Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1; Soil properties were predicted at fixed depths: Surface soil = s0..0cm, Subsoil 1 = s30..30cm, Subsoil 2 = s60..60cm, Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020; To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values: log.oc = 15 → 0.3% SOC; log.oc = 20 → 0.6% SOC; log.oc = 25 → 1.1% SOC; log.oc = 30 → 1.9% SOC; log.oc = 35 → 3.2% SOC; log.oc = 40 → 5.3% SOC; log.oc = 50 → 14.8% SOC;