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403 results for “Satellite data”

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zenodo36/100

Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.

Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.

opencc-by-4.0Nov 2023View details →

zenodo36/100

Global continuous 0.05 degree atmospheric carbon dioxide dataset (GCXCO2) based OCO-2 satellite, CAMS and CarbonTracker simulation data from 2000 to 2020

This dataset provides global seamless 8-day XCO2 (column-averaged CO2 dry air mole fraction) with a spatial resolution of 0.05 degree from 2000 to 2020. The unit is ppm. The detailed process and product validation accuracy can be found in our paper  at https://doi.org/10.1016/j.scitotenv.2024.177051

opencc-by-4.0Nov 2023View details →

dryad36/100

Supporting data for assessing impacts of satellite GPS transmitters on survival, nesting propensity, and nest success of greater sage-grouse

Telemetry technology and data are commonly used to study behavior and demography of wildlife. Satellite-based, global positioning system (GPS) telemetry allows researchers to remotely collect a high volume of fine-resolution animal location data but may also come with hidden costs. For example, recent studies suggested GPS transmitters attached via backpacks may reduce survival of greater sage-grouse (Centrocercus urophasianus) relative to very high frequency (VHF) telemetry transmitters attached via collars. While some evidence suggests GPS backpacks can reduce survival, no studies examined their effects on sage-grouse breeding behavior and success. We compared survival, breeding behavior, and nest success for sage-grouse hens marked with either VHF collars or GPS backpack transmitters in central Idaho, USA. GPS backpacks reduced spring-summer survival relative to VHF collars, yet GPS backpacks did not consistently affect nest success or the likelihood or timing of nest initiation relative to VHF collars. Daily nest survival varied annually and with timing of nest initiation and nest age, but marginal effects of transmitter type were statistically insignificant, and interactions between transmitter type and study year were inconsistent. These results demonstrate the effect of GPS backpacks on sage-grouse survival but also suggest GPS backpacks do not appear to affect components of fecundity.

opencc-zeroDec 2023View details →

zenodo36/100

Data files for Sheehan et al. 2023 'City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona' DOI: https://doi.org/10.3390/rs15245709

Data files for Sheehan et al. (2023) City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona. Remote Sensing. 15(24) DOI: <a href="https://doi.org/10.3390/rs15245709">https://doi.org/10.3390/rs15245709</a> Description of contents:  xView-YOLOv3_Model6_Barcelona_weights.pt This file contains the pre-trained weights for the xView-YOLOv3 model (model code available here: https://github.com/ultralytics/xview-yolov3). These weights were trained on a manually created training data set of vehicles present in WorldView 2/3 imagery covering the city of Barcelona. The weights relate to Model 6 set up: a single vehicle class (parked, static and moving), RGB imagery, Barcelona training data set derived anchor boxes, 1500 x 1500 pixel sized images and to 1000 epochs.

opencc-by-4.0Dec 2023View details →

dryad36/100

Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data

The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.

opencc-zeroJan 2024View details →

zenodo36/100

94 GHz cloud radar simulation data using NICAM and Joint simulator for evaluation of ground-based radar and application to the EarthCARE satellite

Overview These data are snapshots of simulated radar reflectivity and Doppler velocity from the 94 GHz Cloud Profiling Radar (CPR). The simulations were conducted using the Joint-Simulator for Satellite Sensors (Joint-Simulator; Hashino et al., 2013; Roh et al., 2020), and the input data was from the Nonhydorstatic Icosahedral Atmospheric Model (NICAM; Satoh et al., 2014). To focus on the region of interest with high resolution, NICAM transformed the grid (the stretched NICAM; Tomita 2008a) using a G-Level 10 (GL10) horizontal resolution with a stretch-factor of 100 (the ratio between the maximum and minimum grid intervals), where the minimum grid interval is approximately 800 m. We simulated two cases of rain events in September 2019. The first case (case 1) is the tropical cyclone (TC) Faxai. The second is a weak frontal system (case 2). In case 1 the integration and analysis time was from 00 UTC on 8 September to 00 UTC on 9 September 2019. In case 2 the integration and analysis time was from 00 UTC on 20 September to 00 UTC on 21 September 2019. We evaluated the data using the gournd observation and introduced a methodology for using the CPR data for model evaluations. We simulated Doppler velocity of the EarthCARE CPR.    Directory structure and file format There are two files. For Ground_CPR, there are simuation data based on the ground. For Satellite_CPR, there are simulation data bsed on instrument setting of EarthCARE CPR. (Note the order of the array of Satelltie_CPR is different from Ground_CPR.) The files are in the NetCDF4 format. The files have the following name format. AAA_XXX_TIME_EASE.nc AAA: Ecare (CPR simulation withot random errors), Mode(CPR simulation with randome errors based on window observaion mode) XXX: NICAM Single Moment sheme 6 category (NSW6) and NICAM Dobule Moment scheme 6 category(NDW6) TIME: The simulation time EASE:  EarthCARE Active SEnsor simulator (EASE)       These data are only one snapshot data and limited variables becuase of file size issue. If you have any questions or want additional data, please contact the email below Contacts: Woosub Roh  (ws-roh@aori.u-tokyo.ac.jp)

