Knowledge Pool

The term "research data management" refers to the strategic and organizational process for the responsible, planned and sustainable handling of research data, taking into account the entire data life cycle (Fig. 1).

Ideally, research data management begins with the planning of data collection and includes not only technical storage and archiving, but also documentation and data processing. The goal is to ensure that research data can be found and accessed, and that the content is understandable and therefore usable in the long term. Further information on research data management is available here.

The acronym FAIR (Findable, Accessible, Interoperable, Reusable) describes how research data should be organized so they can be easily accessed, understood, exchanged and reused. The FAIR principles have been adopted by the European Commission, and Horizon Europe project proposals must also comply with these principles.

Here are some of the reasons for systematic research data management. They also underscore its importance for good scientific practice:
Research funding
Research data management, and in some cases data sharing, is often required by research funders to enable validation of results and avoid multiple funding.
Reusability:
Research data management according to FAIR principles minimizes the risk of data loss and ensures the long-term usability of data beyond the 10 years required by good scientific practice.
Reproducibility:
If experimentally obtained research data are properly preserved, this will allow long-term reproducibility of results.
Verifiability:
Documentation of research data and how it was obtained ensures that research results are verifiable over time.
Citability:
Data publications are fully citable as stand-alone publications which increases the visibility of your research.

For more information on research data management, please click here.