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6,170 results for “european”
Specialized vitamin D databases- European collection and USDA SR26 collection
<p>These two data sets are collections of best quality data on vitamin D content in foods, available in Europe (hosted by EuroFIR<sup>TM</sup>) and in US (hosted by USDA- in SR26). Data was collected in period of 2014-2015. Search criteria included analytical and manufactures' data sources, and these are sorted in food groups and vitamin D forms- vitamin D total (expressed in ug/100g or IU, and converted to ug), vitamin D3 (ug/100g), vitamin D2 (ug/100g), vitamin 25OHD (ug/100g). </p> <p>A manuscript describing creation process of these two dataset is uploaded as well. </p>
Ecosystem service-multifunctionality in the European transboundary cooperation area EUREGIO
<p>Ecosystem Service-multifunctionality (ES-multifunctionality) is defined as the capacity of ecosystems to supply multiple services within the same spatial unit. The present datasets contain the results of an ES-multifunctionality assessment across a case study region in the eastern European Alps, including the two Italian Autonomous provinces of Trentino and South Tyrol and the Austrian federal state of Tyrol (EUREGIO). ES-multifunctionality was calculated using two diversity indices originating from biodiversity studies: α- and β-diversity. α-multifunctionality is defined as the diversity of ES supplied in terms of ES richness and abundance and accounts for service evenness, favoring a balanced supply of ES. β-multifunctionality is defined as the unique ES contribution of spatial units (e.g., ecosystems, landscape) to ES-multifunctionality at bigger spatial scales (e.g., region). A landscape is considered unique when it supplies specific ES at a higher level compared to other sites in the region.</p> <p>By using 11 ES indicators (carbon sequestration, filtration of surface water, forest protection, lifecycle maintenance, gene pool protection, land use integrity and quality, fuelwood production, grassland production, outdoor recreation, symbolic species, foraging practices), we measured the diversity of ES supplied (i.e., α-multifunctionality) at the patch (100 m pixel resolution) and landscape scale (using the municipality boundaries as landscape units, Local Administrative Level 2). β-multifunctionality was calculated at the landscape scale as the average ES abundance-based dissimilarities between landscape units.</p> <p>The datasets include:</p> <ol> <li>a .tiff raster file containing the records of α-multifunctionality at the patch scale at the pixel resolution of 100 m across the study area.</li> <li>a .csv spreadsheet containing the records of α- and β-multifunctionality at the Local Administrative Level 2 across the study area.</li> </ol> <p> </p>
MCMC simulations from the posterior distributions of European migration flows
<p>This folder contains three RData files each containing MCMC simulations from the posterior distributions of a Bayesian hierarchal model used to estimate European migration flows, developed as part of the project Quantifying Migration Scenarios for Better Policy (QuantMig, <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.quantmig.eu%2F&data=05%7C01%7CP.W.Smith%40soton.ac.uk%7Cc166e7bff7f840a7983b08db94af1244%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C638267252014422806%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=yMDYBngYz%2BnzvXmNj6MqseYwso0ICNbREE8TmqQ%2BRJ0%3D&reserved=0">www.quantmig.eu</a>); see Aristotelous, Smith and Bijak (2022) for details. The first set of estimates are disaggregated by origin, destination and time, a breakdown which we denote as ODT. The second and third sets of estimates are again disaggregated by origin, destination and time, but they are additionally disaggregated by other factors. The second set is further disaggregated by age and sex and the third by just birth region. We respectively denote these breakdowns as ODAST and ODBT. Also, included in this folder is an R script to calculate summaries of these posterior distributions (means, and lower and upper quartiles) and output them to csv files, which are also in the folder. For further details, see deliverable_6_4_v1_2.pdf also in the folder.</p>
Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.
