This dataset contains the processing units for the North American continent from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5- ...

metadata for location and details for raster layers provided by QSEL

This dataset contains the processing unit for Greenland from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

The table Nigeria raster layer metadata is part of the dataset Uganda Geodata, available at https://columbia.redivis.com/datasets/2he4-1tf2z5myv. It contains 4 rows across 9 variables.

DISCLAIMER: CORE is still in development. Interested parties are warmly invited to join common development, to comment, discuss, find bugs, etc. Acknowledgement: The CORE format was proudly inspired by the Cloud Optimized GeoTIFF (COG) format, by considering how to leverage the ability of clients issuing ​HTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).

License: The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

Summary: The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series). WARNING: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved'). With this early public sharing of the format we explicitly support the Open Science agenda, which implies "shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process" (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world). CORE Specifications: 1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers) 2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users) Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to MPEG LA. However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the same quality. 3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous; Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case. 4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264) 5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the OGC GeoTIFF standard or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer 6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size) 7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews) Major issues to be solved: - Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews. - Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length. - Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds) Example Notice: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021]. For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the x264 library. For full reproducibility, we provide the original data set and results, as well scripts for data encoding and extraction (see resources).

OS Open Raster stack of GB for use as base mapping from national scale through to street level data.The currency of the data is: GB Overview Maps - 12/2014 MiniScale - 01/2024 OS 250K Raster - 06/2024Vector Map District Raster - 05/2024Open Map Local Raster - 10/2024The coverage of the map service is GB. The map projection is British National Grid. The service is appropriate for viewing down to a scale of approximately 1:2,500. Updated: 29/10/2024

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service.The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available.The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This dataset is a compilation of geographic rasters from multiple environmental data sources. It aims at making the life of SDM users easier. All rasters cover the metropolitan French territory, but have varying resolutions and projections. Each directory inside the main directory "0_mydata" contain a single environmental raster. Punctual extraction of raster values can be easily done for large sets of WGS84-(longitude,latitude) points coordinates and for multiple rasters at the same time through the R function get_variables of script _functions.R from Github repository: https://github.com/ChrisBotella/SamplingEffort. All data sources are accessible on the web and free of use, at least for scientific purpose. They have various conditions of citations. Anyone diffusing a work using the present data must reference along with the present DOI, the original source data employed. Those source data are described in the paragraphs below. We provide the articles to cite, when required, and webpages for access.

Pedologic Descriptors of the ESDB v2: 1 km × 1 km Raster Library : The library contains multiple soil pedology (physico-chemical properties of the soil) descriptors raster layers covering Eurasia at a resolution of 1 km. We selected 11 descriptors from the library. They come from the PTRDB. The PTRDB variables have been directly derived from the initial soil classification of the Soil Geographical Data Base of Europe (SGDBE) using expert rules. For more details, see [1, 2] and [3]. The data is maintained and distributed freely for scientific use by the European Soil Data Centre (ESDAC) at http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster. The 11 rasters are in the directories "awc_top", "bs_top", "cec_top", "dimp", "crusting", "erodi", "dgh", "text", "vs", "oc_top", "pd_top".

Corine Land Cover 2012, Version 18.5.1, 12/2016 : It is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m. This data base of the European Union is freely accessible online for all use at http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012. The raster of this variable is in the directory "clc".

Hydrographic Descriptor of BD Carthage v3: BD Carthage is a spatial relational database holding many informations on the structure and nature of the french metropolitan hydrological network. For the purpose of plants ecological niche, we focus on the geometric segments representing watercourses, and polygons representing hydrographic fresh surfaces. The data has been produced by the Institut National de l’information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. It is maintained by the SANDRE under free license for non-profit use and downloadable at:
http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX
From this shapefile, we derived a raster containing the binary value raster proxi_eau_fast, i.e. proximity to fresh water, all over France.We used qgis to rasterize to a 12.5m resolution, with a buffer of 50m, the shapefile COURS_D_EAU.shp on
one hand, and the polygons of SURFACES_HYDROGRAPHIQUES.shp with attribute NATURE=“Eau douce
permanente” on the other hand.We then created the maximum raster of the previous ones (So the value of 1 correspond to an approximate distance of less than 50m to a watercourse or hydrographic surface of fresh water). The raster is in the directory named "proxi_eau_fast".

