Exploring research data management planning challenges in practice

Research data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP

HOW RESEARCH DATA MANAGEMENT CAN CONTRIBUTE TO EFFICIENT AND RELIABLE SCIENCE

Research data management (RDM) is an emergent discipline which is increasingly receiving attention from universities, funding agencies and academic publishers. While data management (DM) benefits from a large corpus of data governance and management frameworks adapted to industry, its academic counterpart RDM still struggles at identifying, organizing and implementing the main functions of RDM. In this study we explore the status of research data management at two research organizations in the Netherlands. We identify the main roles and tasks involved in research data governance, services and research. We show that, while the application of the DAMA-DMBOK functions and RDM structures are overlapping, RDM is coping with fundamentally different organizational structures and roles than the roles and tasks listed in professional DM frameworks. As RDM is developed to make science more efficient and reliable, it is questionable whether its current structure is effective. Based on interviews with data managers, researchers and librarians we identified several issues. For instance, at the moment, researchers are responsible for tasks that depend on DM expertise that they, generally, do not possess. At the same time, research data governance as currently implemented fails to capture the complexity of (professional) data management. Similarly, research data support is not well integrated with the wide diversity of research projects. If not addressed, these issues may impede any progress towards open, efficient and reliable science