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23 results for “mmWave”
mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
<p>The ability to estimate 3D human body pose and movement, also known as human pose estimation~(HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few datasets exploit multiple modalities and focus on home-based health monitoring.</p> <p>To bridge this gap, we present <em>mRI</em>, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 5 million frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality.</p> <p>We hope that the release of <em>mRI</em> can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly, facilitate the applications of home-based health monitoring.</p>
CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification
<p>This refers to the gan-generated dataset for the paper" CSI4Free: GAN-Augmented mmWave CSI for<br>Improved Pose Classification.</p> <p>Abstract:In recent years, Joint Communication and Sensing (JC&S), has demonstrated significant success, particularly in utilizing sub-6 GHz frequencies with commercial-off-the-shelf (COTS) Wi-Fi devices for applications such as localization, gesture recognition, and pose classification. Deep learning and the existence of large public datasets has been pivotal in achieving such results. However, at mmWave frequencies (30-300 GHz), which has shown potential for more accurate sensing performance, there is a noticeable lack of research in the domain of COTS Wi-Fi sensing. Challenges such as limited research hardware, the absence of large datasets, limited functionality in COTS hardware, and the complexities of data collection present obstacles to a comprehensive exploration of this field. In this work, we aim to address these challenges by developing a method that can generate synthetic mmWave channel state information (CSI) samples. In particular, we use a generative adversarial network (GAN) on an existing dataset, to generate 30,000 additional CSI samples. The augmented samples exhibit a remarkable degree of consistency with the original data, as indicated by the notably high GAN-train and GAN-test scores. Furthermore, we integrate the augmented samples in training a pose classification model. We observe that the augmented samples complement the real data and improve the generalization of the classification model.</p> <p>The repository is available here: https://github.com/nisarnabeel/Dataset-GAN-Augmented-mmWave-CSI-for-improved-pose-classification</p> <p> </p> <p>Paper Link:https://ieeexplore.ieee.org/document/10646223/</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition
<p>This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information. </p>
An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting
<p>This repository is related with the next paper. Please cite us if this code is useful to you.</p> <p>Barral, V., Dominguez-Bolano, T., Escudero, C. J., & Garcia-Naya, J. A. An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting.</p> <h2>Python Scripts</h2> <ul> <li><em>count_plot.py</em>: Generates a plot comparing image tracking estimation, radar with DBSCAN, and radar with OPTICS. Use example:</li> </ul> <pre><code>python count_plot.py count_video_full.log count_radar_moving_average_30_optics.log count_radar_moving_average_30_dbscan.log "Video" "Radar with OPTICS" "Radar with DBSCAN" average_count_full_optics_dbscan.pdf </code></pre> <h2>Radar measurements</h2> <ul> <li><em>test_0.bag</em>: FMCW mmWave radar measurements from three IWR6843 devices (ISK and AOP). Is a ROS (Robotic Operative System) log, can be played with <code>rosbag play test_0.bag</code></li> </ul> <h3>Radar people counting logs</h3> <ul> <li> <p><strong>count_radar_moving_average_30_dbscan.log</strong>: radar people counting estimation using a 30 seconds moving average and DBSCAN as clustering algorithm.</p> </li> <li> <p><strong>count_radar_moving_average_30_optics.log</strong>: radar people counting estimation using a 30 seconds moving average and OPTICS as clustering algorithm.</p> </li> </ul> <h2>Image tracking</h2> <h3>Videos</h3> <ul> <li><em>test_0_cam_0_anonymized.mp4</em>: Camera 0 capture.</li> <li><em>test_0_cam_1_anonymized.mp4</em>: Camera 1 capture.</li> </ul> <h3>Image tracking logs</h3> <ul> <li><em>test_0_cam_0_interp.txt</em>: People count in camera 0</li> <li><em>test_0_cam_1_interp.txt</em>: People count in camera 1</li> </ul> <p>The files have the following format:</p> <p>frame_number, track_id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z The coordinates x, y, z are always -1 The bb fields define the bounding box of the detection If multiple people are detected in a frame, there are several lines with that frame_number, but with different track_ids.</p> <h3>Image people counting logs</h3> <ul> <li><strong>count_video_full.log</strong>: image tracking people counting estimation using a 30 seconds moving average.</li> </ul>
Supplementary Data and Software for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance"
<p>The file includes supplementary data for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance". If you would like to re-use the software provided please cite the following two items.</p> <p>R. Klus, J. Talvitie, J. Equi, G. Fodor, J. Torsner, and M. Valkama, “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and<br>Performance,” IEEE Transactions on Vehicular Technology, 2024.</p> <p>R Klus et al. (2024). Supplementary materials for “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance”. version v1, 25.06.2024, [Online]. Available: https://doi.org/10.5281/zenodo.12204892</p> <p>If you have any questions about this package, please do not hesitate to contact Roman Klus (roman.klus@tuni.fi).</p>
