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.
55
datasets available to search
ShareScore release 0.7.1
Dataset results
55 results for “satellite tracks”
Ground-truthing of satellite imagery to track harmful algal blooms in Pigeon Lake, Alberta, Canada 2017-2022
This data was collected to create a calibrated model that would enable the use of satellite imagery to track harmful algal blooms by using chlorophyll a estimates as a proxy for cyanobacteria in the lake. Samples from Pigeon Lake were collected on the same day that the Sentinel-2 satellite would pass over the lake. These samples were analyzed for different algal pigments and enumerated to genus level to ensure that the satellite imagery was of cyanobacteria rather than different algal groups. An algorithm was developed which we termed the three band index (TBI) that best matched with the cholorophyll a from the in situ samples. This model was used on satellite imagery from 2017-2022 of Pigeon Lake to get chlorophyll a estimates for every 20 x 20 pixel of each image of the lake. This pixel data was used to determine different bloom metrics like the intensity, the area (extent) and severity.
Satellite tracking data of white sharks in the southwest Indian Ocean (2012-2014)
<p>These data comprise locations and individual metadata from 34 white sharks (<em>Carcharodon carcharias</em>) instrumented March-May 2012 with telemetry devices along the coast of South Africa. These devices were SPOT5 transmitters (SPOT-257, SPOT-258; Wildlife Computers) which transmit locations via ARGOS CLS. All research methods were approved and conducted under the South African Department of Environmental Affairs: Oceans and Coasts permitting authority.</p> <p>This dataset is linked to the manuscript Kock et al. 2021 "Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean".</p> <p>The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows.</p> <p>DeployID: This a factor variable identifying each individual shark. It has 34 levels.</p> <p>SPOT: This is a numeric variable identifying the tag number unique to each shark.</p> <p>Date: This is a date variable (POSIXct) that gives the date and time of a geographic location record in UTC time.</p> <p>Type: This is a character variable identifying the type of location record.</p> <p>Quality: This is a character variable made up of numbers and letters giving the location error associated with each location as provided by ARGOS.</p> <p>Latitude: This is a numeric variable and gives the latitude of the shark at the time of each record.</p> <p>Longitude: This is a numeric variable and gives the longitude of the shark at the time of each record.</p> <p>Area_tagged: This is a character variable that gives the area where the shark was tagged.</p> <p>Sex: This is a character variable identifying the sex of the shark, either "F" or "M" for female and male.</p> <p>TL: This is a numeric variable giving the total length of the shark in centimetres.</p> <p>Maturity: This is a character variable giving the maturity of the shark based on its total length following Malcolm et al. 2001: juveniles (male and female: 175-300 cm TL), sub-adults (male: >300-360 cm TL; females: >300-480 cm TL) and adults (male: >360 cm TL; female: >480 cm TL).</p> <p> </p>
Open Satellite Video Single Target Tracking Datasets (OpenSatSTTD)
<p>We collect the latest open-source datasets for satellite video single target tracking (SatSTT) and launch the OpenSatSTTD project to promote the sharing of the latest research datasets in the SatSTT field. Satellite videos in the OpenSatSTTD project are collected from different sensors and platforms, and four targets (i.e., vehicles, trains, airplanes and vessels) are annotated by oriented bounding boxes. Users can obtain all satellite videos in the OpenSatSTTD project from links in the files.</p> <p>Source:</p> <p>Zheng, Ying., Zhu, Q., Luo, J., Li, Z., Lin, Z., Huang, X., and Zhang L.: Single Target Tracking in High-Resolution Satellite Videos: A Comprehensive Review (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6780820, 2022.</p> <p> </p>
Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.