opencc-by-4.0Mar 2024View details →

zenodo36/100

Satellite Precipitation Products data performance evaluation with observed datasets

Datasets used for evaluation of Satellite Precipitation Products (SPPs). We used Three open-source datasets from different sources for our evaluation of the SPPs applicability in real-time flood forecasting.

opencc-by-4.0Mar 2024View details →

zenodo36/100

Heron Island Satellite Imagery Classified Benthic Data and Halo Analyses and Models

This dataset includes satellite imagery data from Heron Island, Australia downloaded from Google Earth Pro in 2023, with image from Maxar Technologies dated 2016 clipped to the shallow lagoon layer from the Allen Coral Atlas shape file classified into benthic categories: corals, algae and sand using a combination of unsupervised machine learning spectral classification and manual training and assignment of classes. This dataset also includes scoring of selected coral patch reefs for isolated halos across time using historical aerial imagery. We also include 2 notebooks with code used to generate figures and run analyses for data, geometric, and consumer-resource models for coral halo patterns supporting the work entitled, "Consumer-resource interactions reflected in coral halo patterns" by the authors listed. A knitted html for R Markdown file is also included.

openJan 2023View details →

zenodo36/100

Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-by-4.0Sep 2024View details →

zenodo36/100

A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003–2019

In this project, we built a model to predict the mean, maximum, and minimum temperature on each day at each square in a 1-km grid for an area around Mexico City. See the README at <a href="https://github.com/justlab/Mexico_temperature/blob/master/README.rst">https://github.com/justlab/Mexico_temperature/blob/master/README.rst</a> for more information and instructions, or see the publication at: Gutiérrez-Avila, I., Arfer, K. B., Wong, S., Rush, J., Kloog, I., & Just, A. C. (2021). A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003–2019. International Journal of Climatology, 41, 4095–4111. <code>doi:10.1002/joc.7060</code>

opengpl-3.0-or-laterJan 2021View details →

zenodo36/100

The ECS-FVCOM results and also the input variables of FMGDM, the initial particles, satellite pictures, and trajectories data

The data for the manuscript: A Lagrangian-based Floating Macro -algal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tides.  ----------------------------------------------------------------------------- Updata: 2021/03/18 ----------------------------------------------------------------------------- Part 1: The ECS-FVCOM results and also the input variables of FMGDM Part 2: The initial particles position imformation (lat, lon, depth) Part 3: The satellite pictures of green tides in YS, 2014 and 2015 Part 4: Drifters trajectories dataset (lat, lon)   ============================================= Version 5 updata: 2021/12/20 Note: Modified and added some missing variables ('omega') in Part1,           the results of ECS-FVCOM

opencc-by-4.0Jul 2021View details →

zenodo36/100

Unwrapping reworked crust at the Columbia supercontinent margin within Amazonian Craton using satellite potential field data synergy

This file contains the geochronology data inventory (Supplementary Material Table 1) as a MS Excel (*.xls) table used for interpretation in the  the original publication of 'Unwrapping reworked crust at the Columbia supercontinent margin within Amazonian Craton using satellite potential field data synergy'. Paper published in Geoscience Frontiers in early 2022.

opencc-by-4.0Aug 2020View details →

zenodo36/100

Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009

Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI. During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.