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 1
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 6.20% to 43.82% with approximately 2% intervals. In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p>
Data-driven Soil Moisture Sensing with mmWave Radar
<p>This is a mmWave radar soil moisture dataset, which includes two different environments, specifically two conference rooms with different layouts. In Environment 1, soil with 20 different moisture levels was collected, ranging from 6.20% to 43.82%, with an interval of approximately 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p> <p>In Environment 2, soil with 20 different moisture levels was collected, ranging from10.0% to 48.0%, with an interval of 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.</p>
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 2
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 10.0% to 48.0% with approximately 2% intervals.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.</p>
KR, Vehicle quality of service support, Tethering via vehicle using mmWave communication
<p>Use Case Category: <strong>Vehicle quality of service support</strong><br> User Story: <strong>Tethering via vehicle using mmWave communication</strong><br> Location: Korean (KR) trial site</p> <p>According to 3GPP TS 22.186 R16, Vehicle quality of service support “enables a V2X application to be timely notified of expected or estimated change of quality of service before actual change occurs and to enable the 3GPP System to modify the quality of service in line with V2X application’s quality of service needs. Based on the quality of service information, the V2X application can adapt behaviour to 3GPP System’s conditions. The benefits of this use case group are offerings of smoother user experience of service”.</p> <p>User Story: <strong>Tethering via vehicle using mmWave communication</strong></p> <p>Tethering via Vehicle use case enables in-vehicle UEs and pedestrian UEs to access the network with the help of a vehicle relay which is deployed at a vehicle. For in-vehicle UEs, through Tethering via Vehicle use case, it is possible to avoid high penetration loss occurring from the metallic vehicle surface, thereby achieving more reliable wireless connectivity as well as reduced UE power consumption. The in-vehicle UEs are also benefited from the minimized handover operations. Only the vehicle relays involve in the handover operations. For pedestrian UEs, tethering via Vehicle use case enables more reliable connectivity, increased throughput and reduced UE power consumption since it reduces the communication range of the pedestrian UEs.</p> <p>As a deployment scenario for Tethering via Vehicle use case, a vehicle relay can have the Internet connectivity to the network through a base station (BS) including macrocell BS, microcell BS, and BS-type road side unit (RSU). Another UE such as UE-type RSU can also provide the Internet connectivity to the network. Non-terrestrial links such as satellite BS, satellite relay, and high-altitude platform (HAP) can also be used to provide the Internet connectivity to the network.</p> <p>Tethering via Vehicle use case generally supports eMBB-type services such as web surfing, FTP, and video streaming. Hence, it intrinsically requires high data throughput up to several Gbps. In order to satisfy such very high throughput, large bandwidth is necessary which is quite difficult in lower frequency bands below 6 GHz. Therefore, mmWave frequency band should be employed to support such high throughput and to satisfy the Tethering via Vehicle use case.</p>
mmWave-based Fitness Activity Recognition Dataset
<p><strong>Description:</strong></p><p>This mmWave Datasets are used for fitness activity identification. This dataset (FA Dataset) contains 14 common fitness daily activities. The data are captured by the mmWave radar TI-AWR1642. The dataset can be used by fellow researchers to reproduce the original work or to further explore other machine-learning problems in the domain of mmWave signals.</p><p><strong>Format: </strong>.png format</p><p><strong>Section 1: Device Configuration</strong></p><ul><li>A commodity mmWave radar TI AWR1642, which integrates a 2 × 4 antenna array. The detailed information of it can be found at <a href="https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz">https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz.</a></li><li>A TI DCA1000EVM data capture card is used to collect data from the mmWave device and send data to a laptop. The detailed information can be found at <a href="https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM">https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM</a>.</li><li>mmWave radar work at the frequency in the range of 77~81GHz. The sampling rate is fixed at 100 frames per second and each frame has 17 chirps.</li></ul><p><strong>Section 2: Data Format</strong></p><p>We provide our mmWave data in heatmaps for this dataset. The data file is in the png format. The details are shown in the following:</p><ul><li>14 activities are included in the FA Dataset.</li><li>2 participants are included in the FA Dataset.</li><li>FA_d_p_i_u_j.png:<ul><li>d represents the date to collect the fitness data.</li><li>p represents the environment to collect the fitness data.</li><li>i represents fitness activity type index</li><li>u represents user id</li><li>j represents sample index</li></ul></li><li>Example:<ul><li>FA_20220101_lab_1_2_3 represents the 3rd data sample of user 2 of activity 1 collected in the lab</li></ul></li></ul><p><strong>Section 3: Experimental Setup</strong></p><ul><li>We place the mmWave device on a table with a height of 60cm.