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979 results for “spread”
Iceland as stepping stone for intercontinental spread of highly pathogenic avian influenza H5N1 virus between Europe and North America: data set on phylogeographic analysis
<p>Highly pathogenic avian influenza viruses (HPAIV) subtype H5 clade 2.3.4.4b have widely spread within the northern hemisphere since 2020 and threaten wild bird populations as well as poultry production. For the very first time, HPAIV were detected in wild birds and, subsequently, in poultry holdings in Iceland.</p> <p>Here, we present phylogeographic evidence that Iceland has been used as a stepping stone for HPAIV translocation from Northern Europe to North America in 2021 and describe two independent incursions of HPAI H5N1 clade 2.3.4.4b viruses of two different genotypes to Iceland in 2021 and 2022.</p>
Initial Spread Index - ERA-Interim
<p>The Initial Spread Index (ISI) is a numeric rating of the expected rate of fire spread. It combines the effects of wind and the FFMC on rate of spread without the influence of variable quantities of fuel.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
Experimental data for bulk valley transport and Berry curvature spreading at the edge of flat bands
<p>This dataset was used in our study of bulk valley transport and Berry curvature spreading at the edge of flat bands in twisted double bilayer graphene.</p>
Data from: The impact of human mobility networks on the global spread of COVID-19
<p>This is empirical dataset from the paper "The impact of human mobility networks on the global spread of COVID-19". Specifically, the dataset includes several files: (a) the COVID-19 network - an origin/destination matrix (i.e., "covid_network.csv"); (b) the common language network - edgelist format (i.e. "edge_list_comlang.csv"); (c) the same continent network - edgelist format (i.e., "edge_list_continent.csv"; (d) the contiguity network (i.e., "edge_list_contig.csv"); (e) the migration network - edgelist format (i.e., "edge_list_migration_in.csv"; (f) the tourism network - edgelist format (i.e., edge_list_tourism_in.csv"); (g) the list of nodes (countries) corresponding to files (b)-(e) (i.e., "nodes.csv"). Additionally, we uploaded the Rcode used in the paper (i.e. "code"), as a .pdf file format, the data source for the figures included in the paper (i.e., "covid_network_matrix.csv", "matrix_migration_out.csv", "matrix_tourism.csv" - Figure 1; "Fig_2_a_matrix_comlang.csv", Fig_2_b_matrix_contig.csv", "Fig_2_c_matrix_continent.csv" - Figure 2; "Fig_3.graphmlz - Figure 3; Fig_4.graphmlz - Figure 4) and the "global network of COVID-19 onset" (an individual-level data) (i.e., "global_covid_network.csv"). </p> <p>For details, please, see the Methods section of the paper: The impact of human mobility networks on the global spread of COVID-19 (Hancean, M.-G., Slavinec, M., Perc, M). </p> <p> </p> <p> </p> <p> </p>
Molecular Dynamics simulations of spreading droplets
<p>This dataset contains the results of non-equilibrium Molecular Dynamic simulations of 2-dimensional SPC/E water nanodroplets spontaneously spreading over silica-like walls, performed using Gromacs. The main purpose of these simulations is to study the motion of three-phases contact lines over high-friction surfaces and to test contact line friction models.</p> <p>Further details can be found in 'documentation.pdf'.</p>
Supplementary dataset to publication: "Neuroglia Infection by Rabies Virus after Anterograde Virus Spread in Peripheral Neurons"
<p>Supplementary data to the publication: Potratz M., Zaeck L.M., Weigel C., Klein A., Freuling C.M., Müller T., <strong>Finke S.</strong> <strong>2020. </strong>Neuroglia Infection by Rabies Virus after Anterograde Virus Spread in Peripheral Neurons. <strong>Acta Neuropathologica Communications. </strong>8:199. doi.org/10.1186/s40478-020-01074-6.</p>
Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
The arrival and spread of the European firebug Pyrrhocoris apterus in Australia as documented by citizen scientists
<p>Data and R script to reproduce analyses conducted in <strong>The arrival and spread of the European firebug <em>Pyrrhocoris apterus</em> in Australia as documented by citizen scientists</strong></p> <p><strong>Abstract</strong></p> <p>We present evidence of the recent introduction and quick spread of the European firebug <em>Pyrrhocoris apterus</em> in Australia, as documented on the citizen science platform iNaturalist. The first public record of the species was reported in December 2018 in the City of Brimbank (Melbourne, Victoria). Since then, the species distribution has quickly expanded into 15 local government areas surrounding this first observation, including areas in both Metropolitan Melbourne and regional Victoria. The number of records of the European firebug in Victoria has also seen a substantial increase, with a current tally of almost 100 observations in iNaturalist as of July 31<sup>st</sup>, 2021.