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419 results for “airborn”
NASA's Airborne Topographic Mapper (ATM) airborne waveform and ground calibration data for the Arctic Spring campaign 2016
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM airborne waveform data from the T2 lidar, as well as the ground test data and true ranges for the Arctic Spring campaign 2016.</p> <p>A collection of MATLAB® functions to read ground test waveform and airborne waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p> <p><strong><strong>See also:</strong> </strong></p> <ul> <li>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></li> <li>User guide for NASA's Airborne Topographic Mapper HDF5 waveform data products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></li> <li>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></li> <li>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></li> <li>All ATM data products are freely available at the National Snow and Ice Data Center (NSIDC) at <a href="https://nsidc.org/data/icebridge">https://nsidc.org/data/icebridge</a> and can also be downloaded from the NASA Earthdata portal at <a href="https://earthdata.nasa.gov/">https://earthdata.nasa.gov/</a></li> <li>The ILATMW1B airborne waveform data is available at NSIDC: <a href="https://nsidc.org/data/ILNSAW1B/versions/1">https://nsidc.org/data/ILNSAW1B/versions/1</a> (narrow swath) <a href="https://nsidc.org/data/ILATMW1B/versions/1">https://nsidc.org/data/ILATMW1B/versions/1</a> (wide swath)</li> </ul>
Figures: Vortex model of the aerodynamic wake of airborne wind energy systems
<p>Figures in .pdf, .png and .fig format.</p><p>Figures in .fig format can be opened with MATLAB or other open source programming languages (e.g., Python thought the command scipy.io.loadmat or Octave)</p><p>Figures were updated after: Trevisi, F., Croce, A., and Riboldi, C. E. D.: Corrigendum to "Vortex model of the aerodynamic wake of airborne wind energy systems", published in Wind Energ. Sci., 8, 999–1016, 2023, https://doi.org/10.5194/wes-8-999-2023-corrigendum"</p>
Geodetic displacement data from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study.
<p>This repository the results produced by Hurtado-Pulido, Amer, Ebinger, and Holcomb “Variations in subsidence patterns in the Gulf of Mexico passive margin from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study”.</p> <p>This repository presents data sets for figures 4, 5, 6, 7 and 8. Processing methods are described in the paper. The READme file contains details about each file. Please address any questions about this dataset to Hurtado-Pulido.</p> <ul> <li>LiDAR data from 1999 is stored and distributed by the Atlas: The Louisiana Statewide GIS (<a href="https://maps.ga.lsu.edu/lidar2000/">https://maps.ga.lsu.edu/lidar2000/</a>). LiDAR data from 2018 is stored and distributed by the USGS Server through The National Map Download Manager (<a href="https://apps.nationalmap.gov/downloader/">https://apps.nationalmap.gov/downloader/</a>).</li> <li>EnviSAT SAR images were retrieved from the Earth Observation Catalogue (<a href="https://eocat.esa.int/sec/#data-services-area">https://eocat.esa.int/sec/#data-services-area</a>). Sentinel-1 SAR images from the Copernicus Open Access Hub (<a href="https://scihub.copernicus.eu/dhus/#/home">https://scihub.copernicus.eu/dhus/#/home</a>). Both property of the European Space Agency.</li> <li>GNSS information was processed by the Nevada Geodetic Laboratory (Blewitt et al., 2018; <a href="http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html">http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html</a>).</li> <li>Data from water, injection, and extraction wells is stored in the Strategic Online Natural Resources Information System property of the Louisiana Department of Natural Resources (<a href="http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181">http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181</a>).</li> </ul>
Dataset: Multidimensional surrogate modelling for Airborne TDEM data
<p>DataHF contains the synthetic data for a SkyTEM 304 TDEM system, flying at 40 m height for a two-layered model with an interface at an angle, described in parameters.csv, via 3D simulations. DataLF contains the 1D data (without angle) with a 1D analytical forward model.</p> <p>Look out for the published PhD dissertation "Improving Airborne Time-Domain Electromagnetic Imaging with Applications to Groundwater Salinity Mapping" for a description of the dataset in Chapter 5.</p>
Data from: Koalas, friends, and foes – the application of airborne eDNA for the biomonitoring of threatened species
Open the record for dataset details and reuse information.
