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1,832 results for “Cameras”
Polarisation camera movie of single SYTOX Orange molecules on a cover glass
<p>This image dataset is a movie of the fluorescence of single SYTOX Orange molecules (S34861, Invitrogen) dispersed on a cover glass. The data was collected on a fluorescence microscope (Ti-U, Nikon) with a polarisation camera (CS505MUP, Thorlabs). The molecules were excited with a 532 nm diode laser with a measured power density at the sample plane of 0.36 kW/cm^2. An exposure time of 100 ms was used. The following filters were used: dichroic (Di03-R532-t1, Semrock) and emission filter (FF01-582/64, Semrock).</p>
Data from: Camera traps reveal seasonal variation in activity and occupancy of the Alpine mountain hare (Lepus timidus varronis)
<p>Mountain hare is a cold-adapted species threatened by climate change, but despite its emblematic nature, our understanding of the causes of population decline remains limited. Camera traps are increasingly used in ecology as a tool for monitoring animal populations at large spatial and temporal scales. In mountain environments where field work is constrained by difficult access and harsh conditions, camera traps constitute a promising tool for surveying rare and elusive species such as the mountain hare. Our study explored the use of camera traps as a tool for studying seasonal habitat occupancy and daily activity patterns of the mountain hare, in order to carry out long-term monitoring of populations. We installed 46 camera traps along elevation gradients in the Mont-Blanc massif (France) from January 2018 to June 2022. We measured habitat variables at each camera trap site in order to define vegetation composition and habitat structure. We performed multi-season and single-season occupancy models to respectively describe habitat occupancy of the mountain hare throughout the year and identify the environmental variables influencing mountain hare presence during the breeding season. Mountain hares occupy coniferous forest in winter, and then switch to mixed areas of shrubland and grassland above treeline in spring and the beginning of summer. In spring, occupancy probability of the mountain hare increases with relative cover of mixed low shrub and herbaceous layer (i.e. the 10-40 cm vegetation layer), suggesting a link to food resources and protection from predation. Our results also confirm the nocturnal and crepuscular activity of the mountain hare during the breeding season, and strictly nocturnal activity in winter. Our results demonstrate the efficiency of camera traps as tools for monitoring mountain hare habitat occupancy in mountain environments and underline the importance of diverse habitat mosaics for the preservation of the species.</p>
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
High-Speed Camera Videos of Electrical Arc During Hot Switching Tests
<p>This repository contains high-speed camera videos showing the ignition, evolution, and extinguishing of electrical arcs generated by a single break operation. The videos were acquired to obtain a better understanding of the phenomenon of primary arcing prior to the ignition of the main arc. The electrical arcs were generated by a break operation (hot switching) between two electrodes. The static electrode is a hard-gold coated silver-nickel core rivet. The moving electrode consists of different electrical contact materials. Namely: pure silver and copper electrodes (obtained from high-purity rod material, Ag rod and Cu rod), powder metallurgically sintered silver and copper reference samples (Ag0 and Cu0), as well as carbon nanotube reinforced metal matrix composites (Ag1, Ag2, Ag, Cu1, Cu2, and Cu3, where the number indicates the weight percentage of carbon nanotubes). Well-established reference materials were also recorded, i.e., Ag-Ni 90-10, Ag-SnO2 92-8, Ag-SnO2 90-10, and Ag-SnO2 88-12. The electrical load consisted of four 50 W conventional automotive halogen lamps (direct current).</p>
Polarisation camera movies of T cells on a cover glass
