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.
Background: Data sharing presents new opportunities across the spectrum of research and is vital for science that is open, where data are easily discoverable, accessible, intelligible, reproducible, replicable and verifiable. Despite this, it is yet to become common practice. Global efforts to develop practical guidance for data sharing and open access initiatives are underway, however evidence-based studies to inform the development and implementation of effective strategies are lacking. Objective: This study sought to determine the barriers and facilitators to data sharing among health researchers and to identify the target behaviours for designing a behaviour change intervention strategy. Method: Data were drawn from a cross-sectional survey of data management practices among health researchers from one Australian research institute. Determinants of behaviour were theoretically derived using well-established behavioural models. Results: Data sharing practices have been described for 77 researchers, and 6 barriers and 4 facilitators identified. The primary barriers to data sharing included perceived negative consequences and lack of competency to share data. The primary facilitators to data sharing included trust in others using the data and social influence related to public benefit. Intervention functions likely to be most effective at changing target behaviours were also identified. Conclusion: Results of this study provide a theoretical and evidence-based process to understand the behavioural barriers and facilitators of data sharing among health researchers. Implications: Designing interventions that specifically address target behaviours to promote data sharing are important for open researcher practices.
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