</p> <p>The case of the European firebug in Australia adds to the list of examples of citizen scientists playing a key role in not only early detection of newly introduced species but in documenting their expansion across their non-native range. Citizen science presents an exciting opportunity to complement biosecurity efforts carried out by government agencies, which often lack resources to sufficiently fund detection and monitoring programs given the overwhelming number of current and potential invasive species. Recognising and supporting the invaluable contribution of citizen scientists to science and society can help reduce this gap by: (1) increasing the number of introduced species that are quickly detected; (2) gathering evidence of the species’ early expansion stage; and (3) prompting adequate monitoring and rapid management plans for potentially harmful species.</p> <p>Given the range expansion patterns of the European firebug worldwide, their adaptation ability, and future climate scenarios, we suspect this species will continue expanding beyond Victoria, including other parts of Australia, New Zealand, and the South Pacific. We firmly believe that most of the knowledge about how this expansion process continues to happen will be provided by citizen scientists.</p>
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</p>
Dataset for English Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the public on twitterprovides insight into the use of social media duringa pandemic. This paper describes our data collection, sampling, and analysis of 44 million tweets in English in March 2020. We make reference to a parallel dataset and analysis of tweets in Arabic during thesame period. The contribution of this paper is a description of our dataset, our coding process to indicate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions providethe basis for future research on semi-automated classifiers for health-related advice and efforts to reduce thespread of harmful health advice.</p>
AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the general public on Twitter provides insight into the use of social media during health emergencies. This paper describes our data collection, sampling, and analysis of 24 million tweets in Arabic in March and early April 2020. We make reference to a parallel dataset and analysis of tweets in English during the same period. The contribution of this paper is a description of our dataset, our coding process to indiciate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions provide the basis for future research on semi-automated classifiers for health-related advice and efforts to reduce the spread of harmful health advice.</p>
A Hierarchical Network-Oriented Analysis of UserParticipation in Misinformation Spread on WhatsApp
<p>#Authors: Gabriel Peres Nobre, Carlos Henrique Gomes Ferreira, Jussara Marques de Almeida<br> #2021</p> <p>Script to read a Database file of messages and, in the end, extract user communities based on content co-sharing.</p> <p>We provide a database file with the anonymized messages shared in WhatsApp. </p>
Disease Spread in Age Structured Populations with Maternal Age Effects
<p>Fundamental ecological processes, such as extrinsic mortality, determine population age structure. This influences disease spread when individuals of different ages differ in susceptibility or when maternal age determines offspring susceptibility. We show that Daphnia magna offspring born to young mothers are more susceptible than those born to older mothers, and consider this alongside previous observations that susceptibility declines with age in this system. We used a susceptible- infected compartmental model to investigate how age-specific susceptibility and maternal age effects on offspring susceptibility interact with demographic factors affecting disease spread. Our results show a scenario where an increase in extrinsic mortality drives an increase in transmission potential. Thus, we identify a realistic context in which age effects and maternal effects produce conditions favouring disease transmission. </p> <p>epi model R script.R</p> <p>This is the script for the SIR model as well as the associated script for life history data. </p> <p>main.body size.csv</p> <p>This is the data for the body size data collected in the main experiment. This was measured using imageJ, was recorded in pixels and converted into millimetres. </p> <p>main.exposed.csv</p> <p>This is the proportion of infected/not infected individuals from an exposed treatment group. This was a subset of individuals from the entire experiment. This was the result of the exposures from the main experiment. </p> <p>main.reproduction.csv</p> <p>This document records reproduction for individuals from old or young mothers. It is a count of the offspring born at each reproductive event, which occurs generally every three days, though variation in interclutch interval increases with age. This was from the main experiment. Only those who were unexposed to the parasite, where used for this portion of the experimental work. </p> <p>sm.body size.csv</p> <p>This records body size similarly to above, and was an independent replication of the main experiment. </p> <p>sm.infection status.csv</p> <p>This is infection outcomes of exposures carried out as above, in an independent replication of the main experiment. </p> <p>sm.total babies.csv</p> <p>This is the reproductive output, carried out similarly to above, but in an independent replication of the main experiment. </p>
