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338 results for “gps”
Observational Data for Lidar and GPS soundings
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The propagation paths of GPS radio signals over the tropical ocean from 16–18 January 2022
<p>This dataset is the propagation paths of RO radio signals over the tropical ocean from 16–18 January 2022, simulated using the 2D non-local observation operator. The ray integration uses a variable step size of 2 km or smaller. The vertical interval is 100 m. The data format is HDF5. Variables include the 2D simulation bending angle, the COSMIC-2 retrieval bending angle, geometric height, impact heights, the vertical gradient of refractivity, latitude, longitude, and observation time of the RO profile.</p>
Processed GPS tracks for breeding Herring Gulls from four colonies in the eastern Gulf of Maine, Canada
<p>Opportunist gulls use anthropogenic food subsidies, which can bolster populations, but negatively influence sensitive local ecosystems and areas of human settlement. In the eastern Gulf of Maine, Canada, breeding herring gulls <em>Larus argentatus </em>have access to resources from aquaculture, fisheries, and mink farms, but the relative influence of industry on local gull populations is unknown. In 2014, 2015, and 2019, we acquired and processed tracking data from GPS devices on 39 incubating herring gulls at four colonies with access to resources within the Canadian portion of the eastern Gulf of Maine marine and watershed ecosystem: three island colonies in Nova Scotia: Bon Portage (43.47°N, 65.75°W), Whitehead (43.66°N, 65.87°W), Brier (44.26°N, 66.38°W), and one island colony in New Brunswick: Kent (44.58°N, 66.76°W). The data in this repository were processed according to the methods provided in the article indicate below, and were used to address three main objectives (a) assess use of natural and anthropogenic habitats by herring gulls from multiple colonies, (b) evaluate variation among colonies in use of distinct resource types within these habitats, and (c) highlight areas of high gull:industry interaction. The results are published in the journal Wildlife Biology (doi: 10.2981/wlb.00804).</p>
Use of avian GPS tracking to mitigate human fatalities from bird strikes caused by large soaring birds
<p>1. Birds striking aircraft cause substantial economic loss worldwide and, more worryingly, human and wildlife fatalities. Designing effective measures to avoid fatal bird strikes requires in-depth knowledge of the characteristics of this incident type and the flight behaviors of the bird species involved.</p> <p>2. The characteristics of bird strikes involving aircraft crashes or loss of human life in Spain were studied and compared to flight patterns of bird monitored by GPS. We tracked 210 individuals of the three species that cause the most crashes and human fatalities in Spain: griffon and cinereous vultures (<i>Gyps fulvus</i> and <i>Aegypius monachus</i>) and white storks (<i>Ciconia ciconia</i>).</p> <p>3. All the crashes involved general aviation aircraft, while none were recorded in commercial aviation. Most occurred outside airport boundaries, at midday and in the warmest months, which all correspond with the maximum flight activity of the studied species.</p> <p>4. Bird flight altitudes overlapped the legal flight altitude limit set for general aviation.</p> <p>5. Policy implications: Mitigation of fatal bird strikes should especially address the conflict between general aviation and large soaring birds. Air transportation authorities should consider modifying the flight ceiling for general aviation flights above the studied species' maximum flight altitude. Moreover, policymakers should issue pilots with recommendations regarding the dates and times of peak activity of large soaring bird species to improve flight safety.</p>
Codalab ML competition - No GPS, No problem! - Training dataset
<p>See competition <a href="https://epfl-enac.github.io/topo-ml-competition">website</a></p>
Harmony Point Chinstrap penguin GPS and Time-Depth-Recorder processed data
<p>Tracking data from Chinstrap Penguins (<em>Pygoscelis antarcticus</em>) tracked with axy-trek marine loggers in Harmony Point (Nelson Island, Maritime Antarctic Peninsula) in the breeding seasons of 2019/20 and 2021/22.</p> <p>The zip file is composed of two folders containing outputs from the R script named "GPS_TDR_processing.R", which process raw data from Axy-trek marine loggers producing 3 outputs that are within each of the folders. The outputs are: (1) 5-min resampled foraging trips (with trip ID identified), (2) dive statistics and (3) foraging trip summaries.</p> <p> </p> <p>Each file is named as the TagID, type of data (GPS or TDR), period (IB for late incubation / brooding - from late december to early january; CR for Chick-Rearing, from early to mid January) and season (2019/20 as 19 and 2021/22 as 21).</p> <p> </p> <p>GPS data is composed of the fields: Date (YYYY-mm-dd HH:MM:SS), distances, trip number and geographical coordinates in "+proj=longlat +datum=WGS84 +no_defs".</p> <p>TDR data is composed of outputs from dive statistics summarized using the "diveMove" R package. </p>
Codalab ML competition - No GPS, No problem! - test dataset
<p>See competition <a href="https://epfl-enac.github.io/topo-ml-competition">website</a></p>
The velocity difference derived from GPS and well data in the North China Plain and Shaanxi-Gansu region
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Anonymized GPS tracks
