Dataset from an active aeolian system in the Dutch coastal dunes: sediment grain size and shape data obtained between 2016 and 2019 using dynamic image analysis

This dataset includes 1) A sediment grain size-shape dataset from an active aeolian system in the Dutch coastal dunes and 2) artificial grain size-shape datasets. Both were used to prove applicability of a novel method for the detection of sediment transport processes from a sedimentary deposit. The new method makes use of end-member modelling on grain size-shape distributions. The size and shape data were obtained using dynamic image analysis (video's) of sediment samples. End-member modelling was performed using AnalySize (Paterson and Heslop, 2015. DOI: https://doi.org/10.1002/2015GC006070 )The “Dune dataset” consists of sediment samples from the surface and from sediment traps. The samples were obtained from a coastal dune area south of the town Ijmuiden in the Netherlands (for coordinates of- and info on the samples, refer to Coordinates_and_info_dunedataset.xlsx).The samples were measured using dynamic image analysis. For each sediment grain in each sample, various shape variables were measured (aspect ratio = AR, convexity = con, Cox circularity = Cc). Size was also measured as the area equivalent size (D2d).Based on the class-limits described in grainsize_grainshape_classlimits.xlsx, each sediment grain was assigned to the right class. By summing the volume in each size class, and normalising to a sum of 100%, grain size distributions were obtained (D2d).Furthermore, by assigning each grain to the right size-shape class (see size_shape_distribution_classes.xlsx), grain size-shape distributions were obtained (also summing to 100% volume). ConD2d for example, is a distribution of grain convexity and grain size.The artificial datasets consist of similar data as the "Dune dataset", but the data were generated by creating random mixtures of several grain size-shape distributions using a random number generator.