Research data management

Practically every research process generates a wide variety of digital data: Photos, scans, numerical measurement data, transcriptions, videos, ... This data, which is usually generated at great expense, must be adequately indexed, processed, made available for subsequent use and archived in accordance with the FAIR principles. All of this is part of research data management.

Research data life cycle

Within the framework of a research project, research data runs through several typical phases: from data collection to data evaluation to publication and the reuse of the data. In research data management these individual phases are not considered in isolation, but with regard to the whole process and its interconnectedness. This perception is illustrated by the so-called research data life cycle.

FAIR principles

The acronym "FAIR" stands for the four basic principles that should ideally guide the handling of research data. Further information is, amongst others, available on the website of the GO FAIR initiative. Ways in which these principles can be implemented include, for example:

• Findable
  Research data is described through meaningful metadata. Metadata base upon a standardized schema and will be fed into central search engines.
• Accessible
  Every data record is permanently available through a unique identifier. The access on research data happens through a standardized procedure. If access to the data is restricted, access and authentication are clearly regulated.
• Interoperable
  For all data there are recommended data types, which means especially non-proprietary file formats. If the recommended data types cannot be used, it has to be documented why.
• Reusable
  Data should be licensed in a way so that other researchers can reuse it as extensively as possible. Data collection and storage follow subject-specific standards.

With the checklist How FAIR is your research data? you can check the FAIRness of your data.