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35 results for “Database field”
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
Database of weeds in cultivation fields of France and UK, with ecological and biogeographical information
<p>The database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.<br> The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".<br> It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"</p> <p>Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),<br> - 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,<br> - 'binome.discoweed' = corresponding latine name,<br> - 'family' = family name (following APG III),<br> - 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),<br> - 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),<br> - 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.</p> <p>The presence of each taxon in one or several of the following data sources is reported:<br> - Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),<br> * 'jauzein' = national and comprehensive flora in France (Jauzein 1995),<br> - Species lists from plot-based inventories in cultivated fields,<br> * 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),<br> * 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),<br> * 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),<br> * 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)<br> - Reference list of segetal species (species specialist of arable fields),<br> * 'cambacedes' = reference list in France (Cambacedes et al. 2002)</p> <p>Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.<br> The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.</p> <p>Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.<br> The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.<br> In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.<br> In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').<br> A single taxon is classified 'arctic-alpine'.</p> <p>Red list status of weed taxa is derived for France and UK:<br> - 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),<br> - 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.<br> The categories are coded following the IUCN nomenclature.</p> <p>A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).<br> The main habitat categories of weeds are coded following the Divgrass classification,<br> - 1 = Dry calcareous grasslands<br> - 3 = Mesic grasslands<br> - 5 = Ruderal and trampled grasslands<br> - 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)<br> Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.</p> <p>Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.<br> The indexes are network-based metrics proposed by Guimera and Amaral (2005),<br> - c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),<br> - z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean.</p>
Dataset: A database of near-field head-related transfer functions based on measurements with a laser spark source
<p>This is a database of near-field head-related transfer functions (HRTFs) of an artificial head, measured at four distances (0.2, 0.3, 0.4 and 0.5 m), with 49 positions recorded at each distance, for a total of 196 measurement points. The HRTFs were recorded using an acoustic pulse created by a laser-induced breakdown of air (LIB), which realizes a close to ideal, massless, monopole sound source. The repository contains the original measurement data (raw_data.zip), the derived HRTFs both with (NF_LIB_HRTF_LFE.sofa) and without (NF_LIB_HRTF_measured.sofa) a low-frequency extension (LFE) applied, as well as the MATLAB code used to process the measurement data and to apply the LFE (LIB_HRTF_DB.zip). The database is made publicly available to support future research into nearby sound localization, and virtual/augmented reality applications.</p> <p>Please see the accompanying paper for further details: Marschall et al. (2023), <a href="https://doi.org/10.1016/j.apacoust.2022.109173">A database of near-field head-related transfer functions based on measurements with a laser spark source</a>, Applied Acoustics. </p>
A New Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications
<p>This database is created by the Realistic 3D research group at Mid Sweden University, Sundsvall, Sweden. The database details are explained thoroughly in the publication which was published at the 16th International Conference on Quality of Multimedia Experience (QoMEX) in 2024. This database was also reviewed as part of the submission and publication process.</p> <p>You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below:</p> <blockquote> <p>Zerman, E., Gond, M., Takhtardeshir, S., Olsson, R., & Sjöström, M. (2024). A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications. <em>The 16th International Conference on Quality of Multimedia Experience (QoMEX)</em>. IEEE. DOI: <a href="https://ieeexplore.ieee.org/abstract/document/10598264">10.1109/QoMEX61742.2024.10598264</a></p> </blockquote> <p>BibTeX:</p> <blockquote> <p>@inproceedings{zerman2024spherical,<br> title = {A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications},<br> author = {Zerman, Emin and Gond, Manu and Takhtardeshir, Soheib and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},<br> booktitle = {The 16th International Conference on Quality of Multimedia Experience (QoMEX)},<br> year = {2024},<br> organization = {IEEE},<br> doi = {10.1109/QoMEX61742.2024.10598264}<br>}</p> </blockquote> <p>This database contains 20 spherical light fields of 1 x 60 views, captured with a consumer-grade 360-degree camera: Insta360 X3. The capture was done using a dolly to ensure the separation between consecutive views is exactly 1 cm. In addition to the original captures, this database also provides outputs for two different use cases: compression and view synthesis. Several parameters, features, and objective quality metric values are also included.</p> <p>N.B. Only the README file and this description have been updated after the initial submission on 2024-02-09.</p>
