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338 results for “gps”
SCINDA GPS and UHF data supporting analysis in "On the assessment of daily Equatorial Plasma Bubble occurrence modeling and forecasting"
<p>This dataset consists of ionospheric scintillation data collected from August 1, 2013 until July 25, 2014 by a collection of GPS and UHF receiver stations in the Scintillation Network Decision Aid (SCINDA) network (Groves et al., 1997). This particular dataset supports the analysis conducted in Carter et al. (2020).</p> <p><br> Carter, B.A., J.L. Currie, T. Dao, E. Yizengaw, J.M. Retterer, M. Terkildsen, K. Groves and R. Caton (2020), On the assessment of daily Equatorial Plasma Bubble occurrence modeling and forecasting, Submitted to Space Weather, Jun 2020.</p> <p>Groves, K.M., S. Basu, E. J. Weber, M. Smitham, H. Kuenzler, C.E. Valladares, R. Sheehan, E. MacKenzie, J.A. Secan, P. Ning, W.J. McNeill, D.W. Moonan, and M.J. Kendra (1997), Equatorial scintillation and systems support, Radio Science, 32, 2047-2064, doi:10.1029/97RS00836.</p>
GPS locations for mechanistic home range analysis reveals drivers of space use patterns for a non-territorial passerine
<p>1. Home ranging is a near-ubiquitous phenomenon in the animal kingdom. Understanding the behavioural mechanisms that give rise to observed home range patterns is thus an important general question, and mechanistic home range analysis (MHRA) provides the tools to address it. However, such analysis has hitherto been restricted to scent-marking territorial animals, so its potential breadth of application has not been tested.</p> <p>2. Here, we apply MHRA to a population of long-tailed tits <em>Aegithalos caudatus </em>a non- territorial passerine, in the non-breeding season where there is no clear "central place" near which birds need to remain. The aim is to uncover the principal movement mechanisms underlying observed home range formation.</p> <p>3. Our foundational models consist of memory-mediated conspecific avoidance between flocks, combined with attraction to woodland. These are then modified to incorporate the effects of flock size and relatedness, to uncover the effect of these on the mechanisms of home range formation.</p> <p>4. We found that a simple model of spatial avoidance, together with attraction to the central parts of woodland areas, accurately captures long-tailed tit home range patterns. Refining these models further, we show that the magnitude of spatial avoidance by a flock is negatively correlated to both the relative size of the flock (compared to its neighbour) and the relatedness of the flock with its neighbour. </p> <p>5. Our study applies MHRA beyond the confines of scent-marking, territorial animals, so paves the way for much broader taxonomic application. These could potentially help uncover general properties underlying the emergence of animal space use patterns. This is also the first study to apply MHRA to questions of relatedness and flock size, thus broadening the potential possible applications of this suite of analytic techniques.<br> </p>
Data from: Flying south: foraging locations of the Hutton's shearwater (Puffinus huttoni) revealed by time-depth recorders and GPS tracking
1. The Hutton's shearwater Puffinus huttoni is an endangered seabird endemic to Kaikōura, New Zealand, but the spatial and temporal aspects of its at-sea foraging behaviour are not well known. 2. To identify foraging areas and estimate trip durations, we deployed Global Positioning Systems (GPS) devices and Time-Depth Recorders (TDR) on 26 adult Hutton's shearwaters during the chick-rearing period in 2017 and 2018. 3. We found Hutton's shearwaters travelled much further from their breeding grounds at Kaikōura than previously considered, with most individuals foraging in coastal and oceanic areas 125–365 km south and near Banks Peninsula. Trip durations varied from 1 to 15 days (mean = 5 days), and total track lengths varied from 264 to 2157 km (mean = 1092.9 km). 4. Although some diving occurred in near-shore waters near the breeding colony, most foraging was concentrated in four regions south of Kaikōura. Dive durations averaged 23.2 seconds (range 8.1 to 71.3 sec) and dive depths averaged 7.1 m (range 1.5 to 30 m). Foraging locations had higher chlorophyll a levels and shallower water depths than non-foraging locations. Birds did not feed at night, but tended to raft in areas with deeper water than foraging locations. 5. Mapping the spatial and temporal distribution of Hutton's shearwaters at-sea will be fundamental to their conservation, as it can reveal potential areas of overlap with fisheries and other industrial users of the marine environment.