<p>The plasma membrane of live T cells (J8 LFA-1) was labelled using the Nile red-derivative NR4A, and 3D movies were recorded using a polarisation camera. Two unprocessed datasets are included here:</p> <ul> <li><strong>Tcell_NR4A_EPI_stack</strong>: A 3D stack of a single T cell, imaged with the excitation in EPI configuration.</li> <li><strong>Tcell_NR4A_HILO_stack_movie</strong>: A 3D movie of live T cells interacting with an OKT3 antibody-coated cover glass, imaged with the excitation in HILO configuration.</li> </ul> <p>For each .tif file, the corresponding Micro-Manager metadata file is included.</p> <p><strong>Optical setup:</strong></p> <p>Imaging was performed on a widefield microscope equipped with a polarisation camera (CS505MUP, Thorlabs). The sample was excited using a 515 nm laser, coupled into a square-core multi-mode fiber (M97L02, Thorlabs) with a custom vibration-motor-based mode scrambler. The measured power density at the image plane was on the order of 0.004 kW/cm^2. A dichroic (Di03-R514-t1, Semrock) was used to seperate fluorescence from the excitation. The emission was filtered using a long-pass filter (FF01-515/LP, Semrock) and bandpass filter (FF01-650/200, Semrock) before detection.</p> <p><strong>Sample preparation:</strong></p> <p>J8 LFA-1 cells were incubated overnight (~18 h) in complete-RPMI (StableCell RPMI-1640 media (Sigma) supplemented with 10 % (v/v) fetal calf serum (FCS), 1 % (v/v) HEPES buffer, and 1 % (v/v) pen/strep antibiotics. 1 mL of cells were collected by centrifugation and resuspended in phenol-red free RPMI supplemented with 1 % HEPES.</p> <p>Round coverslips were rinsed with IPA, MilliQ, dried, and Ar-plasma cleaned for 20 minutes. Grace Bio-Labs CultureWells were attached, and the slide was incubated with OKT3 antibody (provided by the Human Immunology Unit, WIMM, Oxford) for 30 minutes. The slide was washed 5 times with phenol-red free RPMI supplemented with 1 % HEPES and a final wash with phenol-red free RPMI supplemented with 1 % HEPES and 200 nM NR4A before imaging.</p>
Data and Code from: Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
<p>This record contains the data used in the paper, "Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO", and a link to the corresponding code for calibration of camera networks. The full method and a description of the dataset is given in the companion paper, currently on arXiv: https://arxiv.org/abs/1903.06811 .</p><p>And the code is on Github: https://github.com/amy-tabb/calico</p><p> </p>
Data from: Shooting area of infrared camera traps affects recorded taxonomic richness and abundance of ground-dwelling invertebrates
<p>Ground-dwelling invertebrates are vital for soil biodiversity and function maintenance. Contemporary biodiversity assessment necessitates novel and automatic monitoring methods because of the threat of sharp reductions in soil biodiversity in farmlands worldwide. Using infrared camera traps (ICTs) is an effective method for assessing richness and abundance of ground-dwelling invertebrates. However, the influence that the shooting area of ICTs has on the diversity of ground-dwelling invertebrates has not been strongly considered during survey design. In this study, data from 6 ICTs with two shooting areas (A1, 38.48 cm<sup>2</sup>; A2, 400 cm<sup>2</sup>) were used to investigate ground-dwelling invertebrates in a farm in a city on the Eastern Coast of China from 20:00 on July 31 to 00:00 on September 29, 2022. Over the course of 59 days and 1,420 h, invertebrates within 9 taxa, 2,447 individuals, and 112,909 ind./m<sup>2</sup> were observed from 222,912 images. Our results show that ICTs with relatively large shooting areas recorded relatively high taxonomic richness and abundance of total ground-dwelling invertebrates, relatively high abundance of the dominant taxon, and relatively high daily and hourly abundance of most taxa. The shooting areas of ICTs significantly affected the recorded taxonomic richness and abundance of ground-dwelling invertebrates throughout the experimental period and at fine temporal resolutions. Overall, these results suggest that the shooting areas of ICTs should be considered when designing experiments, and ICTs with relatively large shooting areas are more favorable for monitoring the diversity of ground-dwelling invertebrates. This study further provides an automatic tool and high-quality data for biodiversity monitoring and protection in farmlands.</p>
AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea
<p><strong>Context</strong></p> <p>This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles.</p> <p><strong>Content</strong></p> <p>This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework.</p> <p>A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters.</p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities.</p> <p>By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles.</p> <p><strong>Technical Details</strong></p> <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> <p>More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
PM_110143_Antique_Photo_camera
<u>File Name</u>: PM_110143_Antique_Photo_camera.jpg <br><u>Sublocation</u>: None <br><u>Location</u>: None <br><u>Province</u>: None <br><u>Country</u>: None <br><u>Header</u>: Antiek fototoestel, Zeiss-Ikon, 6x9cm; Privécollectie Paul M.R; Maeyaert; <br><u>Description</u>: Photo camera Zeiss-Ikon, 6x9cm Private collection Paul M. R Maeyaert <br><u>Keywords</u>: Antique photo camera/cinema, Cultural heritage, Museum/private collection, Thematic, privé, privé collecties <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_110133_Antique_Photo_camera