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
Open the record for dataset details and reuse information.
Recorded plant sounds from: Sounds emitted by plants under stress are airborne and informative
Open the record for dataset details and reuse information.
McMurdo Dry Valleys Airborn Sediment Traps / Aeolian Distribution of Soil Biota
Dispersal of soil organisms is crucial for their spatial distribution and adaptation to the prevailing conditions of the Antarctic Dry Valleys. This study investigate the possibility of wind dispersal of soil invertebrates within the dry valleys. Soil invertebrates were evaluated in wind-transported dust particles in collection pans (Bundt pans) 100 cm above the soil surface. Three species of nematode were recovered and Scottnema lindsayae was the most dominant. There were more juveniles (71%) in the transported sediments than adults (29%). The abundance of immobile (dead) nematodes in the Bundt pans was three times higher than active (live) nematodes. Anhydrobiosis constitutes a survival mechanism that allows wind dispersal of nematodes in the McMurdo Dry Valleys.
The airborne lifetime of small speech droplets and their potential importance to SARS-CoV-2 transmission
<p> Movies that show the experimental setup and the full 85-minute observation of speech droplet nuclei.</p> <p> </p> <p>The full movie recording of highly sensitive laser light scattering observations that indicate loud-speaking generates, in addition to hundreds of regular droplets, also many thousands of micro-droplets per second. This movie clip shows the decay of airborne particles.</p> <p> </p> <p>Note: some parts of the audio of the clips were muted for privacy.</p>
Dataset - Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2020) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.txt</strong></em> file.</p> <p>The dataset contains:</p> <p>- libRadtran input files</p> <p>- libRadtran output</p> <p>- name lists</p> <p>- GRAL simulation outputs</p>
Data: Search and Rescue with Airborne Optical Sectioning
<p>This dataset supports the finding of our study "Search and Rescue with Airborne Optical Sectioning".</p> <p>Abstract: We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a recall of 93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft.<br> </p>
Tiles of airborne laser scanning point clouds of Vienna, Austria (2016)
<p>Airborne laser scanning of the city of Vienna, which was organized by the survey department of the City Administration of Vienna (MA41). The data acquisition was performed in eight flight missions in November 2015.</p> <p>After strip adjustment relative accuracy of the point cloud was in the order of 2cm. The absolute accuracy measured as RMSE is better than 5cm in planimetry and better than 4cm in elevation. The dataset was cleaned and does not involve obvious gross errors, e.g. points high up in the air or points far below ground level. The point density is measured as by last echoes per unit area and is more than 15 points/m<sup>2</sup> for 97% of the area. The average point density is 33 points/m<sup>2</sup>.</p> <p>The LiDAR dataset of the city is organized in a number of 1270m×1020m tiles (including a 10m overlap of all neighboring tiles).</p> <p>Reference labels were generated semi-automatically: a rough filtering of main objects was firstly conducted by the software “Terrasolid”, and the final classification was refined by manual labelling. For the purpose of Vienna city administration, five classes are considered, namely ground, buildings, vegetation, others, water and bridges. All common street objects are categorized as others, such as (e.g.) streetlights, benches, shrubs, cars, construction sites and garbage bins. The different classes are defined by the following numeric integer codes: 2: Ground, 5: Vegetation, 6: Buildings, 8: Others, 9: Water and 17: Bridges.</p> <p>The quality of the labelling was manually checked. In 20 sites of 100m×100m the classification was manually verified, and the average accuracy of reference labels is 95%.</p> <p>Total 9 tiles are published, in which 4 tiles were used for the training and 5 tiles for the evaluation in the paper “A Comparison of Deep Learning Methods for Airborne LiDAR Point Clouds Classification”. Their locations in the city of Vienna can be found in the file of metadata, which also provides WGS84/GRS80 latitude and longitude coordinates of the extent corners of each tile (EPSG: 31256). This can be used to access images of the tiles from Google Maps or other public map service.</p> <p> </p>
Data from: Explosion-generated infrasound recorded on ground and airborne microbarometers at regional distances
Recent work in deploying infrasound (low frequency sound) sensors on aerostats and free flying balloons has shown them to be viable alternatives to ground stations. However, no study to date has compared the performance of surface and free floating infrasound microbarometers with respect to acoustic events at regional (100s of kilometers) range. The prospect of enhanced detection of aerial explosions at similar ranges, such as those from bolides, has not been investigated either. We examined infrasound signals from three 1 ton TNT equivalent explosions using microbarometers on two separate balloons at ranges of 280 to 400 km and ground stations at ranges of 6.3 to 350 km. Signal celerities were consistent with acoustic waves traveling in the stratospheric duct. However, significant differences were noted between the observed arrival patterns and those predicted by an acoustic propagation model. Very low background noise levels on the balloons were consistent with previous studies that suggest wind interference is minimal on freely drifting sensors. Simulated propagation patterns and observed noise levels also confirm that balloon-borne microbarometers should be very effective at detecting explosions in the middle and upper atmosphere as well as those on the surface.
