During the past 20 years, the National Institutes of Health (NIH) has implemented policies designed to improve the sharing of research data. The first requirements were established in 2003. Since then other mandated policies to promote access to research data and resulting findings have been adopted, including in 2022: the NIH public access policy for publications, NIH genomic data sharing policy, and National Cancer Institute Cancer Moonshot public access and data sharing policy. In January 2023, a new NIH data sharing policy went into effect, requiring researchers to submit a Data Management and Sharing Plan in proposals for NIH. It requires consideration of: (1) how data management and sharing are addressed in the informed consent process; (2) limitations on subsequent use of data; and (3) whether access to de-identified data should be controlled. Data sharing policies are predicated on the idea that sharing information is an important component of the scientific method. It enables the creation of larger data repositories to support innovative research questions that may not be possible in individual studies.
Data sharing allows valuable information to be used for new hypotheses that may extend beyond original plans for the data. It also has been argued that data sharing represents an ethical obligation by possibly maximizing the learning that comes from federal investment in research and from the contributions of volunteer research participants who assume risks for the benefit of scientific discovery. One important question as data sharing is expanded is: “To whom do benefits of data sharing accrue?” An equally important corollary is the question explored in an article published in the May 2023 issue of the journal Social Science & Medicine: “To whom do benefits not accrue?” Data sharing through a community-engaged research lens is examined from the perspective of helping to ensure that the communities that participate in the generation of data receive benefit from the discoveries and knowledge generated. Otherwise, a significant potential for harm can go beyond the common consideration of re-identification, including de-contextualization and misinterpretation of data and resulting findings, and disenfranchisement of participating communities.