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225 results for “Global database”
Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".
<p>Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".</p>
Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge
<p><strong>1.Summary</strong></p> <p>This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below. The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes. A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov).</p> <p>This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc.</p> <p><strong>2.Polyline geometry</strong></p> <p>The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018). Each line describes a meander and features the following attributes.</p> <p><strong>3.Attribute description</strong></p> <ul> <li><strong>SegmentID:</strong> identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li><strong>lakeFlag:</strong> 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li><strong>Width:</strong> average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li><strong>Elevation:</strong> mean elevation from SRTM (90m) per river meander in meters. SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach. The average elevation of all valid points per meander is reported here. Locations where no elevation could be produced are marked as -9999.</li> <li><strong>Slope:</strong> water surface slope in centimeter per kilometer. Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate. Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination. Locations where no slope could be produced are marked as -9999.</li> <li><strong>Meandwave:</strong> Meander wavelength in meters. This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines. Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li><strong>Sinuosity:</strong> Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li><strong>catch_area:</strong> Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner<em> et al.</em>, 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location. We translated flow accumulation given in number of pixels into catchment area (in m<sup>2</sup>) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li><strong>QWBM:</strong> mean annual flow estimated with the water balance model WBMsed (Cohen<em> et al.</em>, 2014).</li> <li><strong>Strpwr_len:</strong> stream power normalized by width (W/m).</li> <li><strong>Strpwr_are:</strong> stream power normalized by area (W/m<sup>2</sup>).</li> </ul> <p><strong>Acknowledgements</strong></p> <p>Use of this database should be acknowledged appropriately.</p> <p>The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder.</p> <p><strong>References</strong></p> <p>Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, <em>Geophysical Research Letters</em>, <em>42</em>(2), 395-402, doi: 10.1002/2014gl062764.</p> <p>Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, <em>Science</em>, doi: 10.1126/science.aat0636.</p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.</p> <p>LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, <em>Geophysical Research Letters</em>, <em>32</em>(17), doi: 10.1029/2005gl023836.</p> <p>Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, <em>EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION</em>, <em>89</em>(10), 93-94, doi: doi:10.1029/2008EO100001.</p> <p>Leopold, L. B., and M. G. Wolman (1960), River meanders, <em>Geological Society of America Bulletin</em>, <em>71</em>(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.</p> <p> </p> <p> </p>
Global Wood Density Database
Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC, Chave J (2009) Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository. doi:10.5061/dryad.234 Wood performs several essential functions in plants, including mechanically supporting aboveground tissue, storing water and other resources, and transporting sap. Woody tissues are likely to face physiological, structural and defensive trade-offs. How a plant optimizes among these competing functions can have major ecological implications, which have been under-appreciated by ecologists compared to the focus they have given to leaf function. To draw together our current understanding of wood function, we identify and collate data on the major wood functional traits, including the largest wood density database to date (8412 taxa), mechanical strength measures and anatomical features, as well as clade-specific features such as secondary chemistry. We then show how wood traits are related to one another, highlighting functional trade-offs, and to ecological and demographic plant features (growth form, growth rate, latitude, ecological setting). We suggest that, similar to the manifold that tree species leaf traits cluster around the __leaf economics spectrum__, a similar __wood economics spectrum__ may be defined. We then discuss the biogeography, evolution and biogeochemistry of the spectrum, and conclude by pointing out the major gaps in our current knowledge of wood functional traits.
