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11,687 results for “training”
Dataset for training the Surrogate Model of microlaser neurons on the reduced MNIST classification task
<p>This dataset was used to train a surrogate multilayer perceptron surrogate model of microlaser neurons.</p> <p>It is in csv format. It was generated using the Yamada Model as found in </p> <p><span>Selmi F, Braive R, Beaudoin G, Sagnes I, Kuszelewicz R and Barbay S 2014 Relative Refractory Period in an Excitable Semiconductor Laser <em>Phys. Rev. Lett.</em> <strong>112</strong> 183902</span>.</p>
A dataset recorded during development of an affective brain-computer music interface: training sessions
Open the record for dataset details and reuse information.
Silva SSU taxonomic training data formatted for DADA2 (Silva version 138)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 138 release. See https://www.arb-silva.de/documentation/release-138/ for database and citation information. The Silva 138 database is licensed under Creative Commons Attribution 4.0 (CC-BY 4.0); see file "SILVA_LICENSE.txt". The fasta files were generated and checked for consistency with version 132 using the R code in the R-markdown document "silva-v138.Rmd".</p> <p>Version 2 removes the dependence on preprocessed files from mothur, which results in a greater number of bacterial and archeal sequences. It also includes a new version of the assignTaxonomy training set that goes through the species level for use with longer amplicons obtained from long-read amplicon sequencing.</p> <p>If you use these files, please cite one or both of the Silva references below (or at the above link) and the DADA2 paper (reference below). I also recommend citing or linking to the Zenodo record for this specific version in your Methods or published source code to record the specific taxonomic database files used in your analysis.</p> <p><strong>NOTE:</strong><strong> </strong>These Version 2 files are intended for use in classifying prokaryotic 16S sequencing data and are not appropriate for classifying eukaryotic ASVs. The new method implemented within DADA2 for constructing these files only includes 100 eukaryotic sequences for use as an outgroup.</p> <p><strong>NOTE:</strong> These Version 2 files have a known problem in 10/883 families and 114/3838 genera. See https://github.com/mikemc/dada2-reference-databases/blob/main/silva-138/v2/bad-taxa.csv for a list of affected taxa and https://github.com/benjjneb/dada2/issues/1293 for more information.</p>
IPBES Data Management Tutorials - Session 1.2: Introduction to IPBES tutorials and training
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Introduction to the IPBES data management policy</em> chapter provides an overview on data management within the IPBES platform, and the series of the tutorials prepared by the task force on knowledge and data that will assist experts with the implementation of the IPBES data management policy.</p> <p>This session, <em>Introduction to the IPBES tutorials and training</em>, provides<strong> </strong>a short introduction detailing the objectives of these tutorials and the responsibilities of the IPBES secretariat regarding data management. </p>
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Pre-trained models for segmentation and tracking of Coronal Bright Fronts from SDO AIA Base Difference images
<p>Here we present pretrained U-NET-based models followed by SDO AIA Base Difference(BD) validation set after intensity tresholding [-50;150] with predicted feature masks samples. <br>We provide a command-line Python utility for image segmentation using our CNNs designed to process images of solar eruptive phenomena. The https://gitlab.com/iahelio/helios_cnn repository includes regularly updated and newly published models. </p> <p>First model we present is designed to predict the likelihood of each pixel belonging to a certain class or feature in the solar image. A probabilistic output allows for a more nuanced interpretation of ambiguous region. The output can be converted into binary masks through thresholding. The range of values also gives insights into the model's confidence</p> <p>We also present sample segmentation results and the second model designed to produce binary masks.</p>
Smartbay Marine Species Object Detection Training dataset
<h1>Training dataset</h1> <p>The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.</p> <p>The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.</p> <p>The Smartbay Marine Species Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using species names.</p> <p>The imagery used in this training dataset consists of image frame captures from the <a href="https://smartbay.marine.ie">Smartbay</a> video Archive files, CC-BY imagery from the <a href="https://www.minka-sdg.org">www.minka-sdg.org</a> website and images taken by Eva Cullen in the "<a href="https://nationalaquarium.ie/">Galway Atlantaquaria</a>" Aquarium in Galway, Ireland.</p> <p>The imagery were annotated using CVAT, collated on <a href="https://www.roboflow.com/">Roboflow</a> and exported in YOLOv8 training dataset format. </p>