<p><strong>This database provides as polygons the contours of 900 juniper shrubs (<em>Juniperus communis L.</em> and <em>Juniperus sabina L.</em>) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation). </strong></p>
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
<p>The CloudTracks dataset consists of 1,780 MODIS satellite images hand-labeled for the presence of more than 12,000 ship tracks. More information about how the dataset was constructed may be found at <a href="http://github.com/stanfordmlgroup/CloudTracks">github.com/stanfordmlgroup/CloudTracks</a>. The file structure of the dataset is as follows:</p><p>CloudTracks/<br> full/<br> images/<br> (sample image name) mod2002121.1920D.png<br> jsons/<br> (sample json name) mod2002121.1920D.json</p><p>The naming convention is as follows:<br>mod2002121.1920D: the first 3 letters specify which of the sensors on the two MODIS satellites captured the image, mod for Terra and myd for Aqua. This is followed by a 4 digit year (2002) and a 3 digit day of the year (121). The following 4 digits specify the time of day (1920; 24 hour format in the UTC timezone), followed by D or N for Day or Night.</p><p>The 1,780 MODIS Terra and Aqua images were collected between 2002 and 2021 inclusive over various stratocumulus cloud regions (such as the East Pacific and East Atlantic) where ship tracks have commonly been observed. Each image has dimension 1354 x 2030 and a spatial resolution of 1km. Of the 36 bands collected by the instruments, we selected channels 1, 20, and 32 to capture useful physical properties of cloud formations.</p><p>The labels are found in the corresponding JSON files for each image. The following keys in the json are particularly important:</p><p>imagePath: the filename of the image.<br>shapes: the list of annotations corresponding to the image, where each element of the list is a dictionary corresponding to a single instance annotation. The dictionary has a key with value "shiptrack" or "uncertain" which is the label of the annotation and the corresponding value is a linestrip detailing the ship track path.</p><p>Further pre-processing details may be found at the GitHub link above. If you have any questions about the dataset, contact us at:<br><a href="mailto:mahmedch@stanford.edu">mahmedch@stanford.edu</a>, <a href="mailto:lynakim@stanford.edu">lynakim@stanford.edu</a>, <a href="mailto:jirvin16@cs.stanford.edu">jirvin16@cs.stanford.edu</a></p>
Data from: Satellite tracking of American Woodcock reveals a gradient of migration strategies
<p>Diversity in behavior is important for migratory birds in adapting to dynamic environmental and habitat conditions and responding to global change. Migratory behavior can be described by a variety of factors that comprise migration strategies. We characterized variation in migration strategies in American Woodcock (<em>Scolopax minor</em>), a migratory gamebird experiencing long-term population decline, using GPS data from approximately 300 individuals tracked throughout eastern North America. We classified woodcock migratory movements using a step-length threshold, and calculated characteristics of migration related to distance, path, and stopping events. We then used principal components analysis (PCA) to ordinate variation in migration characteristics along axes that explained different fundamental aspects of migration, and tested effects of body condition, age-sex class, and starting and ending location on PCA results. The PCA did not show evidence for clustering, suggesting a lack of discrete strategies among groups of individuals; rather, woodcock migration strategies existed along continuous gradients driven most heavily by metrics associated with migration distance and duration, departure timing, and stopping behavior. Body condition did not explain variation in migration strategy during the fall or spring, but during spring adult males and young females differed in some characteristics related to migration distance and duration. Starting and ending latitude and longitude, particularly the northernmost point of migration, explained up to 61% of the variation in any one axis of migration strategy. Our results reveal gradients in migration behavior of woodcock, and this variability should increase the resilience of woodcock to future anthropogenic landscape and climate change.</p>
Data from: Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking
