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.
403
datasets available to search
ShareScore release 0.7.1
Dataset results
403 results for “Satellite data”
Data from: Satellite image texture for the assessment of tropical anuran communities
The relationship between environmental heterogeneity and biodiversity represents a cornerstone of ecological research. While environmental descriptors over large extents usually have medium to low spatial resolution, in-situ measures provide accurate information for limited areas, and a gap remains in providing remote descriptors that represent local environmental structure. Texture from satellite images can represent fine-scale heterogeneity over wide spatial coverage, but to date, it has mostly been used to predict general aspects of species diversity, such as richness. Here, we assess the utility of image textures from high resolution satellite images (RapidEye 3A) and in-situ variables to predict differences in the composition of anuran communities in a tropical savanna (Cerrado) of Brazil. While in-situ measures accounted for compositional differences of the whole community, two measures of image textures were associated only with the variation of species within the Hylidae family (adj. R² = 0.16 and 0.14). Comparatively, image textures predicted ~2/3 of the variation explained by in-situ measures (adj. R² = 0.23). When both approaches were combined, a greater compositional variation was achieved (adj. R² = 0.28), with 1/5 of it shared by both in-situ and textures, and 1/5 attributed solely to texture. Our findings suggest that image texture can complement the assessment of environmental heterogeneity acting on the assembly of local anuran communities. This approach can be valuable for explicitly including spatial heterogeneity in biological assessments over broad spatial extents, especially for biological groups strongly filtered by environmental conditions.
Data from: Regional movements of satellite-tagged whale sharks Rhincodon typus in the Gulf of Aden
<p>To gain insight into whale shark (<i>Rhincodon typus</i>)<i> </i>movement patterns in the Western Indian Ocean, we deployed eight pop-up satellite tags at an aggregation site in the Arta Bay region of the Gulf of Tadjoura, Djibouti in the winter months of 2012, 2016 and 2017. Tags revealed movements ranging from local-scale around the Djibouti aggregation site, regional movements along the coastline of Somaliland, movements north into the Red Sea, and a large-scale (>1000 km) movement to the east coast of Somalia, outside of the Gulf of Aden. Vertical movement data revealed high occupation of the top ten meters of the water column, diel vertical movement patterns and deep diving behaviour. Long-distance movements recorded both here and in previous studies suggest that connectivity between the whale sharks tagged at the Djibouti aggregation and other documented aggregations in the region are likely within annual timeframes. In addition, wide-ranging movements through multiple nations, as well as the high use of surface waters recorded, likely exposes whale sharks in this region to several anthropogenic threats, including targeted and bycatch fisheries and ship-strikes. Area-based management approaches focusing on seasonal hotspots offer a way forward in the conservation of whale sharks in the Western Indian Ocean. </p>
Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought
<p>The summer drought of 2018 caused major damages to forest ecosystems. In this dataset, various environmental variables (precipitation anomaly, temperature anomaly, climatic water balance anomaly, elevation, slope, aspect, exposition, potential direct incedent radiation, distance to the forest edge, tree type, and species heterogeneity) have been stratified into ten sections and the proportion of forest pixels with severe changes (equal or more than 10% either positive or negative) in the normalized difference water index (NDWI) of the forest canopy from 2017-2018, 2018-2019, and 2017-2019 in Switzerland declared.</p>
Data in the paper Accuracy Evaluation of River Surface Flow Field Measurement Methods based on Satellite Video
Open the record for dataset details and reuse information.
Figure 11 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 11. Main pattern and sources of sea surface oil pollution of the Caspian sea, revealed on the basis of satellite remote sensing data: 1 – Natural leakages of liquid hydrocarbons; 2 – Seabed mud volcanoes; 3 – Natural hydrocarbon seafloor seeps off the Sefid Rud Cape; 4 – Natural hydrocarbon seafloor seeps westward of Cheleken Peninsula; 5 – offshore oil-producing platforms in the Cheleken oil field; 6 – Kashagan oil field; 7 – LUKOIL oil-producing area. Main shipping routes depicted by green lines.
Figure 1 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 1. Maps of the White Sea. Dashed lines show boundaries of the sea and their internal parts (Lebedev et al., 2011).
Figure 2 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 2. Position of the 133 and 209 tracks of the TOPEX/Poseidon and Jason-1/2/3 satellites on water area of the Caspian Sea and coordinate axes for calculation water exchange across these tracks. Dashed lines show geographic division of the Caspian Sea on three parts.
Figure 10 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 10. Manifestations of the fire aftereffects in satellite images: a) the smoke plume from a burning platform. A part of a color composite (R: 620-670 nm, G: 545-55 nm, B: 459-479 nm) MODIS Aqua image of 8 December 2015, 09:00 UTC; b) sea surface oil pollution caused by the accident at the platform. A part of SAR Sentinal-1A image of 13 December 2015, 14:37 UTC. The area of oil pollution due to the accident is 263 km2.
