Am J Epidemiol. 2023 Jun 2;192(6):995-1005. doi: 10.1093/aje/kwad040.
Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
数据共享对于流行病学研究的可重复性、发现结果的复制、联盟努力中的汇总分析以及最大限度地提高研究价值以解决多个研究问题至关重要。然而，与保密性、成本和激励措施相关的障碍常常限制了数据共享的程度和速度。遵循可发现性、可访问性、互操作性、可重用性（FAIR）原则的流行病学实践可以通过使用必要的元数据使数据资源可发现、授权用户可访问以及与其他数据互操作，来解决这些障碍，从而优化资源的重用，并为其创建者提供适当的信用。我们概述了这些原则，并描述了在流行病学中实施这些原则的方法。通过将数据和代码从现场位置移动到远程、可访问的（“云”）数据服务器，使用机器可读和非专有文件，并开发开源代码，可以实现更高程度的 FAIR 性。采用这些实践将改善日常工作和协作分析，并促进遵守资助者和科学期刊的数据共享政策。实现高度的 FAIR 性需要资金、培训、组织支持、认可和共享研究资源（包括数据和代码）的激励措施。然而，通过促进科学界对宝贵研究资源的重复使用，使研究更具可重复性、影响力和公平性，这些成本是值得的。