USGS Digital Elevation Data : The Shuttle Radar Topography Mission achieved in 2010 by Endeavour shuttle measured elevation at three arc second resolution over most of the earth surface. Raw measures have been post-processed by NASA and NGA in order to correct detection anomalies. The data is available from the U.S. Geological Survey, and downloadable on the Earthexplorer (https://earthexplorer.usgs.gov/). One may refer to https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects for more informations. the elevation raster is in the directory named "alti".

Potential Evapotranspiration of CGIAR-CSI ETP : The CGIAR-CSI distributes this worldwide monthly potential-evapotranspiration raster data. It is pulled from a model developed by Antonio Trabucco [4, 5]. Those are estimated by the Hargreaves formula, using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (http://www.worldclim. org/), and radiation on top of atmosphere. The raster is at a 1km resolution, and is
freely downloadable for a nonprofit use at: http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description. This raster is in the directory "etp".

Bioclimatic Descriptors of Chelsea Climate Data 1.1: Those are raster data with worldwide coverage and 1 km resolution. A mechanistical climatic model is used to make spatial predictions of monthly mean-max-min temperatures, mean precipitations and 19 bioclimatic variables, which are downscaled with statistical models integrating historical measures of meteorologic stations from 1979 to today. The exact method is explained in the reference papers [6] and [7]. The data is under Creative Commons Attribution 4.0 International License and downloadable at (http://chelsa-climate.org/downloads/). The 19 bioclimatic rasters are located in the directories named "chbio_X".

ROUTE500 1.1: This database register classified road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approxi-mately 500,000 km of roads. It is produced under free license (all uses) by the IGN. Data are available online at http://osm13.openstreetmap.fr/~cquest/route500/. For deriving the variable “droute_fast”, the distance to the main roads networks, we computed with qGis the distance raster to the union of all elements of the shapefile ROUTES.shp (segments).

References :

[1] Panagos, P. (2006). The European soil database. GEO: connexion, 5(7), 32–33.

[2] Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data
Centre: Response to European policy support and public data requirements. Land Use Policy,
29(2),329–338.

[3] Van Liedekerke, M. Jones, A. & Panagos, P. (2006). ESDBv2 Raster Library-a set of rasters
derived from the European Soil Database distribution v2. 0. European Commission and the
European Soil Bureau Network, CDROM, EUR, 19945.

[4] Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D. & Singh, V. (2007). Trees and
water: smallholder agroforestry on irrigated lands in Northern India.

[5] Zomer, R., Trabucco, A., Bossio, D. & Verchot, L. (2008). Climate change mitigation: A
spatial analysis of global land suitability for clean development mechanism afforestation and
reforestation. Agriculture, ecosystems & environment, 126(1), 67–80.

[6] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler,
M. (2016). Climatologies at high resolution for the earth’s land surface areas. arXiv preprint
arXiv:1607.00217.

[7] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler, M.
(2016). CHELSEA climatologies at high resolution for the earth’s land surface areas (Version
1.1).

Basic raster layers from the MCM-LTER spatial data holdings have been exported and symbolized. The dataset files offered here include: 30m DEM made from USGS Topo map SPOT Satellite Image 39-558 LANDSAT 7 Satellite Image Note - the SPOT and LANDSAT layers are not MCM-LTER data products.  These resources were updated last in 2007, for more up-to-date layers, and potentially, higher resolution layers, please visit the Polar Geospatial Center and other affine geospatial data clearinghouses.Â

