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88 results for “gestures”
UC2017 Static and Dynamic Hand Gestures
<p>We introduce the UC2017 static and dynamic gesture dataset. Most researchers use vision-based systems such as the Microsoft Kinect to acquire and classify hand gesture data. Despite that, we believe that we can achieve more reliable results and allow the use of more complex gestures with wearable systems. There are not many datasets with wearable systems due to the plethora of data gloves in the market and their relative high cost. For these reasons, we opted by creating a new dataset to present and evaluate our gesture recognition framework. The objectives of the dataset are: (1) provide a superset of hand gestures for HRI, (2) have user variability, (3) to be representative of the actual gestures performed in a real-world interaction.</p> <p>We divide the dataset in two types of gestures: SG and DG. SG are described by a single timestep of data, therefore representing a single hand pose and orientation. DGs are variable-length timeseries of poses and orientations with particular meanings. Some of the gestures of the dataset are correlated with a certain meaning in the context of HRI, while others are arbitrary, to enrich the dataset and add complexity to the classification problem.</p> <p>The library is composed of 24 SG classes and 10 DG. The dataset includes SG data from eight subjects with a total of 100 repetitions for each of the 24 classes (2400 samples in total). The DG samples were obtained from six subjects and has cumulatively 131 repetitions of each class (1310 samples in total). All of the subjects are right-handed and performed the gestures with their left hand.</p> <p>We used a data glove (CyberGlove II) and a magnetic tracker (Polhemus Liberty) to capture the hand shape, position and orientation over time. The glove provides digital signals that are proportional to the bending angle of each one of the 22 sensors which are elastically attached to a subset of the hand's joints. In this way we have an approximation of the hand's shape. The tracker's sensor is rigidly attached to the glove on the wrist and measures its position and orientation in respect to a ground-fixed frame. The orientation is the rotation between the fixed frame and the frame of the sensor, given a quaternion (WXYZ). We fuse the sensor data together online since the sensors have slightly different acquisition rates -- 100Hz for the glove and 120Hz for the tracker. The tracker data are under-sampled by gathering only the closest tracker frame in time.</p> <p>The files are in the h5df format. The dimensions are (sample, time, variables).</p>
Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures (Dataset)
<p>The accompanying dataset and code for the ICRA 2020 publication:</p> <p>B. Gromov, J. Guzzi, L. Gambardella, and A. Giusti, "Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures," in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020.</p> <p>The dataset contains a log of user actions and states of the system collected during the user study. The subjects had to fly a nano quadcopter (Bitcraze Crazyflie 2.0) between three targets placed at different heights by using a conventional joystick interface (Logitech F710) and pointing. The pointing is reconstructed using an inertial sensor (mbientlab MetaWearR+) placed on the user's wrist.</p>
SaGA++ Speech-Gesture Dataset Extension
<p>This is the dataset extension release for <a href="https://pub.uni-bielefeld.de/record/2001935">the SaGA dataset</a>.</p> <p>We have extracted anonymous features from all the 25 recordings and release them all now instead of only 6 recordings that were previously released. The modalities included are text annotations, gesture annotations, prosodic audio features, and movement trajectories. More details are provided in the README document included in the dataset.</p> <p><strong>Please reference the following papers in any publication making use of the Dataset:</strong></p> <p>Kucherenko, T., Nagy, R., Neff, M., Kjellström, H., & Henter, G. E. (2022). Multimodal analysis of the predictability of hand-gesture properties. 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS). </p> <p>Lücking, A., Bergman, K., Hahn, F., Kopp, S., & Rieser, H. (2013). Data-based analysis of speech and gesture: The Bielefeld Speech and Gesture Alignment Corpus (SaGA) and its applications. <em>Journal on Multimodal User Interfaces</em>, <em>7</em>(1), 5-18.</p>
Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments (LMHGIf3DVMI)
<p>The aim of the dataset is to investigate machine learning real-time gesture recognizer captured with a Leap Motion sensor to control the performance of a virtual 3D musical instrument. The dataset includes from 10-15 samples for each of the 8 gesture classes collected from 10 participants (5 female and 5 male) using the Leap Motion sensor.</p> <p> </p>
UC2018 DualMyo Hand Gesture Dataset
<p>This is set of data obtained from two consumer-market EMG sensors (Myo) with a subject performs 8 distinct hand gestures.</p> <p>There are a total of 110 repetitions of each class of gesture obtained across 5 recording sessions.</p> <p>Besides the data set, which is saved in a python pickle file, we include a python test script to load the data, generate random synthetic sequences of gestures and classify them with multiple models.</p> <p><strong>Gesture library:</strong></p> <ol> <li>Rest</li> <li>Closed fist</li> <li>Open hand</li> <li>Wave in</li> <li>Wave out</li> <li>Double-tap</li> <li>Hand down</li> <li>Hand up</li> </ol> <p><strong>Device placement:</strong></p> <ol> <li>The two Myos are placed on the forearm with the usb port pointing outwards, palm and sensor 5 facing upwards.</li> <li>The Myos are next to one another with their middle position close the the thickest section of the forearm.</li> <li>The outwards Myo is rotated slightly so that sensor 5 is aligned with the axis of the palmaris longus tendon.</li> <li>The Myo inside is rotated so that it has an angle of 22.5 degrees with the first Myo, in clockwise direction (subject perspective).</li> </ol> <p><strong>Acquisition protocol:</strong></p> <p>The subjects wear the armbands according to the instructions above. The sensors are run for a few minutes to warm-up.</p> <p>The subjects are requested to hold the positions of the gestures for a few seconds while we record 2 seconds of data. The gestures are repeated in random order in several sessions.</p>
Hand gesture dataset based on sEMG data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of five different/independent hand gestures are provided. The data were generated in a sEMG system with two bracelets (eight sEMG sensors and six sEMG sensors) worn in the right forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetForSegmentation.mat" was used to train a classifier whose purpose is the execution of Segmentation process. On the other hand, the file "datasetForRecognition.mat" was used to train a classifier whose purpose is the execution of gesture Recognition process.</p> <p> </p>
Arm gesture dataset based on IMU data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of ten different/independent hand gestures are provided (seven static gestures and three dynamic gestures). The data were generated in a IMU system with five sensors worn in the right forearm, right arm, chest, left arm and left forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetStaticGestures.mat" was used to train and test a classifier whose purpose is the recognition of static gestures. On the other hand, the file "datasetDynamicGestures.mat" was used to train and test a classifier whose purpose is the recognition of dynamic gestures. The latter file contains an extra class (gesture) which represents non-gestures.</p>
VRGestures: Controller and Hand Gesture Datasets for Virtual Reality
<p>15 VR Controller gestures</p> <p>11 one-handed VR Hand Gestures for each hand</p> <p>2 two-handed VR Hand Gestures </p>
Dataset used for making conclusions in article Gesture-controlled image management for operating room: A randomized crossover study.