Database for Towards ice thickness inversion: an evaluation of global DEMs in the glacierized Tibetan Plateau
<p>x, latitude of ICESat-2 point</p> <p>y, longitute of ICESat-2 point</p> <p>icesat-2, elevation from icesat-2</p> <p>sigma-icesat-2, error of elevation from icesat-2</p> <p>date-icesat2, acquiring date of icesat-2 point</p> <p>dhdx, along track slope of icesat-2 data</p> <p>dhdx_sigma, error of along track slope of icesat-2 data</p> <p>elevation range min, the minimum elevation of glaciers where icesat-2 data is located</p> <p>elevation range med, the median elevation of glaciers where icesat-2 data is located</p> <p>elevation range max, the maximum elevation of glaciers where icesat-2 data is located</p> <p>dh, glacier surface elevation change from Shean et al. (2020)</p> <p>aw3d30, elevation from aw3d30 in EGM96 geoid</p> <p>srtmgl1, elevation from srtmgl1 in EGM96 geoid</p> <p>tandem, elevation from TanDEM-X in WGS84 ellipsoid</p> <p>srtmv41, elevation from srtmv41 in EGM96 geoid</p> <p>nasadem, elevation from NASADEM in WGS84 ellipsoid</p> <p>merit, elevation from merit in EGM96 geoid</p> <p>aw3d30slp, slope from aw3d30</p> <p>srtmgl1slp, slope from srtmgl1</p> <p>tandemslp, slope from tandem</p> <p>srtmv41slp, slope from srtmv41</p> <p>nasademslp, slope from nasadem</p> <p>meritslp, slope from merit</p> <p>aw3d30asp, aspect from aw3d30</p> <p>srtmgl1asp, aspect from srtmgl1</p> <p>tandemasp, aspect from tandem</p> <p>srtmv41asp, aspect from srtmv41</p> <p>nasademasp, aspect from nasadem</p> <p>meritasp, aspect from merit</p> <p>The elevation should be <strong>converted</strong> using geoidheight function in MATLAB</p> <p> </p> <p> </p> <p> </p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of 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 Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric 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><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of 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 Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric 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>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
PhanSST - A global database of Phanerozoic sea surface temperature proxy data
<p>Beta pre-publication release of the PhanSST database of globally distributed Phanerozoic paleo-sea surface temperature proxy data. The database is currently accepted at Scientific Data.</p>
Database underlying the scientific publication: A global database of seahorse research and innovation, from the beginning to 2022
<p>The present database supports the study "A global database of seahorse research and innovation, from the beginning to 2022". </p> <p>Scientific knowledge on seahorses is rapidly expanding in response to declines in wild populations due to habitat loss and fishing. A plethora of information has accumulated and up until now, a global, publicly available, curated database has never been produced in a transparent and systematic way. Here, we present the largest open-access repository of scientific publications addressing seahorses and, for the first time, of theses and patents. Compilation followed the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) Statement for systematic reviews and meta-analyses, with modifications. The current repository duplicates the number of scientific publication records found from previous bibliometric/literature/review studies, using three extra repositories of source publications, and a lifetime window, <em>e.i</em>. from the beginning to March 2022. A total of 977 scientific publications, 101 theses and 533 patents are gathered in the dataset, covering 41 seahorse species out of 48 currently recognized. In addition, current work presents for the first time new metrics on authors, institutions, and research subject/field/discipline/thematic, as well as the organism’s stage of development (embryo, newborn, juvenile, subadult and adult). To expand metadata usage, the database was also made available in the Dublin Core™ Metadata Initiative format. This contribution can be used as a core reference for scientists, aquaculturists and conservationists, and is useful to rapidly identify relevant literature and knowledge gaps, better understand seahorse research and discover new trends in seahorse research and innovation.</p> <p>The database is available in two formats:</p> <p>1) <a href="https://zenodo.org/api/files/5651b70d-a7a0-45e6-8ce6-cb92bbe6f7e5/SeahorseBibliometricDatabase.xlsx">SeahorseBibliometricDatabase.xlsx</a></p> <p>and</p> <p>2) <a href="https://zenodo.org/api/files/5651b70d-a7a0-45e6-8ce6-cb92bbe6f7e5/SeahorseBibliometricDatabase_DublinCore.xlsx">SeahorseBibliometricDatabase_DublinCore.xlsx</a>, which is a vocabulary standardized (Dublin Core™ Metadata Initiative) version of the previous one, for metadata reuse.</p>
Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) Database