Smartbay Marine Types Object Detection Training dataset
<h1>Training Dataset</h1> <p>The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.</p> <p>The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.</p> <p>The Smartbay Marine Types Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using broad "Marine Type" classes.</p> <p>The imagery used in this training dataset consists of image frame captures from the <a href="https://smartbay.marine.ie">Smartbay</a> video Archive files, CC-BY imagery from the <a href="https://www.minka-sdg.org">www.minka-sdg.org</a> website and images taken by Eva Cullen in the "<a href="https://nationalaquarium.ie/">Galway Atlantaquaria</a>" Aquarium in Galway, Ireland.</p> <p>The imagery were annotated using CVAT, collated on <a href="https://www.roboflow.com/">Roboflow</a> and exported in YOLOv8 trainign dataset format. </p>
ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC
<p>This data set is used for the training, validation and evaluation of retrievals of temperature and specific humidity profiles, as well as integrated water vapour from simlulated or measured microwave brightness temperatures (TBs), which are described in <strong>[1]</strong>.</p> <p>The data set consists of yearly files (2001-2018, 6-hourly resolution) that include data from the European Centre for Medium-Range Weather Forecasts's ERA5 reanalysis <strong>[2]</strong> and simulated TBs in the microwave spectrum. TB simulations were performed with PAMTRA <strong>[3,4]</strong> on the native ERA5 model level resolution at frequencies of a low frequency Humidity and Temperature Profiler (HATPRO, 22-58 GHz) and of a Low Humidity Profiler (LHUMPRO-243-340, aka MiRAC-P, 175-340 GHz). Afterwards, the ERA5 model level data has been interpolated to a new height grid (dimension 'z'), of which the lowest 43 indices equal the height grid of the retrieval that is developed with this data set. The upper 11 indices are included for additional TB simulations needed for the information content estimation performed and are not used for the retrievals to avoid the tropopause.</p> <p>The trained retrieval is applied to observations from the HATPRO and MiRAC-P that were installed onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.</p> <p><strong>[3]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[4]:</strong> Mech, M., Maahn, M., Ori, D., Kneifel, S., and Orlandi, E.: PAMTRA Package – Passive and Active Microwave TRANsfer, available at: https://github.com/igmk/pamtra (last access: 6 September 2020), 2019c.</p>
Training and test dataset of STED images of microtubules in fixed cells
<p>Training and test dataset of microtubule used in the manuscript "Denoising diffusion models for high-resolution microscopy image restoration".</p>
Training Images for "ImmuNet" Convolutional Neural Network
<p>This dataset contains all annotations and images for training the machine learning architecture presented in this manscript:</p> <p>Shabaz Sultan, Mark A. J. Gorris, Lieke L. van der Woude, Franka Buytenhuijs, Evgenia Martynova, Sandra van Wilpe, Kiek Verrijp, Carl G. Figdor, I. Jolanda M. de Vries, Johannes Textor:<br>ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.<br>Biology Methods and Protocols 10(1), bpae094, 2025. doi: 10.1093/biomethods/bpae094</p> <p>The .tar.gz file contains several multichannel images stored as TIFF files, and arranged in a folder structure that is convenient for matching the files to the annotations provided in the .json.gz file. We also provide an .h5 file that contains the final trained network that was used to generate the figures in this manuscript.</p> <p>Further information on the data can be found in the manuscript cited above. Instructions on how to use the annotations and the code can be found on our GitHub page at: https://github.com/jtextor/immunet</p>
Super-resolving ocean dynamics from space with computer vision algorithms: training datasets
<p>We provide here the datasets used for the development of the dilated Adaptive Residual Network for the super-resolution of ocean Absolute Dynamic Topography described in <em>Buongiorno Nardelli et al.</em> (2022). The model is designed to combine satellite altimetry and thermal observations and provides super-resolved dynamic topography. The training/test datasets have been built starting from the data originally prepared for an Observing System Simulation Experiment carried out in the framework of the European Space Agency CIRCOL project [<em>Ciani et al.</em>, 2021]. They consist of one year of synthetic daily Absolute Dynamic Topography (ADT), surface geostrophic currents and sea surface temperature data obtained from Copernicus Marine Service Mediterranean Forecasting System (MFS) (Product ID: MEDSEA-ANALYSIS- FORECAST-PHY-006-013) [<em>Clementi et al. 2021</em>]. Synthetic Altimeter-derived ADT maps were obtained by first sampling the model output along the actual tracks of a synthetic constellation composed of 4 Radar Altimeters: Jason-3, Sentinel-3A, SARAL/Altika, and Cryosat-2 missions (this step is achieved by running the SWOT simulator software [<em>Gaultier et al.