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62 results for “area covered”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024
This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.
Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.
This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.
Land transformation on multi-decadal timescales reveals expanding croplands and settlements at the expense of tree-covered areas and mangroves in Nigeria
<p>A comparative assessment of the change patterns was conducted for seven categories using multi-decadal timescales in seven agroecological zones during three time-intervals (i.e., 1986 – 2000, 2000 – 2013, and 2013 – 2022). These selected periods cover important epochs in Nigeria’s recent history. To examine how much humans have appropriated natural cover (HANLC) in Nigeria over the last four decades, we differentiated natural covers (e.g., tree-covered areas, grasslands, wetlands, and waterbodies) from human activity-related uses (e.g., cropland, artificial surfaces and otherland). To identify trajectories of changes signifying human appropriation of land cover, we evaluated the drivers and processes underlying these major transitions, 1) Natural regeneration and afforestation, 2) Cropland expansion, and 3) Settlement and infrastructure development. Cropland expansion is Nigeria’s most widespread change process with much loss of croplands related to natural regeneration and settlement expansion. The transition matrix is provided showing the extent of land-cover changes in Nigeria over almost four decades (1986 - 2022). Major land cover transitions in each agroecological zone is presented. Analysis of land cover change in each agroecological zone is over 100% when areas of persistence (i.e., areas of no change) are not considered in the analysis.</p>
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
RIV04 Moss cover in streams in wooded riparian areas and areas where canopy had been cut at Konza Prairie
Our project was designed to test if woody removal in a riparian zone allowed the system to rebound to a grassland stream state. We hypothesized that removal would increase light and decrease moss biomass.
Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data
<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(< 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and –3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics. </span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>
Database of pyroclastic cover deposit thickness measurements (PT-Cam) in peri-volcanic areas of Campania (Italy)
<p>In an eruptive event, tephra deposits (i.e. ash and pumice) disperse in the atmosphere and deposit on the ground surface according to the speed and direction of the wind. Because the geotechnical and hydraulic properties of the unconsolidated pyroclastic fall deposits usually differ from the bedrock, their spatial thickness significantly influences geomorphological and hydrogeological processes such as landscape evolution, erosion, landslide, and hillslope hydrogeology.</p> <p>The PT-Cam database presents the thickness of tephra deposits (i.e. the unconsolidated materials over the bedrock) in Campania region (Italy), measured through in-situ investigations of some territories around the Somma-Vesuvius, Campi Flegrei, Roccamonfina, and Ischia volcanoes during the last decades. The measurements were conducted with probing tests, dynamic penetration tests, trenches, man-made pits, seismic surveys, and outcrops.</p> <p>Explanation for database attribute:</p> <ul> <li>CODE: identification code of the measurement;</li> <li>z: measured thickness expressed in cm;</li> <li>type_z: thickness type (i.e. if investigation has reached to the bedrock the type is “total” otherwise it is “partial”);</li> <li>type_investigation: method of in-situ investigation;</li> <li>locality: municipality to which the measurement point belongs;</li> <li>Longitude, Latitude: km coordinates in UTM WGS 84 system.</li> </ul>
SGS-LTER Ecosystem Stress Area - long-term point-frame (percent basal cover) dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1982-2011, ARS Study Number 3 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/521/7. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mec
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Peat depth and occurrence in areas covered by airborne radiometric surveys, southeastern and western Norway, 1983-2023.
This data package contains point measurements of peat depth and occurrence from two study sites in Norway: Skrimfjella, located in eastern Norway, and Ørskogfjellet, located in western Norway. These sites represent contrasting physical geographies, with diverse bedrock and landscape types, including inland hills and mountains, coastal plains, and inland valleys. The data were collected primarily during field campaigns in August 2020 (Skrimfjella) and August 2023 (Ørskogfjellet). The data package also contains peat depth measurements from beyond these primary field campaigns. These ancillary data were provided by the Norwegian Public Roads Administration and the Norwegian Institute of Bioeconomy Research. Peat depths were measured using manual probing, boreholes, and ground-penetrating radar (GPR). The data package is intended for use in digital soil mapping (DSM) applications at the landscape-regional scale, particularly for predicting peat depth and assessing relationships between peat depth and environmental variables. It supports research on peatland carbon stocks, land use planning, and climate change mitigation. The peat depth measurements are intended to be representative of sampled locations within the study areas, with a focus on peatland environments. The data package does not include comprehensive peatland extent mapping but provides insights into the spatial variability of peat depths in Norwegian landscapes.
SGS-LTER Ecosystem Stress Area - long-term point-frame (percent basal cover) dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1982-2011, ARS Study Number 3 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/331/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/521/7. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persiste
PIE LTER plant species percent cover in quadrats along vegetation transects at the Argilla Rd. salt marsh restoration site (Ipswich) and Rough Meadows reference marsh (Rowley – Stackyard Road area), Massachusetts.
Plant species percent cover in quadrats along vegetation transects at the Argilla Rd. salt marsh restoration site (Ipswich) and Rough Meadows reference marsh (Rowley – Stackyard Road area), Massachusetts. A long term study not directly part of the PIE LTER, but a companion study related to tidal restrictions and hydrological alterations of salt marshes in the Plum Island ecosystem.