GPS 50 Hz data set for the case study entitled "Comparison of TEC calculations based on Trimble, Javad, Leica and Septentrio GNSS receiver data". Part 1
<p>This dataset contains 50 Hz GPS data to explore JAVAD and Leica receivers quality to reconstruct slant TEC.</p> <p>GNSS receiver is to some extent a “black box” when its data is used for ionospheric studies. The results based on Javad, Septentrio, Trimble and Leica GNSS receivers (which is placed in this data set) proved that the accuracy of the slant Total Electron Content (TEC) calculation can differ significantly if data used for its calculation is from different GNSS receiver make/type. This is because TEC measurements depend on the carrier phase tracking technique applied in a receiver. Based on this data it was found that:</p> <p>1) Correlation coefficient between carrier phase noises in L1 and L2 channel outputs may be considered as an indicator showing whether the L1-aided tracking technique or independent tracking is applied.</p> <p>2) The empirical model of TEC noise component was provided to determine TEC noise value in GNSS receivers of different make/type.</p>
Data from: GPS tracking and population genomics suggest itinerant breeding across drastically different habitats in the Phainopepla
Migratory birds generally divide the annual cycle between discrete breeding and non-breeding ranges. Itinerant breeders, however, reproduce twice at different geographic locations, migrating between them. This unusual flexibility in movement ecology and breeding biology suggests that some species can rapidly modulate the conflicting physiological and behavioral traits required for migration and reproduction. The Phainopepla (Phainopepla nitens), a songbird of the southwestern USA, has long been suspected to breed first in desert habitats in spring, then migrate to woodland habitats to breed again in summer. However, direct evaluation of movement and gene flow among individuals breeding in different locations has previously been logistically intractable. We deployed GPS tags on free-flying Phainopeplas in southern California, all of which migrated to hypothesized woodland breeding habitats after desert breeding (an average distance of 232 km). GPS data also revealed previously unknown fall and spring stopover sites. Population genomic analyses revealed no genetic differentiation among desert and woodland breeding populations, indicating significant movement and gene flow across the region. Finally, we used random forest analyses to quantify substantial environmental differences among temporal stages. Our results provide direct evidence that individual Phainopeplas do indeed move between two drastically different breeding habitats in the same year, representing a rare and extreme example of life-history flexibility.
SCINDA GPS and UHF data supporting analysis in "On the Generation of an Unseasonal EPB Over South East Asia"
<p>This dataset contains ground-based GPS ionospheric scintillation data collected from Bandung, Indonesia during July 2014. This GPS receiver is part of the Scintillation Network Decision Aid (SCINDA) network (Groves et al., 1997). This particular dataset supports the analysis conducted in Currie et al. (2020).</p> <p><br> Currie, J. L., B. A. Carter, J. Retterer, T. Dao, R. Pradipta, R. Caton, K. Groves, Y. Otsuka, T. Yokoyama, K. Hozumi, T Le Truong, M. Terkildsen (2020), On the Generation of an Unseasonal EPB Over South East Asia, Submitted to JGR: Space Physics, Sept 2020.</p> <p>Groves, K.M., S. Basu, E. J. Weber, M. Smitham, H. Kuenzler, C.E. Valladares, R. Sheehan, E. MacKenzie, J.A. Secan, P. Ning, W.J. McNeill, D.W. Moonan, and M.J. Kendra (1997), Equatorial scintillation and systems support, Radio Science, 32, 2047-2064, doi:10.1029/97RS00836.</p>
Westland petrel data combined GPS and accelerometer data 2016 & 2017
<p>This study investigated the foraging niche of dimorphic males and females Westland petrel during the chick-rearing period. At-sea movements were recorded with GPS, behaviours and foraging behaviour were recorded with accelerometers, and trophic niche was inferred with stable isotopes (carbon, nitrogen). Altogether, these fine-scale data allowed to look at the foraging niche used by males and females.</p>
Postseismic Corrected Walker Lane GPS velocities
<p>The interseismic motion of GPS stations in a tectonically active, diffuse, strike-slip shear zone<br> provide constraints on the overall deformation budget that can be compared to the summation<br> of geologically-estimated fault slip rates to understand regional strain accommodation. The<br> Walker Lane GPS velocities in this dataset represent a subset of GPS stations included in the<br> Nevada Geodetic Laboratory MIDAS velocity solution (Blewitt et al., 2016, 2018; accessible at<br> <a href="http://geodesy.unr.edu/velocities/midas.NA12.txt,">http://geodesy.unr.edu/velocities/midas.NA12.txt,</a> last accessed 11/19/2020) . This dataset<br> includes velocities for all GPS stations located between 34° N – 43° N latitude and 114° W –<br> 123° W longitude with time series longer than 2.5 years from the semi-continuous MAGNET<br> network operated by the Nevada Geodetic Laboratory (Blewitt et al., 2009) and neighboring<br> continuous GPS stations. The MIDAS velocities are calculated using daily position data collected<br> through August 2019 presented in the NA12 reference frame (Blewitt et al., 2013) , and<br> corrected for the postseismic effects of historic ruptures in and surrounding the Walker Lane.<br> The MIDAS algorithm is a median trend estimator that mitigates both seasonality and step<br> discontinuities in the times series (Blewitt et al., 2016). The resulting velocities are insensitive to<br> the coseismic and postseismic effects of earthquakes that occurred after the midpoint of the<br> time series (Blewitt et al., 2016) , such as the July 2019 Ridgecrest, CA M w 6.4 and 7.1 sequence,<br> but must be corrected for the post-seismic effects of earthquakes that occurred prior to the<br> middle of time series, such as the historic surface rupturing earthquakes in Central Nevada<br> Seismic Belt and the 1993 Landers M w 7.3, and 1999 Hector Mine M w 7.0 events. We apply the<br> viscoelastic postseismic relaxation correction from Bormann et al. (2013) that was developed<br> using the method of Hammond et al. (2010) and the preferred western Basin and Range lower<br> crust (10 20.5 Pa-s) and upper mantle (10 19 Pa-s) viscosity model of Hammond et al. (2009) .<br> When using this dataset, please also cite Blewitt et al (2018) as the authors of the original<br> MIDAS NA12 reference frame velocity solution is the basis for our postseismic corrected Walker<br> Lane velocities:<br> Blewitt, G., Hammond, W.C., Kreemer, C., 2018. Harnessing the GPS Data Explosion for<br> Interdisciplinary Science. Eos. <a href="https://doi.org/10.1029/2018EO104623">https://doi.org/10.1029/2018EO104623</a>.<br> <br> References:<br> Blewitt, G., Hammond, W.C., Kreemer, C., 2018. Harnessing the GPS Data Explosion for<br> Interdisciplinary Science. Eos. <a href="https://doi.org/10.1029/2018EO104623">https://doi.org/10.1029/2018EO104623</a>.<br> Blewitt, G., Kreemer, C., Hammond, W.C., Gazeaux, J., 2016. MIDAS robust trend estimator for<br> accurate GPS station velocities without step detection. Journal of Geophysical Research,<br> Solid Earth. <a href="https://doi.org/10.1002/2015JB012552">https://doi.org/10.1002/2015JB012552</a>.<br> Blewitt, G., Kreemer, C., Hammond, W.C., Goldfarb, J.M., 2013. Terrestrial reference frame<br> NA12 for crustal deformation studies in North America. Journal of Geodynamics.<br> <a href="https://doi.org/10.1016/j.jog.2013.08.004">https://doi.org/10.1016/j.jog.2013.08.004</a>.<br> Bormann, J.M., Hammond, W.C., Kreemer, C., Blewitt, G., Jha, S., 2013. A Synoptic Model of<br> Fault Slip Rates in the Eastern California Shear Zone and Walker Lane from GPS Velocities<br> <br> for Seismic Hazard Studies. Presented at the 2013 Seismological Society of America Annual<br> Meeting, Seismological Research Letters, Salt Lake City, UT, p. 323.<br> Hammond, W.C., Kreemer, C., Blewitt, G., 2009. Geodetic constraints on contemporary<br> deformation in the northern Walker Lane: 3. Postseismic relaxation in the Central Nevada<br> Seismic Belt., in: Late Cenozoic Structure and Evolution of the Great Basin-Sierra Nevada<br> Transition, Geological Society of America Special Paper 447.<br> <a href="https://doi.org/10.1130/2009.2447(03)">https://doi.org/10.1130/2009.2447(03)</a>.<br> Hammond, W.C., Kreemer, C., Blewitt, G., Plag, H.-P., 2010a. Effect of viscoelastic postseismic<br> relaxation on estimates of interseismic crustal strain accumulation at Yucca Mountain,<br> Nevada. Geophysical Research Letters. <a href="https://doi.org/10.1029/2010GL042795">https://doi.org/10.1029/2010GL042795</a>.</p>
FIGURE 27–28. Distributional GPS records. 27 Gelotia robusta Wanless, 1984 in New record of Gelotia Thorell, 1890 and a new species of Parahelpis Gardzińska & Żabka, 2010 (Araneae: Salticidae) from Australia
FIGURE 27–28. Distributional GPS records. 27 Gelotia robusta Wanless, 1984 (blue circle); 28 Parahelpis abnormis (Żabka, 2002) (green circle), P. smithi Gardzińska & Żabka, 2010 (yellow square) and P. wandae sp. nov. (red triangle).
Data from: Linking GPS telemetry surveys and scat analyses helps explain variability in black bear foraging strategies
Studying diet is fundamental to animal ecology and scat analysis, a widespread approach, is considered a reliable dietary proxy. Nonetheless, this method has weaknesses such as non-random sampling of habitats and individuals, inaccurate evaluation of excretion date, and lack of assessment of inter-individual dietary variability. We coupled GPS telemetry and scat analyses of black bears Ursus americanus Pallas to relate diet to individual characteristics and habitat use patterns while foraging. We captured 20 black bears (6 males and 14 females) and fitted them with GPS/Argos collars. We then surveyed GPS locations shortly after individual bear visits and collected 139 feces in 71 different locations. Fecal content (relative dry matter biomass of ingested items) was subsequently linked to individual characteristics (sex, age, reproductive status) and to habitats visited during foraging bouts using Brownian bridges based on GPS locations prior to feces excretion. At the population level, diet composition was similar to what was previously described in studies on black bears. However, our individual-based method allowed us to highlight different intra-population patterns, showing that sex and female reproductive status had significant influence on individual diet. For example, in the same habitats, females with cubs did not use the same food sources as lone bears. Linking fecal content (i.e., food sources) to habitat previously visited by different individuals, we demonstrated a potential differential use of similar habitats dependent on individual characteristics. Females with cubs-of-the-year tended to use old forest clearcuts (6–20 years old) to feed on bunchberry, whereas females with yearling foraged for blueberry and lone bears for ants. Coupling GPS telemetry and scat analyses allows for efficient detection of inter-individual or inter-group variations in foraging strategies and of linkages between previous habitat use and food consumption, even for cryptic species. This approach could have interesting ecological implications, such as supporting the identification of habitats types abundant in important food sources for endangered species targeted by conservation measures or for management actions for depredating animals.
Data from: Jaguar Movement Database: a GPS-based movement dataset of an apex predator in the Neotropics
The field of movement ecology has rapidly grown during the last decade, with important advancements in tracking devices and analytical tools that have provided unprecedented insights into where, when, and why species move across a landscape. Although there has been an increasing emphasis on making animal movement data publicly available, there has also been a conspicuous dearth in the availability of such data on large carnivores. Globally, large predators are of conservation concern. However, due to their secretive behavior and low densities, obtaining movement data on apex predators is expensive and logistically challenging. Consequently, the relatively small sample sizes typical of large carnivore movement studies may limit insights into the ecology and behavior of these elusive predators. The aim of this initiative is to make available to the conservation-scientific community a dataset of 134,690 locations of jaguars (Panthera onca) collected from 117 individuals (54 males and 63 females) tracked by GPS technology. Individual jaguars were monitored in five different range countries representing a large portion of the species' distribution. This dataset may be used to answer a variety of ecological questions including but not limited to: improved models of connectivity from local to continental scales; the use of natural or human-modified landscapes by jaguars; movement behavior of jaguars in regions not represented in this dataset; intraspecific interactions; and predator-prey interactions. In making our dataset publicly available, we hope to motivate other research groups to do the same in the near future. Specifically, we aim to help inform a better understanding of jaguar movement ecology with applications towards effective decision making and maximizing long-term conservation efforts for this ecologically important species.
Data from: Modelling flight heights of lesser black-backed gulls and great skuas from GPS: a Bayesian approach
Wind energy generation is increasing globally, and associated environmental impacts must be considered. The risk of seabirds colliding with offshore wind turbines is influenced by flight height, and flight height data usually come from observers on boats, making estimates in daylight in fine weather. GPS tracking provides an alternative and generates flight height information in a range of conditions, but the raw data have associated error. Here, we present a novel analytical solution for accommodating GPS error. We use Bayesian state-space models to describe the flight height distributions and the error in altitude measured by GPS for lesser black-backed gulls and great skuas, tracked throughout the breeding season. We also examine how location and light levels influence flight height. Lesser black-backed gulls flew lower by night than by day, indicating that this species would be less likely to encounter turbine blades at night, when birds' ability to detect and avoid them might be reduced. Gulls flew highest over land and lowest near the coast. For great skuas, no significant relationships were found between flight height, time of day and location. We consider four 'collision risk windows', corresponding to the airspace swept by rotor blades for different offshore wind turbine designs. We found the highest proportion of birds at risk for a 22–250 m turbine (up to 9% for great skuas and 34% for lesser black-backed gulls) and the lowest for a 30–258 m turbine. Our results suggest lesser black-backed gulls are at greater risk of collision than great skuas, especially by day. Synthesis and applications. Our novel modelling approach is an effective way of resolving the error associated with GPS tracking data. We demonstrate its use on GPS measurements of altitude, generating important information on how breeding seabirds use their environment. This approach and the associated data also provide information to improve avian collision risk assessments for offshore wind farms. Our modelling approach could be applied to other GPS data sets to help manage the ecological needs of seabirds and other species at a time when the pressures on the marine environment are growing.
Data from: Combined use of GPS and accelerometry reveals fine scale three-dimensional foraging behaviour in the short-tailed shearwater
Determining the foraging behaviour of free-ranging marine animals is fundamental for assessing their habitat use and how they may respond to changes in the environment. However, despite recent advances in bio-logging technology, collecting information on both at-sea movement patterns and activity budgets still remains difficult in small pelagic seabird species due to the constraints of instrument size. The short-tailed shearwater, the most abundant seabird species in Australia (ca 23 million individuals), is a highly pelagic procellariiform. Despite its ecological importance to the region, almost nothing is known about its at-sea behaviour, in particular, its foraging activity. Using a combination of GPS and tri-axial accelerometer data-loggers, the fine scale three-dimensional foraging behaviour of 10 breeding individuals from two colonies was investigated. Five at-sea behaviours were identified: (1) resting on water, (2) flapping flight, (3) gliding flight, (4) foraging (i.e., surface foraging and diving events), and (5) taking-off. There were substantial intra- and inter- individual variations in activity patterns, with individuals spending on average 45.8% (range: 17.1–70.0%) of time at sea resting on water and 18.2% (range: 2.3–49.6%) foraging. Individuals made 76.4 ± 65.3 dives (range: 8–237) per foraging trip (mean duration 9.0 ± 1.9 s), with dives also recorded during night-time. With the continued miniaturisation of recording devices, the use of combined data-loggers could provide us with further insights into the foraging behaviour of small procellariiforms, helping to better understand interactions with their prey.
Complete Dataset links and GPS timestamps and locations of our recordings at the Morton Arboretum.
<p>This repository contains all the data to reproduce the experiments conducted in the research article:<br><br>"Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring"</p> <p>The complete dataset for reproducing the experiments conducted in this research are available at<a href="https://web.lcrc.anl.gov/public/waggle/datasets/morton_arb.tar"> Complete Morton Arboretum Data</a>.</p> <p>For a sub-sample of the complete version of the dataset, go to the following link: <a href="https://web.lcrc.anl.gov/public/waggle/datasets/morton_arb_small.tar">2021/06/30 study</a></p> <p>This repository also contains GPS timestamps, and GPS locations of our recordings at the Morton Arboretum, an internationally recognized tree-focused botanical garden and research center in Lisle, IL.</p> <p> </p> <p> </p>
GPS trajectories of tourists in Öregrund 2022 from INCULTUM Sweden Pilot
<p>This dataset is part of ongoing work about changes in tourists' behaviour in Öregrund.</p>
Code and utilities for integrating adafruit GPS tags with UAV video footage
Open the record for dataset details and reuse information.
GPS data and plotting codes for Remote Sensing paper titled Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity
<p>Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper Gajek et al., Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity</p>
GPS_coordiantes_Health_centres_dale_wonsho
<p>This is the location information for mapping the rural Health centres of Dale and Wonsho districts. We used globalid to build the point maps of the health centre.</p>
Insights into short and long-term crop-foraging strategies in a chacma baboon (Papio ursinus) from GPS and accelerometer data
<p>Crop-foraging by animals is a leading cause of human-wildlife 'conflict' globally, affecting farmers and resulting in the death of many animals in retaliation, including primates. Despite significant research into crop-foraging by primates, relatively little is understood about the behaviour and movements of primates in and around crop fields, largely due to the limitations of traditional observational methods. Crop-foraging by primates in large scale agriculture has also received little attention. We used GPS and accelerometer bio-loggers, along with environmental data, to gain an understanding of the spatial and temporal patterns of activity for a female in a crop-foraging baboon group in and around commercial farms in South Africa over one year. Crop fields were avoided for most of the year, suggesting that fields are perceived as a high-risk habitat. When field visits did occur, this was generally when plant primary productivity was low, suggesting that crops were a 'fallback food'. All recorded field visits were at or before 15:00. Activity was significantly higher in crop fields than in the landscape in general, evidence that crop-foraging is an energetically costly strategy and that fields are perceived as a risky habitat. In contrast, activity was significantly lower within 100m of the field edge than in the rest of the landscape, suggesting that baboons wait near the field edge to assess risks before crop-foraging. Together this understanding of the spatiotemporal dynamics of crop-foraging can help to inform crop protection strategies and reduce conflict between humans and baboons in South Africa.</p>
Deer GPS and animal-borne camera data showing effects of gray wolves on niche overlap between mule and white-tailed deer in eastern Washington state
<p><span><span><span><span><span><span><span><span><span><span><span>Predators may alter niche overlap between prey species by eliciting divergent anti-predator behavior. Accordingly, we exploited heterogeneous gray wolf (<i>Canis lupus</i>) presence in Washington, USA, to contrast patterns of resource and dietary overlap between mule (<i>Odocoileus hemionus</i>) and white-tailed deer (<i>O. virginianus</i>) at sites with and without resident packs. Mule deer run (stot) in a way that is less effective as a means of fleeing from predators than the galloping gait of white-tailed deer. Consequently, mule deer manage risk from coursing predators like wolves by avoiding encounters, whereas white-tailed deer respond to such predators by exploiting areas where they are most likely to escape pursuit. Thus, under the "refuge partitioning hypothesis" whereby predators reduce prey niche overlap by eliciting use of different refugia, we predicted wolf exposure to (i) decrease resource and dietary overlap between these ungulates, and (ii) induce segregation consistent with each species using different parts of the landscape to reduce their wolf risk. At the home range scale, the ways in which resource overlap diminished in the wolf areas were consistent with the prey species reducing their respective risks, particularly with respect to slope, with mule deer separating from white-tailed deer by seeking steeper areas where wolf encounters are less likely. At the within-home range scale, the manner in which spatial overlap decreased in relation to forest cover was consistent with species-specific risk management, with mule deer avoiding wolf encounters by shifting toward this resource. Reduced resource overlap between the deer in areas occupied by wolves did not correspond with dietary divergence. Our findings suggest that wolf risk mediates spatial but not necessarily dietary overlap between sympatric ungulates, divergent anti-predator behavior is a non-consumptive pathway by which predators can reduce interspecific competition among prey, and use of disparate refugia by prey may not result in dietary divergence.</span></span></span></span></span></span></span></span></span></span></span></p>
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.