</em>, 2016]) and successively applying the DUACS (<em>Data Unification and Altimeter Combination System)</em> mapping method. The original input images cover the entire Mediterranean domain at 1/24° spatial resolution, leading to an individual image size of 380x1000 pixels. Here, we have randomly chosen 40 dates (~11% of the total) to be kept aside as fully independent test data, and successively re-sampled the original images extracting much smaller tiles (76x100), which are used as input to the network training. The tiles are extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%. Full details on data pre-processing (e.g.normalization strategies) are given in the paper:</p> <ul> <li>Buongiorno Nardelli, B.; Cavaliere, D.; Charles, E.; Ciani, D. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. <em>Remote Sens.</em>, <strong>2022</strong>, 14, 1159. https://doi.org/10.3390/rs14051159</li> </ul>
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
A list of items in the FAIRsFAIR training library
<p>A file containing a list of items, with basic metadata, that were included in the <a href="https://www.fairsfair.eu/competence-centre/training-library">FAIRsFAIR training library</a> </p> <p> </p>
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
Training data for bathymetry estimation via EO satellite - Hel Peninsula
<p>This dataset contains satellite image from Sentiel-2A (bands B2, B3, B4, B8) and reference sonar based bathymetry measurements.</p> <p>The reference data was acquired from Polish Maritime Administration (htttp://www.um.gdy.pl) and is publicly available. File reference_data_34.csv contains in-situ measrements aquired at northen shore of Hel Peninsula. Points coordinates are expressed in UTM34N coordinate system.</p> <p>The satellite and the reference datasets were preprocessed by the authors for adjust them for Machine Learning algorithms used in the research.</p> <p> </p>
Training data for 'Upload data to ENA' (Galaxy Training Material)
<p>The data here is a subset of the data published in 10.5281/zenodo.3732359 to be used in GTN 'Upload data to ENA' tutorial.</p> <p>Human traces have been removed following <a href="https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html">https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html</a></p> <p>We produced consensus sequences (*.fasta) for the Illumina PE data following SARS-CoV-2-PE-Illumina-WGS-variant-calling (https://workflowhub.eu/workflows/113?version=4), SARS-CoV-2-variation-reporting (https://workflowhub.eu/workflows/109?version=5) and COVID-19-consensus-construction (https://workflowhub.eu/workflows/138?version=4) workflows.</p>
MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training
<p>Dataset of a leaf segment of a hybrid poplar (<em>P. maximowiczii x P. nigra</em> ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.</p> <p> </p> <p><strong>Data acquisition methodology</strong></p> <p>Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstruction.</p> <p> </p> <p><strong>Dataset description</strong></p> <p>On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks.</p> <p>For all stacks, files start with:<br> <em>DEHYDRATION_small_Leaf4_time_N_</em><br> where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes.</p> <p>Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest.</p> <p>Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>):</p> <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> <p>A general idea of the slice range within one LABELLED stack is presented after the orientation, as:<br> <em>STARTtoENDbyRANGE</em><br> The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. <strong>Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).</strong></p> <p> </p> <p><strong>References</strong></p> <p>Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037.</p> <p>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.</p>
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from EEG Time Series
<h2>The files include the 20 training datasets, in csv format, from 20 epileptic patients. Each set of data is described by 1080 features extracted using the sliding window technique.</h2>
Training data for: CoastSat image classification
<p><strong>CoastSat image classification training data </strong></p> <p>CoastSat is an open-source global shoreline mapping toolbox, available at https://github.com/kvos/CoastSat, which enables users to extract time-series of shoreline change from 30+ years of publicly available satellite imagery (Landsat 5, 7, 8 and Sentinel-2).</p> <p>The automated shoreline extraction relies on a classifier (Multilayer Perceptron from scikit-learn) which labels each pixels on the images with one of four classes: sand, water, white-water and other land features.</p> <p>The data used to train the classifier is stored here, the README.md file provides information on the data organisation and content of each file.</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.