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12,237 results for “2019”
First motion data and focal mechanism solutions of 108 earthquakes occurred between 1928 and 2019 in the Southeastern Alps
<p>This dataset contains the P-wave polarities readings (FPS_polarities_input.zip) and the focal mechanisms (FPFIT_solution.pdf, FPFIT_solution.csv) obtained by the FPFIT algorithm (Reasenberger and Oppenheimer, 1985) of 108 earthquakes with 1.9 ≤ M<sub> </sub>≤ 4.8 occurring between 1928 and 2019 in the Southeastern Alps area (latitude 45°N-47.5°N and longitude 10°E-15°E). The preferred solution for each earthquake has been reported in the focal mechanism catalogue of Saraò et al. (2020).</p> <p>The first polarities used to compute the focal mechanisms were manually picked from seismograms of the National Institute of Oceanography and Applied Geophysics (OGS) northeastern Italy seismic and deformation network (Priolo et al., 2005; Bragato et al., 2011, Bragato et al., 2020)or extracted from the Bulletin of the International Seismological Centre the Seismological Bulletin of Slovenia. The polarities were also read from the seismograms archived in various Italian and European seismological observatories, many of which are no longer operating (Osservatorio meteorico-sismico nel Seminario - Chiavari; ENEL, Osservatorio Ximeniano - Florence, Osservatorio Astronomico "Brera" -Milan, &nbspDipartimento di Fisica dell’Università di Padova - Padua,;Osservatorio S. Domenico – Prato, Osservatorio meteoro-sismico nel Santuario di N.S. - Oropa, Osservatorio Bina - Perugia, Osservatorio "Valerio"- Pesaro, Osservatorio meteorico-sismico nel Collegio Alberoni - Piacenza, Osservatorio Meteorico Istituto Fisica - University of Siena,Sismografi Lungo Periodo di Mantovani (Bologna, Bolzano, Grosseto, Naples, Olbia, Palermo, Turin), Osservatorio meteorico-sismico nel Seminario Maggiore - Treviso, Osservatorio meteorico-sismico nel Seminario Patriarcale – Venice, Ljubljana, Munich, Stuttgart, Vienna).</p> <p>For more details</p> <p>Saraò, A., Sugan, M., Bressan, G., Renner, G., and Restivo, A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2020-369, in review, 2021.</p> <p> </p> <p>References:</p> <p>Bragato, P.L., Di Bartolomeo, P., Pesaresi, D., Plasencia Linares, M., and Saraò A.: Acquiring, archiving, analyzing and exchanging seismic data in real time at the Seismological Research Center of the OGS in Italy, Ann. Geophys. 54, 67–75, https://doi.org/10.4401/ag-4958, 2011.</p> <p>Bragato P.L., P. Comelli, A. Saraò, D. Zuliani, L. Moratto, V. Poggi, G. Rossi, C. Scaini, M. Sugan, C. Barnaba, P. Bernardi, M. Bertoni, G. Bressan, A. Compagno, P. Di Bartolomeo, E. Del Negro, P. Fabris, M. Garbin, M. Grossi, A. Magrin, E. Magrin, D. Pesaresi, B. Petrovic, M.P. Plasencia Linares, M. Romanelli, A. Snidarcig, L. Tunini, S. Urban, E. Venturini and S. Parolai (2020). The OGS- North-Eastern Italy Seismic and Deformation Network: current status and outlook. Submitted to Seism. Res. Lett. </p> <p>Priolo, E., Barnaba, C., Bernardi, P., Bernardis, G., Bragato, P.L., Bressan, G., Candido, M., Cazzador, E., Di Bartolomeo, P., Durì, G., Gentili, S., Govoni, A., Klinc, P., Kravanja, S., Laurenzano, G., Lovisa, L., Marotta, P., Michelini, A., Ponton F., Restivo, A., Romanelli, A., Snidarcig, A., Urban, S., Vuan, A., Zuliani, D.: Seismic monitoring in northeastern Italy: A ten-year experience, Seismol. Res. Lett., 76, 446–454, https://doi.org/10.1785/gssrl.76.4.446, 2005.</p> <p>Reasenberg, P., Oppenheimer, D.: FPFIT, FPPLOT and FPPAGE: Fortran computer programs for calculating and displaying earthquake fault-plane solutions, Open-File Rep., 85-739, USGS, Menlo Park, 109 pp., 1985.</p> <p>Saraò A., Sugan M., Bressan G., Renner G., Restivo A., 2020: Focal mechanisms of Southeastern Alps and surroundings, doi: 10.5281/zenodo.4284971 .</p>
International Day of the World's Indigenous Peoples in Yakutsk (2019)
<p>Photos taken during the celebration of the International Day of the World's Indigenous Peoples in Yakutsk (8th August 2019). </p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Características Generales de las Revistas en el Portal Universidad Nacional, Costa Rica, 2019
