Information on Research Data Management (RDM)

Professional research data management is a prerequisite for successful applications from almost all funding providers. The German Research Foundation (DFG) has already included this aspect in the Code of Good Scientific Practice. In addition to publishing papers, publishing data further enhances the visibility of research achievements.

The way in which research data is managed is almost as individual as the research project itself. In addition to how fundamental infrastructure is organized (such as access to data storage), handling the datasets themselves in a logical and traceable way plays a significant role.

Research data management is very subject-specific and is based on existing processes. If these have not yet been digitalized, major changes in research projects are necessary. Consortia are funded within the framework of the National Research Infrastructure (NDFI), which define standards for the individual disciplines. The CDI is responsible for supporting the implementation of NDFI at FAU.

From the outset, it is important to always focus on the reusability of data by third parties. However, all the included information assists those involved in using their own data in a sustainable manner. Start by creating an inventory of existing data or data likely to be generated.

  • Which data formats are in use?
  • Which devices generate data/data flows?
  • Where is data stored?
  • Who has access to which data?
  • Are some data subject to special protection?
  • Which data volume needs to be managed?
  • How is data exchanged within the group and externally?
  • How are datasets published?
  • …

Many funding providers, above all DFG and the EU, request that researchers publish as many datasets as possible in accordance with the FAIR principles. Written out in full, the acronym stands for:

  • F = findable (a person or a machine can easily find datasets via a search engine)
  • A = accessible (there is information about how a person or a machine can access the datasets)
  • I = interoperable (the use of standards)
  • R = reusable (clearly-defined user rights)

Further information on each area is available at forschungsdaten.org. Researchers must pay particular attention to interoperable. In this area, common standards, coordinated workflows, rules for data annotation and ways of recording as much supplementary data (rich meta-data) as possible, at least in the subject itself and preferably interdisciplinary data, must be developed, adjusted and utilized. In Germany, the DFG provides funding to various consortia within the National Research Data Infrastructure (NFDI) for developing these standards. Several academic societies have already started defining comprehensive catalogs for research data management. Ensuring that data is permanently stored is a challenge for universities, since the datasets should be kept available for at least 10 years after projects have been completed.

At FAU, research data management (RDM) is accompanied by