<u>File Name</u>: PM_110133_Antique_Photo_camera.jpg <br><u>Sublocation</u>: None <br><u>Location</u>: None <br><u>Province</u>: None <br><u>Country</u>: None <br><u>Header</u>: Antiek fototoestel, Kodak, Retina Reflex S (type 034), 1959-1960; Privécollectie Paul M.R; Maeyaert; <br><u>Description</u>: Photo camera Kodak, Retina Reflex S (type 034), 1959-1960 Private collection Paul M. R Maeyaert <br><u>Keywords</u>: Antique photo camera/cinema, Cultural heritage, Museum/private collection, Thematic, privé, privé collecties <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
MicroED/3DED datasets of (E)-3,3'-dioxo-[2,2'-biindolinylidene]-1-carbaldehyde captured with a Ceta camera on Talos Arctica
<p>Continuous rotation data sets were collected on a Talos Arctica (200 kev) equipped with a ceta camera. Datasets were collected from 4 different crystals. Each dataset was collected with an approximate flux of 0.0895 e-/ Å^2/s-1 which means a total exposure of 10.74 e-/Å^2 for a 120-degree wedge of data. Delphi was used to rotate the crystal while TEM Imaging & Analysis (TIA) software was used to collect the data.Datasets were were processed in DIALS directly without any conversion.</p> <p>Experimental parameters are as follows:</p> <p>Accelerating voltage: 200 Kev </p> <p>Wavelength: 0.02508 </p> <p>Cameralength: 1004 mm </p> <p>Exposure time: 1 s </p> <p>Rotation speed: 1 degrees per second </p> <p>Rotation: -60 to 60 degrees </p> <p>Detector area: 2048 x 2048 (Bin 2) </p> <p>Pixel size: 0.028 nm </p> <p>Spot size: 9 </p> <p>C2 aperture: 50 µm </p> <p>SA aperture: 40 µm </p> <p>Notes: The data collection and processing for this sample was originally done in July 2023.</p> <p> </p>
All-sky camera images from Kjell Henriksen Observatory (Svalbard) on January 7th, 2016 05:45-07:30 UT
<p>This dataset contains the images from the Sony A7S All-Sky camera located at the Kjell Henriksen Observatory operated by the University Centre in Svalbard (UNIS) on 2016-01-07 05:45-07:30 UT. The camera provides all-sky color images. The images files are in the jpg format.</p>
MicroED/3DED datasets of acetaminophen collected with a Ceta camera on Talos Arctica
<p>Continuous rotation data sets were collected on a Talos Arctica (200 kev) equipped with a ceta camera. Each dataset was collected with an approximate flux of 0.17 e-/ Å<sup>2</sup>/s<sup>-1</sup> which means a total exposure of 20.4 e-/Å<sup>2</sup> for a 120-degree wedge of data. Delphi was used to rotate the crystal while TEM Imaging & Analysis (TIA) software was used to collect the data.</p> <p>Datasets were processed in DIALS directly without any conversion. The structure was solved to 0.84 Å with 99% of overall completeness by merging two datasets (attached).</p> <p>Experimental parameters are as follows:</p> <p>Accelerating voltage: 200 Kev </p> <p>Wavelength: 0.02508 </p> <p>Cameralength: 1012 mm </p> <p>Exposure time: 1 s </p> <p>Rotation speed: 1 degrees per second </p> <p>Rotation: 60 to -60 degrees </p> <p>Detector area: 4096 x 4096 (Bin 1) </p> <p>Pixel size: 14 µm </p> <p>Spot size: 8 </p> <p>C2 aperture: 50 µm </p> <p>SA aperture: 40 µm </p>
MicroED/3DED datasets of carbamazepine collected with a Ceta camera on Talos Arctica
<p>Continuous rotation data sets were collected on a Talos Arctica (200 kev) equipped with a ceta camera. Each dataset was collected with an approximate flux of 0.17 e-/ Å<span><sup>2</sup></span>/s<span><sup>-1</sup></span> which means a total exposure of 20.4 e-/Å<span><sup>2</sup></span> for a 120-degree wedge of data. Delphi was used to rotate the crystal while TEM Imaging & Analysis (TIA) software was used to collect the data. Datasets were processed in DIALS directly without any conversion.</p> <p>Experimental parameters are as follows:</p> <p>Accelerating voltage: 200 Kev </p> <p>Wavelength: 0.02508 </p> <p>Cameralength: 1012 mm </p> <p>Exposure time: 1 s </p> <p>Rotation speed: 1 degrees per second </p> <p>Rotation: 60 to -60 degrees </p> <p>Detector area: 4096 x 4096 (Bin 1) </p> <p>Pixel size: 14 µm </p> <p>Spot size: 8 </p> <p>C2 aperture: 50 µm </p> <p>SA aperture: 40 µm </p>
The processing results, dataset and original codes of Coral reef PtCloud segmentation model (based on proposed underwater camera systems)
