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      Exchanging words: Engaging the challenges of sharing qualitative research data

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          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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          Most cited references58

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant.

            In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.
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              Critical Analysis of Strategies for Determining Rigor in Qualitative Inquiry.

              Criteria for determining the trustworthiness of qualitative research were introduced by Guba and Lincoln in the 1980s when they replaced terminology for achieving rigor, reliability, validity, and generalizability with dependability, credibility, and transferability. Strategies for achieving trustworthiness were also introduced. This landmark contribution to qualitative research remains in use today, with only minor modifications in format. Despite the significance of this contribution over the past four decades, the strategies recommended to achieve trustworthiness have not been critically examined. Recommendations for where, why, and how to use these strategies have not been developed, and how well they achieve their intended goal has not been examined. We do not know, for example, what impact these strategies have on the completed research. In this article, I critique these strategies. I recommend that qualitative researchers return to the terminology of social sciences, using rigor, reliability, validity, and generalizability. I then make recommendations for the appropriate use of the strategies recommended to achieve rigor: prolonged engagement, persistent observation, and thick, rich description; inter-rater reliability, negative case analysis; peer review or debriefing; clarifying researcher bias; member checking; external audits; and triangulation.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                13 October 2023
                24 October 2023
                13 October 2023
                : 120
                : 43
                : e2206981120
                Affiliations
                [1] aBioethics Research Center, Department of Medicine, Washington University School of Medicine , St. Louis, MO 63110
                [2] b ICPSR, Institute for Social Research, University of Michigan, Ann Arbor, MI 48106
                Author notes
                1To whom correspondence may be addressed. Email: duboisjm@ 123456wustl.edu .

                Edited by Lee Humphreys, Cornell University; received February 2, 2023; accepted September 4, 2023 by Editorial Board Member Mary C. Waters

                Author information
                https://orcid.org/0000-0002-3712-7051
                https://orcid.org/0000-0002-4942-4571
                https://orcid.org/0000-0001-9447-6496
                https://orcid.org/0000-0003-1174-6118
                Article
                202206981
                10.1073/pnas.2206981120
                10614603
                37831745
                341b0e03-75fc-46af-955b-de6e2c0ecd94
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 10, Words: 7650
                Funding
                Funded by: HHS | NIH | National Human Genome Research Institute (NHGRI), FundRef 100000051;
                Award ID: R01HG009351
                Award Recipient : Jessica Mozersky
                Funded by: HHS | NIH | National Center for Advancing Translational Sciences (NCATS), FundRef 100006108;
                Award ID: UL1TR002345
                Award Recipient : James M DuBois
                Categories
                pers, Perspective
                soc-sci, Social Sciences
                432
                447
                Perspective
                Social Sciences
                Social Sciences

                data sharing,qualitative research,research compliance,fair principles,data de-identification

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