<p><strong>Abstract</strong></p> <p>Satellite and GPS tracking technology continues to reveal new migration patterns of birds which enables comparative studies of migration strategies and distributional information useful in conservation. Bar-tailed godwits in the East Asian–Australasian Flyway <em>Limosa lapponica baueri </em>and <em>L. l. menzbieri</em> are known for their long non-stop flights, however these populations are in steep decline. A third subspecies in this flyway, <em>L. l. anadyrensis</em>, breeds in the Anadyr River basin, Chukotka, Russia, and is morphologically distinct from <em>menzbieri</em> and <em>baueri</em> based on comparison of museum specimens collected from breeding areas. However, the non-breeding distribution, migration route and population size of <em>anadyrensis </em>are entirely unknown. Among 24 female bar-tailed godwits tracked in 2015–2018 from northwest Australia, the main non-breeding area for <em>menzbieri</em>, two birds migrated further east than the rest to breed in the Anadyr River basin, i.e. they belonged to the <em>anadyrensis </em>subspecies. During pre-breeding migration, all birds staged in the Yellow Sea and then flew to the breeding grounds in the eastern Russian Arctic. After breeding, these two birds migrated southwestward to stage in Russia on the Kamchatka Peninsula and on Sakhalin Island en route to the Yellow Sea. This contrasts with the other 22 tracked godwits that followed the previously described route of <em>menzbieri</em>, i.e. they all migrated northwards to stage in the New Siberian Islands before turning south towards the Yellow Sea, and onwards to northwest Australia. Since the Kamchatka Peninsula was not used by any of the tracked <em>menzbieri</em> birds, the 4 500 godwits counted in the Khairusova–Belogolovaya estuary in western Kamchatka may well be <em>anadyrensis</em>. Comparing migration patterns across the three bar-tailed godwits subspecies, the migration strategy of <em>anadyrensis </em>lies between that of <em>menzbieri </em>and <em>baueri</em>. Future investigations combining migration tracks with genomic data could reveal how differences in migration routines are evolved and maintained.</p> <p> </p> <p><strong>Data set</strong></p> <p>Stopping sites and migration timing of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_Stops_Timing.xlsx</p> <p>The sheet 'stopping_sites' contains stopping sites of bar-tailed godwits tracked with solar Argos satellite transmitters, and their respective arrival and departure times at each site. The sheet 'timing' contains departure and arrival times at the non-breeding and breeding sites in 2017. The transmitters were deployed in Roebuck Bay and Eighty Mile Beach, Australia, and were operating on an 8 h on and 25 h off duty cycle. </p> <p> </p> <p>Measurements of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_measurements.csv</p> <p>The datafile contains bill, wing and tarsus lengths and sex of bar-tailed godwits tracked with solar Argos satellite transmitters. The birds were captured in Roebuck Bay and Eighty Mile Beach, Australia. </p> <p> </p> <p><strong>Journal Article</strong></p> <p>Chan, Y.-C., Tibbitts, T. L., Dorofeev, D., Hassell, C. J. and Piersma T. (2022) Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking. J Avian Biol e02988. <a href="https://doi.org/10.1111/jav.02920">https://doi.org/10.1111/jav.02988</a></p>
Figure 9 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 9. Comparison of frequency distributions between sea surface temperatures (SSTs) and time percent temperatures (TPTs) experienced by satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL) after winter 2011. (A) SSTs collected for locations of eight turtles leaving the Sea of Japan. (B) TPTs collected for locations of eight turtles leaving the Sea of Japan.
Figure 8 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 8. Comparison of frequency distributions between sea surface temperatures (SSTs) and time percent temperatures (TPTs) experienced by satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL) during summer 2011. (A) SSTs collected for locations of eight turtles leaving the Sea of Japan. (B) SSTs collected for locations of seven turtles staying in the Sea of Japan. (C) TPTs collected for locations of eight turtles leaving the Sea of Japan. (D) TPTs collected for locations of three turtles staying in the Sea of Japan.
Figure 7 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 7. Ten-day mean (± SD) sea surface temperatures (SSTs) experienced by satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL). Filled circles: six turtles staying in the Sea of Japan, open circles: three turtles leaving the Sea of Japan.
Figure 5 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 5. Sea surface temperature (SST) in the Sea of Japan and western North Pacific and positions of satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL). Open star: start position, circles: positions. (A) Positions of turtle IDs 22270, 25695, 42712, 50135, 57144 and 65426 leaving the Sea of Japan at summer 2011–2013. (B) Positions of turtle IDs 22270, 25695, 42712, 50135, 57144 and 65426 leaving the Sea of Japan at winter 2011–2013. (C) Positions of turtle IDs 19593 and 53771 that stayed in the Sea of Japan at summer 2011–2013. (D) Positions of turtle ID 19593 that stayed in the Sea of Japan at winter 2011–2013.
