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247 results for “PM2.5”
Fine particulate matter (PM2.5) concentrations in downtown Phoenix, Arizona (USA) on August 20, 2024
This dataset contains a collection of estimated particulate matter (PM2.5) concentrations for downtown Phoenix, Arizona (USA), on a typical summer day (August 20, 2024) at three critical times of day (7 a.m., 1 p.m., and 5 p.m.). The 100-m resolution estimates were generated using a pre-trained support vector regression model. This dataset can inform public health interventions related to air quality.
ChinaHighPM2.5: VIIRS 6 km Ground-level PM2.5 Dataset for China
<p>ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.</p> <p>This is the VIIRS derived yearly 6 km ground-level PM<sub>2.5</sub> dataset in China from 2013 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) reaching 0.88 and a root-mean-square error (RMSE) of 16.52 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighPM<sub>2.5</sub> dataset for related scientific research, please cite the corresponding reference (Wei et al., TGRS, 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Sun, L., Xue, X., Ma, Z., Liu, L., Fan, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-TGRS-2022.pdf">Extending the EOS long-term PM<sub>2.5</sub> data records since 2013 in China: application to the VIIRS Deep Blue aerosol products</a>. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2022, 60, 4100412. https://doi.org/10.1109/TGRS.2021.3050999</p> </li> </ul> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
PM2.5, PM10, NO2, O3 from Copernicus Air Quality Forecast March-June 2019, 2020 and 2021
<p>PM2.5, PM10, NO2, O3 Copernicus Air Quality Forecasts March-June 2019, 2020 and 2021 retrieved from the ADAM platform data cube (http://reliance.adamplatform.eu). Datasets are monthly averaged.</p> <p>The resulting extracted datasets are stored in netCDF format and cover Europe.</p>
LAPSO PM2.5 over Europe and US
<p>Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO</p> <p>Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( R2> 0.8 in polluted areas and uncertainty ≪5 μg/m3 for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.</p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
LAPSO PM2.5 in Scotland
<p>Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO</p> <p>Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( R2> 0.8 in polluted areas and uncertainty ≪5 μg/m3 for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.</p>
Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020
<p>The dataset "Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020" contains the emissions analysed in the manuscript "Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates", published in Fire 2023, 6(11), 426; <a href="https://doi.org/10.3390/fire6110426">https://doi.org/10.3390/fire6110426</a>.</p>
Daily 1-km gap-free PM2.5 grids in China, v1 (2000–2020)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM2.5 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.</p>
Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000–2020)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide 21-year-long (2000–2020) gap free annual mean AOD, PM2.5 and PM10 concentration data with a 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.</p>
PM2.5 emissions from Siberian forest fires 2004-2021
<p>The dataset contains Supplementary Materials for the article <em>''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis''</em> in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis. <br> <br> <strong>Supplementary Figures</strong>:<br> - Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line); </p> <p>- Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha. </p> <p>- Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map);</p> <p>- Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region;</p> <p>- Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic;</p> <p>- Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region; </p> <p>- Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory; </p> <p>- Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory. <br> </p> <p>We share Copernicus Atmosphere Monytoring Service <strong>PM2.5 emissions maps</strong> (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594. </p> <p><br> To determine emissions from the territory of Russia, we provide <strong>shapefiles</strong> with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334). </p> <p>Also, there are<strong> initial dataset</strong> for analysis (Initital data_SARIMAX archive) and <strong>SARIMAX model settings</strong> (doc.). </p>
A study of comparative (2019-2023) trends and current acceleration in Particulate Matter (PM2.5) concentration in India
<p><span> For PM<sub>2.5</sub><span> </span>monitoring model, the data was procured from the Central Pollution Control Board’s functional and selected air monitoring stations. The data is available online at the <span> Central Pollution Control Board but in form of daily trends with numerous air quality monitoring stations in an area; monthly and Annual average level especially PM2.5 trends processed from the original data. </span></span></p>
Global health burden of ambient PM2.5 and the role of anthropogenic black carbon and organic aerosols
<p><strong>SI Dataset S1 (</strong><strong>SI DataS1)</strong></p> <p>Excess mortality from ambient PM<sub>2<em>.</em>5 </sub>exposure among adults, children, and neonates.</p> <p><strong>SI Dataset S2 (</strong><strong>SI DataS2)</strong></p> <p>Pie charts showing distribution of excess death by disease among adults, children, and neonates.</p> <p><strong>SI Dataset S3 (</strong><strong>SI DataS3)</strong></p> <p>Sector contribution to ambient PM<sub>2<em>.</em>5</sub>-related excess death under EqT and 2BSP assumptions</p> <p><strong>SI Dataset S4 (</strong><strong>SI DataS4)</strong></p> <p>Excess death from ambient BC exposure and contributions of major anthropogenic sectors.</p> <p><strong>SI Dataset S5 (</strong><strong>SI DataS5)</strong></p> <p>Excess death from ambient POA exposure and contribution of major anthropogenic sectors.</p> <p><strong>SI Dataset S6 (</strong><strong>SI DataS6)</strong></p> <p>Excess death from ambient aSOA exposure and contribution of major anthropogenic sectors.</p> <p><strong>SI Dataset S7 (</strong><strong>SI DataS7)</strong></p> <p>Sector contribution to excess death under EqT and 2BSP relative toxicity assumptions by major regions.</p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at ZIP-code level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct ZIP-code areas in Germany. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each ZIP-code area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles provided by ESRI Deutschland (<a href="https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0">https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0</a>). These shapefiles link the air quality data to precise ZIP-code areas .</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each ZIP-code polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p> <p>Generated using Copernicus Atmosphere Monitoring Service Information 2013-2022</p>
WRF-Chem simulation results of Aerosol Optical Depth and PM2.5 concentrations
<p>This is the data for the manuscript submitted to JGR-Atmosphere: Simulations and characteristics of extreme aerosol events over eastern North America. </p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2003)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2004)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2000)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2005)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2002)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2007)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</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.