opencc-by-4.0Jan 2022View details →

zenodo36/100

The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China

The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.

opencc-by-4.0Aug 2021View details →

dryad36/100

Satellite telemetry data for Egyptian Geese in southern Africa

This archive contains all currently available satellite GPS telemetry data for Egyptian Geese in southern Africa over the period from 2008 to 2016. The data were collected with two primary aims: (1) to understand the movement ecology of this species; (2) to better evaluate the potential role of Egyptian Geese in spreading avian influenza in southern Africa. Data colelction was undertaken in several phases. The first phase focused on just three sites (Manyame, Barberspan, Strandfontein) and occurred at the same time as a series of extensive bird counts and captures. Birds captured during this period were tested for avian influenza. We also undertook an extensive colour-ringing exercise on Egyptian Geese during the first phase. The second phase of the program involved extending our activities to some new locations (Voelvlei, Jozini) to test specific hypotheses about movement and the timing of moult, and to improve the generality of our findings. The third phase involved a translocation experiment in which six birds were moved from Barberspan to Strandfontein. The data have been analysed and published in a number of different venues and publications, as listed in the associated metadata.

opencc-zeroMar 2022View details →

zenodo36/100

Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN

This dataset is for the article titled "Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN" which is being submitted to the Remote Sensing for review.  Abstract: Thunderstorm activity over the Qinghai–Tibet Plateau (QTP) has important climatic effects and disaster impacts. Using the thunderstorm feature dataset (TFD) established based on the black body temperature (TBB) and cloud classification (CLC) products of the Fengyun-2E (FY-2E) geostationary satellite, as well as the lightning data of the World Wide Lightning Location Network (WWLLN), the temporal and spatial distributions and some cloud properties of the thunderstorms over the QTP were analyzed. Approximately 93.9% and 82.7% of thunderstorms over the QTP occur from May to September and from 12 to 21 o'clock local time, and the corresponding peaks are in August and at 14:00, respectively. There are three centers featuring frequent thunderstorms in the southeast, south-central, and southwest regions of the QTP. The average thunderstorm cloud area (the region with TBB ≤ −32℃) is 1.8 × 104 km2. Approximately 32.9% of thunderstorms have strong convective cells (SCCs) composed of areas with TBB ≤ −52℃.The average number and area ratio of SCCs are 3.6 and 25.4%, respectively, and their spatial distribution is given. The average cloud area and the number and area ratio of SCCs of extreme-lightning thunderstorms (thunderstorms with the top 10% of lightning numbers) are approximately 30.0, 3.9, and 1.5 times those of normal thunderstorms. The spatial distribution of the thunderstorm activity is quite different from that of lightning activity given by the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) over the northeastern and southwestern QTP, which may mean that the convection intensity, cloud structure, and charge structure of the thunderstorms over the QTP are different between different regions and seasons.

opencc-by-4.0Apr 2022View details →

zenodo36/100

SDUST2021GRA: Global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data

SDUST2021GRA is the global marine gravity anomaly model on  a grid of 1′×1′, which is established from the altimeter data of  Ka-band and Ku-band  altimetry satellite including HY-2A. Its spatial coverage is 80°S-80°N. Assessed by the shipborne gravity data, the accuracy of SDUST2021GRA in the global is 2.37 mGal, and that in the open ocean is about 1.5 mGal.

opencc-by-4.0Jun 2022View details →

zenodo36/100

Satellite-derived water quality data for Lake Harsha (USA) 2015-2019

This dataset contains satellite-derived water quality (WQ) data of Lake Harsha (USA) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).

opencc-by-nc-sa-4.0Jun 2022View details →

zenodo36/100

Satellite-derived water quality data for Lake Hume (Australia) 2015-2019

This dataset contains satellite-derived water quality (WQ) data of Lake Hume (Australia) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).

opencc-by-nc-sa-4.0Jun 2022View details →

zenodo36/100

Satellite-derived water quality data for Western Water Treatment Plant (Melbourne, Australia) 2015-2019

This dataset contains satellite-derived water quality (WQ) data of Western Water Treatment Plant (Melbourne, Australia) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022).

opencc-by-nc-sa-4.0Jun 2022View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record