Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,337
datasets available to search
ShareScore release 0.9.0
Dataset results
1,337 results for “antarctica”
A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland
<p>Supplementary material for</p> <p>Luo, X. & Lin, T. (2022). “A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland.” Climate Dynamics, in press. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1007%2Fs00382-022-06317-x&data=05%7C01%7CXiao.Luo%40ttu.edu%7C2a6949c147dd4aa9880f08da345e55ae%7C178a51bf8b2049ffb65556245d5c173c%7C0%7C0%7C637879877007681605%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2G3mD6n0ZOogcEFQcuCCElFmgKE0%2B%2F5KPTtzBeq8kM4%3D&reserved=0">https://doi.org/10.1007/s00382-022-06317-x</a></p> <p>This repository contains data and scripts that are used to facilitate the analysis of Greenland and Antarctica ice sheet melting induced by ocean warming.</p> <p>The GCM directory contains ocean potential temperature time series for both Antarctica ice shelves near open sea retrieved from FGOALS-s2 under RCP 6.0 and Greenland store glacier retrieved from MIROC5 under RPC8.5 and HadGEM3-GC31-MM projections under update of RCP8.5 based on SSP5. The raw data are accessed from CMIP5 (https://esgf-node.llnl.gov/search/cmip5/) and CMIP6 (https://esgf-node.llnl.gov/search/cmip6/) projects respectively and the processed data deposited here are stored in Excel format.</p> <p>The WOD directory contains the NetCDF files of Present-Day ocean conditions including temperature, salinity, depth, and other variables retrieved from World Ocean Database (WOD). The data are deposited as NetCDF format.</p> <p>The Analysis directory contains 3 subdirectories that illustrates the analysis workflow for both Greenland and Antarctica.</p> <p> The Antarctica subdirectory contains the analysis codes to reproduce Antarctica ice shelf melting response and future projection. Antarctica_analysis.m is the main routine for computation and plotting, and the other files are input files including PISM/PICO simulations outputs and ocean temperature time series retrieved from CMIP5 project that can be found on the previous GCM directory.</p> <p> The Greenland subdirectory contains the main routine store_analysis.m for Greenland Store glacier computation and plotting, the response time series (store.mat), perturbed response time series (store_pert.mat), ocean temperature projection (store_temp.mat) generated by MIROC5, and subglacial runoff time series (store_runoff.mat) estimated from annual basin surface runoff using MAR data forced by MIROC5 under RCP8.5.</p> <p> The Historical subdirectory contains the data and scripts used for the historical analyses in the main text. AIS_melt_historical.m is the main script of the analysis, along with two other plotting functional scripts including plot_melt_rate.m and plot_ocean_temp.m. The input files include the PISM/PICO simulations outputs, which are the same files in Antarctica subdirectory, and other 2 input files that contain the temperature time series from 6 CMIP5 GCMs.</p>
Supplementary Tables for the article"One-to-one Coupling between Southern Ocean Productivity and Antarctica Climate"
<p>Supplementary Tables for the article"One-to-one Coupling between Southern Ocean Productivity and Antarctica Climate".</p> <p>Table S1. Tie points in the age model of Site U1537; </p> <p>Table S2. Age-depth model and uncertainties at Site U1537;</p> <p>Table S3. Natural gamma radiation data at Site U1537;</p> <p>Table S4. Color reflectance component b* data at Site U1537;</p> <p>Table S5. Ca counts data at Site U1537;</p> <p>Table S6. Magnetic susceptibility data at Site U1537.</p>
WAIS model for "Antarctic Peninsula warming triggers enhanced basal melt rates throughout West Antarctica"
<p>This data set contains the code and input files for WAIS-1080 model, which is based on the Massachusetts Institute of Technology general circulation model (MITgcm), including dynamic/thermodynamic sea-ice model and sub-ice-shelf cavity model for freezing/melting processes.</p> <p>code_was.tar contains source files for compiling model;</p> <p>init.tar and input.tar contain run-time parameter files and binary files for running model</p> <p> </p> <p> </p>
Supplementary Material for "Joint Inversion Based on Variation of Information - A Crustal Model of Wilkes Land, East Antarctica"
<p>Inversion_Results_Cluster.nc - contains geographical and vertical information of clusters</p> <p>Inversion_Results_RealData_Run1.nc - contains inverted susceptibilities and densities after the first inversion run with high coupling</p> <p>Inversion_Results_RealData_Run2.nc - contains inverted susceptibilities and densities after the second continuous inversion run with low coupling</p> <p>Plot.ipynb - python script for visualization</p>