<p>Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.</p>
Data for ms. Do people really care less about their cats than about their dogs? A comparative study in three European countries
<p>The present dataset is based on a questionnaire which is also part of this package. The enclose questionnaire includes identifiable and relevant variables names (yellow highlighted).</p> <p>Participants were recruited by Norstat, a European-based survey company, with the aim of gaining a representative sample of Austrian, Danish and UK citizens, including pet owners. The survey company administers and hosts online panels comprising citizens from many European countries. We aimed for a sample that is representative in terms of age, gender, and region. Therefore, a stratified sampling principle was set up where individuals within each stratum were randomly invited to participate. The invitations were issued through e-mail that contained a link to the online questionnaire. Data was collected from 11-25<sup>th</sup> of March 2022 in Austria, from 11-24<sup>th</sup> of March 2022 in Denmark and from 8-23<sup>rd</sup> of March 2022 in the UK. The invitation provided information about the background of the study, the participating universities, ethical approval, estimated time for questionnaire completion and further, participants were informed that the completion of the questionnaire was voluntary and anonymous, and that they could exit the survey at any point. Before participants were directed to the survey, they ensured informed consent by confirming that they are over 17 years old, and consent to participate in this survey. </p> <p>Besides the questionnaire the dataset includes a csv and an Excel file consisting of the data that is used in the ms. and an rtf and a pdf file with data variable names/labels, and value labels.</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>
Data from air, englacial and permafrost temperature measurements on Mt. Ortles (Eastern European Alps)
<p>The *.csv files report the temperature data recorded between 2010 and 2016 and presented in the paper “Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps” (Carturan et al., 2023, submitted). The data were used to display the time series reported in the paper, which details variable names, data quality flags, maintenance logs of field operations, and characteristics of measurement sites.</p> <p>The data files contain measurements of air temperature, englacial temperature, soil surface temperature and rockwall temperature.</p> <p>The file named ‘Ortles_Temperature_Metadata.pdf’ contains information regarding variable names, structure of data files, quality codes, geolocation, and topographic and geomorphological characteristics of sites instrumented for temperature measurements.</p> <p>Reference:</p> <p>Carturan, L., De Blasi, F., Dinale, R., Dragà, G., Gabrielli, P., Mair, V., Seppi, R., Tonidandel, D., Zanoner, T., Zendrini, T. L., and Dalla Fontana, G.: Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-164, in review, 2023.</p>
Genotyping of European Toxoplasma gondii strains by a new high-resolution next-generation sequencing-based method
<p>The data set comprises 164 FASTQ files generated with an Ion AmpliSeq-based genotyping method for <em>Toxoplasma gondii </em>and<em> </em>a BED file used for the design of the Ion AmpliSeq primer panel. The FASTA file named as "AmpliSeq-ME49-Reference" was used as a reference for mapping and data analysis of the FASTQ files. The GZ file named as "Tgondii_IonAmpliSeq_Results_SNPs_VCF" is a VCF file, which contains all SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference. The VCF file was converted into a FASTA file named as "Tgondii_IonAmpliSeq_Results_SNPs", which also contains the SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference.</p> <p>The work is published in the European Journal of Clinical Microbiology & Infectious Diseases with the title "Genotyping of European <em>Toxoplasma gondii</em> strains by a new high‑resolution next‑generation sequencing‑based method"; https://doi.org/10.1007/s10096-023-04721-7</p>
PDST sensor files European eel (Anguilla anguilla) Belgium
<p><strong>Brief data description</strong></p> <p>This data consists of the raw sensor data from a tagging study on European eel (<em>Anguilla anguilla</em> L.) caught and released in Nieuwpoort Belgium. The applied tags were pop-off G5 data storage tags (CEFAS Technology, Lowestoft, UK). Temperature was measured every 10 seconds and pressure (i.e. depth) every 2 seconds. The sensor data contains the raw data per eel with the tag ID as an individual eel. The tags were externally attached to eels and came off before or at a preprogrammed time, drifted to the surface, washed ashore and when found, the data could be downloaded when the tag was retrieved. Note that some recovered tags were reused hence a tag ID can occur more than once.</p> <p>This data is part of the eel-pdst-analysis GitHub repository: https://github.com/PieterjanVerhelst/eel-pdst-analysis. The sensor files have the following destination: eel-pdst-analysis\data\interim\sensorlogs</p> <p> </p> <p><strong>Files</strong></p> <p>Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/8398240/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <p> </p> <p><strong>More Info</strong></p> <p>For more information on the data collection and analysis, see the following two research papers:</p> <p>Scientific Reports: https://doi.org/10.1038/s41598-021-04052-7</p> <p>Science of The Total Environment: <a href="https://doi.org/10.1016/j.scitotenv.2023.167341">https://doi.org/10.1016/j.scitotenv.2023.167341</a></p>