Airborne daily pollen concentrations in Turku Finland 1974-2013
<p>This data contains daily airborne pollen and spore concentrations in Turku, Finland from 1974 to 2013. The sampling was done with Hirst-type sampler on roof-level and te pollen types were identified to family or genus level. The spore data contains genera Alternaria and Cladosporium. The data has been created by the Aerobiology Unit of the University of Turku.</p>
Data and scripts for: Airborne DNA reveals predictable spatial and seasonal dynamics of fungi
<p><span>Fungi are among the most diverse and ecologically important kingdoms of life. However, the distributional ranges of fungi remain largely unknown, as do the ecological mechanisms that shape their distributions. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardised aerial sampling of fungal spores. The vast majority of OTUs were detected only within one climatic zone, and the spatio-temporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal, and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating only for a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with microbial lifestyles follow the global biodiversity paradigms known for macro-organisms.</span></p> <p>The analyses presented in the paper can be reproduced with the R-script pipeline provided here. The starting point for the scripts is the datafile allData.RData that was published originally by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). The datafile allData.RData is provided also here for convenience, and it includes the following three objects: metadata, taxonomy, and otu.table (see Ovaskainen et al. for details). The script pipeline consists of the following elements (for deltails, see the Methods of the paper):</p> <ul> <li>Scripts S01: data preparation<br> <ul> <li>S01.1_download_clim_data.R. This script downloads daily climatic data for the entire world.</li> <li>S01.2_select_and_preprocess_clim_data.R. This script selects the data relevant for the study locations and preprocesses it.</li> <li>S01.3_add_climatic_data_to_metadata.R. This script adds the preprocessed climatic data to the metadata.</li> <li>S01.4_otu_guild_assignment.R. This script performs the guild assignment to the OTUs. It utilizes the datafiles Fung_LifeStyle_Data.RDS and funguild_db.rds provided here, and it utilizes the taxonomy of ProtaxFungi provided by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). Note that while the paper presents analyses and results only for the trait database of Aguilar-Trugueros et al., the scipts repeat the trait analyses also for the FunGuild database. The reason for not showing the results for the FunGuild database in the paper was that the database of Aguilar-Trugueros et al. contains FunGuild as one of the data sources, and that the results were highly coherent between the two databases.</li> <li>S01.5_add_trait_data_to_taxonomy_and_metadata.R. This script adds the guild data and spore size data to taxonomy (taxon-specific traits) as well as to metadata (community-weighted mean traits). It utilizes the datafile Spore_data_12Nov21.RDS provided here. This script can also be used to generated simulated contamination to the OTU table by setting contaminate=TRUE.</li> </ul> </li> <li>Scripts S02: exploratory analyses <ul> <li>S02.1_show_descriptive_statistics.R. This script outputs basic desriptive statistics from the data.</li> <li>S02.2_make_study_design_maps.R. This script plots the study design map shown in the paper.</li> <li>S02.3_compute_site_and_biome_profiles.R. This script computes site_profiles (needed in ordinations) and biome_profiles (needed to create Venn diagrams).