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
<p>We present a <strong>multi-temporal</strong>, <strong>multi-modal</strong> remote-sensing dataset for predicting <strong>how active wildfires will spread</strong> at a resolution of 24 hours. The dataset consists of <strong>13.607 images</strong> across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a <strong>full time series of daily observations</strong>, containing detected active fires and variables related to <strong>fuel, topography and weather conditions</strong>.</p><h2>Documentation</h2><p><i><strong>WildfireSpreadTS_Documentation.pdf</strong></i> includes further details about the dataset, following Gebru et al.'s <strong>"Datasheets for Datasets"</strong> framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. </p><h2>Code: Getting started</h2><p>Get started working with the dataset at <a href="https://github.com/SebastianGer/WildfireSpreadTS">https://github.com/SebastianGer/WildfireSpreadTS</a>. </p><p>The code includes a <strong>PyTorch Dataset</strong> and <strong>Lightning DataModule </strong>to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository.</p><p> </p><p>This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.</p>
Input data for Episim Berlin Corona spreading simulation
<p>This dataset is supplementary material for </p> <ul> <li>Müller, S. A., Balmer, M., Charlton, W., Ewert, R., Neumann, A., Rakow, C., Schlenther, T. &<br>Nagel, K. Predicting the effects of COVID-19 related interventions in urban settings by combining<br>activity-based modelling, agent-based simulation, and mobile phone data. PLOS ONE 16 (ed Benenson, I.) (Oct. 2021) <a href="https://doi.org/10.1371/journal.pone.0259037">https://doi.org/10.1371/journal.pone.0259037</a></li> </ul> <p>The dataset is also used in the <strong>Math+ project EF4-13 "Modeling Infection Spreading and Counter-Measures in a Pandemic Situation Using Coupled Models"</strong> to perform the epidemic simulation studies for Berlin. </p> <p>The open dataset contains a 25 percent sample of the original dataset. The code for running the simulation is also available in this Github repository: <a href="https://github.com/matsim-org/matsim-episim">https://github.com/matsim-org/matsim-episim</a>. </p> <p>For the terms of use, please see the associated LICENSE file.</p> <p>More information can be found on our website: <a href="https://covid-sim.info/">https://covid-sim.info/</a>. If you have questions, please contact <a href="mailto:covid19@vsp.tu-berlin.de">covid19@vsp.tu-berlin.de</a> .</p> <p>Available files:</p> <ul> <li>be_2020-week_snz_entirePopulation_emptyPlans_withDistricts_25pt_split.xml.gz: Population including all persons having activities in one of the events files. The person attributes are homeId, homeCoordinates, age, district of home. The coordinates are in grid accuracy of 500m.</li> <li>be_2020-week_snz_episim_events_sa_25pt_split.xml.gz be_2020-week_snz_episim_events_s_25pt_split.xml.gz be_2020-week_snz_episim_events_wt_25pt_split.xml.gz The episim events files for a weekday, Saturday and Sunday. The events files are filtered for the only necessary types of events (actend, actstart, PersonEntersVehicle, PersonLeavesVehicle).</li> <li>be_2020-vehicles.xml.gz File includes a mapping of vehiclesIds to the vehilce type.</li> <li>be_2020-facilities_assigned_simplified_grid.xml.gz Including the facilities used in the events files. The coordinates are in grid accuracy of 500m.</li> <li>be_2020-mobility_data.csv Daily mobility data for Berlin for the simulated period.</li> </ul>
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability
<p>This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research. </p>
FIG. 1 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 1. — Map of Anatolia and the Levant with the main sites mentioned in the text (modified data courtesy of National Centers for Environmental Infor- mation – ETOPO1, Natural Earth and Geo Network opensource. https://doi. org/10.7289/V5C8276M).
FIG. 4 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 4. — Arslantepe, tarsometatarsi (left and right) of rooster from level IIIB. Photo credits: R. Ceccacci, ©MAIAO. Scale bar: 3 cm.
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.