Data life cycle management pilot projects and implications for research data management at universities of applied sciences

Publicly available research data (Open Research Data) are a main pillar of Open Science and can be considered as a good measure to increase the effectiveness, transparency and reproducibility of scientific research. However, the rather new scientific practice of Open Research Data sets new demands on best practices in research data management and raises questions regarding the data publication itself, for example finding a suitable data repository or the consideration of legal aspects. To investigate these practical questions, 12 pilot projects were carried out within the DLCM 2.0 project. Research data were published in a variety of disciplines and related processes where reflected in workshops within the project consortium. The pilot projects have provided an insight into the characteristics of individual research data life cycles. A key finding is that the path to open research data is very domain specific. Based on this experience, we think that the individual research communities – as predominant re-users of research data – must develop discipline-specific standards, best practices and data processing workflows. We believe that this is the most important success criterion for data exchange and should be promoted in parallel with meeting the FAIR data principles and an appropriate data curation. To promote this development, support measures are needed at various levels. On the one hand, there is a need for cross-border initiatives to support the communities developing their standards and best practices. On the other hand, researchers must have the appropriate infrastructure, training and support on local level. We consider the latter to be particularly important. That is why we have set up a data stewardship model at our university, where researchers can receive active support over the entire research data lifecycle.