<p>Dataset used for article titled:</p> <p>Gesture-controlled image management for operating room: A randomized crossover study.</p>
Rising tones and rustling noises: metaphors in gestural depictions of sounds
<p>Data for the observational and the experimental study. The quality of the video has been downsized to make the sharing tractable. </p> <p>Videos are arranged by referent sound. The filenames follow this format: subject number_referentsound.mp4</p>
Rising tones and rustling noises: metaphors in gestural depictions of sounds. Data
<p>Data for the article. </p> <p>The file ObservationalData.mat contains the following variables:</p> <p>- Data is 4 x 8 x 10 x 3 x 10 matrix. Dimension 1 corresponds to the four annotators. Dimension 2 corresponds to the 8 referent sounds. Dimension 3 corresponds to the 10 imitators. Dimension 4 corresponds to the three response fields. Dimension 5 corresponds to the different possible answers. When the content of the matrix is 0, it means that the annotator has not indicated this answer for this particular sound and response field. When the content of the matrix is 1, this means that the annotator has selected this answer.</p> <p>- Annotators is the list of annotators: 'HS4' 'OH4' 'GL4' 'PS2'</p> <p>- SoundNames is the list of sound names: ‘Upward sweep' 'Downward sweep' 'Stationnary noise' 'Scraping' 'Filling' 'Door' 'Refrigerator' 'Printer'</p> <p>- Imitators are the imitator IDs: '08' '16' '20' '22' '25' '26' '28' '29' '30' '32'</p> <p>- Fields are the field names: 'Main gestural features' 'Main vocal features' 'Direction of the gestures'</p> <p>- NMax are the number of possible answers for each field: 10, 8, 7</p> <p> </p> <p> </p> <p> </p> <p>Data for the experimental study are stored in two files: GesturalDescriptor.txt and VocalDescriptor.txt</p> <p>Each file is a table, where each row is an observation: one imitator imitating one referent sound. The columns correspond to the imitators, the referent sounds, the factors (granularity, profile, and toneless), and the features.</p>
Ergo: A Gesture-Based Computer Interaction Device
<p>This dataset accompanies the Master of Science (Computer Science) thesis by B.R. Kane titled "Ergo: A Gesture-Based Computer Interaction Device". It contains the raw sensor recordings in CSV format (`train/`), the pre-processed training-validation and testing datasets `trn_20_10.npz` and `tst_20_10.npz`, the dataset of the literature in `bibtex` format as well as in `csv` format.</p><p>The code to train machine learning models on the raw sensor data is available on GitHub: https://github.com/beyarkay/masters-code/</p><p>The code to analyse the gesture recognition is also available on GitHub (along with the source code of the thesis): https://github.com/beyarkay/masters-thesis/</p>
EMG from Combination Gestures following Visual Prompts
<p>Dataset of surface EMG recordings from 10 subjects performing single and combination gestures, from Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder.</p> <p>Each subject contributes 1224 gesture examples (584 single gestures and 640 combination gestures).</p> <p>For more details and example usage, see the following:</p> <ul> <li>Paper pdf - <a href="https://arxiv.org/pdf/2311.14675.pdf">https://arxiv.org/pdf/2311.14675.pdf</a></li> <li>Experiment code - <a href="https://github.com/nik-sm/com-hom-emg">https://github.com/nik-sm/com-hom-emg</a></li> </ul> <h2>Contents</h2> <p>Dataset of single and combination gestures from 10 subjects. Each subject contributes 1224 gesture examples (584 single gestures and 640 combination gestures).</p> <h2>Data</h2> <p>Each gesture trial consists of 500 ms of activity from 8 recording electrodes around the mid forearm.</p> <p>Recording was performed at 1926 Hz with built-in 20–450 Hz bandpass filtering applied.</p> <h3>Data Collection Details</h3> <p>In each gesture trial, subjects began from a neutral position, with the arm on an armrest. Subjects followed visual cues to perform either a direction gesture, a modifier gesture, or both. The timing structure and number of gestures obtained from each visual prompt varied across experimental blocks.</p> <p>Data for a single gesture trial come from the central 500ms when that gesture is being performed (the window is centered to be as far from gesture onset and gesture end as possible).