  In the context of the EU GRACIOUS project, we propose a novel procedure for
similarity assessment and grouping of nanomaterials. This methodology is based
on the (1) Arsinh transformation function for scalar properties, (2) full curve
shape comparison by application of a modified Kolmogorov-Smirnov metric for
bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based
grouping distance, and (4) hierarchical clustering. The approach allows for
grouping of nanomaterials that is not affected by the dataset, so that group
membership will not change when new candidates are included in the set of
assessed materials. To facilitate the application of the proposed methodology,
a software script was developed by using the R programming language which is
currently under migration to a web tool. The presented approach was tested
against a dataset, derived from literature review, related to immobilisation of
Daphnia magna and reporting information on several nanomaterials.