Cloud-free snow cover area in the Pyrenees from MODIS
<p>This dataset contains the output of a gapfilling algorithm applied to MODIS snow products for the Pyrenees mountains as presented by Gascoin et al. (2015) and updated to the period 2000-Sep-01 to 2015-08-31 (15 hydrological years)</p> <ol> <li>Pirineos_gapfilled.tif: a multiband geotiff raster file in WGS84 UTM30N (EPSG:32630) at 500 m resolution with values 200 (snow) or 25 (no snow); <p>Corner Coordinates:<br> Upper Left ( 607750.000, 4789250.000) ( 1d40'21.72"W, 43d14'54.08"N)<br> Lower Left ( 607750.000, 4665250.000) ( 1d41'46.55"W, 42d 7'55.05"N)<br> Upper Right ( 973750.000, 4789250.000) ( 2d49'18.48"E, 43d 6'27.65"N)<br> Lower Right ( 973750.000, 4665250.000) ( 2d43' 8.76"E, 41d59'47.87"N)<br> Center ( 790750.000, 4727250.000) ( 0d32'47.24"E, 42d38'34.10"N)</p> </li> <li>Pirineos_gapfilled_dates.csv: a csv file indicating the date corresponding to each band (year, month, day)</li> <li>dem_Pirineos_UTM30_px500.tif: a geotiff raster of the elevation in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>aspect_Pirineos_UTM30_px500.tif: a geotiff raster of the slope aspect in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>Pirineos_gapfilled_probamap.png: a map of the mean annual number of snow days (snow cover duration) made from 1.</li> <li>Pirineos_gapfilled_scats.png: a plot of the timeseries of the daily snow cover area in km² over the Pyrenees mountain range from 2000-Sep-01 to 2015-08-31 made from 1.</li> </ol> <p><strong>Reference</strong></p> <p>Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337-2351, doi:10.5194/hess-19-2337-2015, 2015. http://doi.org/10.5194/hess-19-2337-2015</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/63NQASRDPDB0.</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Aqua Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD.</p>
SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information
<p>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular).</p> <p>The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation.</p> <p>The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type.</p> <p>1-Road-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>1-Road-Mask</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>2-NoRoad-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p> </p>
Text-fig. 1. Type specimen of Palmocarpon cretaceum MIQUEL, 1853, as illustrated by Miquel (1853: pl. 7). This figure does not include the piece of flint covering the central part of the fossil shown in the photograph (Text-fig. 2). in The Type Of Palmocarpon Cretaceum Miq., 1853 Described From The Cretaceous Of The Sint-Pietersberg, The Netherlands, Is An Eocene Nypa Burtinii (Brongn.) Ettingsh., 1879, Most Likely From The Brussels Area, Belgium
Text-fig. 1. Type specimen of Palmocarpon cretaceum MIQUEL, 1853, as illustrated by Miquel (1853: pl. 7). This figure does not include the piece of flint covering the central part of the fossil shown in the photograph (Text-fig. 2).
Figure 1 in Coral cover percentage and health condition in Tioman Island marine protected area, Pahang, Malaysia
Figure 1. The location of the study sites [Tiong Point (2°49'49.12"N; 104° 9'45.10"E), Tekek (2°49'23.97"N; 104° 9'34.77"E) and Renggis Island (2°48'37.52"N, 104° 8'9.60"E)] in Tioman Island, Pahang, Malaysia.
Figure 3 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 3. The dendrogram of cluster analysis on soil macrofauna from different habitats with Bray–Curtis similarity by paired groups method (A: Uncultivated land; B: Bulrush land; C: Wheat farm; D: Poplar forest; E: Metasequoia forest).
Figure 2 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 2. One-way ANOVA on taxonomic richness and abundance, Margalef 's richness index (R) and Shannon-Weaver diversity index (H') among different habitats (Mean ± SE). The means with different scripts are significantly different by SNK test, α = 0.05.
US Emission Facilities Land Cover Area Derived At Parcel Scale
<p>This dataset includes US industrial facilities from the Environmental Protection Agency's (EPA) 2017 National Emissions Inventory (NEI), combined with location data from the EPA's Facility Registry Service and land cover classes from the United States Geological Survey's (USGS) National Land Cover Data (NLCD). These land cover classes are measured in square meters at the parcel scale. The matching of facility parcels was done using a tiered approach to enhance spatial accuracy. The parcel data was provided by Homeland Infrastructure Foundation-Level Data (HIFLD) US Parcel Data. Because this parcel data is proprietary, the parcel geometries and fields were removed from the final dataset. However, centroid latitude and longitude coordinates were derived to allow spatial joins with publicly available parcel data.</p> <p>This dataset is organized by unique EPA NEI facilities data fields. Unique facility observations are identified by the field <code>cleaned_name</code> which represents the concatenated address and/or place name for the facility (dependent on data availability). Each row represents a facility matched to parcel scale land cover information and are associated with the unique identifier <code>MatchID</code>. NLCD land cover fields are described as the total area in meters squared of each facility parcel. Please see data README file for more information on individual data fields. </p> <h3>Known Limitations</h3> <ul> <li>Parcels are matched to the facility and in some cases multiple facilities are matched to the same parcel. Data users may want to omit these multiple match parcels and there is a data flag called <code>multi_match</code> that enables this.</li> <li>Approximately 15% of the dataset includes facilities where the latitude/longitude coordinates are over 200 meters from the matched parcel. Spot checking these instances revealed that, in many cases, the facility latitude and longitude locations did not accurately match the street address, city, or postal zip code associated with the facility. These instances are flagged as a potential source of inaccuracy and can be removed at the users discretion.</li> </ul> <p> </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.