Figure 6 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 6. Sea surface temperature (SST) in the Sea of Japan and western North Pacific and positions of satellite-tagged loggerhead turtles>50 cm straight carapace length (SCL). Circles: positions, open star: start position. (A) Positions of turtle IDs 52695 and 53759 that left the Sea of Japan at summer 2011–2013. (B) Positions of turtle IDs 52695 and 53759 that left the Sea of Japan at winter 2011–2013. (C) Positions of turtle IDs 23513, 23542, 53758 and 53770 that stayed in the Sea of Japan at summer 2011–2013. (D) Positions of turtle IDs 23513, 23542 and 53758 that stayed in the Sea of Japan at winter 2011–2013.
Figure 4 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 4. Movement route maps of satellite-tagged loggerhead turtles>50 cm SCL after their release off Kanazawa-shi, Sea of Japan, on July 15, 2011. Circles: final positions. Open star: start position. **: recaptured. (A) Tracks of turtle IDs 52695 and 53759. Inlet: track of turtle ID 53759 leaving the Sea of Japan on September 26, 2011 through the Tsugaru Strait. (B) Tracks of turtle IDs 23513, 23542, 53770 and 53758.
Figure 3 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 3. Movement route maps of satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL) after their release off Kanazawa-shi, Sea of Japan, on July 15, 2011. Circles: final positions. *: likely stranded. **: recaptured. (A) Tracks of turtle IDs 19593, 40470, 50144, 50152 and 57152. (B) Tracks of turtle IDs 8552, 22275, 29060, 65424 and 57151. (C) Tracks of turtle IDs 40605, 53771, 57148, 65435, 71916 and 88060.
Figure 1 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 1. Map of study sites. Location of release and recapture sites of the loggerhead turtles. Dashed lines: northern and southern limits of turtle nesting. Gray lines and arrows: typical year-round tracks of the Kuroshio and Tsushima Warm Currents with directions of current flow. Open star: release site.
Figure 2 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 2. Movement route maps of satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL) after their release off Kanazawa-shi, Sea of Japan, on July 15, 2011. Circles: final positions. Open star: start position. (A) Tracks of turtle IDs 22270, 42712, 50135 and 65426. Inlet: track of turtle ID 22270 leaving the Sea of Japan on August 22, 2011 through the Soya Strait. (B) Tracks of turtle IDs 23537, 25329 and 57144. (C) Track of turtle ID 25695.
Figure 4A-B. Migration pathways from the Laniakea, O in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 4A-B. Migration pathways from the Laniakea, O'ahu foraging site to French Frigate Shoals for two females and one male. The male tracking documented a round-trip migration with the return to Laniakea followed by a move to Kāne'ohe Bay, O'ahu. Year of tracking is indicated on the map.
Figure 9 A-E in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 9 A-E. Home ranges for four females and one male green turtle that migrated from French Frigate Shoals to five other neritic foraging areas. Large colored circles indicate 1 km radius around each position. Black circles indicate positions with LC 1, 2 or 3 data. Black lines outline the Minimum Convex Polygons for Minimum Home Range and Full Home Range areas.
Figure 5 in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 5. Foraging areas destinations/origins for 19 of the 20 green turtles tracked from 1992- 2014. Transmissions from one turtle stopped midway between French Frigate Shoals and the Main Hawaiian Islands.
Figure 8 A-B in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 8 A-B. Home range for two female green turtles that migrated to 'Ewa Beach, O'ahu from French Frigate Shoals. Large colored circles indicate 1 km radius around each position. Black circles indicate positions with LC 1, 2 or 3 data. Black lines outline the Minimum Convex Polygons for Minimum Home Range and Full Home Range areas. Both 'Ewa turtles occupied two Minimum Home Range areas.
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.