Data Sharing in Biomedical Sciences: A Systematic Review of Incentives

Background: The lack of incentives has been described as the rate-limiting step for data sharing. Currently, the evaluation of scientific productivity by academic institutions and funders has been heavily reliant upon the number of publications and citations, raising questions about the adequacy of such mechanisms to reward data generation and sharing. This article provides a systematic review of the current and proposed incentive mechanisms for researchers in biomedical sciences and discusses their strengths and weaknesses. Methods: PubMed, Web of Science, and Google Scholar were queried for original research articles, editorials, and opinion articles on incentives for data sharing. Articles were included if they discussed incentive mechanisms for data sharing, were applicable to biomedical sciences, and were written in English. Results: Although coauthorship in return for the sharing of data is common, this might be incompatible with authorship guidelines and raise concerns over the ability of secondary analysts to contest the proposed research methods or conclusions that are drawn. Data publication, citation, and altmetrics have been proposed as alternative routes to credit data generators, which could address these disadvantages. Their primary downsides are that they are not well-established, it is difficult to acquire evidence to support their implementation, and that they could be gamed or give rise to novel forms of research misconduct. Conclusions: Alternative recognition mechanisms need to be more commonly used to generate evidence on their power to stimulate data sharing, and to assess where they fall short. There is ample discussion in policy documents on alternative crediting systems to work toward Open Science, which indicates that that there is an interest in working out more elaborate metascience programs.