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23,429 results for “Region”
Coastal Forest Aboveground Biomass Data at six sites in the Chesapeake Bay and Delaware Bay region, 2021
This dataset contains aboveground biomass measurement and vegetation inventory of 17 coastal forest sites collected during June 1-8 of 2021 across Virginia (n = 6 in Goodwin Island and Phillips Creek), Maryland (n = 4, Monie Bay and Moneystump Swamp) and Delaware (n = 7, Milford Neck and Donas landing). The aboveground biomass was computed with allometric equations and all study sites were located within a narrow elevation range of 0-5m above sea level.
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
Open the record for dataset details and reuse information.
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Phytogeographic regions of Ukraine according to the "Flora Fungorum Ucrainicae"
<p>Origin of the data</p> <p>This regionalization was originally published by Heluta (1989), to illustrate the distribution of powdery mildew fungi across Ukraine, and further was used in the series "Flora Fungorum Ucrainicae", as well as individual publications and thesis in Mycology. The regionalization was based mainly on the current at that time Geobotanical zonation of the URSR (Barbarych et. al, 1977).<br>Since both names and accepted abbreviations of regions originally were in Russian, we adopted the translation made by Akulov et al. (2003), with some additions from a later publication by Prylutskyi & Chvikov (2020):<br>CF – Carpathian Forests, DGMS – Donetsk Gramineous-Meadow Steppe, FSCr – Forest-Steppe Crimea, KFS – Kharkiv Forest-Steppe, LFS – Left Bank Forest-Steppe, LGS – Left Bank Gramineous Steppe, LGMS – Left Bank GramineousMeadow Steppe, LP – Left Bank Polissya, MRF – Middle-Russian Forests, MCr – Mountain Crimea, PF – Precarpathian Forests, RF – Roztocze Forests, RFS – Right Bank Forest-Steppe, RGS – Right Bank Gramineous Steppe, RGMS – Right Bank Gramineous-Meadow Steppe, RP – Right Bank Polissya, SP – Small Polissya, SSCr – South Seaside of Crimea, SGMS – Starobilsk Gramineous-Meadow Steppe, SCr – Steppe Crimea, TR – Transcarpathia, VFS – Volyn Forest-Steppe, WFS – Western Forest-Steppe, WP – Western Polissya, WUF – West-Ukrainian Forests, WS – Wormwood Steppe.</p> <p><strong>UPD:</strong> Ukrainian names and abbreviations, as well as English names of the regions, updated according to <a href="https://ukrbotj.co.ua/archive/80/3/199" rel="nofollow">Heluta, 2023</a>.</p> <p>Dataset description</p> <p>Dataset (zip-archive) contains GIS vector layers with the polygons of regions, in the following formats: Geopackage, KML, and Esri shapefile. Polygons have been drawn manually using QGIS software, following verbal descriptions of the borders of regions from Heluta (1989).<br>CRS: EPSG:3857 - WGS 84 / Pseudo-Mercator<br>Charset Encoding: UTF-8</p> <p>Attribute table's fields descriptions</p> <p>fid - Unique identifier for each polygon<br>Name - Accepted abbreviated name for the region in Ukrainian<br>NameEng - Abbreviated name for the region, translated into English<br>NameFullUA - Full name of a region, in Ukrainian<br>NameFul - Full name of a region translated into English<br>NatZone - Natural zone according to the source (Heluta, 1989), in Ukrainian<br>Ecoregions - Name of the Terrestrial Ecoregion (TEOW) (Olson et al., 2001), which covers most of the area of a given region<br>Note: KML file has additional system fields, not contain attribute information.</p> <p>References</p> <p>Heluta, V.P. (2023) A critical revision of the powdery mildew fungi (Erysiphaceae, Ascomycota) of Ukraine: Erysiphe sect. Microsphaera. Ukrainian Botanical Journal. 2023. 80 (3). <a href="https://doi.org/10.15407/ukrbotj80.03.199" rel="nofollow">https://doi.org/10.15407/ukrbotj80.03.199</a></p> <p>Heluta, V.P. (1989) Powdery Mildews. Flora Fungorum Ucrainicae. Kyiv: Naukova dumka [In Russian: Гелюта, В.П. (1989) Флора грибов Украины: Мучнисторосяные грибы. Киев: Наукова думка]</p> <p>Barbarych, A.I. (Ed.) (1977)Geobotanical zonation of the URSR. Kyiv: Naukova Dumka [in Ukrainian: Геоботанічне районування Української РСР. Київ: Наукова думка]</p> <p>Akulov, O.Yu.; Usichenko, A.S.; Leontyev, D.V.; Yurchenko, E.O.; Prydiuk, M.P. (2003) Annotated checklist of aphyllophoroid fungi of Ukraine. Mycena 2:1–76.</p> <p>Chvikov, V.; Prylutskyi, О. (2020) Annotated checklist of Hygrophoraceae (Agaricales, Basidiomycota) of Ukraine. Biodivers. Ecol. Exp. Biol. 22, 6–23. https://doi.org/10.34142/2708-5848.2020.22.2.01</p> <p>Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</p>
