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81 results for “sonar”
Data from: Abundance, survival, and annual rate of change in Cuvier's beaked whales (Ziphius cavirostris) on a Navy sonar range
<p>Bayesian mark-recapture estimates of survival, abundance, and trend are reported for Cuvier's beaked whales (<em>Ziphius cavirostris</em>) using a Navy training range off southern California. The deep-diving beaked whale family is exceptionally vulnerable to mid-frequency active sonar (MFAS), which has been implicated in mass strandings and altered foraging behavior. Extremely low sighting probabilities impede studies of population-level impacts of MFAS on beaked whales. The San Nicolas Basin hosts a Navy training range subject to frequent MFAS use and attracts high densities of <em>Z. cavirostris</em>. An eleven-year (2007–2018) photo-identification program leveraged automated acoustic detection and location capabilities on the range's 1,800-km<sup>2</sup> hydrophone array to enhance capture probability. Estimated population parameters for <em>Z. cavirostris </em>using the range included mean (90% credibility intervals) apparent annual survival of 0.950 (0.899–0.986), the annual number of individuals as 121 (71–219), and the annual rate of change of -0.8% (-5.6 – 4.1%). Simulations show the probability of detecting abundance changes is currently low, but can be greatly improved through continued monitoring and increased effort. Complementary data collection on habitat use and demographic rates in San Nicolas and surrounding basins is also essential to relating the direct effects of MFAS use to changes in vital rates and broader population outcomes.</p>
Behavioral response of delphinids to sonar - supporting acoustic data
<p>Oceanic delphinids around naval operations are regularly exposed to intense military sonar broadcast within the frequency range of their hearing. However, empirically measuring the impact of sonar on the behavior of highly social, free-ranging dolphins is challenging. Additionally, baseline variability or the frequency of vocal state-switching among social oceanic dolphins during undisturbed conditions is lacking, making it difficult to attribute changes in vocal behavior to anthropogenic disturbance. Using a network of drifting acoustic buoys in controlled exposure experiments, we investigated the effects of mid-frequency (3-4 kHz) active sonar (MFAS) on whistle production in short-beaked (<em>Delphinus delphis delphis</em>) and long-beaked common dolphins (<em>Delphinus delphis bairdii</em>) in southern California. Given the complexity of acoustic behavior exhibited by these group-living animals, we conducted our response analysis over varying temporal windows (10 min – 5 s) to describe both longer-term and instantaneous changes in sound production. We found that common dolphins exhibited acute and pronounced changes in whistle rate in the 5 s following exposure to simulated Navy MFAS. This response was sustained throughout sequential MFAS exposures within experiments simulating operational conditions, suggesting that dolphins may not habituate to this disturbance. These results indicate that common dolphins exhibit brief yet clearly detectable acoustic responses to MFAS. They also highlight how variable temporal analysis windows – tuned to key aspects of baseline vocal behavior as well as experimental parameters related to MFAS exposure – enable the detection of behavioral responses. We suggest future work with oceanic delphinids explore baseline vocal rates a-priori and use information on the rate of change in vocal behavior to inform the analysis time window over which behavioral responses are measured.</p>
The accuracy of length measurements made using imaging SONAR is inversely proportional to the beam width
<p>Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz, and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm, and at ranges between 1 m and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beam width. The measurement error can be reduced by normalising for beam width. However, the variation of measurements (i.e. the precision), was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beam width on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beam width that produces an estimated level of error that is acceptable for that study. Keywords: acoustic camera, imaging SONAR, underwater acoustics, underwater measurements.</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>
Vulcano Summer School 2018 - Sonar Data -
<p>This dataset of Humminbird Sonar data was acquired during the Vulcano Summer School in June 2018. It consists of four folders with data acquired on 14th, 15th, 16th, 19th and 20th June. The data format is the standard Humminbird DAT file format that can be read by a variety of software programs such as <a href="https://reefmaster.com.au/">ReefMaster</a>. The data was acquired to map regions of high seafloor gas seepage and extensions of onshore slope failure structures into the shallow waters.</p> <p>The fourth International Summer School was on Vulcano (Eolian Islands, Italy) in J<strong>une 2018. </strong>The Summer School brought together experts from space and deep-sea research jointly developing technologies for the exploration of highly inaccessible terrain, such as the deep sea and Polar Regions, as well as the Moon and other planets. The summer school is an extension and continuation of the very successful previous three Summer Schools which were supported by the <a href="https://www.helmholtz.de/">Helmholtz Alliance</a> <em><a href="http://www.robex-allianz.de/en/">Robotic Exploration of Extreme Environments (ROBEX).</a> More information at http://robexsummerschool.de/</em></p>
