list_datasets returns information on the supported datasets.
Arguments
- terrestrial
logical. When
TRUE, then datasets that only have terrestrial data (seamasked) are returned.- marine
logical. When
TRUE, then datasets that only have marine data (landmasked) are returned.- freshwater
logical. When
TRUE, then datasets that only have freshwater data are returned.
Details
By default it returns all datasets, when both marine, freshwater and
terrestrial are FALSE then only datasets without land- nor
seamasks are returned.
Examples
list_datasets()
#> dataset_code terrestrial marine url
#> 1 WorldClim TRUE FALSE http://www.worldclim.org/
#> 2 Bio-ORACLE FALSE TRUE https://bio-oracle.org/
#> 3 MARSPEC FALSE TRUE http://marspec.org/
#> 4 ENVIREM TRUE FALSE https://envirem.github.io/
#> 5 Freshwater TRUE FALSE https://www.earthenv.org/streams
#> description
#> 1 WorldClim is a set of global climate layers (climate grids). Note that all data has been transformed back to real values, so there is no need to e.g. divide temperature layers by 10.
#> 2 Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms at a spatial resolution 5 arcmin (9.2 km) in the ESRI ascii and tif format.
#> 3 MARSPEC is a set of high resolution climatic and geophysical GIS data layers for the world ocean. Seven geophysical variables were derived from the SRTM30_PLUS high resolution bathymetry dataset. These layers characterize the horizontal orientation (aspect), slope, and curvature of the seafloor and the distance from shore. Ten "bioclimatic" variables were derived from NOAA's World Ocean Atlas and NASA's MODIS satellite imagery and characterize the inter-annual means, extremes, and variances in sea surface temperature and salinity. These variables will be useful to those interested in the spatial ecology of marine shallow-water and surface-associated pelagic organisms across the globe. Note that, in contrary to the original MARSPEC, all layers have unscaled values.
#> 4 The ENVIREM dataset is a set of 16 climatic and 2 topographic variables that can be used in modeling species' distributions. The strengths of this dataset include their close ties to ecological processes, and their availability at a global scale, at several spatial resolutions, and for several time periods. The underlying temperature and precipitation data that went into their construction comes from the WorldClim dataset (www.worldclim.org), and the solar radiation data comes from the Consortium for Spatial Information (www.cgiar-csi.org). The data are compatible with and expand the set of variables from WorldClim v1.4 (www.worldclim.org).
#> 5 The dataset consists of near-global, spatially continuous, and freshwater-specific environmental variables in a standardized 1km grid. We delineated the sub-catchment for each grid cell along the HydroSHEDS river network and summarized the upstream environment (climate, topography, land cover, surface geology and soil) to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum). All variables were subsequently averaged across single lakes and reservoirs of the Global lakes and Wetlands Database that are connected to the river network. Monthly climate variables were summarized into 19 long-term climatic variables following the \xd2bioclim\xd3 framework.
#> citation
#> 1 Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.
#> 2 Tyberghein L., Verbruggen H., Pauly K., Troupin C., Mineur F. & De Clerck O. Bio-ORACLE: a global environmental dataset for marine species distribution modeling. Global Ecology and Biogeography. doi: 10.1111/j.1466-8238.2011.00656.x
#> 3 Sbrocco, EJ and Barber, PH (2013) MARSPEC: Ocean climate layers for marine spatial ecology. Ecology 94: 979. doi: 10.1890/12-1358.1
#> 4 Title, P.O., Bemmels, J.B. 2017. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography doi: 10.1111/ecog.02880.
#> 5 Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2:150073 doi: 10.1038/sdata.2015.73
list_datasets(marine=TRUE)
#> dataset_code terrestrial marine url
#> 2 Bio-ORACLE FALSE TRUE https://bio-oracle.org/
#> 3 MARSPEC FALSE TRUE http://marspec.org/
#> description
#> 2 Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms at a spatial resolution 5 arcmin (9.2 km) in the ESRI ascii and tif format.
#> 3 MARSPEC is a set of high resolution climatic and geophysical GIS data layers for the world ocean. Seven geophysical variables were derived from the SRTM30_PLUS high resolution bathymetry dataset. These layers characterize the horizontal orientation (aspect), slope, and curvature of the seafloor and the distance from shore. Ten "bioclimatic" variables were derived from NOAA's World Ocean Atlas and NASA's MODIS satellite imagery and characterize the inter-annual means, extremes, and variances in sea surface temperature and salinity. These variables will be useful to those interested in the spatial ecology of marine shallow-water and surface-associated pelagic organisms across the globe. Note that, in contrary to the original MARSPEC, all layers have unscaled values.
#> citation
#> 2 Tyberghein L., Verbruggen H., Pauly K., Troupin C., Mineur F. & De Clerck O. Bio-ORACLE: a global environmental dataset for marine species distribution modeling. Global Ecology and Biogeography. doi: 10.1111/j.1466-8238.2011.00656.x
#> 3 Sbrocco, EJ and Barber, PH (2013) MARSPEC: Ocean climate layers for marine spatial ecology. Ecology 94: 979. doi: 10.1890/12-1358.1
list_datasets(terrestrial=TRUE)
#> dataset_code terrestrial marine url
#> 1 WorldClim TRUE FALSE http://www.worldclim.org/
#> 4 ENVIREM TRUE FALSE https://envirem.github.io/
#> 5 Freshwater TRUE FALSE https://www.earthenv.org/streams
#> description
#> 1 WorldClim is a set of global climate layers (climate grids). Note that all data has been transformed back to real values, so there is no need to e.g. divide temperature layers by 10.
#> 4 The ENVIREM dataset is a set of 16 climatic and 2 topographic variables that can be used in modeling species' distributions. The strengths of this dataset include their close ties to ecological processes, and their availability at a global scale, at several spatial resolutions, and for several time periods. The underlying temperature and precipitation data that went into their construction comes from the WorldClim dataset (www.worldclim.org), and the solar radiation data comes from the Consortium for Spatial Information (www.cgiar-csi.org). The data are compatible with and expand the set of variables from WorldClim v1.4 (www.worldclim.org).
#> 5 The dataset consists of near-global, spatially continuous, and freshwater-specific environmental variables in a standardized 1km grid. We delineated the sub-catchment for each grid cell along the HydroSHEDS river network and summarized the upstream environment (climate, topography, land cover, surface geology and soil) to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum). All variables were subsequently averaged across single lakes and reservoirs of the Global lakes and Wetlands Database that are connected to the river network. Monthly climate variables were summarized into 19 long-term climatic variables following the \xd2bioclim\xd3 framework.
#> citation
#> 1 Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.
#> 4 Title, P.O., Bemmels, J.B. 2017. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography doi: 10.1111/ecog.02880.
#> 5 Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2:150073 doi: 10.1038/sdata.2015.73