</li> <li>S02.4_make_venns.R. This script produces Venn diagrams.</li> </ul> </li> <li>Scripts S03: ordination analyses <ul> <li>S03.1_make_ordination_maps.R. This script makes the ordination analyses.</li> </ul> </li> <li>Scripts S04: univariate analyses <ul> <li>S04.1_conceptualize_univariate_models.R. This script produces a figure that illustrates conceptually the differenent model variants. </li> <li>S04.2_make_univariate_analysis.R. This script implements the univariate analyses.</li> <li>S04.3_show_univariate_results.R. This script summarizes the results of the univariate analyses by producing tables of AIC and R2.</li> <li>S04.4_plot_univariate_results.R. This script plots the univariate models.</li> <li>S04.5_compute_temporal_turnover.R. This script computes site-specific indices of temporal turnover.</li> <li>S04.6_show_temporal_turnover.R. This script generates a plot illustrating temporal turnover.</li> </ul> </li> <li>Scripts S05: Hmsc analyses <ul> <li>S05.1_define_Hmsc_models.R. This script defines the Hmsc models. It utilizes the R-function as.phylo.formula provided here.</li> <li>S05.2_export_Hmsc_models_for_fitting.R. This script exports the unfitted Hmsc-models for fitting with Hmsc-HPC that operates on python/tensorflow.</li> <li>S05.3_import_fitted_Hmsc_models.R. This script imports the fitted Hmsc-models back to Hmsc-R.</li> <li>S05.4_postprocess_Hmsc_results.R. This script postprocesses the results of the fitted Hmsc model.</li> <li>S05.5_show_Hmsc_results.R. This script generates a plot that illustrates the postprocessed results.</li> </ul> </li> </ul>
Datasets for airborne in-situ quantification of methane emissions from oil and gas production in Romania
<p>This dataset includes airborne in-situ measurements taken around target clusters and regions of oil and gas production sites in Romania, as well as two models outputs interpolated to the flight tracks during the ROMEO (ROmanian Methane Emissions from Oil and gas) campaign that took place in Romania in 2019. The dataset is used for the evaluation presented in the manuscript titled: "Airborne in-situ quantification of methane emissions from oil and gas production in Romania."</p>
Global Airborne Observatory: Example Rapid Water Quality Maps of Hawaiian Island Coastline
<p>Example maps from the publication:</p> <p>Nicholas R. Vaughn, Marcel König, Kelly L. Hondula, Dominica E. Harrison and Gregory P. Asner. 2024. Rapid Water Quality Mapping from Imaging Spectroscopy with a Superpixel Approach to Bio-Optical Inversion. <em>Remote Sensing</em>. (in press)</p> <h2>Data:</h2> <p>Maps of water quality and auxiliary information produced from Global Airborne Observatory (GAO) VSWIR Spectrometer data for five example sites:</p> <table> <tbody> <tr> <td>Site</td> <td>Island</td> <td>Date Collected</td> <td>Time Collected (24hr)</td> <td>Latitude</td> <td>Longitude</td> </tr> <tr> <td>1. Pelekane Bay</td> <td>Hawaiʻi</td> <td>30 October 2023</td> <td>10:02</td> <td>20.0243</td> <td>−155.8256</td> </tr> <tr> <td>2. East Kaho’olawe</td> <td>Kaho’olawe</td> <td>22 January 2024</td> <td>09:15</td> <td>20.6027</td> <td>−156.5638</td> </tr> <tr> <td>3. Hilo Bay</td> <td>Hawaiʻi</td> <td>15 January 2023</td> <td>11:30</td> <td>19.7353</td> <td>−155.0636</td> </tr> <tr> <td>4. Mā’alaea Bay</td> <td>Maui</td> <td>22 January 2024</td> <td>10:19</td> <td>20.7805</td> <td>−156.4842</td> </tr> <tr> <td>5. South Moloka’i</td> <td>Moloka’i</td> <td>7 January 2024</td> <td>11:31</td> <td>21.0866</td> <td>−157.2159</td> </tr> </tbody> </table> <div> <div> <div> <p>There are 41 maps for each example site:</p> <ol> <li>A single 3-band true-color map of the example site