SWDD: Sonar Wall Detection Dataset
<p>This repository contains three side scan sonar datasets:</p> <ul> <li><strong>SWDD: Sonar Wall Detection Dataset,</strong> which is part of the paper <a href="https://arxiv.org/abs/2403.09313"><strong>Knowledge Distillation in YOLOX-ViT for Side Scan Sonar Object Detection</strong></a>. </li> <li><strong>SWDD-Validation </strong>is an extension version and<strong> </strong>part of the paper <strong><a href="https://arxiv.org/abs/2410.10554">ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</a></strong>.</li> <li><strong>SWDD-Adversarial</strong> is another extended version and<strong> </strong>part of the paper <a href="https://arxiv.org/abs/2410.10554"><strong>ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</strong></a>.</li> </ul> <p><strong>SWDD Dataset</strong></p> <p>SWDD has been recorded with a <em>lightweight autonomous underwater vehicle</em> (LAUV), operated by OceanScan-MST and carrying a Klein 3500 side scan sonar (SSS). The AUV has been deployed in the Porto de Leixões harbor following the harbor's walls while collecting SSS raw data. The SSS operated at a high frequency of 900kHz with a range of 75m for a total range of 150m from port to starboard. This setup produced a resolution of 4.168 pixels per line. The data were processed using Neptus software, transforming the raw data into waterfall images. The 216 images have been manually annotated with two different classes, <em>wall</em> and <em>noWall</em>. Data augmentation such as noise, flips, and combined noise-flip transformations have been applied, increasing the data to 864 images. Across the 864 images, there are a total of 2,616 labeled samples. To ensure robust training, with respect to the data quality, the original dataset has been mixed with the augmented images. The dataset is divided into 70% for training, 15% for validation, and 15% for testing. The authors chose to generate images with 500 lines, meaning the images have a resolution of 4.168x500. Finally, the images have been resized to 640 × 640 to use this data in specific computer vision algorithms. The dataset is annotated following the COCO annotation format.</p> <p>YOLOX and YOLOX-ViT have been trained and compared using the SWDD dataset. A 6-minute 57-second video from another survey was used for model comparison. From this video, 6243 frames have been extracted with its manually annotated ground truth. </p> <p>Thus, this dataset repository offers an SSS dataset, a 6-minute 57-second SSS video, and 6243 extracted frames from this video with its manually annotated ground truth. </p> <p>The Knowledge Distillation in YOLOX-ViT code using the SWDD dataset is publicly available at <a href="https://github.com/remaro-network/KD-YOLOX-ViT">KD-YOLOX-ViT.</a></p> <p><strong>For (re-)using/publishing SWDD-Validation, please include the following copyright text:</strong></p> <p><em><strong>SWDD</strong> is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> <p> </p> <p><strong>⚠️The two following datasets (SWDD-Validation and SWDD-Adversarial) are parts of the paper <em>ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection</em>, submitted to the 2025 IEEE Symposium on Maritime Informatics & Robotics (MARIS 2025). The paper is still under review.<em> </em>⚠️</strong></p> <p> </p> <p><strong>SWDD-Validation</strong></p> <p>The SWDD dataset has been recorded using the LAUV with the Klein 3500 side scan sonar deployed in the Porto de Leixões harbor following the harbor's walls. The SWDD-Validation comports three field-collected detected: <em>SWDD-Clean</em>, <em>SWDD-Surface</em>, and <em>SWDD-Noisy</em>. The SWDD-Clean dataset, which includes data from the same mission as the original SWDD dataset; the SWDD-Surface dataset, captured while the LAUV was on the surface during windy weather, featuring a non-straight wall and wave-induced variations; and the SWDD-Noisy <em>dataset,</em> collected under stormy conditions, where the SSS transducer intermittently exited the water, resulting in data loss represented by black lines in the images.</p> <p>The SWDD-Validation dataset provides three datasets collected under different weather and sonar setups aiming to study object detection models' robustness variation under different sonar and noise conditions. The metadata of the SWDD-Validation dataset is depicted in the following table.