GHG Dataset for the frontiers publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region"
<p>GHG Dataset used in the Frontiers Publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region". Additionally including CO2 besides N2O and CH4. Includes 3 cropping seasons.</p> <p>The data is also available online on the GHG flux visualisation and calculation tool "gasflxvis": https://sae-interactive-data.ethz.ch/gasflxvis/</p> <p>Further details on the calulation are provided both on gasflxvis and the Frontiers publication. Calculation procedure according the following PLOS ONE publication: http://dx.doi.org/10.1371/journal.pone.0200876</p>
Orbicella faveolata and O. franksi coral metagenome assemblies from the Lower Florida Keys region of Florida, USA
<div> <p>The enclosed files include mostly <em>Orbicella faveolata</em> and three <em>Orbicella franksi</em> coral metagenome assemblies collected from the Lower Keys in Florida’s Coral Reef, USA. Metadata for the files is included in this repository. Apparently healthy coral tissue cores were collected between May 28 and June 21, 2021. The DNA was extracted from the host and associated microorganisms and sequenced in a paired-end 150 bp format on an Illumina NovaSeq. Trimming and quality filtering of DNA sequence reads proceeded, followed by host and photoendosymbiotic dinoflagellate DNA removal. The host-cleaned reads were assembled individually by coral sample into longer contigs using MegaHit v1.1.4. The “Assembly_Fastas” zipped file contains 41 metagenome assemblies from the individual <em>Orbicella faveolata</em> corals and 3 assemblies from the individual <em>Orbicella franksi </em>colonies for a total of 44 assemblies. In addition, these assemblies were annotated with eggnog-mapper v2.1.6 to generate both predicted gene regions and annotation output files. The “Predicted_Gene_Fastas” zipped file contains nucleotide fasta files of the predicted gene regions for all 44 coral metagenome assemblies. The fasta header of each gene includes the contig ID it originated from in the associated “Assembly_Fasta”. The “Predicted_Gene_Annotations” zipped file contains either .csv or .xlsx files with the eggnog-mapper-based annotations. These files contain a “query contig” that corresponds to the contig ID in the fasta header of the “Predicted_Gene_Fasta”. </p> <p>In addition to individual assemblies, a co-assembly was generated that included all 41 <em>Orbicella faveolata</em> coral samples. Prior to co-assembly, further removal of eukaryotic DNA proceeded by splitting the indiviudual assemblies into eukaryotic and prokaryotic content with the program EukRep v0.6.7, followed by mapping of the host-clean reads to the eukaryotic DNA to remove them. The eukaryote-clean reads from all 41 corals were input into MegaHit to generate a co-assembly. The co-assembly is included (FLK_OFAV_MG_coassembly_final.contigs.fa). Predicted genes from the co-assembly were generated with Prodigal v2.6.3 and the nucleotide fasta of the output is included in this repository (FLK_OFAV_MG_pred.fna). Like with the indiviudal assemblies, eggnog-mapper was used to generate annotations of the predicted genes from Prodigal (FLK_OFAV_MG.emapper.annotations.xlsx). Additionally, the abundance of each predicted gene was generated using Salmon to map the eukaryote-clean reads to the predicted genes. The number of reads (counts) for each gene across each coral sample were aggregated as integers into one table and included in this repository (FLK_OFAV_MG_pred_NumReads.tsv). </p> </div> <div> <p>These data were processed and generated by Julie Meyer’s Lab at the University of Florida, using funding from the Florida Department of Environmental Protection. </p> </div>
The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)
