How to teach good research data management to next generation researchers?

Research work is now subject to comply with FAIR principles. Additionally, it is also subject to the practices of Open Science. Different stakeholders e.g. DFG are setting the goals of reproducible research work. This not only requires the adequate handling of data but also the record of related information and practices during the research work. Therefore, different tools and workflows are being developed and suggested to achieve the goals of good research and its data management. Those tools and workflows facilitate researchers and ease the research management tasks e.g. by the means of standardisation, automation of processes and record of corresponding information. The researches of now a days are interdisciplinary and work collaboratively where participants are located at distinct locations, belongs to different domains and have different levels of competencies. In such cases, provision of tools and specification of workflows is not enough. Just like other management, good research data management is a skill that need to be taught to the researchers in a systematic and detailed way. So that they could make right decisions where and when needed. As a result, the contents and the materials for the education of good research data management become important.

In this presentation, mainly approaches to deliver good RDM practices are presented. The presented approaches and experiences, which may be attributed to didactic and pedagogy methods, are based on the work for the Information infrastructure project which is part of DFG funded Collaborative Research Centre, TRR277 AMC. AMC is hosted by TU Munich and TU Braunschweig. It is an interdisciplinary center where researchers are using and generating heterogeneous, large amount of data. During the work we collaborated with LRZ, Gauss-IT center and the libraries of both universities.

In this presentation, common skills and understanding that every researcher should know before and during the course of research work are listed and presented on one hand. On the other hand, skills and knowledge relating to the common aspects of RDM systems are addressed. Additionally, contents and methodologies for both in person and online as well as for hybrid environments to increase acceptance and understanding are presented.

In conclusion, the skills which relates to the pre-research learning and the practices relating to good RDM are emphasised. Therefore, such contents could be suggested for teaching to the future researchers and data stewards during the regular academic discourse. The presented contents and strategies may also be adopted for small projects to large scale CRC projects.