Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia. Issue 2 (May 2020)

• Record Type: Journal Article
• Authors: Krahe, Michelle A; Toohey, Julie; Wolski, Malcolm; Scuffham, Paul A; Reilly, Sheena
• Is Part Of: Health information management journal. Volume 49:Issue 2/3(2020)
• Page Start: 108
• Page End: 116
• Publication Date: 2020-05
• Subjects: medical informatics -- health information management -- data collection -- research -- academies and institutes -- best practices; Medical records -- Management -- Periodicals; Medical records -- Management -- Australia -- Periodicals; Medical records -- Management; Australia; Periodicals; Periodicals; 651.50426105
• DOI: 10.1177/1833358319831318
• Languages: English
• ISSNs: 1833-3583

Abstract

Background: Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. Objective: This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. Method: A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Results: Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Conclusion: Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. Implications: This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.