<p><strong>extrAIM </strong>dataset is a <strong>new merged daily precipitation product</strong> (extraim_merged_data.nc) for the Mediterranean region with the following characteristics:</p> <ul> <li><strong>Dataset format:</strong> NetCDF</li> <li><strong>Spatial resolution:</strong> 25 x 25 km</li> <li><strong>Temporal resolution:</strong> 1 day</li> <li><strong>Spatial coverage:</strong> Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li><strong>Temporal coverage: </strong>01-01-2007 to 30-09-2021</li> <li><strong>Merging approach: </strong>Two-step merging (classification and regression) <ul> <li><strong>Algorithm: </strong>Random Forest for both classification and regression</li> <li><strong>Training strategy:</strong> Full training strategy</li> </ul> </li> <li><strong>Merged precipitation products: </strong>SM2Rain-ASCAT and GPM Late Run</li> <li><strong>Reference precipitation product:</strong> EMO5</li> <li><strong>Static covariates: </strong>Longitude, Latitude and Elevation, in both classification and regression step <ul> <li><strong>Classification step:</strong> probability dry and probability dry of the 5 neighboring points around the target locations</li> <li><strong>Regression step:</strong> mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> <p>In addition, an <strong>ensemble of 20 synthetic realizations</strong> (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.</p>
Microscopic trip chains for Brunswick (Germany) region
<p>The data set contains microscopic trip chains for the Brunswick (Braunschweig) area in Germany on an average day. All synthetic persons within Braunschweig are shown, as well as all households outside Braunschweig where at least one synthetic person had an activity in Braunschweig.</p> <p>The generation of this data set is based on a two-stage process. The starting point is the macroscopic transport demand model DEMO (Winkler and Mocanu, 2020: https://doi.org/10.1016/j.trd.2020.102476) and a population upscaled from the MiD 2017 ("Mobilität in Deutschland") for Germany, which was spatially distributed according to the BKG household dataset (households, inhabitants, federal government). In the first step of the process, the trip chains between the DEMO traffic cells were generated based on the daily schedules of the MiD population (Mocanu and Joshi, 2022: https://elib.dlr.de/188443/). In the second step of the process, corresponding locations were assigned within the target traffic cells. The locations were previously extracted from OpenStreeMap and attributed with activities according to their attributes/metadata (key/value pairs) (Malkus et al., 2024: https://doi.org/10.1016/j.procs.2024.06.043).</p>
Exploring Vocatives in Folk Songs of the Podillia Region
<p>This dataset is based on the folklore collection <em>Pisni Podillia: zapysy Nasti Prysiazhniuk v seli Pohrebyshche. 1920-1970 rr.</em> (Myshanych 1976). The collection consists of 850 songs, encompassing 13,005 lines and 78,888 tokens. Vocatives were manually distinguished and recorded in a separate column in the corpus without the assistance of RStudio, due to the complexity of distinguishing vocatives in Ukrainian.</p> <p>Vocatives in Ukrainian folk songs were analysed using the R programming language along with RStudio. </p> <p>Code written for text analysis in Estonian Literary Museum. </p> <p> </p> <p>This dataset consists of the following files:</p> <p>1. <strong>vocatives_Podillia_folk_songs.R</strong>: R script used for analyzing the corpus, including vocative counting, song length analysis, POS-tag analysis, semantic group and structural types analysis. </p> <p>2. <strong>corpus_vocatives.csv</strong>: Contains the text data of Podillia folk songs with manually distinguished vocatives. </p> <p>3. <strong>corpus_POS_tokens.csv</strong>: Contains verified the POS-tagged tokens of the corpus.</p> <p> </p>
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories
<p>CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (<a href="https://coco2-project.eu/">https://coco2-project.eu/</a>). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available. </p> <p>Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories. </p> <p>For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.</p>
Map of Soil Organic Carbon: Region of Murcia (Spain)
