the datasets themselves and their locations, we provide a suite of associated meta-data. Evaluating the findings of this effort, we demonstrate that the majority of the datasets identified have regional to global coverage, have Local Administrative Unit to gridded (10m - 10km) granularity, are provided annually, are free and open and of moderate relevance in terms of indicator generation for rural areas. With the completion of this deliverable, exploration can begin on the development of the next generation of rural indicators.",api,True,findable,0,0,0,0,1,2024-03-14T14:29:41.000Z,2024-03-14T14:29:41.000Z,cern.zenodo,cern,"rural data,novel data,rural areas","[{'subject': 'rural data'}, {'subject': 'novel data'}, {'subject': 'rural areas'}]",,
10.26302/sshade/experiment_ap_20210622_0001,Vis-NIR reflectance spectra of mixtures of Chelyabinsk light-colored lithology with impact melt or shock-darkened lithologies,SSHADE/UH-ApS (OSUG Data Center),2024,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.",Vis-NIR reflectance spectra of mixtures of Chelyabinsk light-colored lithology with impact melt or shock-darkened lithologies.,mds,True,findable,0,0,0,0,0,2024-03-14T12:39:46.000Z,2024-03-14T12:39:47.000Z,inist.sshade,mgeg,"laboratory measurement,diffuse reflection,macroscopic,Vis,Visible,NIR,Near-Infrared,reflectance factor,Olivine Fa28,Orthopyroxene Fs23 Wo1,Plagioclase Ab86,Troilite,Kamacite,extraterrestrial,nesosilicate,inosilicate,tektosilicate,sulfide,metallic alloy,ordinary chondrite,LL","[{'subject': 'laboratory measurement', 'subjectScheme': 'main'}, {'subject': 'diffuse reflection', 'subjectScheme': 'main'}, {'subject': 'macroscopic', 'subjectScheme': 'main'}, {'subject': 'Vis', 'subjectScheme': 'variables'}, {'subject': 'Visible', 'subjectScheme': 'variables'}, {'subject': 'NIR', 'subjectScheme': 'variables'}, {'subject': 'Near-Infrared', 'subjectScheme': 'variables'}, {'subject': 'reflectance factor', 'subjectScheme': 'variables'}, {'subject': 'Olivine Fa28', 'subjectScheme': 'name'}, {'subject': 'Orthopyroxene Fs23 Wo1', 'subjectScheme': 'name'}, {'subject': 'Plagioclase Ab86', 'subjectScheme': 'name'}, {'subject': 'Troilite', 'subjectScheme': 'name'}, {'subject': 'Kamacite', 'subjectScheme': 'name'}, {'subject': 'extraterrestrial', 'subjectScheme': 'family'}, {'subject': 'nesosilicate', 'subjectScheme': 'compound type'}, {'subject': 'inosilicate', 'subjectScheme': 'compound type'}, {'subject': 'tektosilicate', 'subjectScheme': 'compound type'}, {'subject': 'sulfide', 'subjectScheme': 'compound type'}, {'subject': 'metallic alloy', 'subjectScheme': 'compound type'}, {'subject': 'ordinary chondrite', 'subjectScheme': 'meteorite group'}, {'subject': 'LL', 'subjectScheme': 'meteorite class'}]",['2 spectra'],['ASCII']
10.15778/resif.3t2019,"France 2019, nodal seismic array in the Rhône Valley, DARE project - Preliminary campaign",RESIF - Réseau Sismologique et géodésique Français,2023,,Dataset,,"Preliminary campaign of the dense 400-node array carried out in the same area in 2020 (French-German DARE project). 30 all-in-one 3-component seismic nodes were deployed 3 months before the massive deployment (November 2019) for 2 weeks. These nodes recorded the Mw4.9 Le Teil earthquake (November 11, 2019) that occurred at about 20 km north of the 30-node network. Due to earthquake proximity and magnitude, the earthquake recordings are clipped on most of the stations.",fabrica,True,findable,0,0,1,1,0,2024-03-12T15:00:04.000Z,2024-03-12T15:01:27.000Z,inist.resif,vcob,"Seismic Hazard,Site Effects,Passive experiment,Le Teil Earthquake","[{'subject': 'Seismic Hazard'}, {'subject': 'Site Effects'}, {'subject': 'Passive experiment'}, {'subject': 'Le Teil Earthquake'}]","['30 stations, 42Go (miniseed format)']","['Miniseed data', 'stationXML metadata']"
