Michelle Dawson posts

Post published on Bluesky on 15 Oct 2023 19:32

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James M. DuBois, Jessica Mozersky, Meredith Parsons, Heidi A. Walsh, Annie Friedrich, Amy Pienta (2023). Exchanging words: Engaging the challenges of sharing qualitative research data. Proceedings of the National Academy of Sciences, 120(43), e2206981120. National Academy of Sciences.

Publication date
13 Oct 2023
Identifier
10.1073/pnas.2206981120
Authors
James M. DuBois, Jessica Mozersky, Meredith Parsons, Heidi A. Walsh, Annie Friedrich, Amy Pienta
Source
Proceedings of the National Academy of Sciences
Details
120(43), e2206981120
Reference type
article
Publisher
National Academy of Sciences
Metadata source
crossref

Abstract

In January 2023, a new NIH policy on data sharing went into effect. The policy applies to both quantitative and qualitative research (QR) data such as data from interviews or focus groups. QR data are often sensitive and difficult to deidentify, and thus have rarely been shared in the United States. Over the past 5 y, our research team has engaged stakeholders on QR data sharing, developed software to support data deidentification, produced guidance, and collaborated with the ICPSR data repository to pilot the deposit of 30 QR datasets. In this perspective article, we share important lessons learned by addressing eight clusters of questions on issues such as where, when, and what to share; how to deidentify data and support high-quality secondary use; budgeting for data sharing; and the permissions needed to share data. We also offer a brief assessment of the state of preparedness of data repositories, QR journals, and QR textbooks to support data sharing. While QR data sharing could yield important benefits to the research community, we quickly need to develop enforceable standards, expertise, and resources to support responsible QR data sharing. Absent these resources, we risk violating participant confidentiality and wasting a significant amount of time and funding on data that are not useful for either secondary use or data transparency and verification.

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