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691 results for “Vegetation Data”
Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Vegetation and invertebrate communities in 500 plots in the Duplin and Dean Creek watersheds: ground truth data for matching hyperspectral imagery
We measured characteristics of vegetation (Aster tenuifolius, Batis maritima, Borrichia frutescens, Distichlis spicata, Iva frutescens, Juncus roemerianus, Limonium carolinianum, Salicornia biglovii, Salicornia virginica, Spartina alterniflora, Spartina patens, Sporobolus virginicus), soil (salinity, proportion organic and proportion water) and densities of common gastropods and bivalves in 500 plots in the Duplin and Dean Creek watersheds on Sapelo Island on June 20-26, 2006. Plot locations were determined using a high precision hand-held GPS. These data were used to help ground-truth hyperspectral aerial images collected at the same time by Dr. John Schalles.
Geographic coordinates, soil properties, plant species composition and vegetation survey data in the study on tidal marshes of the Ogeechee, Altamaha and Satilla estuaries in Georgia, USA
We examined patterns of habitat function (plant species richness), productivity (plant aboveground biomass and total C), and nutrient stocks (N and P in aboveground plant biomass and soil) in tidal marshes of the Satilla, Altamaha, and Ogeechee Estuaries in Georgia, USA. We worked at two sites within each salinity zone (fresh, brackish, and saline) in each estuary, sampling a transect from the creekbank to the marsh platform. Site-scale and plot-scale species richness decreased from fresh to saline sites. Standing crop biomass and total carbon stocks were greatest at brackish sites, followed by freshwater then saline sites.
CDRRC growing season aridity and grazing season vegetation biomass data
Growing season aridity and livestock grazing seasonality can influence biomass production of perennial grasses in dryland systems. For this study, we used a long-term dataset (1967-2004) to investigate the independent and joint effects of growing season aridity (De Martonne aridity index calculated for the months of June through September) and grazing season (yearlong continuous, fall, winter/spring, or summer season grazing) on the mean annual biomass (kg per hectare) of the perennial grasses Bouteloua eriopoda (black grama), Aristida spp. (threeawn), and Sporobolus spp. (dropseed) in a southwestern United States Chihuahuan Desert rangeland system. Biomass data were collected from 78 permanent sampling transects that were within one mile (1609.34 m) distance to water. Over the 37-year study period, total perennial grass biomass decreased as growing season aridity increased, but the extent of this relationship depended upon season of grazing and specific grass taxon. Aridity-related decreases in total perennial grass biomass were most severe in the summer and fall summer seasonal grazing pastures, primarily due to inherently low black grama levels. Our findings indicate that over time, summer and fall grazing can potentially exacerbate the negative effects of increasing aridity on perennial grass biomass.
Long-Term Elevation Plots (LTEP) (Altitudinal transects vegetation data along three rivers in the Luquillo Experimental Forest)
The composition of plant communities changes with elevation in the Luquillo Experimental Forest (LEF). The goal of this project is to document the patterns of these changes, and in particular, to determine whether the distributions of individual species are independent of one another, or whether they are related, in either a congruent or a hierarchical manner. Thirty-two permanent vegetation plots, each 50m by 20m are being established in the LEF, with 5 plots along the Icacos river, 11 along the Mamayes river and 16 along the Sonadora stream. The plots were established at every 100m in elevation, starting at 200m above sea level. All woody, free-standing stems greater than 1cm dbh were marked, identified and mapped into 5x5 subquadrats. We anticipated that gradient analysis will show whether the distributions of species are coincident or independent, enabling us to evaluate whether separate, genuine plant communities exist in the LEF. Because the plots are permanent, we also expected that they allow us to better evaluate how different vegetation types, at different elevations, respond to large scale disturbances, especially hurricanes. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Tropical green roofs vegetation dynamics data
The data archive is here: https://doi.org/10.2737/RDS-2021-0024 please use this DOI when citing this dataset. This publication contains data collected in 2017 from three green roofs at the International Institute of Tropical Forestry in San Juan, Puerto Rico and one green roof at the Social Sciences Faculty of the University of Puerto Rico in Río Piedras. Data from these extensive green roofs include substrate depth as well as species counts within a sampled quadrant, as well as species identification information. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Gunnison's Prairie Dog Restoration Experiment (GPDREx): Vegetation Cover Data from the Sevilleta National Wildlife Refuge, New Mexico (2011-2016)
Prairie dogs (Cynomys spp.) are burrowing rodents considered to be ecosystem engineers and keystone species of the central grasslands of North America. Yet, prairie dog populations have declined by an estimated 98% throughout their historic range. This dramatic decline has resulted in the widespread loss of their important ecological role throughout this grassland system. The 92,060 ha Sevilleta NWR in central New Mexico includes more than 54,000 ha of native grassland. Gunnison's prairie dogs (C. gunnisoni) were reported to occupy ~15,000 ha of what is now the SNWR during the 1960's, prior to their systematic eradication. In 2010, we collaborated with local agencies and conservation organizations to restore the functional role of prairie dogs to the grassland system. Gunnison's prairie dogs were reintroduced to a site that was occupied by prairie dogs 40 years ago. This work is part of a larger, long-term study where we are studying the ecological effects of prairie dogs as they re-colonize the grassland ecosystem.