This zip file contains 24 raster layers representing data from a variety of landscape metrics used to analyze the landscape context of the greater Yellowstone area. Their names, descriptions and categorization are as follows: Housing This raster dataset contains seventeen housing layers which are all named in the format "bhc1940," where ‘bhc’ is Built Housing Capacity and year represents the decades from 1940 through 2100. The layers depict the location and density of private land housing unit classes, as described below. The classifications were produced using the SERGoM v3 model (see Theobald, D. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. http://www.ecologyandsociety.org/vol10/iss1/art32). These data are based on existing US Census datasets and growth projections. SERGoM_bhc_metrics: Value CLASSNAME 0 Private undeveloped 1 2,470 units / square km 12 Commercial/industrial Land Cover Land cover and impervious surface data comes from version 2 of the circa 2001 National Land Cover Dataset (NLCD), the circa 2006 NLCD, and the NLCD 2001/2006 Land Cover Change product, which was acquired from the Multi-Resolution Land Characteristics Consortium. The names and descriptions of the five land cover raster layers are as follows: - NLCD_2001v2. This raster layer depicts 16 land cover classes using data from version 2 of the circa 2001 National Land Cover Dataset. - NLCD_2006. This raster layer depicts 16 land cover classes using data from the circa 2006 National Landcover Database. - LandCover_2001. This raster layer depicts 16 land cover classes using data from the circa 2001 National Land Cover Dataset. Land cover classes for NLCD_2001, NLCD_2006 and LandCover_2001 are shown below. Value Land Cover 11 Open Water 12 Perennial Snow/Ice 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands - ImperviousSurfaces_2001. This raster layer represents impervious surfaces using data from the circa 2001 National Land Cover Dataset. - ImperviousSurfaces_2006. This raster layer represents impervious surfaces using data from the circa 2006 National Landcover Database. - LandCoverChange_2001_2006. This raster layer represents land cover change from 2001 to 2006 using data from the NLCD 2001/2006 Land Cover Change product, which was acquired from the Multi-Resolution Land Characteristics Consortium. Wildlife - Grizzly_Connectivity. This raster layer represents grizzly habitat connectivity in the GYA. For more information, please consult the corresponding reference citations from the report. Agriculture - GYA_Agriculture is a CSV file containing tabular county-level data from the 2002 and 2007 U.S. Department of Agriculture (USDA) Census of Agriculture. Data were obtained from National Agriculture Statistics Service (NASS).

The currency of the data is;GB Overview Maps - 12/2014MiniScale - 01/2015OS 250K Raster - 06/2014Vector Map District Raster - 09/2014StreetView - 10/2014The coverage of the map service is GB.The map projection is British National Grid.The service is appropriate for viewing down to a scale of approximately 1:5,000.Updated: 10/04/2015

This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from OpenStreetMap, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and Copernicus HRL Imperviousness data.

The land use / land cover classes are:

buildings.commercial

buildings.industrial

buildings.residential

cemetery

construction.site

dump.site (landfill)

farmland

farmyard

forest

grass

greenhouse

harbour

meadow

military

orchard

quarry

railway

reservoir

road

salt

vineyard

The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.

This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)

Each layer can be viewed interactively on the Open Data Science Europe data viewer at maps.opendatascience.eu.

This dataset contains the processing units for the European continent from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

This dataset holds the unpublished map “Carte physionomique des faciès savaniens de Lamto" drawn by de la Souchère; P. and Badarello, L. in 1969. We georeferenced the scanned paper map using ground control points derived from Google Maps. The dataset contains the scanned map, the ground control points and the raster layer of the georeferenced map.

This data set was processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. The original polygon data were received from Linnet Graphics, the distributor of data for MNR. In the case of the species layer, the percentages of species composition were removed. This reduced the amount of information contained in the species layer of the gridded product, but it was necessary in order to make the gridded product easier to use. The original maps were produced from 1:15,840-scale aerial photography collected in 1988.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format.

