Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
979
datasets available to search
ShareScore release 0.9.0
Dataset results
979 results for “image dataset”
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Maestro Platform-Generated Dataset of Classified Bird Images
<p>The bird dataset, mentioned in the publication titled "Evaluation of Maestro, an extensible general-purpose data gathering and data classification platform" comprises two files: a zip file containing the classified files and a JSON file that includes the corresponding classification results.</p>
Maestro Platform-Generated Dataset of Classified Bird Images
<p>The bird dataset, mentioned in the publication titled "Maestro: An Extensible General-Purpose Data Gathering and Data Classification Platform," comprises two files: a zip file containing the classified files and a JSON file that includes the corresponding classification results.</p>
image classification dataset on tailored textiles quality control
<p>This dataset was geared towards representing practical quality control scenarios, specifically involving the quality inspection of glass fiber fabric. Continuous rolls of glass fiber fabric were cut into samples of 300x200 mm. Half of these samples were reinforced with a single carbon fiber. These samples were then classified into six different categories based on the presence of common defects or if they were error-free textiles. Each category consists of 300 images, with a resolution of 4288x2848 pixels.</p>
Supplementary dataset for 'Super-resolution vibrational imaging based on photoswitchable Raman probe'
<p>Here's a dataset for 'Super-resolution vibrational imaging based on photoswitchable Raman probe.' </p>
Turbulence-distorted infrared imaging dataset.
<p>This dataset contains the real data used in the paper and the full simulation data of our constructed turbulence-distorted infrared imaging dataset. </p>
A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup
<p>We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4].</p> <table> <caption>Details of this dataset</caption> <tbody> <tr> <td>Domain</td> <td>Cases in Each Domain<br> (Training/Test)</td> </tr> <tr> <td>REFUGE</td> <td>320/80</td> </tr> <tr> <td>Drishti-GS</td> <td>50/51</td> </tr> <tr> <td>ORIGA</td> <td>500/150</td> </tr> <tr> <td>BinRushed (RIGA)</td> <td>156/39</td> </tr> <tr> <td>Magrabia (RIGA)</td> <td>76/19</td> </tr> </tbody> </table> <p>[1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570.</p> <p>[2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56.</p> <p>[3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068.</p> <p>[4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre> <p> </p>
WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Train part
<p>The training parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</p> <p>The validation and testing parts can be found on: <a href="https://doi.org/10.5281/zenodo.8035349">https://doi.org/10.5281/zenodo.8035349</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,<a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p>[2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, <a href="http://10.1109/JSTARS.2022.3211857">http://doi.org/10.1109/JSTARS.2022.3211857</a>.</p>
Physics-driven universal twin-image removal network for digital in-line holographic microscopy - dataset
<p>Dataset (Matlab files) containing holograms and reference reconstructions employed in:<br> <br> M. Rogalski, P. Arcab, L. Stanaszek, V. Micó, C. Zuo and M. Trusiak, "Physics-driven universal twin-image removal network for digital in-line holographic microscopy", Submitted 2023<br> <br> This dataset should be used together with the codes present at:<br> https://github.com/MRogalski96/UTIRnet</p>
Supplementary Dataset for the Report "Imaging the Western Edge of the Aegean Shear Zone: The South Evia 2022-2023 Seismic Sequence"
<p>Supplementary Dataset for the Fast Report "<strong>Imaging the Western Edge of the Aegean Shear Zone: The South Evia 2022-2023 Seismic Sequence</strong>" by Christos P. Evangelidis and Ioannis Fountoulakis.</p> <p>The Fast Report can be found in <em><strong>Seismica Vol. 2 No. 1 (2023)</strong></em>.</p> <p><strong>DOI:</strong> <a href="https://doi.org/10.26443/seismica.v2i1.1032">https://doi.org/10.26443/seismica.v2i1.1032</a></p> <p><em><strong>Abstract:</strong></em></p> <p>This report presents the 2022-2023 South Evia island seismic sequence, in the western Aegean sea. An automated workflow, undergoing testing for efficient observatory monitoring in the wake of dense aftershock sequences, was employed to enhance the seismic catalog. It includes a deep-learning phase picker, absolute and relative hypocenter relocation, and moment tensor automatic calculations. The relocated catalog reveals a concentration of earthquake epicenters in a narrow NW-SE zone, with sinistral strike-slip fault movement. The findings of the study indicate the occurrence of an asymmetric rupture within conjugate fault structures in the western Aegean region. These fault structures, although not necessarily both active, play a significant role in marking the transition from dextral (SW-NE) to sinistral (NW-SE) strike-slip ruptures, connecting the Aegean shear zone with normal faulting in mainland Greece. The South Evia 2022-2023 seismic sequence has revealed the activation of this NW-SE strike-slip structure, contrary to previous assumptions of low seismicity in the region. The study highlights the importance of reassessing seismic hazard maps and considering the potential activation of similar zones further south in the future. It also emphasizes the need for the expansion and the densification of seismic networks within the Aegean.</p> <p><em><strong>Files:</strong></em></p> <p><strong>Relocated_Catalog.txt</strong>: Relocated earthquake catalog for the South Evia sequence. Included in the document are details concerning the origin time, the hypocentral positions, errors in determining the location, and the local magnitude of each earthquake.</p>