Global vegetation productivity from 1981 to 2018 estimated from remote sensing data
<p>The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The detail information of the MUSES 5-km global GPP and NPP products are as below:</p> <p>Name: MUSES 5-km global GPP and NPP products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Data type: integer (16bit)</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 100</p> <p>Unit: gCm<sup>-2</sup>d<sup>-1</sup></p> <p> </p> <p><span>Citation (Please cite these papers when these data are used)</span></p> <p><span>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L.,</span><span> </span><span>New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. </span><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</span></p> <p><span>2. Wang, M.J.; Sun, R.; </span><span>Zhu, A.R.;</span><span> Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency</span><span> </span><span>and Gross Primary Productivity Using Three</span><span> </span><span>Different Approaches. <span>Remote Sensing</span>. <span>2020</span>, 12, 1003.</span></p> <p><span>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. <span>Remote sensing. </span><span>2018, </span>10, 327.</span></p>
Data: Cutting the costs of coastal protection by integrating vegetation in flood defences.
<p>File: levee_crest_height_reduction_per_country_version_July2021.nc<br>Fields: (1) Crest height reduction m per km along the populated coastline susceptible to flooding (return period = 100 years)<br> (2) Crest height reduction cost saving per country in million USD<sub>2005</sub> PPP along the populated coastline susceptible to flooding (return period = 100 years)<br> (3) Cost savings as percentage of GDP<sub>2005</sub> along the urban populated coastline susceptible to flooding (return period = 100 years)</p> <p>File: transectdata_version_July2021.nc<br> Transectdata of vegetated transects within the study area.<br>Fields: <br>(1) rps = return period <br>(2) fid = id of the transects<br>(3) centroids = coordinates of the transects<br>(4) inun = (1) in area susceptible to flooding<br>(5) urban = (1) in urban area, (0) not in urban area<br>(6) veg_width = derived coastal vegetation belt width along the foreshore<br>(7) veg_type = derived coastal vegetation type along the foreshore (1: salt marshes, 2: mangroves)<br>(8) hsig = Offshore significant wave heights (multiple return periods) corresponding to the transects<br>(9) wave period = Offshore peak wave period (multiple return periods) corresponding to the transects<br>(10) surge = Extreme water level combination of surge and tide (m +MSL) (multiple return periods)<br>(11) veg_z0 = elevation at the start of the vegetated zone (m +MSL)<br>(12) hrms_end_noveg = root mean square wave height at the end of the foreshore (without vegetation) (multiple return periods)<br>(13) hrms_endveg = root mean square wave height at the end of the foreshore (with vegetation) (multiple return periods) <br>(14) pdens_15km = population density derived using buffer of 15 kilometre radius</p>
Data from: Effects of dispersal and geomorphology on riparian seedbanks and vegetation in a boreal stream
<p>SiteData: information that describes 20 riparian zones along Svartån, a boreal free-flowing stream, indicated per LocationID (column A). Coordinates are given in SWEREF 99 TM (column B and C) and degrees of longitude and latitude (column D and E). RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Side of stream indicates plot placement when looking towards downstream. Data collection is described in the paper linked to below. </p> <p> </p> <p>LitterData: information that describes species lists of litter seedbanks from 20 riparian sites. Litter samples were taken in an unstandardised manner at each location. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing.</p> <p> </p> <p>SeedData: information that describes the soil seedbank composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Layer refers to samples that are taken from from layer 0-1 cm in the soil, 1-5 cm or from 5-10 cm deep. Data collection is described in the paper linked to below. </p> <p> </p> <p>VegetationData: information that describes vegetation composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Abundance is indicated following the categories in Table 1. Data collection is described in the paper linked to below. </p> <p> </p> <p>Table 1. Vegetation cover classes.</p> <table> <tbody> <tr> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Cover (%)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p><1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1-3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3-5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5-15</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>15-25</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>25-50</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>50-75</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>75-100</p> </td> </tr> </tbody> </table> <p> </p> <p>For more information, help or collaboration, please contact Jacqueline.Hoppenreijs@kau.se. If you use the data here in your work or research, please cite the publication appropriately.</p>
Data from: Saltmarsh vegetation and secured woody debris facilitate mangrove re-colonization
