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.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “Subglacial topography”
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Subglacial Topography Training Image Database
<p>This database contains elevation data for 166 topographic training images. These training images should be cited as Yin et al., (2022). The file contains a 40,000 x166 matrix, where each column contains one TI. Each column should be reshaped to 200x200. The resolution is 500 m, and each TI is 100x100 km<sup>2</sup>. The elevation data were obtained from ArcticDEM (Porter et al., 2018) and IBCSO (Arndt et al., 2013). </p> <p>Arndt, J. E., Schenke, H. W., Jakobsson, M., Nitsche, F. O., Buys, G., Goleby, B., ... & Wigley, R. (2013). The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0—A new bathymetric compilation covering circum‐Antarctic waters. <em>Geophysical Research Letters</em>, <em>40</em>(12), 3111-3117.</p> <p>Porter, C., Morin, P., Howat, I., Noh, M. J., Bates, B., Peterman, K., ... & Bojesen, M. (2018). ArcticDEM. <em>Harvard Dataverse</em>, <em>1</em>, 2018-30.</p> <p>Yin, Z., Zuo, C., MacKie, E. J., & Caers, J. (2022). Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1). <em>Geoscientific Model Development</em>, <em>15</em>(4), 1477-1497.</p>
Impacts of bed topography resolution on sea-level rise projections from coupled subglacial hydrology and ice dynamics for Thwaites Glacier, Antarctica
Open the record for dataset details and reuse information.
Supplementary data for the publication: 'Impact of ice topography, basal channels and subglacial discharge on basal melting under the floating ice tongue of 79N Glacier, northeast Greenland'
Open the record for dataset details and reuse information.
Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier
<p>Abstract:</p> <p>These data products accompany the paper "Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier" (MacKie et al., in review). In this study, geostatistical techniques were used to generate an ensemble of topographic realizations that retain the spatial statistics of radar bed elevation measurements. The simulation was conditioned to local radar data and mass conservation bed estimates. This repository contains the radar and mass conservation conditioning data, the ensemble of topographic realizations, and coordinate data.</p> <p> </p> <p>Content and processing steps:</p> <p>The study area is 75.15 x 48.90 km^2. The grid cell resolution is 150 meters. Each digital elevation model (DEM) has 501 x 326 grid cells. The mass conservation DEM was obtained from BedMachine Greenland (Morlighem and others, 2017). The radar data were acquired from the Center for Remote Sensing of Ice Sheets (CReSIS) 2009 flights (Gogineni, 2012; Gogineni and others, 2014). A probabilistic modeling technique called sequential Gaussian co-simulation (Verly, 1993; Almeida and Journel, 1994; Journel, 1999; Remy, 2005) was used to generate the topographic realizations. The datasets are as follows:</p> <p> </p> <p>1) Jakobshavn_mass_conservation.txt - Mass conservation conditioning data</p> <p>2) Jakobshavn_radar_data.txt - Radar conditioning data</p> <p>3) Jakobshavn_simulation.txt - 250 topographic realizations. The shape of this file is 250 x 163326, where each column corresponds to one topographic realization. Each column should be reshaped to 501 x 326 to view the DEM.</p> <p>4) Jakobshavn_x_data.txt - Polar stereographic X coordinates in meters</p> <p>5) Jakobshavn_y_data.txt - Polar stereographic Y coordinates in meters</p> <p> </p> <p>References:</p> <p>Almeida, A. S., & Journel, A. G. (1994). Joint simulation of multiple variables with a Markov-type coregionalization model. <em>Mathematical Geology</em>, <em>26</em>(5), 565-588.</p> <p>Gogineni, P. (2012). CReSIS radar depth sounder data. <em>Center for Remote Sensing of Ice Sheets, Lawrence, KS https://data. cresis.-ku. edu</em>.</p> <p>Gogineni, S., Yan, J. B., Paden, J., Leuschen, C., Li, J., Rodriguez-Morales, F., ... & Gauch, J. (2014). Bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica. <em>Journal of Glaciology</em>, <em>60</em>(223), 813-833.</p> <p>Journel, A. G. (1999). Markov models for cross-covariances. <em>Mathematical Geology</em>, <em>31</em>(8), 955-964.</p> <p>Morlighem, M., Williams, C. N., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., ... & Fenty, I. (2017). BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation. <em>Geophysical research letters</em>, <em>44</em>(21), 11-051.</p> <p>Remy, N. (2005). S-GeMS: the stanford geostatistical modeling software: a tool for new algorithms development. In <em>Geostatistics banff 2004</em> (pp. 865-871). Springer, Dordrecht.</p> <p>Verly, G. W. (1993). Sequential Gaussian cosimulation: a simulation method integrating several types of information. In <em>Geostatistics Troia’92</em> (pp. 543-554). Springer, Dordrecht.</p>
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.