Database of fields of education, with explanatory note
<p>In addition to respondents’ highest educational qualification, some surveys also collect data on their main field of education. Current measurement practice involves either a closed question with highly aggregated response categories, which are difficult to use for respondents, or an open question, requiring expensive post-coding. Therefore, a measurement tool for fields of education was developed in the SERISS-project in work package 8, Task 8.3. In deliverable D8.9 we provide a database of fields of education and training in 34 languages, including the definition of a search tree interface to facilitate navigation of categories for respondents. All 120 standard categories and classification codes are taken from UNESCO's International Standard Classification of Education for Fields of Education and Training (ISCED-F). For most languages, detailed 3-digit information is available. The database, including a live search feature, is available at the surveycodings website at https://surveycodings.org/articles/codings/fields-of-education. The search tree can be used for respondents’ self-identification of fields of education and training in computer-assisted surveys. The live search feature can also be used for post-coding open answers in already collected data. </p>
FIGURE 1 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 1 First page of field notebook number 156. This trip was conducted from Port Augusta to Norseman, Western Australia, by Ken Key, Murray S Upton and Jim Balderson from 28/9/1963 to 23/10/1963. Plant specimens were identified by Nancy T Burbidge. On 28th of September, they started from Mildura, took Arumpo road at a vehicle odometer 6827 and travelled 6 mi to reach 6833. Site description and general observations for collection at stop 6833: Flat with sparse belah and Callitris robusta to 25 ft on pale brown sandy loam with?Cassia sp. abundant to 8 ft and regrowth. Ground layer of Bassia spp. and Kochia spp. to 6 in. and occasional Kochia?pyramidata to 2 ft barley grass and succulents drying off, considerable bare ground. Return. Grasshoppers collected at this site: Cratilopus sp. 1, Chortoicetes terminifera, Caperrala sp. 1 (j.), Apotropis vittata (j.).
FIGURE 4 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 4 Comparison of survey consistency among different surveyors. (a) Between-site distance maintained by different lead surveyors. (b) The total number of grasshoppers recorded per site by different lead surveyors. Numbers in the middle of boxplot show median value. (c) Seasonal variation in the number of species counted per site by different lead surveyors. (d) Proportion of total surveys conducted in each season by different surveyors. Values on pie charts show the number of surveys conducted by each surveyor. Seasons are indicated by colours.
FIGURE 3 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 3 Spatial bias in historic grasshopper surveys in Western Australia and Tasmania (inset). (a) Thiessen polygon network drawn based on survey sites as centre of each polygon, showing the intensity of survey activity; the smaller polygons, the more intensive the survey activity because each polygon represents a sampling site. Colours in the background represent bioregions. (b) Bioregions bias: Positive scores indicate positive survey bias (higher survey effort than expected from a random allocation) and vice versa. Bars on right side of vertical dash line represent Tasmania. Bioregions were abbreviated as: AvWh, Avon Wheatbelt; BL, Ben Lomond; Ca, Carnarvon; CeKi, Central Kimberley; CeRa, Central Ranges; Co, Coolgardie; Da, Dampierland; EsPl, Esperance plains; F, Furneaux; Ga, Gascoyne; GeSa, Geraldton Sandplains; GiDe, Gibson Desert; GrSaDe, Great Sandy Desert; GrViDe, Great Victorian Desert; Ha, Hampton; JaFo, Jarrah Forest; K, King; LiSaDe, Little Sandy Desert; Ma, Mallee; Mu, Murchison; NoKi, Northern Kimberley; Nu, Nullarbor; OrViPl, Ord Victoria Plain; Pi, Pilbara; SwCoPl, Southwest Coastal Plain; Ta, Tanami; TCH, Tasmanian Central Highlands; TNM, Tasmanian Northern Midlands; TNS, Tasmanian Northern Slopes; TSE, Tasmanian South East; TSR, Tasmanian Southern Ranges; TW, Tasmanian West; ViBo, Victorian Bonaparte; Wa, Warren; Ya, Yalgoo.
FIGURE 2 in Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
FIGURE 2 Grasshopper species count and survey effort in Western Australia and Tasmania (inset) per 50-km grid cells. Only records with both genus and species (either confirmed based on formal taxonomy for genus and species or putative taxonomy used for genus or species) names were included in these analyses. (a) Species richness (total number of recorded species per cell); (b) total number of survey sites per cell; (c) for each cell, the average number of species per survey site. No survey was conducted in white cells. Bioregions were abbreviated as: Ca, Carnarvon; CeKi, Central Kimberley; Co, Coolgardie; Da, Dampierland; EsPl, Esperance Plains; Ga, Gascoyne; Ha, Hampton; Nu, Nullarbor; Pi, Pilbara; SwCoPl, Southwest Coastal Plains; ViBo, Victorian Bonaparte.
