Performance Evaluation of the Metadata-Driven MASi Research Data Management Repository Service

Research data is increasingly important in order to gain insights from scientific data. To optimally foster this, the management of research data is required to be usable, customizable and fast. We enable this by building up the MASi research data management repository service, based on the KIT DM framework. The aim is on utilizing a single repository instance to serve multiple arbitrary community use cases. Due to their diverse data characteristics the performance of the MASi service has to be fitting across the different cases. We evaluate the performance along three initial heterogeneous use cases. Various aspects are investigated; First, the object insertion and query performance of the database along the object fill level. Second and third, the ingest and download performance of digital objects using real-life data sets. Highly favorable performance characteristics are shown.