DataScore was developed in an effort towards alternative metrics for the assessment of the additional efforts and impacts associated to datasets provided by scientist. Within the scope of the project, we have established a network comprising repository operators (SupraBank, Chemotion), infrastructure facilities (SCC), scholar projects (DataLiteracy) global bibliometric services (Datacite), theoretical bibliometricians as well as infrastructure constructors (DataScore itself).

Two demonstrators have been created featuring on the project page. Firstly, an interactive dashboard enabling the ad hoc generation of a “DataScore Profile” based on the persistent identifier ORCID has been developed. Secondly, another demonstrator constitutes a collaborative bibliometric development environment based on JupyterHub allowing for diverse data operations.

The DataScore Project offers guidelines and recommendations to stress the importance of research data management already in the graduate and post graduate programs. Furthermore, we offer a set of recommendations and “best practice guides” for repository operators to enhance the analyzability of dataset metrics.