Synergize Multidisciplinary Research via Research Data Management

Scientific disciplines are generating large volumes of valuable data with the potential to drive future discoveries across various domains. However, the effective management of these research data has now become indispensable and increasingly crucial in all scientific disciplines. Research Data Management (RDM) critically addresses challenges related to data integrity and curation throughout the research data life cycle. While RDM practices and procedures often exhibit substantial variations due to the diverse nature of projects, making it challenging to find overlap between disciplines, there are still similarities regarding expectations, requirements, and familiarity with tools, techniques, and infrastructure. This is due to the evolving dynamics of data generation and analytics in every scientific discipline. In this study, we conducted two comprehensive surveys by focusing on a multidisciplinary research environment involving 15 research projects and formulated the questionnaires to evaluate the RDM prerequisites and workflows for these projects. The surveys are associated with different activities in the research data life cycle, which we illustrate in the concept of the RDM Impact Cycle. We present acquired observations and provide comprehensive insights into full-cycle RDM best practices. Our discussion then emphasizes the influence of RDM on interdisciplinary research paradigms, paving the way for exciting data-driven studies supported by robust RDM practices.