20191129 13350700 1.0 TRUE This is total carbon stock across Scotland. The dataset was made through integration of past survey data, recent monitoring work for peatland, spatial covariates from biophysical data such as topography and climate, and remote sensing data. A neural network model was used to integrate these data, and this was then used to generate soil properties data including this dataset. soil The purpose of the dataset is to provide estimates of total carbon across Scotland’s soils in order to allow more effective carbon stock mapping. The James Hutton Institute, Aberdeen None Hutton_Total_carbon_stock 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