Abstract
This report describes the results of a workshop on research data management (RDM) that took place in June 2019. More than 50 experts from 46 different non-university institutes covering all Leibniz Sections participated. The aim of the workshop was the intra- and transdisciplinary exchange among RDM experts of different institutions and sections within the Leibniz Association on current questions and challenges but also on experiences and activities with respect to RDM. The event was structured in inspiring talks, a World Café to discuss ideas and solutions related to RDM and an exchange of experts following their affiliation to the different Leibniz sections. The workshop revealed that most institutions, independent of scientific fields, face similar overarching problems with respect to RDM, e.g. missing incentives and no awareness of the benefits that would arise from a proper RDM and data sharing. The event also endorsed that the Research Data Working Group of the Leibniz Association (AK Forschungsdaten) is a place for the exchange of all topics around RDM and enables discussions on how to refine RDM at all institutions and in all scientific fields.
Highlights
Research data is the basis for all scientific work
The workshop started with an introductory talk and two inspiring talks opening the exchange and discussion on research data management (RDM)
A special effort was made to ensure that all Leibniz Sections were covered and that, where possible, participants were in charge of, or had experience with RDM at their institution
Summary
Research data is the basis for all scientific work. The increasing digitisation of scientific processes and methods calls for new approaches in the way research data is handled. Publishing the conclusions resulting from an analysis of collected research data is no longer sufficient. Well-structured and annotated research data is becoming an increasingly important resource for researchers. Ensuring that data is accessible and can be interpreted creates a range of diverse challenges for research funding bodies, research institutions, researchers and research support staff. It often requires a discipline-specific approach in research data management (RDM) or an adaptation of generic processes
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