<p>A commonly used method to examine the relationship between global water consumption and production is input--output analysis. However, between approximately 70% and 90% of freshwater consumption occurs in agricultural primary production, which is often represented by only a small percentage of the total number of sectors in input-output databases. In addition, the assessment of the impact of water consumption is usually carried out at the national level.</p> <p><br> Therefore, the primary objective of the Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) approach was to improve assessments of water use and its impacts in input-output analysis.</p> <p><br> To achieve this objective, a global spatial model of agricultural primary production <em>MapSPAM</em> (IFPRI, 2019) was integrated into the existing input-output database <em>GLORIA</em> (Lenzen et al., 2017, 2021) via prorating. The resulting IO-GHAAPP approach includes (1) a disaggregated input-output database and novel environmental extensions for freshwater consumption and scarcity. The IO-GHAAPP database consists of 150 categories and 164 regions, resulting in a total of 24,600 region-category combinations. Forty-two of the categories are dedicated to agricultural primary production (28%). In comparison, the source input--output data consist of 120 categories and 164 regions, resulting in a total of 19,680 region-category combinations, of which 14 are dedicated to agricultural primary production (12%).</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Bunsen, Jonas, Vlad Coroamă, and Matthias Finkbeiner. 2023. ‘Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAPP) Database’. <em>Sustainability</em> 15 (2). <a href="https://doi.org/10.3390/su15129351">https://doi.org/10.3390/su15129351</a>.</p> <p> </p> <p><strong>References:</strong></p> <ul> <li>IFPRI. 2019. ‘Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0’. Harvard Dataverse. <a href="https://doi.org/10.7910/DVN/PRFF8V">https://doi.org/10.7910/DVN/PRFF8V</a>.</li> <li>Lenzen, Manfred, Arne Geschke, Muhammad Daaniyall Abd Rahman, Yanyan Xiao, Jacob Fry, Rachel Reyes, Erik Dietzenbacher, et al. 2017. ‘The Global MRIO Lab - Charting the World Economy’. <em>Economic Systems Research</em> 29 (2): 158–86. <a href="https://doi.org/10.1080/09535314.2017.1301887">https://doi.org/10.1080/09535314.2017.1301887</a>.</li> <li>Lenzen, Manfred, Arne Geschke, James West, Jacob Fry, Arunima Malik, Stefan Giljum, Llorenç Milà i Canals, et al. 2021. ‘Implementing the Material Footprint to Measure Progress towards Sustainable Development Goals 8 and 12’. <em>Nature Sustainability</em>, December. <a href="https://doi.org/10.1038/s41893-021-00811-6">https://doi.org/10.1038/s41893-021-00811-6</a>.</li> </ul>
Supplemental Files to "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs"
<p>These are supplemental files to the manuscript, "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs". Supplements contain excel spreadsheets, DNA alignments, Newick tree files, FigTree files, and csv files.</p>
Water and Planetary Health Analytics (WAPHA) global metal mines database
<p class="Teaser"><span>An estimated 23 M people live on floodplains affected by potentially dangerous concentrations of toxic waste derived from past and present metal mining activity. We analyze the global dimensions of this hazard, particularly Pb, Zn, Cu and As, using a geo-referenced global database detailing all known metal mining sites, and intact/failed tailings storage facilities. We then use process-based and empirically tested modelling, to produce a global assessment of metal mining contamination in river systems, and the number of human populations, and livestock exposed. Worldwide, metal mines impact 479,200 km of river channels and 164,000 km<sup>2 </sup>of floodplains. The number of people exposed to contamination sourced from long-term discharge of mining waste into rivers is almost fifty times greater than the number directly impacted by tailings dam failures.</span></p>
Database of nitrification and nitrifiers in the global ocean
<p>This is a dataset compiling the observations of nitrification rates and nitrifiers' abundance in the global ocean. A template for scientists who want to add their data to the database is also provided. </p> <p>Tang, W., Ward, B. B., Beman, M., Bristow, L., Clark, D., Fawcett, S., Frey, C., Fripiat, F., Herndl, G. J., Mdutyana, M., Paulot, F., Peng, X., Santoro, A. E., Shiozaki, T., Sintes, E., Stock, C., Sun, X., Wan, X. S., Xu, M. N., and Zhang, Y.: Database of nitrification and nitrifiers in the global ocean, Earth Syst. Sci. Data, 15, 5039–5077, https://doi.org/10.5194/essd-15-5039-2023, 2023.</p>
Fig. 4 in DarkCideS 1.0, a global database for bats in karsts and caves
Fig. 4 Biogeographical comparison (mean, 95% CI) of landscape parameters at 1-km resolution.
Global database of cement production assets and upstream suppliers
<div> <div> <div> <div> <p>Cement producers and their investors are navigating evolving risks and opportunities as the sector's climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority offrom carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company's greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics.</p> </div> </div> </div> </div>
GLOWCAD: A global database of woody tissue carbon concentrations/fractions
Open the record for dataset details and reuse information.
EntomoFun 1.0: A global database of entomopathogenic fungi and associations with their hosts
Open the record for dataset details and reuse information.
TraitAM, a global spore trait database for arbuscular mycorrhizal fungi
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Global database of cement production assets and upstream suppliers
Open the record for dataset details and reuse information.
Water and Planetary Health Analytics (WAPHA) global metal mines database
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FreshLanDiv: A global database of freshwater biodiversity across different land uses
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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.