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.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “COSMO-SkyMed”
Rutford Ice Stream, Antarctica M_sf tidal velocity components derived from COSMO-SkyMED SAR data
<p>This repository provides rasters for velocity components of a tidal (periodic) model for Rutford Ice Stream (RIS), Antarctica. The tidal model consists of a secular (constant) term and a single sinusoidal component corresponding to the M_sf tidal cycle (14.76529 days). The tidal model is fit to time-dependent velocity fields over RIS derived from speckle tracking of COSMO-SkyMed SAR data, collected over 9 months beginning in August 2013. The original methodology and source dataset are described in the publication:</p> <p>Minchew, B. M., Simons, M., Riel, B., & Milillo, P. (2017). Tidally induced variations in vertical and horizontal motion on Rutford Ice Stream, West Antarctica, inferred from remotely sensed observations. <em>Journal of Geophysical Research: Earth Surface</em>, <em>122</em>(1), 167-190. doi: <a href="https://doi.org/10.1002/2016JF003971">10.1002/2016JF003971</a></p> <p>The rasters are provided in GeoTIFF format in the Polar Stereographic South (EPSG: 3031) coordinate system. The velocity components are also referenced to Polar Stereographic South coordinates. The pixel spacing is 400 meters (in both the X- and Y-directions). The individual files are:</p> <ol> <li>vx_secular.tif: secular velocity in X-direction in meters/day.</li> <li>vy_secular.tif: secular velocity in Y-direction in meters/day.</li> <li>vx_amp.tif: M_sf velocity amplitude in X-direction in meters/day.</li> <li>vy_amp.tif: M_sf velocity amplitude in Y-direction in meters/day.</li> <li>vx_phase.tif: M_sf velocity phase delay in X-direction in days.</li> <li>vy_phase.tif: M_sf velocity phase delay in Y-direction in days.</li> </ol>
Pre-eruption InSAR time-series at Kīlauea (Hawai`i, USA): COSMO-SkyMed Descending 2018
<p>InSAR time-series data for Kīlauea (Hawai`i, USA), between Jan 2010 and Sep 2011 . Data were obtained by processing COSMO-SkyMed descending SAR data (track 165). Data were processed using the JPL-developed InSAR Scientific Computing Environment (<code>ISCE</code>) open-source software package, and further time-series analysis was performed using the <code>MintPy</code> software toolbox (<a href="https://github.com/insarlab/MintPy">Miami INsar Time-series software in PYthon</a>), developed at the University of Miami. </p> <p>The following file is available in Hierarchical Data Format:</p> <p><code>geo_timeseries_tropHgt_demErr_cskDT165.h5</code>: Descending Track timeseries file. Dates available:</p> <p><code>['timeseries-20101001', 'timeseries-20101009', 'timeseries-20101017', 'timeseries-20101025', 'timeseries-20101102', 'timeseries-20101110', 'timeseries-20101118', 'timeseries-20101126', 'timeseries-20101204', 'timeseries-20101212', 'timeseries-20101220', 'timeseries-20110129', 'timeseries-20110206', 'timeseries-20110214', 'timeseries-20110222', 'timeseries-20110302', 'timeseries-20110303', 'timeseries-20110310', 'timeseries-20110318', 'timeseries-20110319', 'timeseries-20110322', 'timeseries-20110326', 'timeseries-20110403', 'timeseries-20110404', 'timeseries-20110407', 'timeseries-20110411', 'timeseries-20110419', 'timeseries-20110420', 'timeseries-20110423', 'timeseries-20110505', 'timeseries-20110506', 'timeseries-20110509', 'timeseries-20110513', 'timeseries-20110521', 'timeseries-20110522', 'timeseries-20110525', 'timeseries-20110529', 'timeseries-20110606', 'timeseries-20110607', 'timeseries-20110614', 'timeseries-20110622', 'timeseries-20110630', 'timeseries-20110708', 'timeseries-20110709', 'timeseries-20110716', 'timeseries-20110724', 'timeseries-20110725', 'timeseries-20110801', 'timeseries-20110809', 'timeseries-20110810', 'timeseries-20110817', 'timeseries-20110825', 'timeseries-20110826', 'timeseries-20110902', 'timeseries-20110910', 'timeseries-20110918']</code></p> <p> </p> <p>These data are supplemental to: Farquharson, J. I. and Amelung, F. [2020], "<em>Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano.</em>" <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a></p>
Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data
<p>This repository contains the file 'Natalya_Maslennikova_data_2024.zip,' which includes 171 DInSAR interferograms (both phase and coherence of a DInSAR signal) and three versions of the grounding lines: manually mapped ('gl_manual_mapping.shp'), mapped by the neural network ('gl_neural_network.shp'), and mapped using the phase gradient-based approach ('gl_phase_gradient.shp'). The X-band DInSAR data were acquired by the ASI’s COSMO-SkyMed mission between 2020 and 2022 over five East Antarctica glaciers: Stancomb-Wills (STA: 32 interferograms), Veststraumen (VES: 35 interferograms), Jutulstraumen (JUT: 42 interferograms), Moscow University (MOS: 27 interferograms), and Rennick (REN: 35 interferograms).</p> <p>The 'gl_neural_network.shp' and 'gl_phase_gradient.shp' shapefiles contain the following fields: the name of the glacier (the 'Glacier' column), four dates in YYYYMMDD format when the corresponding DInSAR interferogram was acquired (the 'Primary1', 'Secondary1', 'Primary2', 'Secondary2' columns), and the double difference name in 'Primary1_Secondary1-Primary2_Secondary2' format (the 'DD' column). The 'gl_manual_mapping.shp' shapefile contains 'Glacier', 'Primary1', 'Secondary1', 'Primary2', 'Secondary2', and 'DD' columns as well, along with the 'Revisit' column (difference in days between the 'Primary1' and 'Primary2' images were acquired), the 'Time' column (acquisition time in 'HHMMSS' format), the 'Coherence' column (average coherence of the corresponding DInSAR interferogram), and the 'Train_1', 'Train_2', 'Train_3' columns, which contain indicators 'Y' for 'yes' and 'N' for 'no', showing whether or not the DInSAR interferogram was used in the corresponding training session.</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.