Figure 9. Fresh oil spill from a in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 9. Fresh oil spill from a moving vessel seen in SAR imagery of the Caspian Sea. SAR Sentinel-1A, 7 June 2019, 14:38 UTC. Length of oil spill - 89 km.
Figure 5 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 5. Examples of the manifestations of oil slicks from a natural seep on the seafloor in the Cheleken area in satellite images. Parts of SAR-C Sentinel-1A images: a) September 16, 2017, 14:28 UTC; b) July 25, 2018, 14:28 UTC; c) June 8, 2019, 02:36 UTC. Parts of OLI-TIRS Landsat-8 images (color composites of 4, 2 and 1 spectral channels): d) June 28, 2013, 07:09 UTC; e) June 15, 2014, 07:07 UTC; August 14, 2016, 07:13 UTC.
Figure 1 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 1. Map of the Caspian Sea. Dashed lines indicate the division into Northern, Middle and Southern Caspian.
IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations
<p>IT-SNOW is a serially complete and multi-year snow reanalysis for Italy. The dataset includes daily maps of Snow Water Equivalent (SWE), snow depth (HS), bulk-snow density (RhoS), and liquid water content (Theta_W). </p> <p>Data are organized in monthly netCDF files, each providing time and lat/lon information for georeference. Units are as follows: HS is in cm, SWE is in mm w.e., RhoS is in kg/m3, and Theta_W is in %. Note that maps are instantaneous snapshots at 11AM UTC, here assumed as representative values for the day. </p> <p>As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 1000+ on-the-ground snow-depth sensors. Additional information are available in the following paper submitted to Earth System Science Data: </p> <p>"IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2009-2021)", Francesco Avanzi et al., 2022. </p> <p>The initial time span of data is September 1, 2010 to August 31, 2021, with future updates envisaged on an annual basis (see updates below).</p> <p><strong>UPDATES</strong></p> <ul> <li>September 29, 2025: released v5 with the complete 2025 water year (September 2024 - August 2025).</li> <li>November 12, 2024: released v4 with the complete 2024 water year (September 2023 - August 2024).</li> <li>September 02, 2024: released v3.1 with the complete 2023 water year (September 2022 - August 2023) AND all previous water years (which were inadvertently NOT carried over while creating v3).</li> <li>September 02, 2024: released v3 with the complete 2023 water year (September 2022 - August 2023).</li> <li>December 20, 2023: released v2 with the complete 2022 water year (September 2021 - August 2022).</li> </ul> <p>LICENSE INFORMATION</p> <p>IT-SNOW is distributed under a CC BY-NC 4.0 license. you are free to: </p> <p>1. Share — copy and redistribute the material in any medium or format; <br>2. Adapt — remix, transform, and build upon the material;</p> <p>under the following terms: </p> <p>a. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.<br>b. NonCommercial — You may not use the material for commercial purposes.</p> <p><br>DATA ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE DATA, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p>For details about the CC BY-NC 4.0 license, see: https://creativecommons.org/licenses/by-nc/4.0/deed.en</p>
Characterising the Chilean megadrought using satellite data of precipitation and evapotranspiration (supplementary material)
<p>This is the supplementary material accompanying the article submitted to Remote Sensing on Environment on February 26th, 2019.</p>
Data attached to Radio Science paper "Model to Scale Rain Attenuation Time Series with Link Elevation Angle for LEO Satellite Based Systems"
<p>Data attached to Radio Science paper "Model to Scale Rain Attenuation Time Series with Link Elevation Angle for LEO Satellite Based Systems"</p>
Replication data for "Spatial Resolution in Inverse Problems: The EZIE satellite mission"
<p>Pandas dataframe contain synthetic measurements of an EZIE satellite.</p>
Satellite precipitation data (extracted)
<p>Daily satellite precipitation datasets extracted for hydrological modeling</p>
Data accompanying the article "Mapping Antarctic Crevasses and their Evolution with Deep Learning Applied to Satellite Radar Imagery"
<p>Fracture and backscatter maps from June 2021 at 100m resolution, covering the Antarctic Ice Sheet.</p> <p>Maps showing estimated change in fracture density between January 2015 and July 2022 covering the Amundsen Sea Sector of West Antarctica, and accompanying uncertainty estimates at 1km resolution.</p> <p>Each of these four datasets is in GeoTiff form with CRS: EPSG:3031 - WGS 84 / Antarctic Polar Stereographic</p> <p> </p>
Data from: Bidirectionality of hormone-behavior relationships and satellite-caller dynamics in green treefrogs
Open the record for dataset details and reuse information.
Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index
Open the record for dataset details and reuse information.
Data from: The organization and evolution of the Responder satellite in species of the Drosophila melanogaster group: dynamic evolution of a target of meiotic drive
Open the record for dataset details and reuse information.
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.