</p> <div> <div> <p> </p> <table> <tbody> <tr> <td><em>Dataset</em></td> <td><em># Image</em></td> <td><em># Bbox</em></td> <td><em>Freq. (kHz)</em></td> <td><em>Range (m)</em></td> <td><em>Resolution</em></td> </tr> <tr> <td>SWDD-Clean</td> <td>148</td> <td>248</td> <td>900</td> <td>50</td> <td>4168 x 500</td> </tr> <tr> <td>SWDD-Surface</td> <td>98</td> <td>153</td> <td>900</td> <td>75</td> <td>6552 x 500</td> </tr> <tr> <td>SWDD-Noisy</td> <td>551</td> <td>800</td> <td>455</td> <td>100</td> <td>4168 x 500</td> </tr> </tbody> </table> </div> <div> </div> <div> <p><strong>For (re-)using/publishing SWDD-Validation, please include the following copyright text:</strong></p> <p><em><strong>SWDD-Validation</strong> is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> </div> <div> </div> <div> </div> <div><strong>SWDD-Adversarial</strong></div> <div> </div> <div> <p>The SWDD-Adversarial dataset has been generated by the ROSAR framework proposed in the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection, which, by leveraging adversarial PGD and Patch attack on the SWDD dataset, generates the three following datasets: P1-SWDD, P2-SWDD, and Patch-SWDD. The ROSAR framework is publicly available in our GitHub [<a href="https://github.com/remaro-network/ROSAR-framework">repository</a>].</p> <p><strong>P1 and P2-SWDD</strong> datasets result from adversarial PGD attacks, each resulting from different safety properties (<em>P1</em> and <em>P2</em>), resulting in 1017 and 1462 images for <em>P1</em> and <em>P2</em>. </p> <p><strong>Patch-SWDD</strong> dataset results from the adversarial patch attack, resulting in 151 images provided by the following repository: <a href="https://github.com/SamSamhuns/yolov5_adversarial" target="_blank" rel="noopener">Patch Attack.</a></p> </div> <div> </div> <div><strong>For (re-)using/publishing SWDD-Adversarial, please include the following copyright text:</strong></div> <div> <p><em><strong>SWDD-Adversarial</strong> is a public dataset based on the original SWDD dataset, which was collected using a Light Autonomous Underwater Vehicle by Oceanscan-MST and is within the scope of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> </div> </div>
e2e-Dutch model trained on Sonar-1
<p>This is a model to be used with the <a href="https://zenodo.org/badge/latestdoi/276878416">e2e-Dutch</a> code, trained on the <a href="https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/">SoNaR corpus</a>.</p> <p> </p>
Raw dataset for "A Bio-Inspired SONAR System for Autonomous Flying Drones".
<p>Created on Mon Sep 26, 2023<br> Written by Yasufumi Yamada</p> <p>This repository was uploaded for the original article entitled <br> "A Bio-Inspired SONAR System for Autonomous Flying Drones".</p> <p>This repository includes the raw dataset for Fig.1, Fig.2, and Fig.3.</p> <p>Note that, the ".data" format can be read by changing the format name to ".txt".</p> <p>all "chk_" files indicate signal data which was recorded with a sampling frequency of 1MHz. <br> chk_rxwv : time amplitude signal of the received sound.<br> chk_txwv : digital time amplitude signal for emission.<br> chk_env : Cross-correlation signal processed with received sound and digital emission sound. <br> Each data was conducted the Hilbert transform to extract the envelope of the cross-correlation signal. </p>
Large differences in the distribution of pelagic biomass as a result of sonar frequency choice
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Data from: Abundance, survival, and annual rate of change in Cuvier's beaked whales (Ziphius cavirostris) on a Navy sonar range
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Data from: Sonar estimation of summer chum and pink salmon in the Anvik River, Alaska, 2018
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Behavioral response of delphinids to sonar - supporting acoustic data
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The accuracy of length measurements made using imaging SONAR is inversely proportional to the beam width
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Data for: Modeling short-term energetic costs of sonar disturbance to cetaceans using high resolution foraging data
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Data from: First direct measurements of behavioural responses by Cuvier's beaked whales to mid-frequency active sonar
Most marine mammal strandings coincident with naval sonar exercises have involved Cuvier's beaked whales (Ziphius cavirostris). We recorded acoustic and animal movement data on two tagged Ziphius and obtained the first direct measurements of behavioural responses of this species to mid-frequency active (MFA) sonar signals. Each recording included a 30-minute playback (one 1.6-s simulated MFA sonar signal repeated every 25 s); one whale was also incidentally exposed to MFA sonar from distant naval exercises. Whales responded strongly to playbacks at low received levels (89-127 dB re1µPa): after ceasing normal fluking and echolocation, they swam rapidly, silently away, extending both dive duration and subsequent non-foraging interval. Distant sonar exercises (78-106 dB re 1 µPa) did not elicit such responses, suggesting that context may moderate reactions. The observed responses to playback occurred at received levels well below current regulatory thresholds; equivalent responses to operational sonars could elevate stranding risk and reduce foraging efficiency.