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Why, what and how do European healthcare managers use performance data? Results of a survey and workshop among members of the European Hospital and Healthcare Federation (Data set; anonymised)
<p>The dataset presents results of a descriptive cross-sectional study based on a survey, delivered through an online self-reported questionnaire. The questionnaire was distributed to managers of hospitals and other health care organisations in a purposive sample of participants to the Exchange Programmes of the European Hospital and Health Care Federation (HOPE) eliciting information on the actual use of performance data in hospitals and other healthcare organisations in Europe in 2019.<br> Data collected through the online questionnaire was analysed using univariate descriptive statistics. Analyses were conducted using the R statistical program version 3.6.1. Respondents were, for certain parts of the analysis, sub-grouped by their reported managerial position and experience, as well as the type of organisation they work for. Analysis was done on a full sample of respondents, including the primary, 2019 HOPE Exchange Programme participants, and the secondary study population, 2015-2018 Exchange Programme alumni and local hosts.</p>
DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats
<p>Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.</p>
FIG. 5 in Additional data towards the knowledge of european Podismini Jacobson, 1905 (Orthoptera, Acrididae, Melanoplinae)
FIG. 5. — Epiphallus of male in dorsal view: A, Peripodisma llofizii n. sp.; B, Peripodisma tymphii Willemse, 1972. Abbreviations: Ap, anterior projection; Lp, lateral pons; Pp, posterior projection; Lo, lophus; Po, pons; An, ancora. Scale bar: 1 mm.
FIG. 3 in Additional data towards the knowledge of european Podismini Jacobson, 1905 (Orthoptera, Acrididae, Melanoplinae)
FIG. 3. — Abdominal apex of male: A, Peripodisma llofizii n. sp.; B, Peripodisma tymphii Willemse, 1972.
European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data
<p>The files uploaded under this doi number represent the updated data sets used in the manuscript submitted to ESSDD in its revised version entitled: "European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data" Excel files including the data behind all the manuscript figures are available for download only for review purposes. We added, as sugggested by the referees, metadata belonging to UNFCCC 2018, FAOSTAT, EDGAR v4.3.2, CAPRI and CBM.</p>
Insights into European research funders policies and practices - public dataset
<p>This is the dataset arising from a survey of European research funders on Open Access (OA) and Research Data (RD) policies, commissioned by SPARC Europe, in consultation with representatives from the following organisations: <a href="https://www.allea.org/">ALLEA</a>, the <a href="https://www.efc.be/">European Foundation Centre</a> and <a href="http://scienceeurope.org/">Science Europe</a> and a wider advisory group.</p> <p>Launched in the spring of 2019, the survey, which targeted about 400 funders, garnered just over 60 responses from 29 countries. The cohort includes important national funding agencies (almost 50%), pan-European funders, national and regional academies, foundations and philanthropic organisations and research charities. </p>
ERT data collected at the Corona volcano (Lanzarote, Canary Islands) during the European Space Agency (ESA) testing campaign PANGAEA-X 2017
<p>This dataset contains the ERT (Electrical Resistivity Tomography) data collected between 22 and 23 November 2017 at the Corona volcano (Lanzarote, Canary Islands, Fig. 1) for the detection of lava tubes and the stratigraphic investigation of planetary volcanic analogues. This geophysical survey was carried out within the European Space Agency (ESA) testing campaign PANGAEA-X 2017 (Bessone et al., 2018), aimed at integrating astronaut training-data collection, documentation, analogue field geology procedures with remote sensing and in situ geophysical methods. </p> <p>Two ERT profiles were acquired in NE-SW and NNE-SSW orientations (Fig. 1). These were located roughly orthogonal to the Corona lava tube system and as far as possible on top of the main lava tube axes. The longer profile, profile D, is 470 m in length and was obtained using 48 electrodes spaced 10 m apart. The profile orientation is from SW to NE (electrode 1 to 48). The profile was acquired to detect lava tubes in test site D (sub-area south) where the exact location of a lava tube was known thanks to a LiDAR TLS (Terrestrial Laser Scan) subsurface survey (Santagata et al., 2018). A shorter profile, profile E, is 235 m long and was obtained using 48 electrodes 5 m apart. The profile orientation is from SSW to NNE (electrode 1 to 48). This profile was acquired in test site E (sub-area north) to provide a more detailed investigation of the potential existence of inaccessible sections of the tube whose location could be indicated by the evidence of closely-spaced aligned collapse structures.