Dataset: Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture
<p>Dataset for Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture manuscript submitted to Plant Phenomics. The dataset contains raw and processed root architecture images, RhizoVision trait outputs, and the associated R scripts for statistical analysis and model parameterization.</p>
Swahili Image Captioning Dataset
<p>The SwaFlickr8k dataset is an extension of the well-known Flickr8k dataset, specifically designed for image captioning tasks. It includes a collection of images and corresponding captions written in Swahili. With 8,091 unique images and 40,455 captions, this dataset provides a valuable resource for research and development in the field of image understanding and language processing, particularly in the context of the Swahili language.</p>
Datasets for a data-centric image classification benchmark for noisy and ambiguous label estimation
<p>This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at</p> <p>Paper: https://arxiv.org/abs/2207.06214</p> <p>Source Code: https://github.com/Emprime/dcic</p> <p>The license information is given below as download.</p> <p><strong>Citation</strong></p> <p>Please cite as</p> <pre><code>@article{schmarje2022benchmark, author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd}, journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks}, title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}}, year = {2022} }</code></pre> <p>Please see the full details about the used datasets below, which should also be cited as part of the license.</p> <pre><code>@article{schoening2020Megafauna, author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J}, doi = {10.5194/bg-17-3115-2020}, journal = {Biogeosciences}, number = {12}, pages = {3115--3133}, title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}}, volume = {17}, year = {2020} } @article{Langenkamper2020GearStudy, author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W}, doi = {10.3389/fmars.2020.00506}, issn = {2296-7745}, journal = {Frontiers in Marine Science}, title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}}, volume = {7}, year = {2020} } @article{peterson2019cifar10h, author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga}, doi = {10.1109/ICCV.2019.00971}, issn = {15505499}, journal = {Proceedings of the IEEE International Conference on Computer Vision}, pages = {9616--9625}, title = {{Human uncertainty makes classification more robust}}, volume = {2019-Octob}, year = {2019} } @article{schmarje2019, author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard}, doi = {10.1007/978-3-030-33676-9_26}, issn = {23318422}, journal = {DAGM German Conference of Pattern Regocnition}, number = {November}, pages = {374--386}, publisher = {Springer}, title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}}, volume = {11824 LNCS}, year = {2019} } @article{schmarje2021foc, author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, doi = {10.3390/s21196661}, issn = {1424-8220}, journal = {Sensors}, number = {19}, pages = {6661}, title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}}, volume = {21}, year = {2021} } @article{schmarje2022dc3, author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard}, journal = {Proceedings of the European Conference on Computer Vision (ECCV)}, title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}}, year = {2022} } @article{obuchowicz2020qualityMRI, author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam}, doi = {10.1186/s12880-020-00505-z}, issn = {1471-2342}, journal = {BMC Medical Imaging}, number = {1}, pages = {109}, title = {{Interobserver variability in quality assessment of magnetic resonance images}}, volume = {20}, year = {2020} } @article{stepien2021cnnQuality, author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz}, doi = {10.3390/s21041043}, issn = {1424-8220}, journal = {Sensors}, number = {4}, title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}}, volume = {21}, year = {2021} } @article{volkmann2021turkeys, author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit}, doi = {10.3390/ani11092655}, journal = {Animals 2021}, pages = {1--13}, title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}}, volume = {11}, year = {2021} } @article{volkmann2022keypoint, author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard}, doi = {10.3390/s22145188}, issn = {1424-8220}, journal = {Sensors}, number = {14}, pages = {5188}, title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}}, volume = {22}, year = {2022} }</code></pre> <p>Addition: This repository also contains the original data from the paper "Annotating Ambiguous Images" (https://arxiv.org/abs/2306.12189). The data is created based on the original datasets and license from https://osf.io/t98fz/ and https://osf.io/nqjyw/</p>
Dataset for "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique"