GRD Dataset Maitri Antarctica
<p>Potential Gradient, Near Earth Current Density, Concentration of air ions, Conductivity, Wind Speed & Direction, Temperature Measurements over Maitri, Antarctica during December 2018 to November 2019.</p>
Heat tolerance of marine ectotherms in a warming Antarctica
<p><span>Global warming is affecting the Antarctic continent in complex ways. Because Antarctic organisms are specialized to living in the cold, they are vulnerable to increasing temperatures, though quantitative analyses of this issue are currently lacking. Here we compiled a total of 184 estimates of heat tolerance belonging to 39 marine species and quantified how survival is affected concomitantly by the intensity and duration of a thermal stress. Species exhibit thermal limits displaced towards colder temperatures, with contrasting strategies between arthropods and fish that exhibit low tolerance to acute heat challenges, and brachiopods, echinoderms and molluscs that tend to be more sensitive to chronic exposure. These differences might be associated with mobility. A dynamic mortality model suggests that Antarctic organisms already encounter temperatures that might be physiologically stressful and indicate that these ecological communities are indeed vulnerable to ongoing rising temperatures.</span></p>
1-km bed topography digital elevation model (DEM) of the Weddell Sea sector, West Antarctica
<p>We present a new bed elevation digital elevation model (DEM) of the Weddell Sea sector, West Antarctica. The DEM consists a total area of ~125,000 km<sup>2</sup> covering the Institute, Möller and Foundation ice streams and the Bungenstock Ice Rise with a 1 km spatial resolution. </p> <p>In order to produce the bed elevation DEM, ice thickness DEM was formed from the available radio-echo sounding (RES) data using the 'Topo to Raster' function in ArcGIS. The RES data used in this study were compiled from four main sources which are (1) Scott Polar Research Institute (SPRI) survey collected during several campaigns in the 1970s (Drewry, 1983), (2) British Antarctic Survey (BAS) airborne radar survey conducted during the austal summer 2006/07 (GRADES/IMAGE) (Ashmore et al., 2014). , (3) BAS airborne survey accomplished during the Institute and Möller Antarctic Funding Initiative (IMAFI) in 2010/2011 (Ross et al., 2012) and (4) Center for the Remote Sensing of Ice Sheet (CReSIS) data during the NASA Operation IceBridge (OIB) programme in 2012, 2014 and 2016 (Gogineni, 2012). The ice thickness picks were gridded at a uniform 1-km spacing using the Nearest Neighbour interpolation within the Topo to Raster. The ice thickness DEM was later subtracted from the 1-km ice-sheet surface elevation derived from the combined European Remote Sensing Satellite-1 (ERS-1) radar and Ice, Cloud and land Elevation Satellite (ICESat) laser satellite altimetry DEM (Bamber et al., 2009), to produce the bed topography referenced to the Polar Stereographic projection (Snyder, 1987). </p>
Dataset of sediment cores and box cores collected in Central Basin, Ross Sea (Antarctica)
<p>dataset of sediment cores and box cores collected in Central Basin, Ross Sea (Antarctica)</p>
ECHAM6-wiso and ECHAM5-wiso isotope daily data at Dome Concordia station, East Antarctica
<p>ECHAM5-wiso and ECHAM6-wiso isotope daily data at Dome Concordia station (75.1° S, 123.3° E), East Antarctica.</p> <p>ECHAM5-wiso is described in Werner et al. (2011). The simulation was nudged to ERA-Interim dataset. The simulation spans the 2008-2017 period.</p> <p>ECHAM6-wiso is described in Cauquoin et al. (2019) and Cauquoin and Werner (2021). The simulation was nudged to ERA5 dataset. The simulation spans the 2008-2021 period.</p> <p>ECHAM5/6-wiso data from both nudged simulations were extracted at the nearest grid cell from Concordia Station, providing modeled daily-averaged values for the surface temperature, 2 m air temperature, precipitation amount, δ18O, δ2H, and d-excess values.</p> <p>For ECHAM6-wiso, surface specific humidity and its isotopic content are also provided for the period 2016-2021. </p> <p>These data are discussed in the following pubblications:</p> <ul> <li>Dreossi, G., Masiol, M., Stenni, B., Zannoni, D., Scarchilli, C., Ciardini, V., Casado, M., Landais, A., Werner, M., Cauquoin, A., Casasanta, G., Del Guasta, M., Posocco, V., and Barbante, C.: A decade (2008–2017) of water stable-isotope composition of precipitation at Concordia Station, East Antarctica, <em>The Cryosphere</em>, <strong>18</strong>, 3911–3931, <a href="https://doi.org/10.5194/tc-18-3911-2024" target="_blank" rel="noopener">https://doi.org/10.5194/tc-18-3911-2024</a>, 2024.</li> <li>Ollivier, I., Steen-Larsen, H. C., Stenni, B., Arnaud, L., Casado, M., Cauquoin, A., Dreossi, G., Genthon, C., Minster, B., Picard, G., Werner, M., and Landais, A.: Surface processes and drivers of the snow water stable isotopic composition at Dome C, East Antarctica – a multi-datasets and modelling analysis, <em>The Cryosphere</em>, <strong>19</strong>, 173–200, <a href="https://doi.org/10.5194/tc-19-173-2025">https://doi.org/10.5194/tc-19-173-2025</a>, 2025.</li> </ul>
Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions
<p>Model data for probabilistic projections of the Amery Ice Shelf catchment, <br>Antarctica, under high ice-shelf basal melt conditions<br>======================================================================</p> <p>This archive contains ice-sheet model output and statistical models <br>used in the manuscript:<br>"Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under<br>high ice-shelf basal melt conditions" by:<br>Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro<br>Perego, Stephen Price, John D. Jakeman</p> <p>OVERVIEW<br>--------</p> <p>Statistical models and corresponding datasets are archived in the file<br>Amery_UQ_Study_Statistical_Models.zip. That file contains a README that<br>describes its contents.<br>Contact for statistical models:<br>Sanket Jantre, Brookhaven National Laboratory, sjantre@bnl.gov</p> <p>The remaining files in this archive contain output from ice-sheet model <br>simulations using the MPAS-Albany Land Ice (MALI) model <br>(Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM)<br>applied to a regional domain of the Amery Ice Shelf catchment<br>of Antarctica with mesh resolution varying from 4 to 20 km.<br>The contents of these files are described below.<br>Contact for MALI simulations:<br>Matthew Hoffman, Los Alamos National Laboratory, mhoffman@lanl.gov</p> <p>ENSEMBLES<br>---------</p> <p>The ensembles consist of all or a subset of 200 model runs with values for 6<br>parameters sampled from a Sobol' sequence over a specified range.</p> <p>Parameters and Sampled Ranges:<br>1. Ice stiffness scaling factor, Cφ: (0.8, 1.2)<br>2. Basal friction scaling factor, Cμ: (0.8, 1.2)<br>3. Basal slip exponent, q: (0.1, 0.333)<br>4. Calving yield stress, σmax: (80, 180) kPa<br>5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−1<br>6. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1</p> <p>SCENARIOS<br>---------</p> <p>The archive contains output from 4 scenarios described in the manuscript:</p> <p>Historical relaxation (RELX): For each ensemble member, we conducted a 50 year<br>relaxation from the initial condition using historical climate forcing to<br>integrate out fast transient behavior. For surface mass balance, we applied a<br>1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; van<br>den Broeke, 2019). The ocean thermal forcing was the observation-based<br>climatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018<br>(Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation duration<br>was chosen as the most rapid adjustments occur in the first few decades of<br>integration, while the long term adjustment to a fully steady state takes<br>thousands of years. Relaxation to full steady state would require<br>substantially more computing resources than our entire set of ensembles and<br>also leads to the complication of different runs having potentially very<br>different initial states. Future improvements to model initialization that<br>account for surface elevation change (Perego et al., 2014) may reduce model<br>drift and adjust this requirement. The final model state in each run at the<br>end of RELX was given the nominal date of January 1, 2015, and all three<br>projection ensembles were branched from these states.</p> <p>Control projection (CTRL): The CTRL projection ensemble were an extension of<br>the RELX configurations, continuing the same surface mass balance and ocean<br>thermal forcing from January 1, 2015, to January 1, 2300. This ensemble was<br>used to assess model drift relative to the forced response of the climate<br>scenarios. </p> <p>SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface mass<br>balance and ocean thermal forcing derived from a UKESM SSP1-2.6 climate<br>scenario (expAE10 from Seroussi et al., 2024). Surface mass balance and ocean<br>thermal forcing were applied as anomalies relative to the climatological mean<br>forcings in RELX/CTRL to avoid issues related to climate model bias and abrupt<br>changes in forcing. This ensemble was also run from January 1, 2015, to<br>January 1, 2300.