</li><li>The participants are asked to perform fitness activity in front of a mmWave device with a distance of 2m.</li><li>The data are collected at an lab with a size of (5.0m×3.0m).</li></ul><p><strong>Section 4: Data Description</strong></p><ul><li>We develop a spatial-temporal heatmap to integrates multiple activity features, including the range of movement, velocity, and time duration of each activity repetition.</li></ul><p> </p><ul><li>We first derive the Doppler-range map of the users' activity by calculating Range-FFT and Doppler-FFT. Then, we generate the spatial-temporal heatmap by accumulating the velocity of every distance in every Doppler-range map together. Next, we normalize the derived velocity information and present the velocity-distance relationship in time dimension. In this way, we transfer the original instantaneous velocity-distance relationship to a more comprehensive spatial-temporal heatmap which describes the process of a whole activity.</li></ul><p> </p><ul><li>As shown in Figure attached, in each spatial-temporal heatmap, the horizontal axis represents the time duration of an activity repetition while the vertical axis represents the range of movement. The velocity is represented by color.</li></ul><p> </p><ul><li>We create 14 zip files to store the the dataset. There are 14 zip files starting with "FA", each contains repetitions from the same fitness activity.</li></ul><p>14 common daily activities and their corresponding files </p><p><strong>File Name Activity Type File Name Activity Type</strong></p><p>FA1 Crunches FA8 Squats</p><p>FA2 Elbow plank and reach FA9 Burpees</p><p>FA3 Leg raise FA10 Chest squeezes</p><p>FA4 Lunges FA11 High knees</p><p>FA5 Mountain climber FA12 Side leg raise</p><p>FA6 Punches FA13 Side to side chops</p><p>FA7 Push ups FA14 Turning kicks</p><p> </p><p><strong>Section 5: Raw Data and Data Processing Algorithms</strong></p><ul><li>We also provide the mmWave raw data (.mat format) stored in the same zip file corresponding to the heatmap datasets. Each .mat file can store one set of activity repetitions (e.g., 4 repetations) from a same user.<ul><li>For example: FA_d_p_i_u_j.mat:<ul><li>d represents the data to collect the data.</li><li>p represents the environment to collect the data.</li><li>i represents the activity type index</li><li>u represents the user id</li><li>j represents the set index</li></ul></li></ul></li><li>We plan to provide the data processing algorithms (heatmap_generation.py) to load the mmWave raw data and generate the corresponding heatmap data.</li></ul><p><strong>Section 6: Citations</strong></p><p>If your paper is related to our works, please cite our papers as follows.</p><p><a href="https://ieeexplore.ieee.org/document/9868878/">https://ieeexplore.ieee.org/document/9868878/</a></p><p><i>Xie, Yucheng, Ruizhe Jiang, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave." In 2022 International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2022.</i></p><p>Bibtex:</p><p><i>@inproceedings{xie2022mmfit,</i></p><p><i> title={mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave},</i></p><p><i> author={Xie, Yucheng and Jiang, Ruizhe and Guo, Xiaonan and Wang, Yan and Cheng, Jerry and Chen, Yingying},</i></p><p><i> booktitle={2022 International Conference on Computer Communications and Networks (ICCCN)},</i></p><p><i> pages={1--10},</i></p><p><i> year={2022},</i></p><p><i> organization={IEEE}</i></p><p><i>}</i></p>
Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned~Dataset
<p>This refers to dataset of the paper Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned to appear in IEEE WoWMoM 2023. Those files labeled with "beamsnr" correspond to 60 GHz while others with "data_processed" correspond to CSI at 5GHz. "ENV1" and "ENV2" represent two environments. "orient90" means user performs gestures 90 degree with respect to LoS.</p> <p> </p> <p>Abstract:</p> <p>In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave WiFi signals so that they can be used not only for high data rate communication but also for improved sensing e.g., for extended reality (XR) applications. For this reason, we extract spatial beam signal-to-noise ratios (SNRs) from the periodic beam training employed by IEEE 802.11ad devices. We consider a set of 10 gestures/poses motivated by XR applications. We conduct experiments in two environments and with three people. As a comparison, we also collect CSI from IEEE 802.11ac devices. To extract features from the CSI and the beam SNR, we leverage a deep neural network (DNN). The DNN classifier achieves promising results on the beam SNR task with stateof-the-art 96.7% accuracy in a single environment, even with a limited dataset. We also investigate the robustness of the beam SNR against CSI across different environments. Our experiments reveal that features from the CSI generalize without additional re-training, while those from beam SNRs do not. Therefore, retraining is required in the latter case.</p>
The mmWave radar point cloud dataset for person identification under various occluded conditions
<p>The dataset is collected by a COTS, Freqency Modulated Continuous Wave radar and an RGB-D camera. The dataset has been collected from 9 recruited individuals, which are instructed to walk behind the obstacle in an inbound/outbound manner, with each subject perfoms 5 consecutive minutes of walking. The RGB dataset is accessible on https://zenodo.org/record/8401329</p>
mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
Open the record for dataset details and reuse information.
Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring
<h2><strong>Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring</strong></h2><h3><strong>Description</strong></h3><p>The archive contains a dataset that can be used for multi-sensor-based vehicle detection/classification. Each part of the dataset is divided into four separate subfolders. All the footage was collected from different parts of Tallinn during the late winter and early spring. The dataset contains 8393 frames. Each frame comes with a corresponding annotation in XML and YOLO formats and a JSON file containing mmWave radar point cloud data. </p>
mmWave IWR1642 dataset
<p>Dataset consists of IQ data for multiple targets taken in the presence and in the absence of spot-jammer for ultra short range and short range mode of mmWave radar sensor IWR1642. AWR1642 is a spot-jammer. Each receiving elements data is in the form of number of ADC (analog to digital converter) samples by number of chirps by number of frames collected experimentally.</p>
mmWave IWR1642 dataset with interfering radar
<p>Dataset consists of IQ data for multiple targets taken in the presence of an interfering radar for short range mode of mmWave radar sensor IWR1642. AWR1642 is an interfering radar. Each receiving elements data is in the form of number of ADC (analog to digital converter) samples by number of chirps by number of frames collected experimentally. </p> <p>An interfering radar was placed at 30deg line of sight and -30deg angles for 2m, 3m and 5m distances.</p> <p>Eg: Receiver1_srr_twotarget_withjammer_5m_1 has one of the receiver's element data for which radar was configured to srr mode, interfering radar is at 5m at an angle of -30deg .</p> <p>Receiver2_srr_twotarget_withjammer_5m has one of the receiver's element data for which radar was configured to srr mode, interfering radar is at 5m at an angle of 30deg .</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
Breathing Rate and Heart Rate Dataset using Integrated mmWave FMCW Radar and Camera Steering System
<p>The presented dataset consists of raw and transformed CWT images of the breathing and heart waveforms obtained from a radar<br> and IP camera setup. The setup is a self-proposed setup with a mmWave radar mounted over an IP camera and can capture<br> the breathing and heart waveforms of the person in the room in front of the system in any orientation. The dataset can be used to estimate the vital signs using any machine learning model and important information about the respiration rate and pulse rate<br> can then be obtained. The dataset is a total of 1280 images- 720 raw and 720 processed. The processed dataset is labeled in six different classes. The first bifurcation is between breath and heart, breath signal is classified as low, normal, and high whereas the heart signal is classified as low, normal, and slightly low. The subject is oriented in differently so that the setup can steer towards the person and capture the breath and heart signals accordingly.</p>
MmWave 28 GHz MIMO channel sounding data collected in a conference room
<p>Channel sounding data sampled using a 28 GHz switched array MIMO channel sounder in a conference room.</p> <p>More information can be found in <a href="https://arxiv.org/abs/2404.19297">the corresponding publication</a> (DOI: 10.1109/TCOMM.2024.3392805), while the underlying data employs a self-explanatory structure, implemented in Matlab.</p>
A Clinical Trial Evaluating the Effectiveness and Safety of a mmWave Radar Based Sleep Respiratory Monitoring System
ClinicalTrials.gov study NCT06038006. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.