<p>This is the dataset used in a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>The content is</p> <ul> <li>Augmented images used in the U-Net training (aug_images.zip) <ul> <li>train/*.png: augmented original images (14,219 files)</li> <li>train_masks/*.png: augmented hand-masked images (14,219 files).</li> </ul> </li> </ul> <p>Note that original images include images obtained using <em>google-image-download</em>, a Python script published on GitHub (<a href="https://github.com/Joeclinton1/google-images-download/tree/patch-1">https://github.com/Joeclinton1/google-images-download/tree/patch-1</a>, Copyright © 2015-2019 Hardik Vasa). The whole images we obtained by <em>google-image-download</em> were labeled as noncommercial reuse with modification.</p> <p>For more details, please refer to a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>Correspondence: Rina Noguchi (r-noguchi@env.sc.niigata-u.ac.jp)</p>
Squint Imaging Dataset
<p>Artifact evaluation for Squint Imaging for "Squint: A Framework for Dynamic Voltage Scaling of Image Sensors Towards Low Power IoT Vision" MobiCom 2023</p>
Dataset for: Star Photometry for DECaLS and SDSS Images Based on Convolutional Neural Networks
<p>The dataset consists of two parts, the training dataset and the comparison dataset. The training dataset and the comparison dataset are each divided into three parts, namely the simulation dataset, the SDSS dataset and the DECaLs dataset.</p> <p>The simulation dataset is simulated by PhoSim software and the original size of the simulation data is 512x512 pixels. The SDSS dataset is from DR12 and the ObjId of the target as well as other parameters are given in the csv file.The DECaLs dataset is from DR9.</p>
Test Dataset for Whole Slide Image Registration
<p>Mouse duodenum fixed in 4% PFA overnight at 4°C, processed for paraffin infiltration using a standard histology procedure and cut at 4 microns were dewaxed, rehydrated, permeabilized with 0.5% Triton X-100 in PBS 1x and stained with Azide - Alexa Fluor 555 (Thermo Fisher) to detect EdU and DAPI for nuclei. The images were taken using a Leica DM5500 microscope with a 40X N.A.1 objective (black&white camera: DFC350FXR2, pixel dimension: 0.161 microns). Next, the slide was unmounted and stained using the fully automated Ventana Discovery xT autostainer (Roche Diagnostics, Rotkreuz, Switzerland). All steps were performed on automate with Ventana solutions. Sections were pretreated with heat using the CC1 solution under mild conditions. The primary rat anti BrDU (clone: BU1/75 (ICR1), Serotec, diluted 1:300) was incubated 1 hour at 37°C. After incubation with a donkey anti rat biotin diluted 1:200 (Jackson ImmunoResearch Laboratories), chromogenic revelation was performed with DabMap kit. The section was counterstained with Harris hematoxylin (J.T. Baker) before a second round of imaging on DM5500 PL Fluotar 40X N.A.1.0 oil (color camera: DFC 320 R2, pixel dimension: 0.1725 microns). Before acquisition, a white-balance as well as a shading correction is performed according to Leica LAS software wizard. The fluorescence and DAB images were converted in ome.tiff multiresolution file with the <a href="https://github.com/BIOP/ijp-kheops">kheops Fiji Plugin</a>.</p> <p>Sampled prepared in the <a href="https://www.epfl.ch/research/facilities/histology-core-facility/">EPFL histology core facility</a> by Nathalie Müller and Gian-Filippo Mancini.</p> <p>Associated documents:</p> <ul> <li><a href="https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/">https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/</a></li> <li>https://imagej.net/plugins/bdv/warpy/warpy</li> </ul> <p>This document contains a full QuPath project with an example of registered image.</p> <p> </p>
External test datasets for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"
<p>In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays.</p> <p>To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results.</p> <p>SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at <a href="https://github.com/FunctionalUrology/SpheroScan">https://github.com/FunctionalUrology/SpheroScan</a>.</p>
Dataset for automated image-based generation of finite element models for masonry buildings
<p>This repository contains the dataset used for computing finite element models for masonry buildings via image-based approach. The method that uses this data set was presented in the paper "Automated image-based generation of finite element models for masonry buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1007/s10518-023-01726-7</p>
Image datasets used in the paper "Revealing invisible cell phenotypes with conditional generative modeling"
<p>- BBBC021_selection_128 is a selection of the BBBC021 image dataset from the Broad Bioimage Benchmarck Collection from the Broad Institute</p> <p>- golgi_256_subset is a subset (one plate) of the Golgi Dataset we used (which is about 3 times larger). It was generated by the Biophenics platform in Institut Curie, Paris, France</p> <p>- translocation_256 is the translocation Dataset we used. It was generated by the Biophenics platform in Institut Curie, Paris, France</p> <p>- LRKK2_256 is the Parkinson LRKK2 mutation dataset we used. It was generated by Ksilink, Strasbourg, France</p> <p>- smala_256 is the Malaria dataset we used. It was generated by IRD, Paris, France and acquired by the Histopathology Platform at Institut Pasteur in Paris, France. </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.