</p> <p>SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5<br>projection forcings (expAE05 from Seroussi et al., 2024), again applied as<br>anomalies and from 2015 to 2300.</p> <p><br>OUTPUT<br>------</p> <p>Each ensemble directory contains at the base level:<br>* branch_ensemble.cfg - configuration file for the ensemble_generator test<br> case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html) <br> of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)<br> used to set up the ensemble.<br>* mesh_vars.nc - netCDF file containing the mesh variables. This file is the<br> same for all ensembles. These fields need to be appended to an output file to<br> visualize in, e.g., Paraview. This can be done with the command:<br> ncks -A mesh_vars.nc output.nc</p> <p>Each ensemble contains subdirectories for each run numbered 000-199. Each run<br>directory contains the files:<br>* globalStats.nc - scalar metrics at every time step<br>* output.nc - spatial fields at 10 year intervals<br>* run_info.cfg - summary of parameter values</p> <p>Notes:<br>* The SSP1 and SSP5 ensembles contain a subset of the full 200 runs<br> because some runs were filtered out as being unnecessary during model<br> calibration (see manuscript).<br>* The RELX ensemble is run for 50 years with an arbitrary start year of 2000.<br> The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state<br> of the corresponding RELX run (nominally 2050). Because spatial data has<br> been saved in this archive at 10 year intervals, the first output for the<br> three projection ensembles is 2020. To get the initial condition for the<br> projection ensembles, use the 2050 state from RELX for the corresponding<br> model run.<br>* Spatial field output at 1 year intervals, as well as run input, log, and<br> restart files are available from the corresponding contact. The complete<br> run data is about 2 TB.</p> <p><br>CITATION<br>--------<br>If you find these data useful, please cite the DOI for this <br>archive (10.5281/zenodo.11166628), as well as our manuscript<br>submitted to The Cryosphere.</p> <p>This archive is approved by Los Alamos National Laboratory<br>for public release under LA-UR-24-24969; distribution is unlimited.</p> <p> </p> <p>REFERENCES<br>----------</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T.,<br>Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.:<br>MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth<br>system modeling using Voronoi grids, Geoscientific Model Development, 11,<br>3747–3780, 2018.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H.,<br>Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in<br>the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134,<br>https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H.,<br>Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A.,<br>Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M.,<br>Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C.<br>M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R.<br>S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.:<br>Experimental protocol for sea level projections from ISMIP6 stand-alone ice<br>sheet models, The Cryosphere, 14, 2331–2368,<br>https://doi.org/10.5194/tc-14-2331-2020, 2020.</p> <p>Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for<br>Coupling Ice Sheet Models to Earth System Models, Journal of Geophysical<br>Research: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181,<br>2014.</p> <p>Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol.<br>https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica. </p> <p>van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica<br>(1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science,<br>https://doi.org/10.1594/PANGAEA.896940, 2019.</p> <p>van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley,<br>B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De<br>Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation<br>of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate<br>Model, Journal of Glaciology, 60, 761–770,<br>https://doi.org/10.3189/2014JoG14J051, 2014.</p>
Antarctica, a game changer for sea level rise
<p>How is Antarctica going to react to global warming? Right now, glaciers in the mountains and the Greenland and Antarctica ice sheets are all contributing roughly equally to sea level rise through melting, but in the near future, once Antarctica passes a tipping point, this might change completely, making sea level rise enough to wipe out all coastal cities of the world. However, if humanity manages to stay below +1,5°C, meaning following the Paris agreement, then we should be able to divide by two the contributions of land ice to global sea level by 2100.</p>