<p><strong># Data Introduction</strong><br>The proposed camera system includes three operation modes: Surface Mode, Horizontal Mode, and Curved Mode.</p> <ul> <li><strong>Surface Mode</strong>: Provides 3D reconstructions (point cloud), DEM, and orthophoto maps of the seabed (covering a line of 70m in length).</li> <li><strong>Horizontal Mode</strong>: Provides 3D reconstructions (point cloud and mesh) and orthophoto maps of a single transect line.</li> <li><strong>Curved Mode</strong>: Offers 3D reconstructions (mesh) of various targets, including artificial coral reefs, coral reefs with snails, and coral reefs with starfish.</li> </ul> <p>All mesh results are saved in <code>.FBX</code> format, and point cloud results are saved in <code>.TXT</code> format.</p> <p><strong># Code Introduction</strong><br>The backbone of our point cloud segmentation model is the KPConv model.<br>If you encounter any issues during code deployment, please refer to the original KPConv repository (<a href="https://github.com/HuguesTHOMAS/KPConv" target="_new" rel="noopener">Original Code: https://github.com/HuguesTHOMAS/KPConv</a>).<br>We provide our modified code (customized for our task) along with the complete point cloud dataset.</p>
Women Writing around the Camera Knowledge Graph
<p>The Women Writing around the Camera Knowledge Graph maps the personal and professional networks about Italian cinema divas' lives, focusing on the dynamics between actresses' writings and films in which they performed, along with a comprehensive taxonomy of concepts and keywords that encapsulate the key themes for studying the lives of Italian actresses.</p>
Estimating wolf abundance from cameras
<p>Detection histories for wolves derived from cameras in 3 study areas in Idaho, USA, 2016-2018. </p>
Evaluating Augmented Reality Light Probe Estimation Techniques using a Single Camera (Supplemental Material)
<p>This repository contains the dataset for the paper "Evaluating Single Camera Augmented Reality Light Probe Estimation Techniques".</p> <p>The data archive includes:</p> <ul> <li>The complete image set (3 scenes with 6 techniques and ground truth in 90 time points)</li> <li>SSIM data of the panoramas as a CSV file</li> <li>SSIM data of the rendered images as a CSV file</li> <li>Various graphs to visualize the data</li> <li>R files used to plot the graphs</li> </ul> <p>The data archive includes additional plots that are not included in the paper.</p>
Spectral reconstruction using iteratively reweighted-regulated model from two illumination camera responses
<p>This document shows spectral reflectance data and RGB values of 380 samples for spectral estimation, which includes 5 databases: 1) Spectral reflectance of ColorChecker semigloss chart (CCSG, 140 patches), 2) Spectral reflectance of ColorChecker DC matte chart (CCDC, 240 patches including 232 mattes, and 8 glossy patches), 3) RGB values of ColorChecker semigloss chart (CCSG, 140 patches) , 4) RGB values of ColorChecker DC matte chart (CCDC, 240 patches including 232 mattes, and 8 glossy patches), 5) Spectral Power Distribution value of 3500K & 6500K illumination conditions.</p>
Speckle image stacks acquired on human skin with a high-speed camera
<p>These datasets all consist of stacks of images of various body parts of of the same 50-year-old Caucasian male subject.</p> <p>For each acquisition, the skin was first illuminated with a near-infrared LASER (wavelength 785 nm). Then, the skin was filmed with a Phantom VEO 710L camera, with an acquisition rate of 2000 to 30,000 Hz.</p> <ul> <li>Datasets <strong>WRIST_1</strong>, <strong>WRIST_2</strong> and <strong>WRIST_3</strong> were acquired by imaging the anterior surface of the left wrist, with respective acquisition frequencies of 3000, 2000 and 10,000 Hz.</li> <li>Dataset <strong>FINGERS</strong> consists of images of the end of two fingers of the left hand. It was acquired at 30,000 Hz.</li> <li>Dataset <strong>EAR</strong> consists of images of the right ear. It was acquired at 2000 Hz.</li> <li>Dataset <strong>PALM</strong> consists of images of the palm of the left hand, which was gently scraped beforehand to trigger a superficial inflammation and draw a smiley face. It was then filmed with an acquisition frequency of 10,000 Hz.</li> </ul> <p>Each dataset is stored in a HDF5 file. The data array is stored as <strong>data </strong>at the root of the tree structure. Attributes <strong>T_exp </strong>and <strong>f_acq</strong> provide respectively the exposure time (in microseconds) and the acquisition rate (in Hertz) of the dataset. The script <strong>test.py </strong>shows how data can be accessed through Python and the library h5py. It can be used as follows:</p> <pre><code class="language-bash">python test.py filename.h5</code></pre> <p>All these datasets are used and referenced in our companion article "Dynamic speckle imaging of human skin vasculature with a high-speed camera" (to be published).</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.