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1,342 results for “aerosol”
Data of Relative errors in derived multi-wavelength intensive aerosol optical proberties
<p>Measurement Data of "Relative errors in derived multi-wavelength intensive aerosol optical<br> properties using cavity attenuated phase shift single-scattering<br> albedo monitors, a nephelometer, and tricolour<br> absorption photometer measurements"</p> <p> </p>
Intital simulation of Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model
<p>This dataset is from a series of “forward projection” interactive stratospheric aerosol simulations of the Jan 2022 Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model. The model experiments predict how the cloud will disperse through 2022, and apply the UM-UKCA model at GA4 (Walters et al., 2014), with GLOMAP v8.2, as applied for the “MajorVolc” datasets for Agung, El Chichon and Pinatubo (Dhomse et al., 2020), those runs aligned with the Historical Eruption SO2 emissions Assessment experiment within ISA-MIP (Timmreck et al., 2018).</p> <p>The “standard” Hunga-Tonga GA4 UM-UKCA experiment emits 0.4Tg of SO2 at 29-31km, within a 24-hour period, matching the detrainment duration specified for the ISA-MIP HErSEA experiment protocol. Following the stronger than expected mid-visible backscatter ratios (BSR) measured by CALIOP satellite-borne lidar, and from ground-based lidar from Reunion Island (very high BSR values > 200), we also ran UM-UKCA simulations with “scaled-up Hunga-Tonga SO2 emission”, at 0.8, 1.2 and 1.6 Tg of SO2 emitted.</p> <p>Unexpectedly strong stratospheric AOD observed from the OMPS satellite months after the eruption further strengthens the motivation for these simulations.</p> <p>Several hypotheses for the high AOD from Hunga-Tonga have been suggested:<br> 1) an unusual amount of (or influence from) co-emitted ultra-fine ash particles<br> 2) “in-plume oxidised sulphate” already converted from SO2 at the time of detrainment<br> (e.g. via aqueous-phase oxidation within water droplets within the eruptive plume).<br> 3) co-emitted marine aerosol (e.g. sea-salt aerosol) from seawater vaporized in the plume<br> </p> <p>There are 4 types of netcdf files, Stratospheric AOD (saod), Effective Radius (reff), Extinction (ext) and sulphate aerosol surface area density (sad).</p> <p><br> <br> For e.g. <br> saod550_HT_0pt4Tg_T2Mz-20220101-20230831.nc contains<br> Stratospheric aerosol optical depth (sAOD) at 550nm (2D-monthly dataset vs latitude and time) with 0.4 Tg SO2 injection Jan2022 to August 2023<br> Whereas other files<br> reff_HT_0pt4Tg_T2Mz_20220101-20230831.nc,<br> sad_HT_0pt4Tg_T2Mz_20220101-20230831.nc<br> ext550_HT_0pt4Tg_T2Mz-20220101-20230831.nc</p> <p>contain particle effective radius (reff), aerosol surface area density, aerosol extinction as 3D-monthly fields (altitude, latitude , time) from the same simulation.<br> Other saod and extinction files are also available at 870 and 1020 nm.</p> <p> </p> <p>Note that these are preliminary simulations, hence we do not expect good match with the observations. We plan to perform additional UM-UKCA simulations, comparing to the satellite and ground-based lidar measurements, and to in-situ balloon observations from Reunion Island rapid response campaign & upcoming high-altitude balloon sampling flights in Brazil.</p> <p> </p> <p>References :<br> Dhomse SS, Mann GW, Antuña Marrero JC, Shallcross SE, Chipperfield MP, Carslaw KS, Marshall L, Abraham NL, Johnson CE. 2020. Evaluating the simulated radiative forcings, aerosol properties, and stratospheric warmings from the 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo volcanic aerosol clouds. Atmospheric Chemistry and Physics. 20(21), pp. 13627-13654</p> <p><br> Timmreck, C., Mann, G. W., Aquila, V., Hommel, R., Lee, L. A., Schmidt, A., Brühl, C., Carn, S., Chin, M., Dhomse, S. S., Diehl, T., English, J. M., Mills, M. J., Neely, R., Sheng, J., Toohey, M., and Weisenstein, D.: The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design, Geosci. Model Dev., 11, 25812608, https://doi.org/10.5194/gmd-11-2581-2018, 2018.</p> <p> </p>
European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets
<p>Organic aerosol (OA) is a key component of total submicron particulate matter (PM<sub>1</sub>), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM<sub>1</sub> levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.</p>
Spectral Effects of Absorbing Aerosols on Backscattered UV Radiation