Data from: Sperm whales reduce foraging effort during exposure to 1-2 kHz sonar and killer whale sounds
The time and energetic costs of behavioral responses to incidental and experimental sonar exposures, as well as control stimuli, were quantified using hidden state analysis of time series of acoustic and movement data recorded by tags (DTAG) attached to 12 sperm whales (Physeter macrocephalus) using suction cups. Behavioral state transition modeling showed that tagged whales switched to a non-foraging, non-resting state during both experimental transmissions of low-frequency active sonar from an approaching vessel (LFAS; 1–2 kHz, source level 214 dB re 1 μPa m, four tag records) and playbacks of potential predator (killer whale, Orcinus orca) sounds broadcast at naturally occurring sound levels as a positive control from a drifting boat (five tag records). Time spent in foraging states and the probability of prey capture attempts were reduced during these two types of exposures with little change in overall locomotion activity, suggesting an effect on energy intake with no immediate compensation. Whales switched to the active non-foraging state over received sound pressure levels of 131–165 dB re 1 μPa during LFAS exposure. In contrast, no changes in foraging behavior were detected in response to experimental negative controls (no-sonar ship approach or noise control playback) or to experimental medium-frequency active sonar exposures (MFAS; 6–7 kHz, source level 199 re 1 μPa m, received sound pressure level [SPL] = 73–158 dB re 1 μPa). Similarly, there was no reduction in foraging effort for three whales exposed to incidental, unidentified 4.7–5.1 kHz sonar signals received at lower levels (SPL = 89–133 dB re 1 μPa). These results demonstrate that similar to predation risk, exposure to sonar can affect functional behaviors, and indicate that increased perception of risk with higher source level or lower frequency may modulate how sperm whales respond to anthropogenic sound.
Data and Code for Sibley et al. Baited Imaging Sonar (BISON)
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Data from sonar beam pattern measurements and modeling of free-flying Rousettus aegyptiacus
<p>Concurrent broadband microphone and high-speed video camera recordings of free-flying Egyptian fruit bat (<em>Rousettus aegyptiacus</em>). The experiment was designed to record and reconstruct broadband, two-dimensional (azimuth and elevation in a bat-centered coordinate system) beam pattern from this interesting lingual-echolocating bat species. These bats produce alternating left- and right-pointing clicks as pairs during navigation and target localization, with very short inter-click intervals (~20 msec) within each pair.</p>
Co-occurrence of beaked whale strandings and naval sonar in the Mariana Islands, Western Pacific
<p>Mid-frequency active sonar (MFAS), used for antisubmarine warfare (ASW), has been associated with multiple beaked whale (BW) mass stranding events. Multinational naval ASW exercises have utilized MFAS offshore of the Mariana Archipelago semi-annually since 2006. We report BW and MFAS acoustic activity near the islands of Saipan and Tinian from March 2010 through November 2014. Signals from Cuvier's (<i>Ziphius cavirostris)</i> and Blainville's beaked whales (<i>Mesoplodon densirostris</i>), and a third unidentified BW species were detected throughout the recording period. Both recorders documented MFAS on 21 August 2011 before two Cuvier's beaked whales stranded on 22-23 August 2011. We compared the history of known naval operations and BW strandings from the Mariana Archipelago to consider potential threats to BW populations. Eight BW stranding events between June 2006 and January 2019 each included 1-3 animals. Half of these strandings occurred during, or within 6 days after naval activities, and this co-occurrence is highly significant. We highlight strandings of individual BWs can be associated with ASW, and emphasize the value of ongoing passive acoustic monitoring, especially for beaked whales that are difficult to visually detect at sea. We strongly recommend more visual monitoring efforts, at sea and along coastlines, for stranded cetaceans before, during, and after naval exercises.</p>
Underwater caves sonar data set
<p>Data set collected with an autonomous underwater vehicle testbed in the unstructured environment of an underwater cave complex. The vehicle is equipped with two mechanically scanned imaging sonar sensors to simultaneously map the caves horizontal and vertical surfaces, a Doppler velocity log, two inertial measurement units, a depth sensor, and a vertically mounted camera imaging the sea floor for ground truth validation at specific points. The testbed collected the data in July 2013, guided by a human diver, to sidestep autonomous navigation in a complex environment. For ease of use, the original robot operating system bag files are provided together with a version combining imagery and human-readable text files for processing on other environments.</p> <p>This dataset was described in:<br> Mallios A, Vidal E, Campos R, Carreras M. Underwater caves sonar data set. The International Journal of Robotics Research. 2017;36(12):1247-1251. doi:10.1177/0278364917732838</p> <p>Web: <a href="https://cirs.udg.edu/caves-dataset/">https://cirs.udg.edu/caves-dataset/</a></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.