Field-wide assessment of differential HT-seq from NCBI GEO database
<p>We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. </p> <p> </p> <p>- This release includes GEO series published up to Dec-31, 2020;</p> <p> </p> <p><a href="https://zenodo.org/api/files/320af641-0181-4e73-adfa-d2d2e61dbf23/geo-htseq.tar.gz?versionId=852d4e0b-3c5b-4156-ac80-9b27cc1e53cb">geo-htseq.tar.gz </a>archive contains following files:</p> <p>- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).</p> <p>- output/document_summaries.csv, document summaries of NCBI GEO series.</p> <p>- output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions.</p> <p>- output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO.</p> <p>- output/publications.csv, publication info of NCBI GEO series.</p> <p>- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series</p> <p>- output/spots.csv, NCBI SRA sequencing run metadata.</p> <p>- output/cancer.csv, cancer related experiment accessions.</p> <p>- output/transcription_factor.csv, TF related experiment accessions.</p> <p>- output/single-cell.csv, single cell experiment accessions.</p> <p>- blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import.</p> <p> </p> <p>Workflow to produce this dataset is available on Github at <a href="https://github.com/rstats-tartu/geo-htseq">rstats-tartu/geo-htseq</a>.</p> <p> </p> <p><a href="https://zenodo.org/api/files/320af641-0181-4e73-adfa-d2d2e61dbf23/geo-htseq-updates.tar.gz">geo-htseq-updates.tar.gz </a>archive contains files:</p> <p>- results/detools_from_pmc.csv, differential expression analysis programs inferred from published articles</p> <p>- results/n_data.csv, manually curated sample size info for NCBI GEO HT-seq series</p> <p>- results/simres_df_parsed.csv, pi0 values estimated from differential expression results obtained from simulated RNA-seq data</p> <p>- results/data/parsed_suppfiles_rerun.csv, pi0 values estimated using smoother method from anti-conservative p-value sets</p> <p> </p> <p> </p>
ASHRAE global database of thermal comfort field measurements
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Biological invasions: a field synopsis, systematic review, and database of the literature
Species introductions of anthropogenic origins are a major aspect of rapid ecological change globally. Research on biological invasions has generated literature on many different aspects of this phenomenon. This literature is enormous and has grown rapidly since the mid‐twentieth century. Therefore, we created this dataset in order to describe and categorize some aspects of this literature, to better understand what has been studied and what we know, mapping well‐studied areas and important gaps. To do so, we employed the techniques of systematic reviewing widely adopted in other scientific disciplines. We identified 2398 relevant studies in a field synopsis of the biological invasions literature. The purpose of the field synopsis was to map and categorize the scope of available information (and what is not known) from the literature addressing a fundamental understanding of biological invasions. We then examined 1537 papers in greater detail in a systematic review. The systematic review addressed the state of our knowledge about the mechanisms that permit species to invade novel environments. We carried this out by attempting to identify and characterize the literature, including what hypotheses have been tested, and what organisms and systems have been studied. A secondary goal of our work was to create a publicly accessible database of this literature for future research.