<p>Does the presence of saltmarsh vegetation affect the long-term regeneration of the pioneer mangrove species <em>Avicennia germinans</em> in a degraded dwarf forest? Does immobilized coarse woody debris (CWD) affect regeneration similarly? Do larger trees suppress or facilitate intraspecific saplings? The study was conducted in a dwarf mangrove forest in the high intertidal zone on Bragança peninsula in northern Brazil. The spatial patterns of <em>A. germinans</em>, the herbaceous halophyte <em>Sesuvium portulacastrum</em>, and CWD were mapped in three sample plots (each 400 m<sup>2</sup>) during two consecutive vegetation surveys, conducted in 2011 and 2014. Inhomogeneous Poisson and Thomas point-process models were used to assess the distribution of <em>A. germinans</em> life-history stages (seedlings, saplings, and adult dwarf trees), conditioned on the presence of <em>S. portulacastrum</em> and CWD. In addition, intraspecific interactions between trees and regeneration were assessed based on crown projection mapping. Bivariate point pattern analyses were used to assess the dependence of advance regeneration on dwarf <em>A. germinans</em> trees and <em>S. portulacastrum</em>. <em>A. germinans</em> saplings and trees were positively associated with <em>S. portulacastrum</em> and CWD, whereas seedlings were located around tree crowns. The density of fruit-bearing trees was positively associated with sapling density, indicating that regeneration relied on locally dispersed propagules. Herbaceous vegetation and CWD have an important ecological function in degraded mangroves by retaining tidally dispersed propagules. Here, we show that herbaceous vegetation does not suppress the growth of seedlings but facilitates mangrove recolonization. Due to limited tidal dispersal, regeneration relies on local propagule supply. In addition to hydrological restoration, the observed vegetation patterns suggest that, in the absence of propagule-retaining vegetation, restoration of high-intertidal mangroves can be facilitated by establishing nuclei of planted trees and installing secured logs.</p>
Data used in the manuscript: "Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh"
<p>The data set presented accompanies the study by Laso Bayas et al. (2011) “Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh”. The data set contains all the observed (not transformed) variables used in the above mentioned study. The accompanying text file describes each of the variables included. A total of 180 transects were employed for the Laso Bayas et al (2011) study. The variables were further standardized and simplified to use them into the statistical models described in the paper.</p>
Data from: Vegetative phenologies of lianas and trees in two Neotropical forests with contrasting rainfall regimes
<ol> <li>Among tropical forests, lianas are predicted to have a growth advantage over trees during seasonal drought, with substantial implications for tree and forest dynamics. We tested the hypotheses that lianas maintain higher water status than trees during seasonal drought and that lianas maximize leaf cover to match high, dry-season light conditions while trees are more limited by moisture availability during the dry season.</li> <li>We monitored the seasonal dynamics of predawn and midday leaf water potentials and leaf phenology for branches of 16 liana and 16 tree species in the canopy of two lowland tropical forests with contrasting rainfall regimes in Panama.</li> <li>In a wet, weakly seasonal forest, lianas maintained higher water balance than trees and maximized their leaf cover during dry-season conditions, when light availability was high, while trees experienced drought stress. In a drier, strongly seasonal forest, lianas and trees displayed similar dry season reductions in leaf cover following strong decreases in soil water availability.</li> <li>Greater soil moisture availability and a higher capacity to maintain water status allow lianas to maintain the turgor potentials critical for plant growth in a wet and weakly seasonal forest but not in a dry and strongly seasonal forest.</li> </ol>
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Data on submerged aquatic vegetation and its water environment in Lake Saint-Pierre, Saint Lawrence River, from 2012 to 2016
<p>This dataset is the result of a large collaborative work lead by the GRIL from 2012 to 2015 on a submerged aquatic vegetation meadow located downstream of two agricultural tributaries (Saint-François and Yamaska rivers) in Lake Saint-Pierre, a fluvial lake of the Saint Lawrence River. The data describe plants (as rake biomass and echosounding) and their environment, including water chemistry, current velocity as well as light, temperature and instantaneous meteo. Only echosounding data are available in 2016 and sediments were collected in 2015. Data are organized as a relational database and the GRIL_LSP_database.png provides keys and links between tables as well as data format. Data are in the tables mesure_integree, mesure_spatiale, mesure_verticale, plante_biomass_taxon, plante_recolte, plante_in_situ. The other tables are metadata about spatiotemporal locations and reported measures. Additional data (e.g. zooplankton, sediments) should eventually be made available and associated to this overall GRIL dataset.</p>
Modern pollen data from the East Asian Pollen Database (EAPD): pollen, vegetation and climate relationship
<p>This is a modern pollen dataset of eastern Asia, in which a total of 1756 sample sites is selected from the original database EAPD (East Asian Pollen Database) which consists of 2858 samples. The sample types are mainly surface soil, moss, sediment top (lake, delta, peatland, river basin, reservoir and so on), and dust capture. The pollen data are mostly count numbers, but a few was originally given in percentage (marked with TRUE for proportion or percentage). We have checked pollen taxonomic nomenclature and combined some synonym pollen types from different original sources.</p> <p>This dataset includes only the samples collected in the areas under natural vegetation or land cover with low human disturbance, that the sites located in the agriculture areas or strong human intervention have been excluded. This screening procedure makes the pollen data readily available for biome and climate reconstructions. The contributors' original research concerning pollen-climate relationship from EAPD sources have been published in Zheng, et al. (2014 and 2008), which have revealed that pollen taxa in the dataset have significant relationship with climate variables. This dataset is potentially useful for multiscale paleovegetation and paleoclimate reconstruction studies in Asia. </p> <p>EAPD is developed and maintained by the Laboratory of Quaternary Science and Palynology in the School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, China.</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.