</p> <p>Each profile was collected using measure sequences compounded by 276 Wenner-Schlumberger array quadrupoles which ensure high vertical resolution and signal amplitude and 328 dipole-dipole array quadrupoles which provide enhanced lateral resolution. A fully automatic multi-electrode resistivity meter SYSCAL Jr Switch-48 by IRIS Instruments (400 V max output voltage, 1200 mA max output current, 100 W max output power, <a href="http://www.iris-instruments.com/syscal-juniorsw.html">http://www.iris-instruments.com/syscal-juniorsw.html</a>), was used for data collection.</p> <p>At most of the measurement points, it was necessary to drill the basalt using a hand drilling machine in order to place the tips of the electrodes into the ground at a depth of approximately 40 cm. The electrodes also needed to kept moist to reduce contact resistance between the electrode and the ground. A large amount of water (up to 2 liters per point) was needed for profile D, situated in an area above the lava tubes with very porous dry soil cover.</p> <p>The dataset is presented as a spreadsheet format which has the "space" as separator and the ".txt" extension. The structure of such a file is the following one:</p> <p>#, El array, Spa1/4, Rho, Dev, M, Sp, Vp, In, Time, Spa5/12, M1/20</p> <p>- #: Data point number</p> <p>- El array: Electrode array</p> <p>- Spa. 1/4: four spacing parameters (corresponding to the electrode array – in m)</p> <p>- Rho: resistivity value (in Ohm.m)</p> <p>- Dev: standard deviation (quality factor, in %)</p> <p>- M: global chargeability value (induced polarization parameter (in mV/V – "=0" if only-resistivity data))</p> <p>- Sp: spontaneous polarization (measured just before the injection, in mV)</p> <p>- Vp: measured primary voltage (in mV)</p> <p>- In: injected current intensity (in mA)</p> <p>- Time: injection time (pulse duration, in s)</p> <p>- Spa. 5/8: other spacing parameters (in m)</p> <p>- Spa. 9/12: electrode elevation (in m)</p> <p>- M1/M20: partial chargeability values (induced polarization window (in mV/V – "=0" if only-resistivity data))</p> <p> </p> <p>Acknowledgements</p> <p>The authors are grateful to ESA and all PANGAEA-X 2017 staff, particularly Loredana Bessone, Matthias Maurer, Herve Stevenin and Igor Drozdovskiy for their participation in data collection during some of the experiments and to the MilesBeyond Team, particularly Francesco Maria Sauro for his logistical support. Regional and local remote sensing data were obtained by the Spanish Instituto Geográfico Nacional (https://www.ign.es) and Gobierno de Canarias (https://www.grafcan.es, <a href="https://opendata.sitcan.es/">https://opendata.sitcan.es</a>).</p> <p> </p> <p>References</p> <p>Bessone, L., et al., 2018, Testing technologies and operational concepts for field geology exploration of the Moon and beyond: the ESA PANGAEA-X campaign, Geophysical Research Abstract, #EGU2018-4013.</p> <p>Santagata, T., Sauro, F., Massironi, M., Pozzobon, R., Del Vecchio, U., Lazzaroni, M., Damiano, N., Tonello, M., Tomasi, I., Martínez-Frìas, J. and Mateo Medero, E., 2018. Subsurface laser scanning and photogrammetry in the Corona Lava Tube System, Lanzarote, Spain, EGU General Assembly 2018, pp. EGU2018-5290.</p>
Foreign workers share in European Union 2018
<p>This is to display Eurostat data about the employed recent immigrants by origin: E.U or not EU. This brings to mind the factors identified in the literature regarding the decision to migrate, such as language, cultural ties, distance, wage levels, quality of life, and how immigrant communities are formed in Europe. </p>
Inventory of the sustainability methodologies, indicators and criteria of research projects funded by the European Union
<p>Based on a screening in the CORDIS database and the experience of project partners, 16 projects were selected for analyses of their contributions regarding sustainability criteria and indicators.</p>
Figure 1 in The European lesser glow worm, Phosphaenus hemipterus (Goeze), in North America (Coleoptera, Lampyridae)
Figure 1. An adult male specimen of Phosphaenus hemipterus (photographed in Switzerland). Note the broad antennae, small eyes, lampyrid-shaped pronotum, and the shortened elytra exposing seven abdominal segments, the last two bearing bioluminescent organs. Photo credit: Urs Rindlisbacher.
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.