A modern, integrated research data management (RDM) enables reproducible and verifiable research, the linking of interdisciplinary expertise, the sharing of research for comparison and integration of different analysis results and metadata studies to derive further knowledge. An increasing adoption of FAIR principles and requirements by funding agencies on open data has significantly benefited overall quality, reuse, and sharing of research results. For scientists, a proper data management is a

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... crucial element to prove their findings and make them reproducible. As a consequence, RDM had to become an integral part of the science support infrastructure in todays research institutions in the past decade. Scientist of various disciplines should be supported over the complete data lifecycle starting from holistic planning of future projects to RDM related services provided: As RDM is a multidimensional endeavor requiring various skills, tasks ranging from community specific to community needs are optimally handed to the best qualified provider. The presented concepts and considerations are work in progress while establishing an organizational frameworks for a research university. Completely reproducible data publications including the relevant data-sets' context are the ultimate goal. These require appropriate service components like EaaSI. The university strives to profit from the overlaps in RDM and digital preservation and to define the handover of tasks from the first to the latter.