Data Sharing and Reuse

Ida Sim ()
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Ida Sim: University of California San Francisco, Division of General Internal Medicine

Chapter 108 in Principles and Practice of Clinical Trials, 2022, pp 2137-2158 from Springer

Abstract: Abstract Traditionally, clinical trials influence science through publications of their results. Increasingly, however, summary-level and individual participant-level results data are being shared with the scientific community and are influencing science through data reuse. Journal and funder mandates are compelling the sharing of summary-level as well as individual participant-level data (IPD) by industry and academic trialists. Patients, too, are becoming more vocal in demanding that their data contributions to clinical trials be re-used to accelerate findings. The move toward clinical trial data sharing is part of a wider movement toward open science in general. Four principles underlie scientific data sharing: Findability, Accessibility, Interoperability, and Reusability (FAIR). To handle the global volume of clinical trials, automated implementation of these principles is needed to complement more manual methods. Close to 100 clinical trial data sharing platforms currently exist worldwide, each meeting the FAIR data sharing principles to varying degrees of automation. This chapter reviews the history, motivations, and current landscape of clinical trial data sharing and reuse. A culture of data sharing is now the norm in the pharmaceutical industry and is starting to take hold in academia, where new mechanisms for crediting and rewarding data sharing are needed. The benefits of data sharing go beyond new publishable findings to include improvements in future study designs informed by analyses of prior IPD. Clinical trial data sharing honors participant contributions to research, enhances public trust in clinical trials, and promises to accelerate scientific findings by maximizing the value of clinical trials data.

Keywords: Data sharing; Data reuse; Individual Participant-level Data; IPD; FAIR data sharing principles; Data repositories; Data sharing platforms; Interoperability (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_190

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