This data package contain four soil organic carbon (SOC) maps resulted from the best data-model agreement of the analysis carried out in the frame of the Ph.D. Thesis ‘MODELING ORGANIC CARBON FOR QUANTIFICATION OF RESERVOIRS IN TERRESTRIAL ECOSYSTEMS AT THE NATIONAL LEVEL’ (Pilar Durante). Theses maps correspond to the estimates of SOC concentration (SOCc, g/kg) and SOC stocks (SOCs, tC/ha), and their associated spatially explicit uncertainties maps, for the Region of Murcia at 0-30 cm and 100 m spatial resolution. To achieve this, we evaluated four different digital soil mapping (DSM) approaches to estimate SOCc and SOCs for the Region of Murcia (11,313 km2), a topographic and climatic complex area in southern Iberian Peninsula, at three spatial resolutions (100m, 250m, 1000m). Using a local SOC database (255 soil profiles), we founded that a Quantile Regression Forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the best balance of accuracy, external validation, and interpretability. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%; meanwhile the mean SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. The analysis showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement between ~40% (for SOCc) and ~65% (for SOCs) when compared with SOC products derived from national and global databases. Our results provided evidence that large discrepancy exists between national and global estimates for reporting SOC at a local scale. Consequently, local-to-regional efforts are needed to better describe SOC spatial variability to reduce uncertainty and improve the assessment of soil resources.
Water temperature in the hidden, subglacial lake at Uruguay Island, Antarctic Peninsula region, 2020-2021, and additional data sets.
The dataset contains temperature measurements in a small subglicer (hidden) lake of Antarctic Peninsula region at several levels of depth. The measurements cover almost a full year and provide an understanding of the temperature and hydrological regime of the water body. Weather measurement data and statistics is provided additionally.
Sustainable Tourism Survey Dataset for the Northern Ecuadorian Amazon Region, 2024-2025
This dataset is based on a sustainability perception survey conducted at Perla Ecological Park, located in the Northern Ecuadorian Amazon, during December 2024 and January 2025. A total of 383 visitors participated in the study, which aimed to: (1) quantify and compare key sustainability indicators across PERLA’s management zones, (2) identify the most influential predictors of overall sustainable performance, and (3) derive and prioritize a set of integrated strategies that balance ecological conservation with economic viability. The survey instrument included Likert-scale, dichotomous, and thematic categorical items designed to assess public perceptions on tourism sustainability, natural and cultural resource management, institutional support, and visitor satisfaction. The research was carried out through a collaborative effort among multiple public universities and independent researchers, including two international institutions—one of them based in Ecuador—as part of a broader scientific initiative to inform evidence-based sustainability planning in protected areas. This dataset provides valuable insight for researchers, practitioners, and policymakers interested in sustainable tourism, visitor management, and participatory planning in biodiversity-rich environments.
Sacramento-San Joaquin Bay-Delta Continuous (15 minute) Water Quality Monitoring: South Delta Region, collected by the North Central Region Office, DWR, 1999 – ongoing
The Department of Water Resources (DWR) Water Quality Evaluation Section (WQES) provides technical expertise and program support for regulatory compliance, water operations, emergency response, and environmental restoration. Wireless telemetry is used to transmit real-time provisional data to the California Data Exchange Center (CDEC), making the data publicly available. The published dataset is quality controlled and quality assured providing detailed information at 15-minute intervals from 18 monitoring stations, using Xylem’s YSI EXO2 and YSI 6600 multiparameter sondes to document individual water quality measurements of multiple water quality parameters. The dataset informs operations for the California State Water Project and supports water quality monitoring required by Water Right Decision D-1641, the Delta Compliance Program, the South Delta Temporary Barriers and the South Delta Improvement Program. It is important to note that the start dates and subsequent equipment upgrades vary between stations and equipment leading to discrepancies in the dataset’s date ranges.