<p>Spatially anonymized GPS tracks for manuscript: "Wild canids and felids differ in memory-related use of travel routes." V2 is the correct version to use in replicated or further analyses.</p>
GPS_tws
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GPS velocity and strain rate of Pamir
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Spoofing Signal Generation Algorithm using the Processing Information of a Receiver for Authentic GPS Signals
<p>Experimental results analyzation files.</p>
MIDAS GPS TEC data for "On the Annual Asymmetry of High-Latitude Sporadic-F" (Space Weather)
<p>GPS-based TEC data related to high-latitude ionospheric variability in 2014</p>
YUTO MMS Dataset, Sequence B (Lidar data, Calibration files, Groundtruth, GPS, IMU, Timestamp)
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GPS tracks and behaviour detection of chick-rearing streaked shearwaters at Funakoshi Oshima Island, Japan, 2018 & 2019
<p>The study of seabird behaviour has largely relied on animal-borne tags to gather information, requiring interpretation to estimate at-sea behaviours. Details of shallow-diving birds' foraging are less known than deep-diving species due to difficulty in identifying shallow dives from biologging devices. Development of smaller video loggers allow a direct view of these birds' behaviours, at the cost of short battery life. However, recordings from video loggers combined with relatively low power usage accelerometers give a means to develop a reliable foraging detection method. Combined video and acceleration loggers were attached to streaked shearwaters in Funakoshi-Ohshima Island (39'N,141'59''E) during the breeding season in 2018. Video recordings were classified into behaviours and a detection method was generated from the acceleration signals. Two foraging behaviours, surface seizing and foraging dives, are reported with video recordings. Surface seizing was comprised of successive take-offs and landings (mean duration 0.6 and 1.5s, respectively), while foraging dives were shallow subsurface dives (1.9s mean duration) from the air and water surface. Birds were observed foraging close to marine predators, including dolphins and large fish. Foraging detections were validated against video recordings (surface seizing true positive 78%, false positive 5%, foraging dive true positive 66%, false positive <1%). The detection method was implemented to data from longer duration acceleration and GPS datasets collected during the 2018 and 2019 breeding seasons. Foraging trips lasted between 1-8 days, with birds performing on average 16 surface seizing events and 43 foraging dives per day, comprising <1% of daily activity, while transit and rest took up 55% and 39%, respectively. This foraging detection method can address the difficulties of recording shallow-diving foraging behaviour and provides a means to measure activity budgets across shallow diving seabird species.</p>
Figure 4 from: Dantas GPS, Hamada N, Mendes HF (2016) Denopelopia amicitia, a new Tanypodinae from Brazil (Diptera, Chironomidae). ZooKeys 553: 107-117. https://doi.org/10.3897/zookeys.553.5988
Figure 4 - Denopelopia amicitia sp. n. Pupa: A frontal apotome B thoracic horn C base of thoracic horn, basal lobe and thoracic comb D shagreen of sternite II E T VII–VIII and Anal lobe.
Figure 3 from: Dantas GPS, Hamada N, Mendes HF (2016) Denopelopia amicitia, a new Tanypodinae from Brazil (Diptera, Chironomidae). ZooKeys 553: 107-117. https://doi.org/10.3897/zookeys.553.5988
Figure 3 - Denopelopia amicitia sp. n. Pupa: A thoracic horn, in frontal view B thoracic horn, in lateral view C abdomen, in dorsal view D T VII–VIII and Anal lobe.
Figure 2 from: Dantas GPS, Hamada N, Mendes HF (2016) Denopelopia amicitia, a new Tanypodinae from Brazil (Diptera, Chironomidae). ZooKeys 553: 107-117. https://doi.org/10.3897/zookeys.553.5988
Figure 2 - Denopelopia amicitia sp. n. Adult male: A wing B fore tibial spur C mid tibial spur D hind tibial spur E hypopygium in dorsal view F hypopygium with tergite IX removed.
Figure 2 from: Stienen EWM, Desmet P, Aelterman B, Courtens W, Feys S, Vanermen N, Verstraete H, Van de walle M, Deneudt K, Hernandez F, Houthoofdt R, Vanhoorne B, Bouten W, Buijs RJ, Kavelaars MM, Müller W, Herman D, Matheve H, Sotillo A, Lens L (2016) GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast. ZooKeys 555: 115-124. https://doi.org/10.3897/zookeys.555.6173
Figure 2 - Left: map of western Europe and northwest Africa, showing the full extent of the gull tracking data, including two migration/wintering seasons. Right: map of the southern North Sea coast, showing mainly breeding season data. Each point represents a recorded occurrence, LBBG are indicated in orange, HG in blue. Overlapping points are brighter in colour. Maps created with CartoDB, basemap based on OpenStreetMap data. https://inbo.cartodb.com/u/lifewatch/viz/da04f120-ea70-11e4-a3f2-0e853d047bba/public_map
Figure 1 from: Stienen EWM, Desmet P, Aelterman B, Courtens W, Feys S, Vanermen N, Verstraete H, Van de walle M, Deneudt K, Hernandez F, Houthoofdt R, Vanhoorne B, Bouten W, Buijs RJ, Kavelaars MM, Müller W, Herman D, Matheve H, Sotillo A, Lens L (2016) GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast. ZooKeys 555: 115-124. https://doi.org/10.3897/zookeys.555.6173
Figure 1 - One of the tracked Lesser Black-backed Gulls ("Hans", ring code: L906682), photographed near its nest in Zeebrugge on May 29, 2013 shortly after he was equipped with a tracker (device info serial: 861). Photo by Misjel Decleer, VLIZ photo gallery.
ScienceDex guides
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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.