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118 results for “Object detection”
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection
<p><strong>Summary</strong></p> <p>This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections have been corrected with flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images). Both the X-ray projections and the ground truth images are resized to 128x128 pixels. The raw data is made available in another (larger) submission for complete reproduction (<a href="https://zenodo.org/record/5866228">https://zenodo.org/record/5866228</a>). All images are stored in .tif format. The data for samples with 5-8 stones are put in a separate folder from the data with 0-3 stones. The size of the completely unpacked dataset is 19.6 GB.</p> <p><strong>NOTE</strong>: Because the dataset consists of 471600 files, fully extracting the dataset may take a while. Therefore, an additional and significantly smaller zip-file is included for previewing the data, with one X-ray projection for each sample.</p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Figure 1 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 1. Examples of images collected for soybean in the VE-VC (A) and R2 (B) growth stages.
Indoor Object Detection Dataset
<p>We introduce a new fully labeled object detection dataset collected from indoor scenes. This indoor dataset consists of 2213 image frames containing seven classes. In contrast to existing indoor datasets, our dataset includes a variety of background, lighting conditions, occlusion and high inter-class differences.<br> For detail information, please refer to our paper: <a href="https://doi.org/10.1109/EUVIP.2018.8611732">10.1109/EUVIP.2018.8611732</a></p>
TUD-GV Dataset for Floating Litter Detection (object detection task)
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>This dataset is a subset of the large-scale "TU Delft - Green Village" (TUD-GV), which includes 9,473 RGB images. More details on the TUD-GV dataset can be found at: <a href="https://doi.org/10.5281/zenodo.7636124" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7636124</a>. This subset used in this publication consists of 1,501 images, selected from the full TUD-GV dataset. All floating litter items in this subset have been annotated with bounding boxes. This subset is specifically for detecting floating litter in object detection tasks.</p> <p>The 1,501 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file.</p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Dump truck object detection with manual annotations
<p>Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are:</p> <p>0 - front wheel <br> 1 - middle wheel<br> 2 - back wheel<br> 3 - tipping body<br> 4 - cap</p> <p>This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.</p>
Object Detection meets Knowledge Graphs Raw Data
<p>Raw data used to reproduce the results of 'Object Detection meets Knowledge Graphs'. Combination of VOC 2007 images, MS COCO 2014 images and ConceptNet knowledge graph. Also the checkpoints of the two trained models for the VOC dataset and COCO dataset.</p>
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
<p>Floral resources are a key driver of pollinator abundance and diversity, yet their quantification in the field and laboratory is laborious and requires specialist skills.</p> <p>Using a dataset of 25000 labelled tags of fieldwork-realistic quality, a Convolutional Neural Network (Faster R-CNN) was trained to detect the nectar-producing floral units of 25 taxa in surveyors' quadrat images of native, weed-rich grassland in the UK.</p> <p>Floral unit detection on a test set of 50 model-unseen images of comparable vegetation returned a precision of 90%, recall of 86% and F1 score (the harmonic mean of precision and recall) of 88%. Model performance was consistent across the range of floral abundance in this habitat. </p> <p>Comparison of the nectar sugar mass estimates made by the CNN and three human surveyors returned similar means and standard deviations. Over half of the nectar sugar mass estimates made by the model fell within the absolute range of those of the human surveyors.</p> <p>The optimal number of quadrat image samples was determined to be the same for the CNN as for the average human surveyor. For a standard quadrat sampling protocol of 10–15 replicates, this application of deep learning could cut pollinator-plant survey time per stand of vegetation from hours to minutes.</p> <p>The CNN is restricted to a single view of a quadrat, with no scope for manual examination or specimen collection, though in contrast to human surveyors its object detection is deterministic and floral unit definition is standardised.</p> <p>As agri-environment schemes move from prescriptive to results-based, this approach provides an independent barometer for grassland management which is usable by both landowner and scheme administrator. The model can be adapted to visual estimations of other ecological resources such as winter bird food, floral pollen volume, insect infestation and tree flowering/fruiting, and by adjustment of classification threshold may show acceptable taxonomic differentiation for presence-absence surveys.</p>
Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for<br> "Invariance of Object Detection in Untrained Deep Neural Networks"</strong><br> Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik*<br> *Contact: sbpaik@kaist.ac.kr<br> <br> To run demo codes for "<a href="https://github.com/vsnnlab/Invariance">Invariance of Object Detection in Untrained Deep Neural Networks</a>", please download files below.<br> <br> <strong>1. Image.zip</strong><br> <strong>- Object dataset (Foldername: selectivity_var)</strong>: This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class.<br> <strong>- Viewpoint dataset for invariance test (Folder name: invariance_test)</strong>: This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset.<br> <strong>- Viewpoint dataset for finding invariant unit (Folder name: invariance_unit)</strong>: This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit<br> <strong>- SVM dataset (Folder name: SVM_var)</strong>: This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.</p>
[Re] Object Detection Meets Knowledge Graphs - Supporting Datasets
<p><strong>Supporting Datasets for [Re] Object Detection Meets Knowledge Graphs</strong></p> <p>The supporting data needed to reproduce the results of <a href="https://www.ijcai.org/Proceedings/2017/230">Object Detection Meets Knowledge Graphs</a> as a part of the submission to ReScience C journal.</p>
Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps
<p>Dataset analyzed in the study "Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps". Use of the data requires proper reference to [1].</p> <p>Dataset contains Electromyographic data from forearm, recorded with an 8-channel sEMG Biometrics Ltd. device. The fields contained in the structure are those detailed in the following scheme::</p> <ul> <li>Group: 0 for healthy subjects; 1 for HOA patients</li> <li>Subject: subject ID;</li> <li>Grasp: grasp ID, according to Figure 1 [1];</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [2].</li> </ul> <p>[1] Jarque-Bou, N.J.; Gracia-Ibáñez, V.; Roda-Sales, A.; Bayarri-Porcar, V.; Sancho-Bru, J.L.; Vergara, M. Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2413. https://doi.org/10.3390/s23052413</p> <p>[2] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p> <p> </p>
Roost-dataset: a remote sensing object detection dataset
<p>We release a multi-channel weather radar sensing dataset with roost annotations, for the purpose of ecological analyses and developing visual object detection and tracking models to recognize biological phenomena in radar data. Please refer to https://github.com/darkecology/roost-dataset for more details. Here we upload arrays rendered from weather radar data. Radar scan lists and roost annotations are released in json files of a COCO-like format in the roost-dataset Github repository.</p>
Sainfoin Fruit Processing - Object Detection Dataset
<p>This dataset consists of 500 images of sainfoin (Onobrychis viciifolia) seed pods, seed, and split seeds. The images were taken as a part of an experiment to determine minimum sample size of seed pods needed to accurately estimate pod threshing trait heritability within sainfoin breeding lines.</p><p>The experiment was a complete factorial design with the following factors:</p><ul><li>Sainfoin named varieties: AAC Mountainview, Delaney, Eski , Rocky Mountain Remont, and Shoshone</li><li>Sample Size: 1, 2, 3, 4, and 5 grams of dried seed pods</li><li>Two different threshing types: Belt thresher processed 3X, Haldrup Impact Thresher (35sec @ Speed 9)</li></ul><p>This makes for a total factorial combination set of 5 varieties X 5 sample sizes X 2 threshing types = 50.</p><p>Each combination was comprised of 10 individual replicates where each replicate in a combination was a unique, random sample of seeds of the same mass (So, 10 random, 2g samples of Eski seed, processed by belt thresher; 10 random, 5g samples of Delaney seed processed by the Haldrup thresher, etc.). This makes for a total of 500 experimental units that comprise the sample set.</p><p>Once the seeds were sampled, weighed, and processed through the threshing equipment, they were weighed again and imaged.