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124 results for “robotic dataset”
Surface Type Classification for Autonomous Robot Indoor Navigation - Dataset
<p>Surface Type Recognition with Inertial Measurement Unit (IMU).</p> <p>The dataset contains time series samples with 10 features each, related to orientation, velocity and acceleration. Each time series (of lenght 128) includes its corresponding surface type annotation.</p> <p>The data has been also divided in groups for easier cross-validation (80 groups present)</p> <p>A total of 9 different surface types are present in the dataset.</p> <p>"X_data.npy" contains the time series samples of dimension 7626x10x128<br> "label.npy" contains the label information for each sample (dimension 7626x1)<br> "groups.npy" contains the group information for each sample (dimension 7626x1)<br> "details.csv" contains for each sample the group information and the corresponding label</p> <p> </p>
[DATASET 9] - PLANT-ROBOT INTERFACES FOR ENERGY HARVESTING
<p>In the framework of GrowBot project, task 7.2 aims at developing bio-hybrid energy harvesting systems based on the triboelectric effect.<br> The energy conversion occurs at plant leaves level during mechanical stimulation (i.e., wind, rain, etc.).<br> Two main components have been developed in GrowBot:<br> - Flexible artificial “leaves” (flexible electrodes covered with tailored materials) to enhance mechanical impacts with the plant leaves and further enhance power output.<br> - Minimal-invasive electrodes that establish electrical contact between GrowBots and real plants.</p> <p>DS9 aims at collecting all the experimental data gathered during these activities.</p>
[DATASET 7] - SOFT "SEARCHER-LIKE" ROBOT
<p>In the framework of GrowBot project, Task 5.4 aims at developing a robotic searcher with sensing and actuation abilities.<br> IIT has developed a modular continuum soft arm taking inspiration from the structural features of climbing plants investigated in WP3. The searcher module can explore the environment via circumnutation movements and tactile feedback.</p> <p>DS7 aims at collecting all the data related to the design and development of the soft searcher robot.</p>
Dataset for the paper "How to Work on Equality and Inclusion when Introducing Computational Thinking and Educational Robotics in Early Childhood Education: A Systematic Review"
<p>Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08).</p> <p>The SLR covers papers in WoS and Scopus from 2011 to 2022.</p>
Dataset AEROARMS - Perception for robot operation
<p><strong>Dataset description: </strong>This dataset contains the images used for validating the perception algorithms to support aerial and ground robot operations.</p> <p><strong>Data description:</strong> The images where obtained using an Intel Realsense d435 camera. Additionally, the calibration of the camera is given in XML format.</p> <p><strong>Dataset size: </strong>72.8Mb</p>
Dataset from: In-situ bidirectional human-robot value alignment
Open the record for dataset details and reuse information.
InHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics
<p><strong>Objectives</strong></p> <p>We introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.<br> Read <strong>00-README.txt</strong> for detailed download instructions.</p> <p>More details on the dataset at <a href="https://github.com/vhavard/InHARD">https://github.com/vhavard/InHARD</a></p> <p>This work has been performed at the CESI LINEACT : <a href="https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/">https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/</a></p>
Dataset for systematic mapping literature about STEAM through Challenge-Based Learning, Robotics and Physical Devices
<p>This is the public dataset for the systematic mapping literature review performed for the paper "Fostering STEAM through Challenge-Based Learning, Robotics and Physical Devices: A systematic mapping literature review".</p> <p>Related to activity O2.A1 of the RoboSTEAM European Project.</p>
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Dataset to "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture"
<p>This dataset is related to the paper "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture".</p> <p>Contents:</p> <p>Recordings comparing a human performance and two robot performances (named case A and case B).</p> <ul> <li>Synchronised motion-capture data (time, bow markers, nut marker and bridge marker; Fs = 240 fps - .csv files)</li> <li>Synchronised audio recordings (sound in front of the instrument; Fs = 44100 Hz - .wav files)</li> <li>Music score</li> <li>Read-me file with further details</li> </ul>
Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)
<pre><code class="language-markdown">This dataset contains data accompanying the work: @ARTICLE{9676410, author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele}, journal={IEEE Robotics and Automation Letters}, title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots}, year={2022}, volume={7}, number={2}, pages={2779-2886}, doi={10.1109/LRA.2022.3141658}} The dataset is organized in folders, whose content can be summarized as follows: - mocap: motion capture data collected from human motion - retargeted_mocap: motion capture data retargeted on the robot - IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator - training_D2_D3_subsampled_mirrored_4ew_98%: training data - inference: data collected while generating trajectories - trajectory_control_simulation: data collected while controlling trajectories in simulation - trajectory_control_real_robot: data collected while controlling trajectories on the real robot - additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.</code></pre>
Dataset of industrial robots
<p>Dataset of industrial robots, including ABB, COMAU, KUKA, NACHI, UFACTORY, and UR robots.</p>
Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)
<p>Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR".</p> <p>Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated:</p> <ul> <li>a dataset that contains YOLO annotations for these images.(this dataset)</li> <li>a second dataset contains images of Sheep, (10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. ( 10.5281/zenodo.11313966)</li> </ul> <p> </p>
Bimanual Robot Arm Cloth Manipulation Dataset
<p>This dataset contains 945 trials of a bimanual robot arm setup, where one arm holds a piece of cloth (e.g., a towel) by one corner while the other arm, equipped with a wrist-mounted camera, captures images from approximately 180 different angles by making a half-circle motion around the cloth. Each trial includes images and the corresponding TCP (Tool Center Point) poses of the second arm. Additionally, the dataset provides annotated positions of the two corners adjacent to the held corner, which are critical for unfolding the cloth by grasping these points. This dataset is valuable for research in robotic manipulation, computer vision, and cloth handling tasks.</p>
Tuta Absoluta Robotic Traps Dataset
<p>This dataset focuses on enabling Tuta absoluta detection, necessitated annotated images. It was created as part of the H2020 PestNu project (No. 101037128) using the SpyFly AI-robotic trap from Agrorobotica. The SpyFly trap features a color camera (Svpro 13MP, sensor: Sony 1/3” IMX214) with a resolution of 3840 × 2880 for high-quality image capture. The camera was positioned 15 cm from the glue-paper to capture the entire adhesive board. In Total 217 images were captured.</p> <p>Expert agronomists annotated the images using Roboflow, labeling a total of 6787 T. absoluta insects, averaging 62.26 annotations per image. Images without insects were excluded, resulting in 109 annotated images, one per day.</p> <p>The dataset was split into training and validation subsets with an 80–20% ratio, leading to 87 images for training and 22 for validation. The dataset is organized into two main folders: “<em>0_captured_dataset</em>" contains the original 217 .jpg images. "<em>1_annotated_dataset</em>" includes the images and the annotated data, split into separate subfolders for training and validation. The Tuta absoluta count in each subset can be seen in the following table:</p> <table> <tbody> <tr> <td><strong>Set</strong></td> <td><strong>Images</strong></td> <td><strong>Tuta Absoluta Instances</strong></td> </tr> <tr> <td>Training</td> <td>87</td> <td>5344</td> </tr> <tr> <td>Validation</td> <td>22</td> <td>1443</td> </tr> <tr> <td>Total</td> <td>109</td> <td>6787</td> </tr> </tbody> </table>
ERL Service Robot Competition Dataset FBM1 and FBM2 (Lisbon 2017 Tournament)
<p>Dataset for ERL Service Robot Competition (Major competition in Lisbon 2017)</p> <p>FBM1:Object Perception and FBM2:Navigation Functionality benchmarks.</p> <p>Log files are in Rosbag format and include two files per trial of the benchmark:</p> <p>1) Internal Robot data (information logged by the robot such as onboard sensors and control commands)</p> <p>2) RSBB data (Information From the Referee box that contains benchmark related information as well as the ground truth data measured by motion capture system)</p> <p> </p>
The behaviour of commercial broilers in response to a mobile robot - Behavioural dataset
<p>The data files associated with the paper titled 'The behaviour of commercial broilers in response to a mobile robot'.</p> <p> </p>
Synthetic Multimodal Dataset using MuJoCo: UR5 Robot Motion
<p>Using the Mujoco environment, we simulated robot trajectory and transitions from one formation<br> to another. Mujoco is a 3D simulator, while Gym serves as an interface to the UR5 robot.<br> The robot has measurement units that allow the acquisition of the angles, positions,<br> and quaternions of the joints and the position of the end-effector. The robot is located on<br> a table with 4 cameras all from the same radius to the center of the robot just<br> rotated by 90° for each of them. Using the described environment, we collected 1999 samples <br> at a rate of 10 samples per second.<br> <br> ################################################################<br> ################################################################<br> <br> camera views:</p> <p> id: 0<br> name: 'camera_0'<br> xmat: array([ 0.70710678, 0.42537261, -0.56485232, -0.70710678, 0.42537261,<br> -0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([-2., -2., 3.])</p> <p> id: 1<br> name: 'camera_1'<br> xmat: array([-0.70710678, 0.42537261, -0.56485232, -0.70710678, -0.42537261,<br> 0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([-2., 2., 3.])</p> <p> id: 2<br> name: 'camera_2'<br> xmat: array([ 0.70710678, -0.42537261, 0.56485232, 0.70710678, 0.42537261,<br> -0.56485232, -0. , 0.79882181, 0.60156772])<br> xpos: array([ 2., -2., 3.])</p> <p> id: 3<br> name: 'camera_3'<br> xmat: array([-0.70710678, -0.42537261, 0.56485232, 0.70710678, -0.42537261,<br> 0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([2., 2., 3.])</p> <p> <br> <br> The camera data is stored as .png files with a size of 256x256 </p> <p><br> <br> ################################################################<br> ################################################################<br> <br> The files angles.pt, angular_velocity.pt, angular_acceleration.pt contains information about<br> the motor data of the joints. The angles, velocity, acceleration is the information about the <br> Motor in each joint in following order:<br> ['base_to_lik', 'base_to_rik', 'elbow_joint', 'shoulder_lift_joint', 'shoulder_pan_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint']<br> <br> <br> ################################################################<br> ################################################################<br> <br> The files pose.pt and quaternion.pt contains information about<br> the body data of the robot. The pose of each element is in following order:<br> ['base', 'base_link', 'box_2_link', 'box_link', 'drop_box', 'ee_link', 'forearm_link', 'left_inner_finger', 'left_inner_knuckle', 'right_inner_finger', 'right_inner_knuckle', 'robotiq_85_base_link', 'shoulder_link', 'upper_arm_link', 'world', 'wrist_1_link', 'wrist_2_link', 'wrist_3_link']<br> <br> <br> ################################################################<br> ################################################################<br> <br> The file action.pt contains information about the used action in the corresponding time-step. <br> The action of each element is in following order:<br> ['forearm_T', 'gripper_motor', 'shoulder_lift_T', 'shoulder_pan_T', 'wrist_1_T', 'wrist_2_T', 'wrist_3_T']<br> <br> </p>
Synthetic Multimodal Dataset using ABB Studio: Single Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Löppenberg et al. (2024), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p>Frame '02655_frame' is corrupted. That means the corresponding values of the other modality needs to be removed as well.</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024). Dynamic robot routing optimization: State-space decomposition for operations research-informed reinforcement learning. Robotics and Computer-Integrated Manufacturing, 90, 102812.</p>
Synthetic Multimodal Dataset using ABB Studio: Dual Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Diprasetya et al. (2023), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters for each robot:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Diprasetya, M. R., Yuwono, S., Löppenberg, M., & Schwung, A. (2023, July). Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation. In 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1-7). IEEE.</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.