Results of the initMIP-Antarctica experiments: an ice sheet initialization intercomparison of ISMIP6
<p>This archive provides the forcing data and ice sheet model output produced as part of the publication " initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6", published in The Cryosphere, <a href="https://www.the-cryosphere.net/13/1441/2019/">https://www.the-cryosphere.net/13/1441/2019/</a></p> <p>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.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and initMIP-Antarctica can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=InitMIP-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>Archive overview<br> ----------------<br> README.txt - this information</p> <p>dSMB.zip - Surface mass balance anomaly forcing data and description<br> dSMB/<br> dSMB_AIS_01km.nc<br> dSMB_AIS_02km.nc<br> dSMB_AIS_04km.nc<br> dSMB_AIS_08km.nc<br> dSMB_AIS_16km.nc<br> dSMB_AIS_32km.nc<br> README_dSMB_AIS.txt</p> <p>dBasalMelt.zip – Ice shelf Basal Melt anomaly forcing data and description<br> dBasalMelt/<br> dBasalMelt_AIS_01km.nc<br> dBasalMelt_AIS_02km.nc<br> dBasalMelt_AIS_04km.nc<br> dBasalMelt_AIS_08km.nc<br> dBasalMelt_AIS_16km.nc<br> dBasalMelt_AIS_32km.nc<br> README_dBasalMelt_AIS.txt</p> <p><group>_<model>_<experiment>.zip - The model output per group, model and experiment (init, ctrl, asmb, abmb)<br> <group1>_<model1>_init/<br> acabf_AIS_<group1>_<model1>_init.nc<br> ...<br> <group1>_<model1>_ctrl/<br> acabf_AIS_<group1>_<model1>_ctrl.nc<br> ...<br> <group1>_<model1>_asmb/<br> acabf_AIS_<group1>_<model1>_asmb.nc<br> ...<br> <group1>_<model1>_abmb/<br> acabf_AIS_<group1>_<model1>_abmb.nc<br> ...</p> <p><group1>_<model2>_init/<br> ...<br> <group1>_<model2>_ctrl/<br> ...<br> <group1>_<model2>_asmb/<br> ...<br> <group1>_<model2>_abmb/<br> ...</p> <p><group2>_<model1>_init/<br> ...<br> <group2>_<model1>_ctrl/<br> ...<br> <group2>_<model1>_asmb/<br> ...<br> <group2>_<model1>_asmb/<br> ...</p> <p>The following script may be used to download the content of the archive.<br> #!/bin/bash<br> wget https://zenodo.org/record/2651652/files/README.txt<br> wget https://zenodo.org/record/2651652/files/dSMB.zip<br> wget https://zenodo.org/record/2651652/files/dBasalMelt.zip</p> <p>for amodel in ARC_PISM1 ARC_PISM2 ARC_PISM3 ARC_PISM4 AWI_PISM1Eq AWI_PISM1Pal CPOM_BISICLES_A_500m CPOM_BISICLES_B_500m DMI_PISM0 DMI_PISM1 DOE_MALI IGE_ELMER ILTS_SICOPOLIS1 ILTS_SICOPOLIS2 IMAU_IMAUICE JPL1_ISSM LSCE_GRISLI NCAR_CISM PIK_PISM3PAL PIK_PISM4EQUI PSU_EQNOMEC PSU_GLNOMEC UCIJPL_ISSM ULB_FETISH1 VUB_AISMPALEO; do<br> wget https://zenodo.org/record/2651652/files/${amodel}_init.zip<br> wget https://zenodo.org/record/2651652/files/${amodel}_ctrl.zip<br> wget https://zenodo.org/record/2651652/files/${amodel}_asmb.zip<br> wget https://zenodo.org/record/2651652/files/${amodel}_abmb.zip</p> <p>done</p>
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
Table I in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
<p>Table I: (continued)</p><table><tbody><tr><th>Species</th><th>ZDDƎ</th><th>ZDIs</th></tr></tbody><tbody><tr><th><i>Grammatophora angulosa</i> Ehrenberg</th><td>r</td><td>–</td></tr><tr><th><i>Grammatophora angulosa</i> var. <i>islandica</i> (Ehrenberg)</th><td>r</td><td>–</td></tr><tr><th>Grunow</th></tr><tr><th><i>Gyrosigma arcuatum</i> (Donkin) Sterrenburg</th><td>–</td><td>r</td></tr><tr><th><b><i>Gyrosigma fasciola</i> (Ehrenberg) Grif</b> fi th <i>et</i> Henfrey</th><td>c</td><td>c</td></tr><tr><th><b><i>Gyrosigma</i> cf. <i>obscurum</i> (W. Smith) Grif</b> fi th <i>et</i> Henfrey</th><td>–</td><td>c</td></tr><tr><th><i>Halamphora holsatica</i> (Hustedt) Levkov e</th><td>vr</td><td>–</td></tr><tr><th><i>Hippodonta hungarica</i> (Grunow) Lange-Bertalot, Met-</th><td>r</td><td>–</td></tr><tr><th>zeltin <i>et</i> Witkowski</th></tr><tr><th><i>Licmophora abbreviata</i> Agardh</th><td>f</td><td>–</td></tr><tr><th><i>Licmophora antarctica</i> Carlson</th><td>f</td><td>f</td></tr><tr><th><i>Licmophora belgicae</i> Peragallo</th><td>–</td><td>f</td></tr><tr><th><i>Licmophora decora</i> Heiden</th><td>r</td><td>–</td></tr><tr><th><i>Licmophora gracilis</i> (Ehrenberg) Grunow</th><td>f</td><td>f</td></tr><tr><th><i>Licmophora luxuriosa</i> Heiden</th><td>–</td><td>r</td></tr><tr><th><i>Lyrella hennedyi</i> (W. Smith) Stickle <i>et</i> D. G. Mann</th><td>–</td><td>r</td></tr><tr><th><i>Melosira adeliae</i> Manguin</th><td>r</td><td>r</td></tr><tr><th><i>Melosira sol</i> (Ehrenberg) Kützing</th><td>–</td><td>f</td></tr><tr><th><i>Minidiscus trioculatus</i> (Taylor) Hasle</th><td>r</td><td>–</td></tr><tr><th><i>Navicula</i> cf. <i>cancellata</i> Donkin</th><td>c</td><td>f</td></tr><tr><th><i>Navicula directa</i> (Smith) Brébisson</th><td>f</td><td>c</td></tr><tr><th><i>Navicula</i> cf. <i>perminuta</i> Grunow</th><td>c</td><td><b>c</b></td></tr><tr><th><b><i>Navicula</i> sp.