1. Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling:  This dataset – HYSOGs250m – represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system. There is one data file of global hydrologic soil groups (HSGs) at 250-m resolution in GeoTIFF format provided with this dataset. The R script used to generate the dataset is included as a companion file. 
  2. A Global Database of Soil Respiration Data, Version 5.0: The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB’s orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global Spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science.
  3. Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3: This dataset provides six global gridded products at a 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.