Assessing Quality Variations in Early Career Researchers’ Data Management Plans

Abstract

This paper aims to better understand early career researchers’ (ECRs’) research data management (RDM) competencies by assessing the contents and quality of  data management plans (DMPs) developed during a multi-stakeholder RDM course. We also aim to identify 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, using content analysis, differences were found between disciplines or course tracks regarding DMP’s key characteristics such as sharing, storing, and preserving data. DMPs that contained a data table (DtDMPs) 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. The results illustrated that the ECRs had learned the basic RDM competencies and grasped their significance to the integrity, reliability, and reusability of data. However, more focused, further training to reach the advanced competency is needed, especially in areas of handling and sharing personal data, legal issues, long-term preserving, and funders’ data policies. Equally important to the cultural change when RDM is an organic part of the research practices is to merge research support services, processes, and infrastructure into the research projects’ processes. Additionally, incentives are needed for sharing and reusing data.