</b> 1</th><td>vr</td><td><b>r</b></td></tr><tr><th><b><i>Navicula</i> sp.</b> 2</th><td>–</td><td>r</td></tr><tr><th><i>Nitzschia angularis</i> W. Smith</th><td>–</td><td>f</td></tr><tr><th><i>Nitzschia</i> cf. <i>australis</i> (Peragallo) A. Mann</th><td>vr</td><td>r</td></tr><tr><th><i>Nitzschia</i> cf. <i>gelida</i> Cleve <i>et</i> Grunow</th><td>–</td><td>r</td></tr><tr><th><i>Nitzschia hybrida</i> Grunow</th><td>r</td><td>c</td></tr><tr><th><i>Nitzschia mitchelliana</i> Greenleaf</th><td>–</td><td>r</td></tr><tr><th><i>Nitzschia</i> cf. <i>obtusa</i> W. Smith</th><td>–</td><td>r</td></tr><tr><th><i>Nitzschia pellucida</i> Grunow</th><td>r</td><td>–</td></tr><tr><th><i>Nitzschia sigma</i> (Kützing) W. Smith</th><td>–</td><td>r</td></tr><tr><th><i>Nitzschia tubicola</i> Grunow</th><td>r</td><td><b>–</b></td></tr><tr><th><b><i>Nitzschia</i> sp.</b> 1</th><td>r</td><td>–</td></tr><tr><th><i>Odontella litigiosa</i> (Van Heurck) Hoban</th><td>f</td><td>c</td></tr><tr><th><i>Paralia sulcata</i> (Ehrenberg) Cleve</th><td>–</td><td>vr</td></tr><tr><th><i>Parlibellus delognei</i> (Van Heurck) E. J. Cox</th><td>r</td><td>–</td></tr><tr><th><i>Parlibellus crucicula</i> (W. Smith) Witkowski, Lange-</th><td>–</td><td>f</td></tr><tr><th><b>Bertalot <i>et</i> Metzeltin</b></th></tr><tr><th><b><i>Parlibellus</i> sp.</b> 1</th><td>vr</td><td><b>–</b></td></tr><tr><th><b><i>Parlibellus</i> sp.</b> 2</th><td>r</td><td>–</td></tr><tr><th><i>Petroneis plagiostoma</i> (Grunow) D. G. Mann</th><td>c</td><td><b>c</b></td></tr><tr><th><b><i>Petroneis</i> sp.</b> 1</th><td>r</td><td><b>c</b></td></tr><tr><th><b><i>Petroneis</i> sp.</b> 2</th><td>r</td><td>–</td></tr><tr><th><i>Pinnularia quadratarea</i> (A. Schmidt) Cleve</th><td>f</td><td>c</td></tr><tr><th><i>Planothidium lanceolatum</i> (Brébisson <i>ex</i> Kützing)</th><td>r</td><td>–</td></tr><tr><th>Lange-Bertalot</th></tr><tr><th><i>Planothidium vicentii</i> Manguin</th><td>vr</td><td>–</td></tr><tr><th><i>Pseudogomphonema kamtschaticum</i> (Grunow) Medlin</th><td>f</td><td>f</td></tr><tr><th><i>Pleurosigma diversestriatum</i> Meister</th><td>–</td><td>r</td></tr><tr><th><i>Pleurosigma eudon</i> var. <i>kerguelensis</i> Heiden <i>et</i> Kolbe</th><td>vr</td><td>vr</td></tr><tr><th><i>Pleurosigma strigosum</i> W. Smith</th><td>–</td><td>c</td></tr><tr><th><i>Podosira stelliger</i> (Bailey) A. Mann</th><td>–</td><td>vr</td></tr><tr><th><i>Porosira glacialis</i> (Grunow) JØrgensen</th><td>–</td><td>f</td></tr><tr><th><i>Rhabdonema arcuatum</i> (Lyngbye) Kützing</th><td>–</td><td>r</td></tr><tr><th><i>Rhoicosphenia abbreviata</i> (Agardh) Lange-Bertalot</th><td>r</td><td><b>–</b></td></tr></tbody></table>
Table I in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
<p>Table I: (continued)</p><table><tbody><tr><th>Species</th><th>ZDDƎ</th><th>ZDIs</th></tr></tbody><tbody><tr><th><i>Staurophora</i> sp.</th><td>–</td><td>r</td></tr><tr><th><i>Stellarima microtrias</i> (Ehrenberg) Hasle <i>et</i> Sims</th><td>–</td><td>f</td></tr><tr><th><i>Surirella fastuosa</i> (Ehrenberg) Ehrenberg</th><td>–</td><td>vr</td></tr><tr><th><i>Surirella</i> sp.</th><td>–</td><td>r</td></tr><tr><th><i>Synedra kerguelensis</i> Heiden</th><td>r</td><td>–</td></tr><tr><th><i>Synedropsis fragilis</i> (Manguin) Hasle, Medlin <i>et</i></th><td>r</td><td>–</td></tr><tr><th><b>Syvertsen</b></th></tr><tr><th><i>Synedropsis recta</i> Hasle, Medlin <i>et</i> Syvertsen</th><td>r</td><td>–</td></tr><tr><th><i>Synedropsis</i> sp.</th><td>–</td><td>f</td></tr><tr><th><i>Tetramphora rhombica</i> (Kitton) Stepanek <i>et</i> Kociolek f</th><td>vr</td><td>–</td></tr><tr><th><i>Thalassionema nitzschioides</i> (Grunow) Mereschkowsky</th><td>r</td><td>r</td></tr><tr><th><i>Thalassiosira eccentrica</i> (Ehrenberg) Cleve</th><td>–</td><td>r</td></tr><tr><th><i>Thalassiosira gracilis</i> (Karsten) Hustedt</th><td>r</td><td>f</td></tr><tr><th><i>Thalassiosira lentiginosa</i> (Janisch) Fryxell</th><td>vr</td><td>–</td></tr><tr><th><i>Thalassiosira maculata</i> Fryxell <i>et</i> Johansen</th><td>vr</td><td>–</td></tr><tr><th><i>Thalassiosira oliveriana</i> (O’ Meara) Makarova <i>et</i></th><td>–</td><td>r</td></tr><tr><th><b>Nikolajev</b></th></tr><tr><th><i>Thalassiosira ritscheri</i> (Hustedt) Hasle</th><td>vr</td><td>–</td></tr><tr><th><i>Thalassiosira tumida</i> (Janisch) Hasle</th><td>r</td><td>–</td></tr><tr><th><i>Thalassiosira</i> sp.