<p>Satellite measurements of backscattered UV radiation are sensitive to the presence of UV-absorbing aerosols in the atmosphere. These measurements are commonly used for determining the concentration of atmospheric trace gases such as O<sub>3</sub>, SO<sub>2</sub>, and H<sub>2</sub>CO.</p> <p>The theoretical results in this dataset describe the effects of UV-absorbing mineral dust and carbonaceous smoke aerosols on these backscatter satellite measurements between 300-400 nm. The information provided is independent of any specific trace gas retrieval algorithm and does not require detailed a priori knowledge of aerosol and surface properties.</p> <p>The results are derived from the analysis of the radiative transfer model simulations performed with the optical property data used in the Ozone Monitoring Instrument (OMI) UV aerosol retrieval algorithm, OMAERUV. Results from this algorithm have been validated with Aerosol Robotic Network (AERONET) observations (Jethva & Torres, 2011; Torres et al., 2018).</p> <p>This dataset is associated with the following publication:</p> <p>Jethva, H., Haffner, D., Bhartia, P. K., & Torres, O. (2022). Estimating Spectral Effects of Absorbing Aerosols on Backscattered UV Radiation, Earth Space Sci., Accepted.</p>
Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state (Data Underlying Figures)
<p>Data underlying figures in A. Bain 'Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state' RSC Environmental Science: Nano, 2022. </p> <p>CA = citric acid<br> NaCl = sodium chloride<br> AS = ammonium sulfate</p> <p>rho = difference in density (g/cm^3)<br> Rh = % relative humidity<br> radius is in micrometers<br> Pe0 are the calculated dimensionless Peclet numbers<br> </p>
IMS sulphate aerosol in the stratospheric plume of the January 2022 Tong aeruption
<p>This animation is made using the IMS sulphate aerosol optical depth product (see https://www?doi.org/10.5281/zenodo.7102472) for all day and night orbits of each day between 13 January and 30 April 2022. The indicated times are those of the intersection of the orbits with the equator. The upper chart of each view is a daily composite of the day orbits and the lower chart is a daily composite of the night orbits. When two orbit swaths overlap, the crossing time of the overlapped orbit is indicated in red. Missing orbits are blanked out. Several days are entirely missing between 8 and 14 March.</p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Data used for doi.org/10.1029/2012JD018338 : Baars et al, 2012: Aerosol profiling with lidar in the Amazon Basin during the wet and dry season
<p><span>This is the data whoch ahs been used for the publication:</span></p> <p><span><span>Baars, H.</span></span><span>, <span>A. Ansmann</span>, <span>D. Althausen</span>, <span>R. Engelmann</span>, <span>B. Heese</span>, <span>D. Müller</span>, <span>P. Artaxo</span>, <span>M. Paixao</span>, <span>T. Pauliquevis</span>, and <span>R. Souza</span> (<span>2012</span>), <span>Aerosol profiling with lidar in the Amazon Basin during the wet and dry season</span>, <em>J. Geophys. Res.</em>, <span>117</span>, D21201, doi:<a title="Link to external resource: 10.1029/2012JD018338" href="https://doi.org/10.1029/2012JD018338" target="_blank" rel="noopener">10.1029/2012JD018338</a>.</span></p> <p><span>For each of the Figures in the Publication the underlaying data is provided in a respective folder.</span></p> <p><span>The raw data (i.e,. the analyzed lidar data for several case during the one-year campaign in 2008) is provided separately.</span></p> <p><span>As the time of data creation is more than 10 years ago, the data description does not comply to current standards. Thus, in case of any questions, please contact the first author.</span></p> <p> </p> <p> </p> <p> </p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window
<p>Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of <em>"Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window"</em> by Chen et al. (2021).</p> <p>This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.</p>
Data for Aerosol Diffuse Fertilization Effect
<p>The simulated GPP and diffuse PAR caused by natural and anthropogenic aerosols under all and clear skies.</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>
Data for the publication "Addressing complexity in global aerosol climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann<br> Titel: Addressing complexity in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.7375978).</p>
Data for: Luo et al., Expiratory aerosol pH: the overlooked driver of airborne virus inactivation, Environmental Science and Technology, 10.1021/acs.est.2c05777
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figures 1 and S3 (inactivation curves for IAV, SARS-CoV-2 and HCoV-229E)</p> <p>Figure 1 (rate constants)</p> <p>Figure 2 (EDB analysis of SLF)</p> <p>Figure S1A (zetasizer analysis to measure virus aggregation)</p> <p>Figure S1B (renilla and plaque assay data for viruses exposed to pH 5, 6 and 7)</p> <p>Figure S4A (EDB analysis of different SLF samples; raw data)</p> <p>Figure S4Amean (EDB analysis of different SLF samples; mean values)</p> <p>Figure S5 (EDB analysis of nasal mucus)</p> <p>Figure S8 (EDB analysis of slow crystal growth stage of SLF and nasal mucus)</p> <p>Figure S13 and S14 (literature data on inactivation of IAV and SARS-CoV-2 in aerosol particles)</p> <p> </p> <p> </p>