<p>Se trata de una base de datos cerrada de características de las revistas que aparecieron publicadas en 2019, del Portal de Revistas Académicas de la Universidad Nacional, Costa Rica.</p> <p>En la base de datos se recopilan el ISSN, área temática, periodicidad, tipos de artículos publicados, políticas declaradas, licenciamiento y portabilidad, números publicados de 2016 a 2019, indizaciones registradas, redes sociales utilizadas, estadísticas declaradas, tipo de arbitraje, formatos de publicación t norma de citación.</p>
3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 3 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 6 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p> <p> </p>
Monthly Global NDVI at 5 km based on AVHRR product - 1982 to 2019
<p>Long-term monthly NDVI calculated through daily images of AVHRR (<a href="https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5?hl=en">NOAA/CDR/AVHRR/NDVI/V5</a>), which were cloud screened and aggregated by percentile 90th on <a href="https://earthengine.google.com/">Google Earth Engine</a>-GEE. All the images were exported from GEE to R environment where the follow procedures were executed on a pixel-basis:</p> <ol> <li><strong>Outlier removal</strong> based on a temporal moving window, used to identify and remove NDVI outlier values (<em>i.e.</em> greater or less than 1.5 standard deviation calculated on 60-months time window) </li> <li><strong>Smoothing and gap filling</strong> approach implemented by the <a href="https://greenbrown.r-forge.r-project.org/">greenbrown package</a> (TSGFssa method)</li> </ol> <p>The final product is composed by 456 NDVI global images without gaps and for each month was produced an ancillary image indicating:</p> <ul> <li><strong>Disregarded pixels</strong>: -25000 (removed) and -32000 (nodata)</li> <li><strong>Gapfilled pixels</strong>: -20000</li> <li><strong>Smoothed pixels</strong>: all values greater than -20000 indicate the difference between the original and smoothed AVHHR NDVI</li> </ul> <p>To access and visualize the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>Publication in preparation.</p> <p>All files were internally compressed using "COMPRESS=DEFLATE" creation option in GDAL.<br> File naming convention <em>(veg_ndvi_avhrr_p90_5km_s0..0cm_</em>20000101..20000131 <em>_v1.0)</em>:</p> <ul> <li>veg = Vegetation theme</li> <li>ndvi = Normalized Difference Vegetation Index </li> <li>avhrr = NOAA CDR AVHRR V5 product</li> <li>p90 = Percentile 90th to aggregate the daily images</li> <li>5km = 5 km of spatial resolution</li> <li>s0..0cm = land surface as vertical reference</li> <li>20000101..20000131 = from 2000-01-01 to 2000-01-31 (time reference)</li> <li>v1.0 = version number</li> </ul>
Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019 - high quality maps
<p>Here you can find the high quality maps published in 'Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019' by EFSA.</p>
Viskari et al. (2019) The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling
<p>Zenodo DOI release for permanent archiving outside of GitHub</p>
Hydro-meteorological dataset for three Siberian river basins from 1936 to 2019
<p>This dataset accompanies the published paper in the Environmental Research Letters: Potential role of permafrost thaw on increasing Siberian river discharge. </p> <p>https://iopscience.iop.org/article/10.1088/1748-9326/abe326</p> <p>The dataset includes observed and naturalized daily river discharge that are collected from public data sources. This dataset also includes reanalysis data for monthly average air temperature, precipitation, potential evapotranspiration. With this dataset, we detected and attributed the changes in streamflow regimes for the three great Siberian rivers (Ob, Yenisei, Lena) during 1936-2019.</p>
Virtually Real eyetracking data (beeswarm) for the short film "Sonnenwende" (2019) byTimo von Gunten
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These quicktime movie represent the Eye-Tracking Data for Scenes 1,2 and 3 of "Sonnenwende" (2019) by Timo von Gunten in beeswarm form for both the real and the virtual version of the film.</p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
SURF Machine Metric Dataset [2019-12-29, 2020-08-07]
<p>An addition to our earlier <a href="https://zenodo.org/record/3878143">uploaded dataset.</a></p> <p>This dataset contains an update of Prometheus: it now contains 327 metrics and spans more than 7 months. Note that the rack and node names have changed with respect to the previous dataset and invalid values are now set to NaN to better indicate what is an invalid value and what is a true zero value.</p>
Review of OpenStreetMap research publications from 2016 to 2019
