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The topics of research data and research data management (RDM) are playing an increasingly important role in the professionalization and digitalization of research in Germany, not least thanks to the establishment of the National Research Data Infrastructures (NFDI) and the European Open Science Cloud (EOSC).

To meet the demands regarding information and consultation about the broad subject area of RDM, EUF offers support on the Services sub-pages. If you want to gain a broad overview of the topic, please go to the Materials page, and if you want to contribute at EUF or beyond, please visit the Working group or the Network.

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In my field of work we don't really produce research data...

At the bottom of this assumption lies a misconception regarding the question: "what is research data?"

A short definition by the inter-institutional information platform forschungsdaten.info helps to provide clarification:

Research data is (digital) data created during research activities (i.e. measurements, surveys, source work). It is at the foundation of scientific practice and documents the results. ^{_{(https://forschungsdaten.info)}}

The wording of this definition is deliberately open, refuting the notion that there are scientific contexts that do not create research data at all. At the same time, research data is not limited to certain formats or forms of representation, but include images, text and audio files as well as measuring sequences, simulations, source code, surveys and so on.

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Research data management is the process of transforming, selecting and saving of research data, aiming at long-term and creator-independent access, reusability and verifiability. ^{_{(https://forschungsdaten.info)}}

Naturally, creators know their own data best - especially mid-project. But how about after several years post completion? How comprehensible are data for third parties? Is there meta data and if so, is it machine friendly? Is it linked data and does it use standardized vocabulary for its description?

"Sustainable" data is not only at the foundation of transparent and professional research, but in a globalized and highly mobile world of inter-disciplinary research projects, it helps keep researchers visible.

Research data management includes all stations of the "data life cycle", beginning with the planning phase.

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FAIR is an acronym describing four principles of especially sustainable research data management:

• (F)indability - how easily is data found? Does it possess unique identifiers? Does it contain detailed metadata describing its contents, methods and tools? Is it registered and indexed in searchable resources?
• (A) accessibility - how easily is data accessed? Is it unstructured or uses proprietary database formats? Is it accessible via standard protocols? Is access free of charge? If it's sensible data, is there an authorization process? Are metadata preserved long term?
• (I)nteroperability - is the data/metadata represented by a formal, understandable, universal language? Is there a shared vocabulary? Do metadata point to other metadata?
• (R)eusability - is the data described extensively? Is the data licensed under common laws? Is the context of creation sufficiently described? Are metadata complying with disciplinary standards?

Implementation of the four FAIR principles is at the heart of transparent research that complies with the guidelines for good scientific practice.