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134 results for “Super Resolution”
An Automated Method for Measuring Tree Rings Based on Super Resolution and Image Segmentation
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BreizhSR: multi-temporal cross-sensor super-resolution of satellite imagery
<h1>BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset </h1> <h2>1. Dataset motivation</h2> <p><strong>BreizhSR</strong> is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.</p> <h2>2. Dataset organization</h2> <p>The dataset folder follows the structure detailed below :</p> <p><code>BreizhSR</code><br><code>├── dataset_test.pkl</code><br><code>├── dataset_train.pkl</code><br><code>├── README.md</code><br><code>├── x</code><br><code>├── x_test</code><br><code>├── y</code><br><code>└── y_test</code></p> <p>The <code>README.md</code> file contains the same information as this description.</p> <p>Actual image patches are stored in the <code>x</code> and <code>x_test</code> folders for Sentinel-2 patches, and in the <code>y</code> and <code>y_test</code> folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. <code>8355</code>) that denote the series identifier. Therefore, for the S2 series <code>x/8355</code>, the corresponding SPOT patch is in subfolder <code>y/8355</code>.</p> <p>This organization and additional metadata are described in two <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">Pandas Dataframes</a> : <code>dataset_train.pkl</code> and <code>dataset_test.pkl</code>. These files are Dataframes serialized using the <a href="https://docs.python.org/3/library/pickle.html">pickle Python serialization protocol</a>. The columns available in these Dataframes are described in the table below.</p> <table> <tbody> <tr> <td>x</td> <td>y</td> <td>wkt</td> <td>spot6_name</td> <td>sen2_acquisitions</td> <td>dates_sen2</td> <td>dates_spot6</td> <td>split</td> </tr> <tr> <td>Latitude of the center point (expressed in Lambert 93 CRS)</td> <td>Longitude of the center point (expressed in Lambert 93 CRS)</td> <td>Area of interest geometry in well-known text format</td> <td>Path to the SPOT ground truth</td> <td>Paths to the Sentinel-2 input series</td> <td>Acquisition dates for the Sentinel-2 images</td> <td>Acquisition date for the SPOT ground truth</td> <td>`train` or `test`</td> </tr> </tbody> </table> <h2>3. Data collection and preprocessing</h2> <h3>Sentinel-2</h3> <p>Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.</p> <p>Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.</p> <h3>SPOT-6/7</h3> <p>Orthorectified SPOT data under the Licence Ouverte is collected from the <a href="https://openspot-dinamis.data-terra.org/">DINAMIS</a> platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm <a href="https://www.orfeo-toolbox.org/CookBook/Applications/app_BundleToPerfectSensor.html">Orfeo ToolBox</a>, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.</p> <h2>4. License</h2> <p>SPOT images and the Sentinel-2 Theia L2A products are released under the <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">Licence Ouverte 2.0</a> from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).</p> <h3>Acknowledgements</h3> <p>We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).</p>
Advances in volumetric super-resolution microscopy and single-particle tracking (associated codes and datasets)
<h2>Overview</h2> <p>This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by <a href="https://www.ch.cam.ac.uk/person/sgd46">Sam G. Daly</a> (Yusuf Hamied Department of Chemistry, University of Cambridge).</p> <p>Managed/updated versions my be avalible at <a href="https://github.com/TheLeeLab">https://github.com/TheLeeLab</a>.</p> <p>The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM.</p> <h2>Available Datasets</h2> <h3>Chapter 4</h3> <ol> <li>Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames.</li> <li>Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames.</li> </ol> <h3>Chapter 5</h3> <ol> <li>B cell receptor imaging on a fixed B cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>SPT of the B cell receptor on a live B cell (PALM); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>PD-1 imaging on a fixed T cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed T cell (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> </ol> <h3>Chapter 6</h3> <ol> <li>SPT of ACBD3 in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> <li>SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> </ol> <h2>Available Code</h2> <ol> <li><strong>Autofocus (BeanShell):</strong> Counteracts axial drift in SMLFM experiments.</li> <li><strong>Calibration (BeanShell):</strong> Controls the piezo scanner for axial calibrations in 3D-SMLM.</li> <li><strong>3D Reconstruction (Matlab):</strong> Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub.</li> <li><strong>Fiducial correction (Matlab):</strong> Removes focal drift artifacts from 3D localisation data.</li> <li><strong>Temporal grouping (Python):</strong> Removes multiple single-molecule blinking events.</li> <li><strong>3D tracking (Matlab):</strong> Converts 3D localisations into tracks and calculates diffusion quantities.</li> <li><strong>Matching (Matlab):</strong> Determines PPV, sensitivity, and Jaccard index from localisation data.</li> <li><strong>Membrane curvature (Python):</strong> Determines the frequency of 3D localisations at a given membrane curvature.</li> </ol> <h3><em>Supported by The Royal Society (RGF\EA\181021) </em></h3>