for reference (Site<<em>X_Name></em>_3band.tif)</li> <li>A single full-resolution water quality map produced using a bio-optical inversion model on each pixel (Site<<em>X_Name></em>_PixelbyPixel.tif)</li> <li>For each of 13 different superpixel size settings (50px, 100px, 250px, 500px, 600px, 750px, 1000px, 1250px, 1500px, 2000px, 3000px, 4000px, 5000px), a map of the superpixel ID of each pixel (Site<X_Name>_Size<####>_ClusterID.tif)</li> <li>For each of 13 different superpixel size settings, a superpixel-level map of water quality as estimated at the cluster level. (i.e., each pixel in a superpixel will have the same values, Site<X_Name>_Size<####>_Cluster_Preds.tif)</li> <li>For each of 13 different superpixel size settings, a full-resolution water quality map produced by interpolation of the superpixel level maps in 4. above (Site<X_Name>_Size<####>.tif)</li> </ol> <p>All files are in GeoTiff format using 32-bit floating point values. Most are compressed with the LZW algorithm. Maps are not georeferenced. No data value for map types 2-5 is -9999.</p> </div> </div> </div>
Individual tree dataset from airborne laser scanning data
<h1>General description</h1> <p>Data about individual tree segments detected from the Finnish national 5 pt/m^2 airborne laser scanning data. The dataset contains 281304 individual trees from a test site of 3km x 3km located in Padasjoki, Finland. The ALS data has been collected in 2019.</p> <h1>Data description</h1> <p>The individual tree segment dataset has been published as a CSV file. The tree height, DBH, stem volume and above-ground biomass have been predicted using Random Forest Machine Learning models. The rest of the features have been calculated directly from the ALS point cloud. The CSV file contains the following columns:</p> <ul> <li><strong>LX</strong> and <strong>LY</strong>: tree location (location of max height in the canopy height model) in the ETRS-TM35FIN coordinate reference system (EPSG:3067)</li> <li><strong>Hmax</strong>: tree height (based on highest returns)</li> <li><strong>Gele</strong>: ground elevation at tree location (interpolated from ground points)</li> <li><strong>lowBranch</strong>: height of lowest branch (limited accuracy)</li> <li><strong>crownV</strong>: crown volume as 3D convex hull</li> <li><strong>species</strong>: predicted species: 1 = pine, 2 = spruce, 3 = deciduous tree</li> <li><strong>H</strong>: predicted tree height</li> <li><strong>DBH</strong>: predicted diameter at breast height</li> <li><strong>Volume</strong>: predicted stem volume</li> <li><strong>Biomass</strong>: predicted above-ground biomass (dry mass)</li> </ul> <h1>Citation</h1> <p>Any scientific publication using the data should cite the following paper:</p> <p>Hyyppä, M., Turppa, T., Hyyti, H., Yu, X., Handolin, H., Kukko, A., Hyyppä, J., & Virtanen, J. -P. (2024). Concepts Towards Nation-Wide Individual Tree Data and Virtual Forests. <em>ISPRS International Journal of Geo-Information</em>, <em>13</em>(12), 424. https://doi.org/10.3390/ijgi13120424</p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Assimilation of NASA's Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system
<p>Data Analysis Scripts and Post-Processed Model Data for a case study using assimilation of ASO Snow Data into the NASA LIS/WRF-Hydro Model. </p> <p>Manuscript Citation:</p> <p>Lahmers T. M., S. V. Kumar, D. Rosen, A. L Dugger, D. Gochis, J. A. Santanello, C. Gangodagamage<sup>,</sup> and R. Dunlap,<strong> </strong>2020: Assimilation of NASA’s Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system,<em>Water Resour. Res.,</em></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.