<p>This dataset consists of a <strong>review of research publications about the <a href="https://www.openstreetmap.org/">OpenStreetMap (OSM) project</a> </strong>published from 2016 to 2019.</p> <p>The dataset was obtained as follows. First, papers published between 2016 and 2019 were extracted using <a href="https://scholar.google.it/">Google Scholar</a> with a query identifying all records with at least one of the keywords “OpenStreetMap” and “OSM” in the title. The extracted records were further filtered to only keep papers having a minimum length of 4 pages and published in academic journals or conference proceedings. In addition, irrelevant papers (e.g. using “OSM” as an acronym for another purpose) and non-English papers were removed from the dataset. The remaining paper were then analyzed and manually classified.</p> <p>The attributes included in the dataset are the following:</p> <ul> <li><strong>id</strong> [paper ID]</li> <li><strong>Paper Citation </strong>[citation of the paper]</li> <li><strong>Authors’ disciplines</strong> — multiplicity: 1-7; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Journal’s discipline</strong> — multiplicity: 1; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Topic(s)</strong> — multiplicity: 1:10; allowed values: application, data quality, contribution behaviours, analyzing contributions, contributors, shaping contributions, OSM effects, data enrichment, development, review</li> <li><strong>Authors' Geography (countries) </strong>— multiplicity: 1:n; allowed values: [list of countries]</li> <li><strong>Authors' Geography (continents) </strong>— multiplicity: 1:n; allowed values: [list of continents]</li> <li><strong>Study Area Geography (countries)</strong> — multiplicity: 1:n; allowed values: [list of countries], [list of continents], Global, NA</li> <li><strong>Geographic Correspondence</strong>— multiplicity: 1; allowed values: no correspondence, partial affiliation/partial location, partial affiliation/full location , full correspondence, NA</li> <li><strong>Perspective on the community</strong> — multiplicity: 1; allowed values: OSM as a data source, data produced by contributors, a collaborative project based on contributors, contributors producing data, a unified community, a diverse community, a social product</li> <li><strong>Evidence of engagement </strong>— multiplicity: 1-8; allowed values: none, acknowledgement, meaningful development, occasional development, object of study - direct, object of study - indirect, contribution, participation</li> <li><strong>General comments </strong>[comments on the paper]</li> </ul>
High resolution global industrial and smallholder oil palm map for 2019
<p>The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The file '<em>grid.shp</em>' contains the grid that covers the potential distribution of oil palm. The file '<em>grid_withOP.shp</em>' shows the 100x100 grid squares with presence of oil palm plantations. The classified images (‘<em>oil_palm_map</em>’ folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm. The file ‘<em>Validation_points_GlobalOilPalmLayer_2019.shp</em>’ includes the 13,495 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the labelled class assigned by visual interpretation, and the attribute ‘predClass, which reflects the predicted class by the convolutional neural network. The ‘Class’ and ‘predClass’ values are the same as the raster files: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.</p> <p>See article for additional information:</p> <p>Descals, Adrià, et al. "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations." <em>Earth System Science Data</em> 13.3 (2021): 1211-1231.</p> <p> </p> <p>Changelog v1:</p> <p>- The analysis was extended to Sri Lanka, South India, and countries in Eastern Africa where oil palm can potentially grow.</p> <p>- The validation dataset only includes the points drawn by simple random sampling and stratified random sampling in the grid cells where the IUCN industrial layer detected oil palm.</p> <p>- The 'Class' and 'predClass' values in the validation dataset were reclassified with the same values as the raster images: [1] Industrial plantations, [2] Smallholder plantations, and [3] Other land covers/uses.</p>
Photovoltaic generation and temperature for the year 2019
