Spatial predictions of the fractions of mud, sand and gravel as continuous response variables for the north-west European continental shelf. Mud, sand and gravel fractions range from 0-1 (i.e. 0-100%). These fractions were generated from two additive log-ratios (ALR), ALRs and ALRm which are independent, unconstrained response variables. These raw predictions as rasters are also included presented in the attached dataset. Predicted fractions have been combined to predict the likely sediment classification based on the EUNIS level 3 sediment classification for broadscale habitats, Folk 5, Folk 7, Folk 11 and Folk 15 classification schemes. These are available as raster tif files with an ArcGIS layer file indicating the appropriate class for each raster value. For all predictions an accompanying map of the spatial distribution of error/accuracy is also included as a separate raster. For the three components of the sediment fraction a smoothed Root-Mean-Squared-Error layer is available. For the classification maps a smoothed local accuracy map is available. Spatial predictions of mud, sand and gravel were generated for the north-west European continental shelf. Based on these fractions sediment classification maps were also generated for the study site. To support the interpretation of these layers maps of the spatial distribution of error/accuracy were also generated. In short, analysis combined the eight continuous predictive layers (Bathymetry, Bathymetric position index at a 50-pixel radii, Bathymetric position index at a 434-pixel radii, Distance from coast, Current speed at the seabed, Wave peak orbital velocity at the seabed, and suspended inorganic particulate matter for summer and winter as two separate variables) with sediment observation data in a statistical regression model to make spatial predictions of the fractions of mud, sand and gravel. Spatial predictions were generated based on two additive log-ratios that could then be back transformed to produce spatial predictions for each fraction. From these spatial predictions any classification scheme based on the percentages of mud, sand and gravel can be applied. Included here are the five classification shemes generated from these maps. The maps of accuracy were also generated to support interpretation. For the maps of the fractions of mud, sand and gravel map error was calculated based on the Root-Mean-Squared-Error of the observed vs predicted fractions from the test samples. A smoothed surface of local RMSE was then generated using the Inverse Distance Weighted (IDW) technique in ArcGIS. Each pixels’ RMSE was determined based on the closest 50 points (up to a maximum distance of 200 km). A weighting power function was applied in the IDW tool (set at 0.3) so nearer points contributed more to the pixel than distant points. For the classified maps spatial accuracy was calculated using a locally constrained confusion matrix. The IDW technique was applied to calculate a local thematic accuracy value. As above, this was applied based on the closest 50 points (maximum distance of 200 km) with a weighting power function of 0.3.