</p><p>The threshed seeds were scattered onto an imaging platform with a blue background, lit by 2 LED panels, and photographed with a Sony ILCE-7RM2 at the following settings:</p><ul><li>ISO: 100</li><li>Exposure: 1/40s</li><li>Focal Length: 55mm</li><li>Format: TIFF</li><li>Size: 7968x5320</li></ul><p>The raw images were converted from TIFF files to JPEG format and annotated in image labeling software. The seed objects were annotated with bounding boxes classified as the following classes</p><ol><li>pod: an enclosed seed pod</li><li>seed: a seed which was successfully threshed from the legume pod carpel</li><li>split: a seed threshed from the pod, but which split in two halves during the threshing process</li></ol><p>All image annotations were exported into the convenient <a href="https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html">COCO format</a>.</p><p>No further image processing was performed.</p><p>The image set was split into a 80/20 training and validation step using `scikit-learn` in Python 3.11 stratifying the datasets equally over the various experimental factor levels.</p><p>The zip file 'train_val_images.zip' contains a 'train' folder with 400 training images, 'val' containing 100 validation images, an image taken with a color correction card named 'color_test.jpg', and a json file with all the annotations.</p><p>Another file called 'seed_weights.csv' contains the image_name to global-key mapping in tabular format as well as the before and after threshing seed weights for each experimental sample.</p><p><strong>Labeling Metrics:</strong></p><ul><li>Pod (48.58%)<ul><li>36,599 objects</li></ul></li><li>Seed (33.83%)<ul><li>25,488 object</li></ul></li><li>Split (17.59%)<ul><li>13,255 objects</li></ul></li><li><strong>TOTAL (100%)</strong><ul><li><strong>75,342 objects</strong></li></ul></li></ul>
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
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3,832 annotated images of scanned Hollywood pressbooks for object detection Model
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Brainport, Automated valet parking, RS camera object detection
<p><strong>Scenario description</strong>:</p> <p>RS Camera object detection and publication of the iot message from type AutoPilot.ObjectDetection to the PMS via IoT platforms</p> <p><strong>Session description</strong>:</p> <p>A car drive on the AVP road segment and stop and the RS camera detect the car as obstacle and send the obstacle information to the PMS for free obstacle route calculation for AD-car</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
SODD – Subaquatic Object Detection Dataset
<p>The SODD dataset consists of 3168 images of underwater objects consistent with underwater installations, including the following categories: propeller, pipe, pipe_type2, net, red_fin, qr_codes. The total number of annotated objects are 8934, with the following distribution among categories: propeller (1092 instances), pipe (2008), pipe_type2 (886), red_fin (760), net (1556), qr_codes (2632). </p><p>The images were acquired from a collection of videos (mp4 format, with HD resolution, and 16 FPS). The videos were acquired in an indoor pool using a Blue Robotics low-light USB camera. The vehicle used is a BlueROV2 from Blue Robotics with the heavy configuration retrofit kit, providing full actuation in 6 degrees of freedom.</p><p>More details in the documentation enclosed in the file SODD_Documentation. </p><p> </p><p><i>Acknowledgements</i></p><p>We would like to thank Professors Damiano Varagnolo and Annette Stahl from the Norwegian University for Science and Technology for the valuable advice during the planning phase of the data collection. </p><p> </p>
TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection
<p>This dataset is from the paper "TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection", which has been accepted by the WACV 2024 CV4EO Workshop.</p>
MOTS-annotated UAV Vineyard Dataset captured using Multiple Perspectives to avoid Leaf Occlusion for Object Detection and Tracking
<p>This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s. </p>
A fine-grained dataset named iSOOD for sewage outfalls objective detection in natural environments
<p><strong>Basic Information:</strong></p> <p>The 10481 images in iSOOD were captured using UAVs and handheld cameras by individuals from the river basin in China. Our study has carefully annotated these images to ensure accuracy and consistency. The iSOOD has undergone technical validation utilizing the YOLOv5 objective detection model. The iSOOD have been publicly released after undergoing desensitization, with the goal of promoting interdisciplinary collaboration and accelerating advancements in the intelligence watershed management. We expect that the iSOOD dataset is anticipated to inspire further research on the SOs detection and the control of pollution migration paths, and serve as a fundamental resource for the use of advanced deep learning visual technology in environmental monitoring.</p> <p><strong>Usage Policy:</strong><br>If you plan to use our data in a scientific analysis paper, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.</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.