Data and scripts for the paper: Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations
<p>This is a collection of the processed data, plotting scripts, and processing scrips used in the making of the publication: "Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations" By Griewank, Schemann, and Neggers. </p> <p>WARNING: These files were used exclusively used by Philipp Griewank on his local work station, and the main purpose of uploading them is for reasons of scientific transparency. They are not highly optimized tools intended for general usage, so comments are hit and miss. Feel free to contact Philipp Griewank (currently philipp.griewank@uni-koeln.de) with any questions. </p>
DRET-DNA PAINT: using Dark dyes for fast super-resolution imaging
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Dataset for "Effective super-resolution method for paired electron microscopic images"
<p>This is the image set used in the paper, Qian, Xu, Drummy, and Ding, 2020, “Effective super-resolution method for paired electron microscopic images,” <em>IEEE Transactions on Image Processing</em>, Vol. 29, pp. 7317–7330.</p>
North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/". </p>
Human STING is a proton channel (Live-cell MGAT Super-Resolution Experiment)
<p>hTERT-immortalized BJ1 cells (ATCC CRL-2522) expressing SEP-mRuby3 targeted to cis/medial Golgi (MGAT) were transduced with pXPR023 (lentiCRISPRv2) expressing an sgRNA targeting STING and selected with 0.1 µg/mL puromycin for 5 days. Cells were then transduced with blasticidin-STING-miRFP680 and selected using 10 µg/mL blasticidin HCl for 5 days. Cells were plated in 96-well glass-bottom plates (Greiner Bio-One) at 6,000 cells/well. After 48 hours, cells were incubated in Fluorobrite DMEM (Thermo Fisher Scientific, cat. #A1896701) medium supplemented with 10% FBS, 1% Pen-strep, and 1x GlutaMAX (Thermo Fisher Scientific, cat. #35050061) and stimulated with 1 µM diABZI (Invivogen, #tlrl-diabzi). All images were acquired using an LSM980 with Airyscan2 (Zeiss) with 37°C with 5% CO2 incubation. 8 z-stacks were acquired with 0.15 µm z-step. Images were acquired using a 63X 1.40 NA DIC M27 objective with Immersol 518F 37°C oil. Acquired images were Airyscan processed and then analyzed as described in the image analysis section.</p> <p>Each frame represens one timepoint imaged every 5 minutes post diABZI treatment. Channels are: SEP (super-ecliptic pHluorin), mRuby3, and STING-miRFP680.</p> <p> </p>
High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components
<p>Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm<sup>2</sup> and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.</p>
FireSR: A Dataset for Super-Resolution and Segmentation of Burned Areas
<p><br># FireSR Dataset</p> <p>## Overview</p> <p>**FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques.</p> <p>## Dataset Structure</p> <p>The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring:</p> <p>- **S2**: Contains Sentinel-2 images.<br> - **pre**: Pre-fire Sentinel-2 images (high resolution).<br> - **post**: Post-fire Sentinel-2 images (high resolution).</p> <p>- **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.<br> - **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.<br> - **post**: Burned area labels corresponding to post-fire conditions.</p> <p>- **MODIS**: Contains post-fire MODIS images (lower resolution).</p> <p>- **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023).</p> <p>- **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries.</p> <p>### File Naming Convention</p> <p>Each GeoTIFF (.tif) file is named according to the format: `CA_<year>_<province>_<id>.tif`, where:<br>- `CA` stands for Canada.<br>- `<year>` is the year of the wildfire event.<br>- `<province>` is the province code (e.g., AB for Alberta, BC for British Columbia).<br>- `<id>` is a unique identifier for the wildfire event.