Assessing Quality Variations in Early Career Researchers’ Data Management Plans - Rantasaari, Jukka (University of Edinburgh, 2025)
By evaluating the real-world data management plans DMPs crafted in a multi-stakeholder RDM course, this study aims to improve understanding of quality variations in early career researchers' (ECRs) DMPs, and to identify gaps in their research data management (RDM) planning skills. We also examine the differences between DMPs in relation to several background variables (e.g., discipline, course track). The Basics of Research Data Management (BRDM) course has been held in two multi-faculty, research-intensive universities in Finland since 2020. In this study, 223 ECRs’ DMPs created in the BRDM of 2020 - 2022 were assessed, using the recommendations and criteria of the Finnish DMP Evaluation Guide + General Finnish DMP Guidance (FDEG). The median quality of DMPs appeared to be satisfactory. The differences in rating according to FDEG’s three-point performance criteria were statistically insignificant between DMPs developed in separate years, course tracks or disciplines. However, content analysis revealed differences in RDM best practices, such as sharing, storing, and preserving data, between disciplines or course tracks. DMPs that contained a structured data table (DtDMP) also differed highly significantly from prose DMPs. DtDMPs better acknowledged the data handling needs of different data types and improved the overall quality of a DMP. Nevertheless, more focused, further training to achieve the excellent quality is needed, especially in areas of handling personal data, legal issues, archiving, and funders’ data policies.
The study provides RDM stakeholders – including researchers, institutions, funders, and publishers – with a standardized framework for the development and evaluation of DMPs. Researchers benefit from enhanced data management descriptions, boosting the integrity and reproducibility of research. Institutions can better identify DMP strengths and areas for improvement, allowing for customized support and training. Educators can leverage this framework to gauge the effectiveness of RDM training. Funders and publishers can set clear DMP standards, promoting transparency, compliance, and data sharing efficiency.