Statistical and scientific computing applications exhibit characteristics that are fundamentally different from classical database system application domains. The CROSS-DB data model presented in this paper is optimized for use in such applications by providing advanced data modelling methods and application-oriented query facilities, thus providing a framework for optimized data management procedures. CROSS-DB (which stands for Classification-oriented, Redundancy-based Optimization of Statistical and Scientific DataBases) is based on a multidimensional data view. The model differs from other approaches by offering two complementary mechanisms for structuring qualifying information, classification and feature description. Using these mechanisms results in a normalized, low-dimensional database schema which ensures both, modelling uniqueness and understandability while providing enhanced modelling flexibility.