系统全面的数据管理是全面健康研究的关键推动因素。数据收集、存储、访问、共享和分析等过程要求研究人员正确和一致地遵循精心设计的数据管理策略。研究表明，可找到、可访问、可互操作和可重用 (FAIR) 的数据有助于在不同科学领域中改善数据共享。本范围审查识别和讨论健康研究数据中FAIR倡议的概念、方法、实施经验和教训。采用了Arksey和O'Malley阶段方法论框架进行范围审查。通过搜索PubMed、Web of Science和Google Scholar，获得相关出版物。包括在2014年至2020年间发表的英文文章，并涉及健康领域的FAIR概念或实践。使用参考管理软件对3个数据来源进行了去重。总共，两位独立的作者根据规定的纳入和排除标准审查了每篇文章的资格。使用绘图工具从全文论文中提取信息。结果按照PRISMA-ScR (范围审查的首选报告项目和系统评价扩展)指南进行报告。在经过筛选的1561篇文章中，共有2.18% (34篇)被纳入最终审查。作者报告了包括插值、包含全面的数据字典、存储库设计、语义互操作性、本体论、数据质量、关联数据和FAIR化工具需求收集等在内的FAIR化方法。还报告了与FAIR化相关的挑战和缓解策略，例如高成本的建立、数据政策、技术和管理问题、隐私问题，以及尽管其敏感性质但在共享健康数据时遇到的困难。我们发现世界各地的不同团体设计了各种工作流程、工具和基础设施以促进健康研究数据的FAIR化。通过使用不同的工作流程、工具和基础设施，我们还发现研究人员正在努力解决各种问题和问题。尽管健康研究领域FAIR数据管理的概念相对较新，但几乎所有大陆都已经通过至少一个网络实现了健康数据的FAIR性。FAIR化努力的记录成果包括同行评审的出版物、改善的数据共享、便于数据重用、回报率和新的治疗措施。成功地将数据FAIR化已经为癌症、心血管疾病和神经系统疾病等各种疾病的管理和预测提供了信息。在新冠肺炎大流行期间，FAIR化更多种类疾病的数据的努力一直在进行中。此工作总结了健康研究数据FAIR化的项目、工具和工作流程。综合审查显示，在大数据和开放式研究出版时代，将FAIR概念应用于健康数据管理将带来改进的研究数据管理和透明度。 RR2-10.2196/22505.©Esther Thea Inau, Jean Sack, Dagmar Waltemath, Atinkut Alamirrew Zeleke. 文章首发于Journal of Medical Internet Research (https://www.jmir.org)，2023年8月28日。

Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.RR2-10.2196/22505.©Esther Thea Inau, Jean Sack, Dagmar Waltemath, Atinkut Alamirrew Zeleke. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.08.2023.