</p> <p>See the paper for more details.</p> <h2>Labels</h2> <p>Each gesture trial in the dataset has a two-part label, describing which gesture was performed.</p> <p>The first label component describes the direction gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:</p> <ul> <li>0 - "Up" (wrist extension)</li> <li>1 - "Down" (wrist flexion)</li> <li>2 - "Left" (wrist abduction; movement towards thumb side)</li> <li>3 - "Right" (wrist adduction; movement towards pinky side)</li> <li>4 - "NoDirection" (absence of a direction gesture; none of the above)</li> </ul> <p>The second label component describes the modifier gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:</p> <ul> <li>0 - "Pinch" (connecting thumb and index finger)</li> <li>1 - "Thumb" (touching thumb to first knuckle of index finger)</li> <li>2 - "Fist" (all fingers curled)</li> <li>3 - "Open" (all fingers extended)</li> <li>4 - "NoModifier" (absence of a modifier gesture; none of the above)</li> </ul> <p>Each subject provided 1224 total examples, consisting of:</p> <ul> <li>- 584 single gesture examples. (584 = 73 examples, for each of the 4+4 possible single gesture classes)</li> <li>- 640 combination gesture examples. (640 = 40 examples, for each of the 4x4 possible combination gesture classes)<br> </li> </ul> <h3>Examples of Label Structure</h3> <p>Single gestures have labels like (0, 4) indicating ("Up", "NoModifier") or (4, 3) indicating ("NoDirection", "Open").</p> <p>Combination gesture have labels like (0, 0) indicating ("Up", "Pinch") or (2, 3) indicating ("Left", "Open").</p> <h2>Loading data</h2> <p>After downloading and unzipping, the examples below can be used to load data in Python or MATLAB.</p> <p>Data have shape `(items, channels, timesteps)`.</p> <p>Labels have shape `(items, 2)`, where the first coordinate is the direction and the second coordinate is the modifier.<br> </p> <h3>Python example</h3> <blockquote> <p><em># Load data</em></p> <p>import numpy as np</p> <p>from pathlib import Path</p> <p> </p> <p>folder = Path("path/to/combination-gesture-dataset/python")<br> </p> <p>all_data, all_labels = [], []</p> <p>for i in range(10):</p> <p> data = np.load(f"{folder}/subj{i}/data.npy")</p> <p> labels = np.load(f"{folder}/subj{i}/labels.npy")</p> <p> all_data.append(data)</p> <p> all_labels.append(labels)</p> <p>all_data = np.concatenate(all_data, axis=0)</p> <p>all_labels = np.concatenate(all_labels, axis=0)</p> <p> </p> <p>print(all_data.shape) <em># (12240, 8, 962)</em></p> <p>print(all_labels.shape) <em># (12240, 2)</em></p> <p> </p> <p><em># Convert numeric labels to text</em></p> <p>def convert_label(label):</p> <p> directions = ["Up", "Down", "Left", "Right", "NoDirection"]</p> <p> modifiers = ["Pinch", "Thumb", "Fist", "Open", "NoModifier"]</p> <p> return directions[label[0]], modifiers[label[1]]</p> <p> </p> <p>print(convert_label(all_labels[0])) <em># ('NoDirection', 'Fist')</em></p> </blockquote> <p> </p> <h3>MATLAB example</h3> <blockquote> <p>% Load data</p> <p>folder = "path/to/combination-gesture-dataset/matlab";<br> </p> <p>all_data = [];</p> <p>all_labels = [];</p> <p>for i = 0:9</p> <p> data = load(sprintf("%s/subj%d/data.mat", folder, i));</p> <p> labels = load(sprintf("%s/subj%d/labels.mat", folder, i));</p> <p> all_data = [all_data; data.contents];</p> <p> all_labels = [all_labels; labels.contents];</p> <p>end</p> <p> </p> <p>% (Optional) convert labels from 0-indexed to 1-indexed, for easier conversion to text</p> <p>all_labels = all_labels + 1;</p> <p> </p> <p>size(all_data) % (12240, 8, 962)</p> <p>size(all_labels) % (12240, 2)<br> </p> <p>% Convert numeric labels to text</p> <p>directions = ["Up", "Down", "Left", "Right", "NoDirection"];</p> <p>modifiers = ["Pinch", "Thumb", "Fist", "Open", "NoModifier"];</p> <p>convert_label = @(label) [directions(label(1) + 1), modifiers(label(2) + 1)];</p> <p> </p> <p>convert_label(all_labels(1, :)) % "NoDirection" "Fist"</p> </blockquote>
EMG from Combination Gestures with Ground-truth Joystick Labels