10.5281/zenodo.10827673,NeoGeographyToolkit/StereoPipeline: 2024-03-17-daily-build,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,Recent additions log: https://stereopipeline.readthedocs.io/en/latest/news.html,api,True,findable,0,0,0,1,0,2024-03-17T16:32:36.000Z,2024-03-17T16:32:36.000Z,cern.zenodo,cern,,,,
10.5281/zenodo.10807438,Screening rural data sources,GRANULAR,2023,en,Text,Creative Commons Attribution 4.0 International,"This document presents an initial screening of datasets that are relevant to capture rural diversity and to create novel indicators for rural areas. Following a semi-structured format of discovery and evaluation, we have documented 90 different datasets to date which are either already used to characterise rural areas, or could underpin novel indicators. In addition to identifying the datasets themselves and their locations, we provide a suite of associated meta-data. Evaluating the findings of this effort, we demonstrate that the majority of the datasets identified have regional to global coverage, have Local Administrative Unit to gridded (10m - 10km) granularity, are provided annually, are free and open and of moderate relevance in terms of indicator generation for rural areas. With the completion of this deliverable, exploration can begin on the development of the next generation of rural indicators.",api,True,findable,0,0,0,0,0,2024-03-14T14:29:41.000Z,2024-03-14T14:29:41.000Z,cern.zenodo,cern,"rural data,novel data,rural areas","[{'subject': 'rural data'}, {'subject': 'novel data'}, {'subject': 'rural areas'}]",,
10.5281/zenodo.10812218,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,0,0,0,0,0,2024-03-15T13:42:36.000Z,2024-03-15T13:42:38.000Z,cern.zenodo,cern,"Ice-sheet modelling, Antarctic ice sheet, Sea-level projections","[{'subject': 'Ice-sheet modelling, Antarctic ice sheet, Sea-level projections'}]",,
10.5281/zenodo.10824503,easystats/insight: insight 0.19.9,Zenodo,2024,,Software,Creative Commons Attribution 4.0 International,"New supported models
Support for models of class serp (package serp).
General
standardize_names() now also recognizes column s.value from objects of
package marginaleffects.
Bug fixes
Fixed issue in find_predictors() for models with splines (s()), where
number of dimensions was indicated with a variable, not a number.
format_ci() now works for factors and character vectors again.
Fixed issues with latest release of tinytable.
Fixed issues with latest release of PROreg.",api,True,findable,0,0,0,1,0,2024-03-16T08:56:29.000Z,2024-03-16T08:56:29.000Z,cern.zenodo,cern,,,,
10.18150/vbwcr1,Coherent imaging and dynamics of excitons in MoSe2 monolayers epitaxially grown on hexagonal boron nitride,RepOD,2024,,Dataset,,"The source data file for a publication: ""Coherent imaging and dynamics of excitons in MoSe2 monolayers epitaxially grown on hexagonal boron nitride""..gwy and .asc files were created using Gwyddion 2.59 software, for more information see http://gwyddion.net/Abstract:Using four-wave mixing microscopy, we measure the coherent response and ultrafast dynamics of excitons and trions in MoSe2 monolayers grown by molecular beam epitaxy on thin films of hexagonal boron nitride. We assess inhomogeneous and homogeneous broadenings in the transition spectral lineshape. The impact of phonons on the homogeneous dephasing is inferred via the temperature dependence of the dephasing. Four-wave mixing mapping, combined with atomic force microscopy, reveals spatial correlations between exciton oscillator strength, inhomogeneous broadening and the sample morphology. The quality of the coherent optical response of epitaxially grown transition metal dichalcogenides now becomes comparable to the samples produced by mechanical exfoliation, enabling the coherent nonlinear spectroscopy of innovative materials, like magnetic layers or Janus semiconductors.",mds,True,findable,0,0,0,0,0,2024-01-05T10:19:33.000Z,2024-03-15T18:29:14.000Z,tib.repod,repod,,,,
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment