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1,832 results for “Cameras”
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>
Scene camera movies from mobile eye tracker
<p>These are the full recorded scene camera scenes. Eye position data for each scene can also be found here as well as an excel file detailing which parts of the clips we used.</p>
Svalbard time-lapse cameras
<p>Time-lapse cameras are important data sources enabling us to observe changes in the Svalbard environment in an efficient and economically favorable way. Focusing on snow cover monitoring using cameras, it is important to identify potential image providers, archived imagery, and processed datasets.</p>
A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing
<p>Dataset to produce the results of the publication: "A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing"</p><p>The dataset contains:</p><ul><li>Large-Eddy Simulation (LES) model configuration files</li><li>Selected output data of the LES experiments</li><li>Data and analysis scripts for the figures</li><li>The rendered camera images</li><li>The cloud field, cloud hulls, and reconstructed hulls</li><li>A frozen version of the open-source Blender code (version 2.90) as used in this study</li></ul><p>For the latest version of Blender, please visit:</p><p><a href="https://chat.openai.com/c/www.blender.org">www.blender.org</a></p><p>It is important to note that the method was specifically tested only on version 2.90.</p><p> </p><p>This research is supported by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1086. The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under projects VIRTUALLAB and RCONGM.</p>
Assessment of the Axial Resolution of a Compact Gamma Camera With Coded Aperture Collimator
<p>The dataset contains 21 images of a point-like gamma source, taken with a compact gamma camera that was equipped with a coded aperture collimator. The pixel intensity represents the accumulated energy deposited by the captured gamma photons. This dataset was acquired as part of the following paper, to which the reader is referred for an in-depth explanation.</p> <p>Note: Although the TIFF files may appear as all black or all transparent images, they will be displayed correctly when opened with an image processing tool such as ImageJ or a Python script.</p> <p><strong>Assessment of the Axial Resolution of a Compact Gamma Camera With Coded Aperture Collimator<br></strong></p> <p><strong>Purpose:</strong> Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods.<br><strong>Methods:</strong> An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08mm in diameter in a 0.11mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12 to 100mm. For each source position, the detector image was reconstructed in 0.5mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. <br>Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM). <br><strong>Results: </strong>While taking 4,400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3mm and 1.8mm at 12mm to 42.2mm and 13.5mm at 100mm for MURA Decoding and 3D-MLEM respectively. <br><strong>Conclusion:</strong> Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.</p>
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
On the use of a consumer-grade 360-degree camera as a radiometer for scientific applications: calibration dataset
<p>Studying the geometric distribution of the light field in terms of absolute radiometry required expensive and complex instruments. New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments. </p> <p>This dataset presents raw format images captured with the camera Insta360 ONE for its calibration and characterization. These experiments include geometric calibration, relative illumination evaluation, spectral response determination, absolute spectral radiance calibration, as well as linearity and dark frame analysis. In addition, we are providing data from a calibration validation experiment based on co-located measurements of the sky's downward radiance using a 360-degree calibrated camera and a scientific radiometer: the Compact Optical Profiling System (C-OPS, Biospherical Instruments Inc.).</p> <p>This repository contains raw files as taken by the camera's imaging sensor. In most cases, no data processing has been carried out. The entire data set is contained in a .zip file which includes the following sub-folders (in alphabetical order):</p> <ul> <li><strong>absolute-radiance</strong>: data (.dng, .tsv , .hdf5) acquired for <em>absolute spectral radiance</em> calibration</li> <li><strong>darkframe: </strong>DNG raw images taken for <em>dark frame</em> analysis</li> <li><strong>geometric: </strong>DNG images used for the <em>geometric calibration</em></li> <li><strong>immersion-factor: </strong>DNG images for the calculation of the immersion factor</li> <li><strong>linearity:</strong> DNG images for <em>linearity</em> assessment (gain and exposure time)</li> <li><strong>relative-illumination: </strong>DNG images for <em>roll-off (relative illumination) </em>characterization</li> <li><strong>relative-spectral-response: </strong>data (.asc, .dng) taken for relative spectral response characterization</li> <li><strong>verification&validation: </strong>time series data (.tsv, .dng) of the calibration validation experiment </li> </ul> <p>Each folder contains a <strong>README </strong>file explaining the additional subfolders and their files. As you may notice, some of the subfolders are named "lensclose" or "lensfar". They refers to the data acquired with the fish-eye optic assembly that is closer or farther from the top of the 360-degree camera respectively. Some calibrations were performed both in water and in air (geometric calibration, relative-illumination). In that regard, the images were placed in folders refering to "air" and "water". The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01">Github repository</a> (master_v01) or the <a href="../records/4660994">Zenodo stored version</a>. The useful scripts are located in the <em>calibration</em> directory, and the<strong> README</strong> for each folder points to the revelant code for analysis of the files they contain. For additional information, all the methodologies are described in the <a href="https://arxiv.org/abs/2305.07103">arXiv preprint</a>. </p>
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
Watching the watchers: Camera identification and characterization using retro-reflections - Dataset
<p>A focused imaging system such as a camera will reflect light directly back at a light source in a retro-reflection (RR) or cat-eye reflection. RRs provide a signal that is largely independent of distance providing a way to probe cameras at very long ranges. We find that RRs provide a rich source of information on a target camera that can be used for a variety of remote sensing tasks to characterize a target camera including predictions of rotation and camera focusing depth as well as cell phone model classification. We capture three RR datasets to explore these problems with both large commercial lenses and a variety of cell phones. This repository contains time-synced videos from the perspective of both a retro-reflective probe and a target camera that can be used to train algorithms for different remote sensing tasks. We include a dataset for cellphone classification, target camera rotation prediction, and target camera focusing depth prediction.</p>
Dataset for the manuscript: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy
<p>These are the data files used to generate the figures in the paper: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy. DOI: 10.1101/2023.06.27.546748</p>
Top view of DR1/DR2 double riffle, each section contains a spawning ground made up of eight gravel-filled trays, a rest area. The "double riffle" was designed to accommodate two groups from 25 to 50 specimens of broodstock in strictly identical conditions. The spawning grounds are equipped with waterproof, motion-sensing cameras with infrared night vision, connected to a 1000 Gb recorder. The diurnal and nocturnal activities of the two groups can therefore be simultaneously recorded over a long period. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Top view of DR1/DR2 double riffle, each section contains a spawning ground made up of eight gravel-filled trays, a rest area. The "double riffle" was designed to accommodate two groups from 25 to 50 specimens of broodstock in strictly identical conditions. The spawning grounds are equipped with waterproof, motion-sensing cameras with infrared night vision, connected to a 1000 Gb recorder. The diurnal and nocturnal activities of the two groups can therefore be simultaneously recorded over a long period.
Data for: Motion adaptive deblurring with single photon cameras
<p>Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme lowlight sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work we harness these capabilities for dealing with motion blur in a passive imaging setting in low illumination conditions. Our key insight is that the data captured by a SPAD array camera can be represented as a 3D spatio-temporal tensor of photon detection events which can be integrated along arbitrary spatio-temporal trajectories with dynamically varying integration windows, depending on scene motion. We propose an algorithm that estimates pixel motion from photon timestamp data and dynamically adapts the integration windows to minimize motion blur. Our simulation results show the applicability of this algorithm to a variety of motion profiles including translation, rotation and local object motion. We also demonstrate the real-world feasibility of our method on data captured using a 32 × 32 SPAD camera.</p>
Dataset: Inferring Inherent Optical Properties of Sea Ice Using 360-Degree Camera Radiance Measurements
<p>New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments. </p> <p>This dataset presents the angular radiance distributions measured with the Insta360 ONE 360-degree camera in sea ice. We report vertical profiles of the light field structure at two sites reprensentative of distinct sea ice types: High Arctic multi-year ice and Chaleur Bay (Quebec, Canada) landfast first-year ice. </p> <p>This repository contains the radiometric data stored in <strong>Hierarchical Data Format (HDF5, h5)</strong> under the following names: </p> <ul> <li><strong><a href="https://zenodo.org/api/records/14263256/draft/files/oden-08312018-imf-fluo.h5/content" target="_blank" rel="noopener noreferrer">oden-08312018-imf-fluo.h5</a></strong></li> <li><strong><a href="https://zenodo.org/api/records/14263256/draft/files/baiedeschaleurs-03232022-imf-fluo.h5/content" target="_blank" rel="noopener noreferrer">baiedeschaleurs-03232022-imf-fluo.h5</a></strong></li> </ul> <p>The High Arctic dataset (<strong>oden-08312018-imf-fluo.h5</strong>) contains only one station, while the Chaleur Bay (<strong>baiedeschaleurs-03232022-imf-fluo.h5</strong>) has four that can be accessed using these tags: "station_1", "station_2", "station_3", "station_4". The radiance measurements at each depth are reported as 2-dimensionals arrays with the azimuth directions (0-359°, 1° resolution) as columns and the zenith directions (0-180°, 1° resolution) as lines. The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01" target="_blank" rel="noopener">Github repository</a> (master_v01) or the <a href="https://zenodo.org/records/4660994" target="_blank" rel="noopener">Zenodo stored version</a>. </p> <p>The methodologies to carefully calibrated the 360-degree camera for radiometry purpose are described in this <a href="https://doi.org/10.1364/AO.524122" target="_blank" rel="noopener">pulibcation</a> and the raw calibration data can be found in this Zenodo <a href="https://zenodo.org/records/10278731" target="_blank" rel="noopener">repository</a>. </p> <p>Additionnal information on the fieldwork and the data analysis are described in the <a href="https://doi.org/10.31223/X5V955" target="_blank" rel="noopener">preprint</a>.</p>
Data from: Rhode Island wildlife camera trap survey 2018 to 2023
<p>Camera trap detections from a statewide survey of Rhode Island wildlife conducted between 2018 and 2023. </p> <p>This dataset contains two .CSV files. "RI_CameraSurvey_Deployments.csv" contains the camera operation dates (start and end dates), and coordinates for all cameras during each survey season. "RI_CameraSurvey_Detections.csv" contains all independent detections of animals at a camera location (Site and camera identifiers, species identification, data and time of detection). The station and camera identifications are consistent between the deployment table and the detection table. </p> <p>Version 2 includes additional fields in "RI_CameraSurvey_Detections.csv" to specify taxonomic Order, Class, and Family.</p> <p>Version 3 "RI_CameraSurvey_Detections.csv" contains all detections of animals (i.e. a row of data for each image captured) at a camera location. Both files include identifiers for the primary survey location and the specific camera location. An additional field for YearSeason is included in both files.</p>
Estimating wolf density from cameras
<p>DNA recapture data used to estimate wolf density in three study areas in Idaho, USA 2016-2018. </p>
MicroED datasets of biotin collected on Titan Krios G4 operated at 300kV and Ceta-D camera
<p>MicroED datasets of biotin microcrystals were collected using Titan Krios G4 (300 kV) with the CMOS camera Ceta-D. The stage was controlled using SerialEM and diffraction images were independently collected using Velox software. For this reason, only frames with constant rotation speed should be used for data processing.</p> <p>Rotation step (continuous) was ~0.96°/frame and each dataset consisted of ~63 images (tilt range is +/-30°). The calibrated camera lengths using <a href="https://www.tedpella.com/calibration_html/TEM_STEM_Test_Specimens.htm">evaporated aluminum</a> were 751.09 mm, 952.85 mm, and 1075.09 mm, corresponding to nominal lengths of 430 mm, 540 mm, and 610 mm, respectively. Biotin crystals belonged to space group <em>P</em>2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with a~5.2, b~10.2, c~20.8 Å, and could be merged at ~0.6 Å resolution.</p> <p>Collection conditions:</p> <ul> <li>gun lens 5, spot 11, C2 aperture 20, beam size 1.5 μm, 0.033 e/Å<sup>2</sup>/sec</li> </ul> <p>Note:</p> <ul> <li>emd files (hdf5 format) were transparently compressed using h5repack -f SHUF -f GZIP=4 command to reduce file size.</li> <li>EMD file can be processed with DIALS using <a href="https://github.com/keitaroyam/yamtbx/blob/master/dxtbx_formats/FormatEMD.py">this dxtbx format</a> file.</li> <li>Metadata (machine parameters, stage tilt angles etc.) is stored as json format in /Data/Image/*/Metadata in emd file. See <a href="https://github.com/keitaroyam/yamtbx/wiki/EMD-file">here</a> for details.</li> <li>If you want to process data using DIALS, please see <a href="https://github.com/keitaroyam/yamtbx/wiki/Processing-biotin-MicroED-data-(Krios-and-CetaD)">the processing note</a>.</li> </ul>
Phenological time lapse images from ground camera MC126 in Lammi Birch stand
<p>This record contains phenological time lapse images from camera Lammi Birch stand. Camera was mounted at ground view level at location 61.05211; 25.04180(N;E, WGS84).</p> <p>First set of images were taken between 11.12.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact john.loehr@helsinki.fi</p>
Phenological time lapse images from ground camera MC116 in Kenttärova Spruce stand
<p>This record contains phenological time lapse images from camera Kenttärova Spruce stand. Camera was mounted at ground view level at location 67.987283;24.242983(N;E, WGS84).</p> <p>First set of images were taken between 07.04.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact mika.aurela@fmi.fi</p>
Phenological time lapse images from crown camera MC122 in Lammi Birch stand
<p>This record contains phenological time lapse images from camera Lammi Birch stand. Camera was mounted at crown view level at location 61.05211; 25.04180(N;E, WGS84).</p> <p>First set of images were taken between 08.05.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact john.loehr@helsinki.fi</p>
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></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.