Database of non-target invertebrates recorded in field experiments of genetically engineered Bt maize and corresponding non-Bt maize: data files
<p><span>This database represents a comprehensive collection of experimental field data from all over the world on non-target invertebrates recorded in genetically engineered/modified Bt and non-Bt maize. The three data files deposited here are described by Meissle et al. (2022), BMC Research Notes, <span><a href="https://doi.org/10.1186/s13104-022-06021-3"><span>https://doi.org/10.1186/s13104-022-06021-3</span></a></span><span> </span>.</span></p> <p><span>The database was created for a systematic review with the question if growing Bt maize changes abundance or ecological function of non-target animals compared to growing of non-GM maize, published by Meissle et al. (2022), Environmental Evidence, <a href="https://doi.org/10.1186/s13750-022-00272-0"><span>https://doi.org/10.1186/s13750-022-00272-0</span></a></span>.</p> <p>Data file 1 contains the database with 7279 records of non-target invertebrate abundance, activity density, or predation or parasitism in Bt and non-Bt maize, extracted from 120 publications. Data file 2 includes the list and definitions of variables in the database, and data file 3 represents the critical appraisal questions and answer options.</p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Dead wood in the forests of Northern Eurasian: field measurements database
<p>Dead wood plays a substantial role in forest ecosystem functioning. However, the amount and dynamics of dead wood in the forests of Northern Eurasia are poorly understood. Here we present a database of field measurements of dead wood, collected from published sources and aggregated data from the Russian national forest inventory. The structure of dead wood by its components includes snags, logs, stumps, and the dry branches of living trees The database is intended to be used to assess the dead wood volume and the amount of dead wood in carbon units as part of the carbon budget calculation of forests at different scales.</p> <p>The database is a supplementary material in the following journal paper.</p> <p>Shvidenko, A.; Mukhortova, L.; Kapitsa, E.; Kraxner, F.; See, L.; Pyzhev, A.; Gordeev, R.; Fedorov, S.; Korotkov, V.; Bartalev, S.; Schepaschenko D. A Modelling System for Dead Wood Assessment in the Forests of Northern Eurasia. Forests 2023, 14, 45. https://doi.org/10.3390/f14010045</p>
Data from: Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
<p>The baseline distribution data for all species of a given group in a region can provide fundamental insights into biogeographic questions about historic patterns of species richness, population trends, and extinction. Grasshoppers are one major group of insects for which a continent-wide perspective on their geographic distribution can be obtained. This is because they were extensively surveyed in Australia for 54 years (1936-1989) as part of Commonwealth expeditions to obtain specimens for the Australian National Insect Collection (ANIC). Field notebooks recorded from those surveys, under the direction of ANIC curator and director K. H. L. Key, form the principal source of historic distribution records for grasshoppers in Australia. We digitized all 223 notebooks (2486 pages) and transcribed all the field trips conducted in Western Australia (WA) and Tasmania (47 notebooks, 590 pages). We then carefully geocoded all sampling sites of the transcribed notebooks, following the odometer readings and descriptions of routes from a suitable reference point using historic topographic maps and Google Earth. In total, we extracted 8975 geographic coordinates for 477 species having a confirmed or putative taxonomic name of (only 170 of these species have been formally described). We found that species richness varied spatially, with highest richness in arid interior and north of WA. Historic grasshopper surveys were non-randomly distributed across both WA and Tasmania with the highest survey intensity around coastal regions. Variation was observed among surveyors in terms of the number of species detected per site, between-site distance, and the season of survey being conducted. Overall, however, the dataset is among the most comprehensive continent-wide surveys of Australian invertebrates and will greatly facilitate future work on their ecology, biogeography, conservation, and responses to environmental change.</p>
Data from: Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
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Database of non-target invertebrates recorded in field experiments of genetically engineered Bt maize and corresponding non-Bt maize: data files
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Regional databases demonstrate macroecological patterns less clearly than systematically collected field data
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Above- and below-ground net primary productivity: a field-based global database of grasslands
<p>Net primary productivity (NPP) over global grasslands is crucial for understanding the terrestrial carbon cycling and for the assessments of wild herbivores food security. During the past few decades, numerous field investigations have been conducted to estimate grassland NPP since the measuring criterion released by the International Biological Program. However, a comprehensive NPP database, particularly for belowground NPP (BNPP), in global grasslands is rare to date. Here, field NPP measurements from 438 publications (1957–2018) in global grasslands were collected, critically filtered, and incorporated in a comprehensive global database with observations for aboveground NPP (ANPP), BNPP, total NPP (TNPP), and BNPP fraction (fBNPP). Associated information on geographical locations, climatic records, grassland types, land use patterns, manipulations subjected to manipulative experiments, sampling year of study sites as well as NPP measurement methods are also documented. This database included 2985 entries from 1785 study sites. Among them, 806 entries contained paired data of ANPP and BNPP, resulting in the 806 fBNPP data. The study sites encompassed global grasslands with latitudinal range of 54.5° S~78.9° N, longitudinal range of 157.4° W~175.8° E, and altitudes from 0 to 5168 m above sea level, covering broad climatic gradients (-17.6 to 28.8 °C in mean annual temperature and 63 to 2052 mm in mean annual precipitation). This global database is the world's largest paired data of ANPP and BNPP field measurements in grasslands. It can be used to study the spatio-temporal patterns of NPP and its allocation, evaluate the responses of above- and below-ground carbon components to future global changes, and validate the NPP estimation by empirical or process-based models in global grasslands. The database can be freely used for non-commercial applications. We kindly request users cite this data paper when using the database, respecting all the hard work during data compilation.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.