</th><td>–</td><td>r</td></tr><tr><th><i>Trachyneis aspera</i> (Ehrenberg) Cleve</th><td>f</td><td>f</td></tr><tr><th><i>Trachyneis</i> cf. <i>robusta</i> Petit</th><td>r</td><td>–</td></tr><tr><th><i>Triceratium favus</i> Ehrenberg</th><td>–</td><td>r</td></tr><tr><th><i>Trigonium arcticum</i> (Brightwell) Cleve</th><td>–</td><td><b>r</b></td></tr></tbody></table><p><b>vr, very rare, few cells found; r, rare,</b> 1 <b>–</b> 5 <b>%; f, frequent,</b> 5 <b>–</b> 20 <b>%; c, common,></b> 20 <b>%; –, not observed. <sup>a</sup> Reported as <i>Amphora</i> cf. <i>fogediana</i> (Grunow) Lange-Bertalot in Al-Handal and Wulff (</b>2008 <b>a). <sup>b</sup> Reported as</b> <b><i>Caloneis</i> sp.</b> 1 <b>in Al-Handal and Wulff (</b>2008 <b>a). <sup>c</sup> Reported as <i>Cocconeis schuettii</i> Van Heurck in Al-Handal and Wulff (</b>2008 <b>a). <sup>d</sup> Reported as <i>Achnanthidium exigua</i> (Grunow) Lange-Bertalot in Al-Handal and Wulff (</b>2008 <b>a). <sup>e</sup> Reported as <i>Amphora holsatica</i> Husted in Al-Handal and Wulff (</b>2008 <b>a). <sup>f</sup> Reported as <i>Amphora rhombica</i> Kitton <i>ex</i> A. Schmidt in Al-Handal and Wulff (</b>2008 <b>a).</b></p>
Table I in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
<p><b>Table I: Comparison of the occurrence of marine benthic diatoms in Potter Cove, King George Island, Antarctica in</b> 2003 <b>(Al-Handal and Wulff</b> 2008 <b>b) and</b> 2015 (present study).</p><table><tbody><tr><th>Species</th><th>ZDDƎ</th><th>ZDIs</th></tr></tbody><tbody><tr><th><i>Achnanthes bongrainii</i> (M. Peragallo) A. Mann</th><td>r</td><td>–</td></tr><tr><th><i>Achnanthes</i> cf. <i>groenlandica</i> (Cleve) Grunow</th><td>–</td><td>f</td></tr><tr><th><i>Actinocyclus actinochilus</i> (Ehrenberg) Simonsen</th><td>r</td><td>c</td></tr><tr><th><i>Actinocyclus curvatulus</i> Janisch</th><td>r</td><td>–</td></tr><tr><th><i>Amphora</i> cf. <i>eximia</i> Carter a</th><td>r</td><td>–</td></tr><tr><th><i>Amphora gourdonii</i> M. Peragallo</th><td>–</td><td>f</td></tr><tr><th><i>Amphora marina</i> W. Smith</th><td>f</td><td>f</td></tr><tr><th><i>Amphora</i> cf. <i>laevissima</i> Gregory</th><td>r</td><td>–</td></tr><tr><th><i>Amphora spectabilis</i> Gregory</th><td>r</td><td><b>–</b></td></tr><tr><th><b><i>Amphora</i> sp.</b> 1</th><td>–</td><td><b>r</b></td></tr><tr><th><b><i>Amphora</i> sp.</b> 2</th><td>–</td><td>r</td></tr><tr><th><i>Asteromphalus parvulus</i> Karsten</th><td>–</td><td>r</td></tr><tr><th><i>Biremis ambigua</i> (Cleve) D. G. Mann <i>Caloneis</i> sp. b</th><td>– vr</td><td>f r</td></tr><tr><th><i>Campyloneis frenguellii</i> Riaux-Gobin <i>et</i> Guerrero</th><td>–</td><td>f</td></tr><tr><th><i>Cocconeis antiqua</i> Tempère <i>et</i> Brun c</th><td>f</td><td>f</td></tr><tr><th><i>Cocconeis costata</i> Gregory</th><td>r</td><td>–</td></tr><tr><th><i>Cocconeis costata</i> var. <i>antarctica</i> Manguin</th><td>r</td><td>–</td></tr><tr><th><i>Cocconeis costata</i> var. <i>hexagona</i> Grunow</th><td>–</td><td>r</td></tr><tr><th><i>Cocconeis dallmannii</i> Al-Handal, Riaux-Gobin, Romero</th><td>–</td><td><b>r</b></td></tr><tr><th><b><i>et</i> Wulff</b></th></tr><tr><th><i>Cocconeis fasciolata</i> (Ehrenberg) Brown</th><td>r</td><td>f</td></tr><tr><th><i>Cocconeis hauniensis</i> Witkowski</th><td>vr</td><td>–</td></tr><tr><th><i>Cocconeis</i> cf. <i>imperatrix</i> A. Schmidt</th><td>r</td><td>c</td></tr><tr><th><i>Cocconeis matsii</i> (Al-Handal, Riaux-Gobin <i>et</i> A.Wulff)</th><td>–</td><td>f</td></tr><tr><th><b>Riaux-Gobin, Compère, Romero <i>et</i> Williams</b></th></tr><tr><th><i>Cocconeis melchioroides</i> Al-Handal, Riaux-Gobin,</th><td>–</td><td>r</td></tr><tr><th><b>Romero <i>et</i> Wulff</b></th></tr><tr><th><i>Cocconeis moronensis</i> A. Schmidt</th><td>vr</td><td>–</td></tr><tr><th><i>Cocconeis pinnata</i> Gregory <i>ex</i> Greville</th><td>r</td><td>–</td></tr><tr><th><i>Cocconeis scutellum</i> Ehrenberg</th><td>vr</td><td><b>–</b></td></tr><tr><th><b><i>Cocconeis</i> sp.</b> 1</th><td>vr</td><td><b>–</b></td></tr><tr><th><b><i>Cocconeis</i> sp.</b> 2</th><td>vr</td><td>–</td></tr><tr><th><b><i>Corethron pennatum</i> (Grunow) Osten</b> fi eld</th><td>r</td><td>f</td></tr><tr><th><i>Coscinodiscus asteromphalus</i> Ehrenberg</th><td>vr</td><td>–</td></tr><tr><th><i>Coscinodiscus radiatus</i> Ehrenberg</th><td>–</td><td>vr</td></tr><tr><th><i>Cylindrotheca closterium</i> (Ehrenberg) Reimann <i>et</i> Lewin</th><td>–</td><td>r</td></tr><tr><th><i>Diploneis smithii</i> (Brébisson) Cleve</th><td>r</td><td>r</td></tr><tr><th><i>Diploneis weiss</i> fl ogii (Schmidt) Cleve</th><td>–</td><td>vr</td></tr><tr><th><i>Diploneis</i> sp.