<p>This dataset has photovoltaic generation data and temperature data regarding a research building in ISEP/P.Porto (Instituto Superior de Engenharia do Porto / Politécnico do Porto). The data was measured using 5-minutes periods during the entire year of 2019. The temperature sensor was located near the photovoltaic panels (without having direct sunlight). The photovoltaic installation has a theoretical peak generation of 7.5 kW.<br>The dataset presents some errors in the data, representing failures in the acquisition system.</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
The database of words and affiliations of the SEG Annual Conferences (1982 - 2019)
<p>The database of the SEG Annual meetings v2.0<br> This repository includes data for the words and phrases frequency of occurrence analysis "SEGgrams.sqlite" and the database "SEG_affiliations_data.sqlite" consisting of the industry companies and different countries academia that presented their research during the 38 Society of Explorational Geophysicists Annual Conferences (1982 - 2019) with the corresponding number of affiliations for the whole period of study.</p>
Artigos brasileiros de estudos de gênero e sobre mulheres: conjunto de dados de publicações de 1959 a 2019
<p>O conjunto de dados trata-se de artigos brasileiros de estudos de gênero e sobre mulheres, indexados na base de dados 1Findr e publicados no período de 1959 a 2019. A VERSÃO CORRETA DO ARQUIVO CONTÉM DADOS SOBRE 31609 ARTIGOS BRASILEIROS DE ESTUDOS DE GÊNERO E SOBRE MULHERES PUBLICADOS ENTRE 1959 E 2019. São artigos de pessoas vinculadas a instituições brasileiras, com metadados de: título; pessoas autoras (nomes padronizados e limpos); suas instituições de vínculo (nomes padronizados e limpos); cidade de cada instituição; país de cada instituição; periódico em que o artigo foi publicado; ano de publicação; dados do fascículo em que o artigo foi publicado; resumo do artigo (quando disponível); área de classificação na 1Findr de cada artigo (quando disponível); área brasileira de cada artigo (classificação CNPq, quando disponível); palavras-chave originais (quando disponível na fonte); palavras-chave limpas e padronizadas em português brasileiro; palavras-chave limpas e padronizadas em inglês. Formato planilha, conjunto de dados coletado em janeiro de 2020. A nova versão do conjunto de dados contém também algumas análises utilizadas na tese e em artigos posteriores. Cite a fonte! Tese originada deste conjunto de dados: <a href="http://hdl.handle.net/10183/220744">http://hdl.handle.net/10183/220744</a> Veja também as demais referências. Contacte-me para dúvidas: natascha.hoppen@ufrgs.br</p>
Model and observational datasets used for evaluating CHASER simulated formaldehyde (HCHO) abundances in 2019 and 2020.
<p>The dataset entails the model simulation results and the observational data (satellite, aircraft, and ground-based MAX-DOAS) used for the study titled " Evaluating CHASER V.40 global formaldehyde (HCHO) simulations using satellite aircraft and ground-based remote sensing observations", submitted for peer-review in JGR: Atmospheres. </p>
Land cover map of the Greater Paramaribo region 2019
<p>This map shows the land cover of the Greater Paramaribo region for 2019 based on Sentinel 2 images. See details in the metadata document. </p><p>The map was made by Razi Taus for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows.</p><p><i>Razia Taus, Lisa Best, Rudi van Kanten, Nina Schwarz, Louise Willemen, 2020, Land cover map of the Greater Paramaribo Region 2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC. DOI: 10.5281/zenodo.7696951</i></p>
Vertical profiles of stable water isotopes and thermodynamic properties from research flights during the L-WAIVE field campaign in June 2019
<p>This datasets contains the measurements of stable water isotopes conducted during the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021). The measurements were conducted using a Picarro laser spectrometer L2130-i that was installed on an ultralight aircraft. The Picarro measurements of atmospheric humidity are merged measurements of thermodynamic properties by a fast-response temperature and humidity probe (iMet XQ-2; see also Chazette et al. 2021) interpolated on 10s temporal resolution.</p> <p>The data is provided on a one file per flight. All variables are described in README.</p> <p>This dataset has been used in Thurnherr et al. (submitted) for a comparison study of stable water isotopes measurements from various platforms and COSMOiso model simulations.</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.