Sulfate Concentrations and Sulfur Stable Isotope Ratios in Surface Water from the Marlborough and Waipara Winegrowing Regions, South Island of New Zealand, 2023
Agricultural sulfur (S) additions are a major anthropogenic source of S to the environment, yet our understanding of the downstream transformations and potential environmental consequences of these S inputs remains incomplete. This dataset includes surface water samples from the Marlborough and Waipara winegrowing regions of the South Island of New Zealand, where frequent applications of S fungicide are widespread. Samples were analyzed for sulfate concentration and S stable isotope ratios – the combination of which forms the S “fingerprint”. We collected surface water samples during the winter of 2023 from a variety of different locations and land use types, including vineyards, forests, pastures, and urban areas. The data table includes water sample sulfate concentrations, sulfate-S stable isotope measurements, and the S stable isotope composition of commonly used S-containing fungicides and fertilizers for comparison.
Bonanza Creek LTER: Active Layer Depth or Permafrost Presence for the Regional Site Network
The initial goal (2000-2013) of these data was to define the presence/absence of permafrost within 2.5m of the surface in the regional site network. Efforts were focused mainly on sites where this was not easily deduced. The final subset of sites (2015 � present) are distributed across the 3 ecoregions of the RSN and primarily in older aged wet sites. The permafrost distribution in interior Alaska is discontinuous and dynamic; susceptible to fire and climate disturbances. Therefore, sites included in this long-term monitoring dataset may cease to be monitored as permafrost degrades and disappears or may be monitored again if permafrost is reestablished.
Organic Horizon Depth in the Regional Site Network
This dataset contains the organic depth from a subset of sites in the regional site network. Each sample was taken from near one of the 20 plots markes located every 10 meters within each site for a total of 20 samples.
Tree Health Conditions (mortality, damage, disease, bark beetles) in Fuel Reduction Treatments Located Near Communities in Interior Alaska and the Cook Inlet Region of Alaska - Observations from July-August 2023
This dataset contains tree-, transect-, and site-level observations of forest stands at sites that received a fuel reduction treatment. Tree-level observations include species, diameter, living status, damage, disease, and bark beetle presence. Transect-level observations include level of coarse woody debris and bark beetle presence. Sites are categorized by region (recent/ongoing spruce beetle oubreak or endemic spruce beetle population levels) and treatment type (hand-thinned or mechanincally felled and masticated). These observations are from July-August 2023. Sites are located near communities in Interior Alaska and the Cook Inlet Region.
Regional E-Atlas of the Greater Phoenix Region: Areas of significant agricultural and residential groundwater use, 1996-2000
Spatial distribution of well water usage (groundwater) for the period 1996 - 2000. These data present significant areas of agricultural and residential groundwater use during this period. This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/). The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.
Regional drinking water quality monitoring program: long-term monitoring of water quality in select canals, reservoirs, and treatment plants of the greater Phoenix, Arizona metropolitan area drinking water system, ongoing since 1998
Regional Drinking Water Quality Monitoring Program ================================================== Arizona Statue University (ASU) has been working with regional water providers (Salt River Project (SRP), Central Arizona Project (CAP)) and metropolitan Phoenix cities since 1998 on algae-related issues affecting drinking water supplies, treatment, and distribution. The results have improved the understanding of taste and odor (T&O) occurrence, control, and treatment, improved the understanding of dissolved organic and algae dynamics, and initiated a forum to discuss and address regional water quality issues. The monitoring benefits local Water Treatment Plants (WTPs) by optimizing ongoing operations (i.e., reducing operating costs), improving the quality of municipal water for consumers, facilitating long-term water quality planning, and providing information on potentially future-regulated compounds. ASU has been monitoring water quality in terminal reservoirs (Lake Pleasant, Saguaro Lake, and Bartlett Lake) continuously from 1998 to the present for algae-related constituents (taste and odors, and more recently metals from the upper reservoirs), nutrients, and disinfection by-product precursors (i.e., total and dissolved organic carbon and organic nitrogen). Additional monitoring has been conducted in the SRP and CAP canal systems and in water treatment plants in Phoenix, Tempe and Peoria. During this work the Valley has been in a prolonged drought and recently one above average wet year, and this data provides important baseline data for development of new or expanded WTPs and management of existing WTPs in the future. The current work has improved the understanding of T&O sources and treatment, but additional research and monitoring into the future is necessary. Reservoir monitoring is conducted once per month at Bartlett Lake, Saguaro Lake, and Lake Pleasant, and quarterly at Roosevelt, Apache, and Canyon Lakes. Samples are depth integrated in the epilimnion a
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.