<p>Dataset of surface EMG recordings from 11 subjects performing single and combination gestures, from "**A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition**" by Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Stephanie Naufel, Tales Imbiriba, Eugene Tunik, Deniz Erdogmus, and Mathew Yarossi.</p> <p>For more details and example usage, see the following:</p> <ul> <li>Paper pdf - <a href="https://arxiv.org/pdf/2309.12217.pdf">https://arxiv.org/pdf/2309.12217.pdf</a></li> <li>Experiment code - <a href="https://github.com/neu-spiral/multi-label-emg">https://github.com/neu-spiral/multi-label-emg</a></li> </ul> <h1>Contents</h1> <p>Dataset of single and combination gestures from 11 subjects. <br>Subjects participated in 13 experimental blocks.<br>During each block, they followed visual prompts to perform gestures while also manipulating a joystick.<br>Surface EMG was recorded from 8 electrodes on the forearm; labels were recorded according to the current visual prompt and the current state of the joystick.</p> <p>Experiments included the following blocks:</p> <ul> <li>1 Calibration block</li> <li>6 Simultaneous-Pulse Combination blocks (3 without feedback, 3 with feedback)</li> <li>6 Hold-Pulse Combination blocks (3 without feedback, 3 with feedback)</li> </ul> <p>The contents of each block type were as follows:</p> <ul> <li>In the Calibration block, subjects performed 8 repetitions of each of the 4 direction gestures, 2 modifier gestures, and a resting pose.<br>Each Calibration trial provided 160 overlapping examples, for a total of: 8 repetitions x 7 gestures x 160 examples = 8960 examples.</li> <li>In Simultaneous-Pulse Combination blocks, subjects performed 8 trials of combination gestures, where both components were performed simultaneously.<br>Each Simultaneous-Pulse trial provided 240 overlapping examples, for a total of: 8 trials x 240 examples = 1920 examples.</li> <li>In Hold-Pulse Combination blocks, subjects performed 28 trials of combination gestures, where 1 gesture component was held while the other was pulsed.<br>Each Hold-Pulse trial provided 240 overlapping examples, for a total of: 28 trials x 240 examples = 6720 examples.</li> </ul> <p>A single data example (from any block) corresponds a window 250ms of EMG recorded at 1926Hz (built-in 20–450 Hz bandpass filtering applied).<br>A 50ms step size was used between each window; note that neighboring data examples are therefore overlapping.</p> <p>Feedback was provided as follows:</p> <ul> <li>In blocks with feedback, a model pre-trained on the Calibration data was used to give realtime visual feedback during the trial.</li> <li>In blocks without feedback, no model was used, and the visual prompt was the only source of information about the current gesture.</li> </ul> <p>For more details, see the paper.</p> <h1>Labels</h1> <p>Two types of labels are provided: </p> <ul> <li>joystick labels were recorded based on the position of the joystick, and are treated as ground-truth.</li> <li>visual labels were also recorded based on what prompt was currently being shown to the subject.</li> </ul> <p>For both joystick and visual labels, the following structure applies. Each gesture trial has a two-part label.</p> <p>The first label component describes the direction gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:</p> <ul> <li>0 - "Up" (joystick pull)</li> <li>1 - "Down" (joystick push)</li> <li>2 - "Left" (joystick left)</li> <li>3 - "Right" (joystick right)</li> <li>4 - "NoDirection" (absence of a direction gesture; none of the above)</li> </ul> <p>The second label component describes the modifier gesture, and takes values in {0, 1, 2}, with the following meaning:</p> <ul> <li>0 - "Pinch" (joystick trigger button)</li> <li>1 - "Thumb" (joystick thumb button)</li> <li>2 - "NoModifier" (absence of a modifier gesture; none of the above)</li> </ul> <h2>Examples of Label Structure</h2> <p>Single gestures have labels like (0, 2) indicating ("Up", "NoModifier") or (4, 1) indicating ("NoDirection", "Thumb").</p> <p>Combination gesture have labels like (0, 0) indicating ("Up", "Pinch") or (2, 1) indicating ("Left", "Thumb").</p> <h1>File layout</h1> <p>Data are provided in Numpy and MATLAB format. Descriptions below apply for both.</p> <p>Each experimental block is provided in a separate folder.<br>Within one experimental block, the following files are provided:</p> <ul> <li>`data.npy` - Raw EMG data, with shape (items, channels, timesteps).</li> <li>`joystick_direction_labels.npy` - one-hot joystick direction labels, with shape (items, 5).</li> <li>`joystick_modifier_labels.npy` - one-hot joystick modifier labels, with shape (items, 3).</li> <li>`visual_direction_labels.npy` - one-hot visual direction labels, with shape (items, 5).</li> <li>`visual_modifier_labels.npy` - one-hot visual modifier labels, with shape (items, 3).</li> </ul> <h1>Loading data</h1> <p>For example code snippets for loading data, see the associated code repository.</p>
Supplementary materials to: Hand gestures and the oratorical taskscape of Late Republican and Augustan Forum Romanum in Rome
<p>Supplementary materials to the article: "Hand gestures and the oratorical taskscape of Late Republican and Augustan Forum Romanum in Rome"<br><br>S1 - Virtual 3d Reconstruction of Forum Romanum, ca. 54 BCE used in the study</p> <p>S2 - Virtual 3d Reconstruction of Forum Romanum, ca. 27 BCE used in the study</p> <p>S3 - Virtual 3d Reconstruction of Forum Romanum, ca. 14 CE used in the study</p> <p>S4 - Parameters used in the Viewshed function in Exploratory 3D Analysis in the ArcGIS Pro 2.9</p> <div></div>
Partially automatically annotated corpus to predict gestural cues in Embodied Conversational Agents
<p>#Structure of the corpus</p> <p>This corpus has been built using speeches of Spanish politicians freely available <a href="http://www.congreso.es/portal/page/portal/Congreso/Congreso/Intervenciones">here</a> along with their transcriptions.</p> <p>Each transcription has been analyzed in terms of:</p> <ul> <li>Surface Syntactic Structure*</li> <li>Deep Syntactic Structure*</li> <li>Morphology (Part of Speech)*</li> <li>Communicative Structure</li> </ul> <p>Gestures (beat vs. no gesture tags) have been annotated using the videos.</p> <p>*All those features have been automatically retrieved using the parser freely available in https://github.com/TalnUPF/miis. The other features have been annotated manually.</p> <p>#Concerns about the corpus</p> <p>This corpus has been mostly annotated manually. Annotation agreement has not been computed.</p> <p>Moreover, it is small. In order to extract reliable correlations from it, it should be extended.</p>
TongueTap: Multimodal Tongue Gesture Recognition with Head-Worn Devices
<p>Please cite the primary paper at <a href="http://doi.org/10.1145/3577190.3614120">https://doi.org/10.1145/3577190.3614120</a> when referencing this dataset.</p> <p>This dataset contains multimodal tongue gesture data as a supplement for "TongueTap: Multimodal Tongue Gesture Recognition with Head-Worn Devices" published in ICMI (International Conference on Multimodal Interfaces) 2023. The data is presented in three formats (XDF, pickle and NumPy) at various stages of pre-processing. Please review the READMEs in each file before working with them. Please also review the paper at <a href="http://doi.org/10.1145/3577190.3614120">https://doi.org/10.1145/3577190.3614120</a> for more information about the data and how it was collected.</p> <p><strong>Abstract</strong></p> <p>Mouth-based interfaces are a promising new approach enabling silent, hands-free and eyes-free interaction with wearable devices. However, interfaces sensing mouth movements are traditionally custom-designed and placed near or within the mouth. TongueTap synchronizes multimodal EEG, PPG, IMU, eye tracking and head tracking data from two commercial headsets to facilitate tongue gesture recognition using only off-the-shelf devices on the upper face. We classified eight closed-mouth tongue gestures with 94% accuracy, offering an invisible and inaudible method for discreet control of head-worn devices. Moreover, we found that the IMU alone differentiates eight gestures with 80% accuracy and a subset of four gestures with 92% accuracy. We built a dataset of 48,000 gesture trials across 16 participants, allowing TongueTap to perform user-independent classification. Our findings suggest tongue gestures can be a viable interaction technique for VR/AR headsets and earables without requiring novel hardware.</p>
EMG dataset for gesture recognition with arm translation
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Data from: Multi-gesture drag-and-drop decoding in a 2D iBCI control task
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Data from: Gesture encoding in human left precentral gyrus neuronal ensembles
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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.