Data sharing policies across health research globally: Cross-sectional meta-research study

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Aidan C. Tan, Angela C. Webster, Sol Libesman, Zijing Yang, Rani R. Chand, Weber Liu, Talia Palacios, Kylie E. Hunter, Anna Lene Seidler
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引用次数: 0

Abstract

Background

Data sharing improves the value, synthesis, and integrity of research, but rates are low. Data sharing might be improved if data sharing policies were prominent and actionable at every stage of research. We aimed to systematically describe the epidemiology of data sharing policies across the health research lifecycle.

Methods

This was a cross-sectional analysis of the data sharing policies of the largest health research funders, all national ethics committees, all clinical trial registries, the highest-impact medical journals, and all medical research data repositories. Stakeholders' official websites, online reports, and other records were reviewed up to May 2022. The strength and characteristics of their data sharing policies were assessed, including their policies on data sharing intention statements (a.k.a. data accessibility statements) and on data sharing specifically for coronavirus disease studies. Data were manually extracted in duplicate, and policies were descriptively analysed by their stakeholder and characteristics.

Results

Nine hundred and thirty-five eligible stakeholders were identified: 110 funders, 124 ethics committees, 18 trial registries, 273 journals, and 410 data repositories. Data sharing was required by 41% (45/110) of funders, no ethics committees or trial registries, 19% (52/273) of journals and 6% (24/410) of data repositories. Among funder types, a higher proportion of private (63%, 35/55) and philanthropic (67%, 4/6) funders required data sharing than public funders (12%, 6/49).

Conclusion

Data sharing requirements, and even recommendations, were insufficient across health research. Where data sharing was required or recommended, there was limited guidance on implementation. We describe multiple pathways to improve the implementation of data sharing. Public funders and ethics committees are two stakeholders with particularly important untapped opportunities.

Abstract Image

全球健康研究数据共享政策:横断面荟萃研究
背景数据共享提高了研究的价值、综合性和完整性,但数据共享率却很低。如果数据共享政策在研究的每个阶段都非常突出且具有可操作性,那么数据共享的情况可能会得到改善。我们的目的是系统地描述整个健康研究生命周期中数据共享政策的流行病学。方法这是对最大的健康研究资助机构、所有国家伦理委员会、所有临床试验注册机构、影响力最大的医学期刊以及所有医学研究数据存储库的数据共享政策进行的横断面分析。对利益相关者截至 2022 年 5 月的官方网站、在线报告和其他记录进行了审查。对其数据共享政策的力度和特点进行了评估,包括其数据共享意向声明(又称数据可访问性声明)和专门针对冠状病毒疾病研究的数据共享政策。人工提取的数据一式两份,并按照利益相关者和特征对政策进行了描述性分析:结果确定了 935 个符合条件的利益相关者:110 个资助者、124 个伦理委员会、18 个试验登记处、273 个期刊和 410 个数据存储库。41%的资助者(45/110)、无伦理委员会或试验登记处、19%的期刊(52/273)和6%的数据存储库(24/410)要求数据共享。在资助者类型中,要求数据共享的私人资助者(63%,35/55)和慈善资助者(67%,4/6)的比例高于公共资助者(12%,6/49)。在有数据共享要求或建议的地方,实施指导也很有限。我们介绍了改善数据共享实施的多种途径。公共资助者和伦理委员会是两个利益相关者,它们拥有尚未开发的重要机会。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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