</p> <p>### Directory Structure</p> <p>The dataset is organized as follows:</p> <p>```<br>FireSR/<br>│<br>├── dataset/<br>│ ├── S2/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── mask/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── MODIS/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── LULC/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── Daymet/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>```</p> <p>### Spatial Resolution and Channels</p> <p>- **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4)<br>- **MODIS Images**: 250 meters (Bands: B7, B2, B1)<br>- **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned)<br>- **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp)<br>- **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland)</p> <p>**Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions:</p> <p>| Name | Units | Min | Max | Description |</p> <p>|------|-------|-----|-----|-------------|</p> <p>| dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. |</p> <p>| prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. |</p> <p>| srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. |</p> <p>| swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. |</p> <p>| tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. |</p> <p>| tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. |</p> <p>| vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. |</p> <p>**ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year.</p> <p>| Class Value | Land Cover Class |</p> <p>|-------------|------------------|</p> <p>| 1 | Water |</p> <p>| 2 | Trees |</p> <p>| 4 | Flooded Vegetation |</p> <p>| 5 | Crops |</p> <p>| 7 | Built Area |</p> <p>| 8 | Bare Ground |</p> <p>| 9 | Snow/Ice |</p> <p>| 10 | Clouds |</p> <p>| 11 | Rangeland |</p> <p><br>## Usage Tutorial</p> <p>To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow:</p> <p>### Step 1: Extract FireSR.tar.gz</p> <p>```bash<br>tar -xvf FireSR.tar.gz<br>```</p> <p>### Step 2: Tiling the GeoTIFF Files</p> <p>The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images:</p> <p>```python<br>import rasterio<br>from rasterio.windows import Window<br>import os</p> <p>def tile_image(image_path, output_dir, tile_size=128):<br> with rasterio.open(image_path) as src:<br> for i in range(0, src.height, tile_size):<br> for j in range(0, src.width, tile_size):<br> window = Window(j, i, tile_size, tile_size)<br> transform = src.window_transform(window)<br> outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")<br> with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:<br> dst.write(src.read(window=window))</p> <p># Example usage<br>tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/')<br>```</p> <p>### Step 3: Loading Data into a Machine Learning Model</p> <p>After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch:</p> <p>```python<br>import torch<br>from torch.utils.data import Dataset<br>from torchvision import transforms<br>import rasterio</p> <p>class FireSRDataset(Dataset):<br> def __init__(self, image_dir, transform=None):<br> self.image_dir = image_dir<br> self.transform = transform<br> self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]</p> <p> def __len__(self):<br> return len(self.image_paths)</p> <p> def __getitem__(self, idx):<br> image_path = self.image_paths[idx]<br> with rasterio.open(image_path) as src:<br> image = src.read()<br> if self.transform:<br> image = self.transform(image)<br> return image</p> <p># Example usage<br>dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor())<br>dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)<br>```</p> <p>## License</p> <p>This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.</p> <p>## Contact</p> <p>For any questions or further information, please contact:<br>- Name: Eric Brune<br>- Email: ebrune@kth.se</p>
Data for: High-throughput expansion microscopy enables scalable super-resolution imaging
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Quantification of endogenous and exogenous protein expressions of Na,K-ATPase with super-resolution PALM/STORM imaging
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Comparing Lifeact and Phalloidin for super-resolution imaging of actin in fixed cells
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Electrochemically controlled switching of dyes for enhanced super-resolution optical fluctuation imaging (EC-SOFI)
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Implementation of a 4Pi-SMS super-resolution microscope - Example data
<p>4Pi-SMS image of indirectly immunostained ɑ-tubulin labelled with CF660C in a COS7 cell in water-based imaging buffer.</p>
Raw data accompanying the manuscript "Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope"
<p>Raw data sets (.tif files) of data utilized to demonstrate 2D SIM with an unmodified multi photon intravital fluorescence microscope.</p>
Spatially Variant Super-Resolution (SVSR) benchmarking dataset
<p>The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 real low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding real noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers. The associated paper can be found here: <a title="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf" href="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf">https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf</a></p>
live cell super-resolution data_dual_color_cell_line_SCR_deletion
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live cell super-resolution data_dual_color_cell_line_E15_deletion
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live cell super-resolution data_dual_color_cell_line_original
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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.