Supporting Researchers in Creating Data Management Plans

Researchers are increasingly encouraged by different stakeholders to make research processes as transparent as possible, to enable reproducible research results and to share their (research) data FAIRly and openly with others. Such requirements can be challenging, as not all researchers are familiar with the concepts of FAIR and open data. At the same time, existing tools (and guidance) to foster the creation of FAIR data – such as templates for data management plans – vary to a great extent, rarely indicating what the best practice solution is. The project Domain Data Protocols for Educational Research in Germany aims to address this issue by developing a standardized tool to create data management plans. Funded by the German Federal Ministry of Education and Research, it brings together twelve German research institutions with diverse areas of expertise on educational research to develop so called Domain Data Protocols (in short DDPs). Based on a concept by Science Europe[1], DDPs are open, standardized, and referenceable data protocols, serving as a ‘model’ data management plan for a specific research domain, i.e. educational research in our case. DDPs are for the benefit of various stakeholders. First, they assist researchers in doing excellent data management, preparing project proposals and funding applications as well as offering support for data archiving and sharing. Second, they enable replication of results by the research community as well as the re-use of data by others in new (research) contexts. Third, DDPs simplify review processes on data management, reducing the efforts of examining funding applications and (periodical) reports on data management by implementing standardized procedures. Finally, DDPs foster data ingest in a data repository or archive, by fostering researchers in the creation of FAIR data. The development of DDPs is not without challenges, as their structure needs to be flexible enough to cover different types of data and methods and to enable researchers to reflect their individual project-specific requirements. DDPs therefore consist of different modules, e.g. in the context of data collection, documentation, legal issues, and data sharing. Each of these modules contains different elements defining a minimum set of requirements on what FAIR data look like and includes use cases, standards, relevant regulations as well as further resources on related data management practices. [1] Science Europe (2018): Science Europe Guidance Document Presenting a Framework for Discipline-Specific Research Data Management. D/2018/13.324/1