Aerosol particles observed onboard the research vessel Mirai over the Southern Ocean in the austral summer of 2017
<p>We have compiled a dataset of field observations to measure aerosol particle size distributions and to collect the aerosols for the following laboratory analyses to quantify the chemical composition and ice nucleating properties of aerosols over the Southern Ocean in the austral summer of 2017 as a part of the research cruise of Japanese research vessel (R/V) Mirai (Cruise number of MR16-09 leg3). The particle size distributions (PSDs) of the submicron aerosols (14–737 nm in the electrical mobility diameter) were measured using a scanning mobility particle sizer, SMPS, which is composed of a differential mobility analyzer, DMA (model 3081, TSI Inc., Minnesota, USA) and a condensation particle counter, CPC (model 3010, TSI Inc.). Since a custom-made inlet system was installed in front of the SMPS, the PSDs of total and non-volatile aerosols upon heating at the 300°C were alternatively measured every 5 min. The PSDs of the coarse fluorescent and non-fluorescent particles (700–3000 nm in the optical diameter) were measured using a waveband integrated bioaerosol sensor, WIBS (type 4A, Droplet Measurement Technologies Ltd., Colorado, USA). Hourly averaged PSDs for the diameter range of 14–3000 nm were analyzed in the associated paper in order to relate the wave breaking state derived from the hourly observations of significant wave height on the R/V. Chemical compositions were derived from the collected samples with the following techniques at the laboratory, ion chromatography for water soluble ions (chloride, nitrate, sulfate, ammonium, sodium, potassium, magnesium, calcium ions), thermal optical transmittance technique for carbonaceous aerosols (organic and elemental carbons), and inductively coupled plasma mass spectrometry for aluminum (Al). Ice nucleating properties of the aerosol particles were analyzed using a droplet freezing method (Cryogenic Refrigerator Applied to Freezing Test, CRAFT) at National Institute of Polar Research (Tobo, 2016 <a href="https://doi.org/10.1038/srep32930">https://doi.org/10.1038/srep32930</a>). All the data indicating the concentrations were reported at standard temperature and pressure (0°C and 1 atm).</p> <p>We prepared five files (comma-separated values) in total, which are hourly aerosol concentrations measured using the SMPS and WIBS, Particle size distributions measured using the SMPS, Particle size distributions measured using the WIBS, Aerosol chemical compositions, and Ice nucleating particle concentrations during the research cruise of MR16-09 leg3. Each file includes the header part to describe the aerosol data including the date and time in UTC, and the positions of the R/V.</p> <p>The associated paper discusses some aspects of data treatment and questions regarding to the methods employed in this study.</p>
Dataset of "Exposure to airborne SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures."
<p>The authors highly appreciate being contacted if the data is to be used for any purpose.</p> <p>The following data set was used in the study entitled "<strong>Exposure to airborne </strong><strong>SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures.</strong>" and published in <em>Heliyon</em> Journal.</p> <p>This study characterized the transmission dynamics of airborne SARS-CoV-2 in normal and intensive care units. The data were collected over the period of 2020. In total, 165 and 62 air and environmental samples, respectively, were collected in four COVID-19 wards and ICUs in Cyprus and analyzed by RT-PCR. The comparison between RT-PCR and an alternative method for SARS-CoV-2 detection in air that provides comparable results but is less cumbersome and time demanding, is also given in the tab "Comparison with BELD".</p> <p>The data from sampling airborne SARS-CoV-2 using a MOUDI impactor are not included in this document but can be found in the supplement of the relevant publication.</p> <p>Please refer to the manuscript and its supplementary material for more information about how the data was collected. </p> <p> </p>
Dataset to: Novel aerosol diluter – Size dependent characterization down to 1 nm particle size
<p>Dataset to: Lampimäki et al. Novel aerosol diluter – Size dependent characterization down to 1 nm particle size. Journal of Aerosol Science 172 (2023) 106180, doi: <a href="https://doi.org/10.1016/j.jaerosci.2023.106180">https://doi.org/10.1016/j.jaerosci.2023.106180</a></p>
Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration
<p>Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and LiDARs, the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from RADARs which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.</p>
Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"
<p>This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.</p>
ScienceDex guides
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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.