</th><td>–</td><td>r</td></tr><tr><th><i>Entomoneis gigantea</i> Grunow</th><td>vr</td><td>r</td></tr><tr><th><i>Entomoneis kjellmanii</i> (Cleve) Poulin <i>et</i> Cardinal</th><td>f</td><td>r</td></tr><tr><th><i>Entomoneis paludosa</i> (W. Smith) Reimer</th><td>f</td><td>c</td></tr><tr><th><i>Entopyla ocellata</i> (Arnott) Grunow</th><td>r</td><td>f</td></tr><tr><th><i>Eucampia antarctica</i> (Castracane) Mangin</th><td>vr</td><td>–</td></tr><tr><th><i>Eunotia praerupta</i> Ehrenberg</th><td>vr</td><td>–</td></tr><tr><th><i>Fogedia</i> cf. <i>giffeniana</i> (Foged) Witkowski, Lange-</th><td>r</td><td>–</td></tr><tr><th><b>Bertalot, Metzeltin <i>et</i> Bafana</b></th></tr><tr><th><i>Fragilaria striatula</i> Lyngbye</th><td>–</td><td>f</td></tr><tr><th><i>Fragilariopsis cylindrus</i> (Grunow <i>ex</i> Cleve) Helmcke <i>et</i></th><td>–</td><td>r</td></tr><tr><th><b>Krieger</b></th></tr><tr><th><i>Fragilariopsis rhombica</i> (O’ Meara) Hustedt</th><td>r</td><td>–</td></tr><tr><th><i>Gogorevia parexigua</i> (Metzeltin <i>et</i> Lange-Bertalot) Kulikovskiy <i>et</i> Kociolek d</th><td>r</td><td><b>–</b></td></tr></tbody></table>
Global warming may turn ice-free areas of Maritime and Peninsular Antarctica into potential soil organic carbon sinks
<h2>Dear researchers and interested parties,</h2> <p>We are excited to announce the publication of our recent research on Zenodo, presenting <strong>high-resolution</strong> (8 m) spatial models of <strong>soil organic carbon (SOC) stocks in ice-free areas of Maritime and Peninsular Antarctica</strong>. This research evaluates the potential impacts of climate change on SOC stocks under three Shared Socioeconomic Pathways (SSPs), providing a comprehensive understanding of the role these regions may play as carbon sinks in the face of intensified global warming.</p> <h2>Available resources:</h2> <h3>SOC stock predictions:</h3> <p>We provide detailed maps of SOC estimates and uncertainties for different soil depths across various IPCC Shared Socioeconomic Pathways, including mean values (Mg ha⁻¹) and coefficients of variation (%). All maps are available in "tif" format, using the South Pole Stereographic projection system (<a href="https://epsg.io/102021" target="_blank" rel="noopener">ESRI:102021</a>).</p> <p>Open-Source Code and Data: The entire analytical workflow, developed in R, <strong>is accessible through our <a href="https://github.com/moquedace/soc_stock_antarctica" target="_blank" rel="noopener">GitHub repository</a></strong>, ensuring reproducibility and transparency. Additional methodological details are provided in our publication:</p> <p>Mello, D., Francelino, M. R., Moquedace, C. M., Baldi, C. G. O., Silva, L., Siqueira, R. G., Veloso, G. V., Fernandes-Filho, E. I., Thomazini, A., Demattê, J., Ferreira, T., Gomes, L. C., Senra, E., Schaefer, C. E. G. R. Global warming may turn ice-free areas of Maritime and Peninsular Antarctica into potential soil organic carbon sinks. <em>Commun Earth Environ</em>, v. 6, n. 1, p. 143, 2025. DOI: <a href="https://doi.org/10.1038/s43247-024-01937-z" target="_blank" rel="noopener">10.1038/s43247-024-01937-z</a></p> <h2>Availability objectives:</h2> <h3>Advancing scientific collaboration:</h3> <p>We invite scientists, researchers, and organizations to explore our findings to support additional studies on soil carbon dynamics and climate change.</p> <h3>Supporting environmental understanding:</h3> <p>By providing open access to these models, we aim to contribute to global knowledge on Antarctic soil carbon dynamics and assist in formulating sustainable climate mitigation strategies.</p> <h3>Fostering innovation:</h3> <p>Sharing this data aims to stimulate advances in spatial modeling and SOC prediction methodologies, especially in high-latitude environments.</p> <h2>We appreciate your interest and collaboration. We look forward to advancing knowledge and promoting sustainable solutions to essential environmental challenges together.</h2>
Figure 5. Plectus telekii A in Five new records of nematodes from East Antarctica
Figure 5. Plectus telekii A - Female Anterior region; B - Female Posterior region.
Figure 1. Helicotylenchus diagonicus A in Five new records of nematodes from East Antarctica
Figure 1. Helicotylenchus diagonicus A - Female Anterior region; B - Posterior region.
Figure 3. Helicotylenchus dihystera A in Five new records of nematodes from East Antarctica
Figure 3. Helicotylenchus dihystera A - Female Anterior region; B - Posterior region.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.