Research Data Management Frameworks: A Systematic Literature Review

Several models and frameworks have been proposed by researchers for the management of research data over the past few years. These models and frameworks may help organizations and researchers evaluate various aspects of implementing research data management systems. Thus, this study aims to identify the principal dimensions and necessary practices of research data management based on a systematic review. A systematic search of PubMed, Web of Science, Scopus, and LISTA databases was conducted by October 2021 to identify relevant studies. Two authors screened all retrieved articles in three stages (title, abstract, and full text). Based on the refined studies, data were extracted to fulfil the objectives and answer the research questions. According to the analysis of articles, research data management can be categorized into four main dimensions: data, researchers, organizations, and technology. Data processing, data protection, data legal issues, and data sharing are the main practices of the data dimension. The researcher's dimension is educating, changing the mindset, and motivating researchers. On the organizational dimension, the essential practices supply human resources, policy-making, interaction, and support. On the technology dimension, the most significant practices include: Establishing software and hardware infrastructures. This study demonstrates that research data management is a systematic process involving various stakeholders and facilities, each cooperating and coordinating various activities. To manage research data, researchers, policymakers, research organizations, and trained staff are among the most important participants, and technical facilities are vital to its success.