The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

Contains 4 folders:1. america_sin_islasShapefile of america, used to crop environmental layer (bioclimatic and PCs).2. environmental2.1 bio_10m_bil (folfer): Bioclimatic layers at 10 arcminutes 2.2 ntbox_envLayers -> CMIP5_rcp85_y50_bio_10 (folders): Bioclimatic layers at 10 arcminutes for representative concentration pathway 8.5 for the CCSM4 general circulation model.2.3 PCA_future:A folder with the projected Principal Components (PCs) layers of the bioclimatic variables of WorldClim (without bio 8, 9, 18, and 19). The layers that were used to project the PCs were the ones for the representative concentration pathway 8.5 for the CCSM4 general circulation model available from (Hijmans et al. 2005a).2.4 PCA_present:A folder withPrincipal Components (PCs) layers of the of the bioclimatic variables of WorldClim (without bio 8, 9, 18, and 19). It also has the scree plot of the variance explained variance by each component.3. filtered_data: 3.1. all_sps_environmental_test_data.csv: A dataset representing georeferenced data used to test the niche-centroid distance's models; it has 21 variables "folder": Folder which the occurrence and abundance data for a species"Species": Species ID"Latitud": Decimal latitude"Longitud": Decimal longitude"n_train": number of data used to train the models"PC1": Value for the 1 Principal Component as extracted from Principal Components Raster Layers"PC10": Value for the 10 Principal Component as extracted from Principal Components Raster Layers"PC11": Value for the 11 Principal Component as extracted from Principal Components Raster Layers"PC12": Value for the 12 Principal Component as extracted from Principal Components Raster Layers"PC13": Value for the 13 Principal Component as extracted from Principal Components Raster Layers"PC14": Value for the 14 Principal Component as extracted from Principal Components Raster Layers"PC15": Value for the 15 Principal Component as extracted from Principal Components Raster Layers"PC2": Value for the 2 Principal Component as extracted from Principal Components Raster Layers"PC3": Value for the 3 Principal Component as extracted from Principal Components Raster Layers"PC4": Value for the 4 Principal Component as extracted from Principal Components Raster Layers"PC5": Value for the 5 Principal Component as extracted from Principal Components Raster Layers"PC6": Value for the 6 Principal Component as extracted from Principal Components Raster Layers"PC7": Value for the 7 Principal Component as extracted from Principal Components Raster Layers"PC8": Value for the 8 Principal Component as extracted from Principal Components Raster Layers"PC9": Value for the 9 Principal Component as extracted from Principal Components Raster Layers."bio1": Value for the bio1 Raster Layer"bio10": Value for the bio10 Raster Layer"bio11": Value for the bio11 Raster Layer"bio12": Value for the bio12 Raster Layer"bio13 Value for the bio13 Raster Layer"bio14": Value for the bio14 Raster Layer"bio15": Value for the bio15 Raster Layer"bio16": Value for the bio16 Raster Layer"bio17": Value for the bio17 Raster Layer"bio18": Value for the bio18 Raster Layer"bio19": Value for the bio19 Raster Layer"bio2": Value for the bio2 Raster Layer"bio3": Value for the bio3 Raster Layer"bio4": Value for the bio4 Raster Layer"bio5": Value for the bio5 Raster Layer"bio6": Value for the bio6 Raster Layer"bio7": Value for the bio7 Raster Layer"bio8": Value for the bio8 Raster Layer"bio9": Value for the bio9 Raster Layer2. all_sps_environmental_train_data.csv: A dataset representing georeferenced data used to train the niche-centroid distance's models3. sps_traits_CleanAllInclude.csv: Dataset with 10 variables representing bird species traits "folder": Folder with the occurrence and abundance data for a species,"Species": Species ID"SciName": Scientific name"n_train": Number of data points to train the niche-centroid distance models."n_abundance": Number of abundance points to test the relationship between population abundance and niche-centroid distance."Include": Does the species was included in the analysis "Aquatic": Is it aquatic"Migratory": Is it migratory"BodyMass": Body Mass"PercentBBS": Percent of the distribution sampled in the Bird Breathing Survive.4. n_amA shapefile of North America. It was used to reproduce figure 4

This dataset contains the processing units for Australasia from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

This dataset contains the processing units for the Asian continent from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.