Abstract

Background FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data.

Objectives This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by “GO FAIR” which addresses the identified gaps that such process has when dealing with health datasets.

Methods A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing.

Results A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow.

Discussion Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets.

Conclusion Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture.

中文翻译：

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从原始数据到公平数据：卫生研究的公平化工作流程。

摘要

背景 FAIR（可发现性，可访问性，互操作性和可重用性）指导原则寻求数据和其他数字研究输入，输出和对象（导致该数据的算法，工具和工作流程）的重用，使其可查找，可访问，可互操作，并且可重复使用。GO FAIR是一个自下而上，由利益相关者驱动和自治的计划，它定义了一个由七个步骤组成的FAIRification流程，重点放在数据上，同时也指出了元数据所需的工作。此FAIRification流程旨在以一般方式解决将原始数据集转换为FAIR数据集的问题，而不考虑处理某些特定类型的数据时可能出现的特定要求和挑战。

目标 该科学贡献致力于基于“ GO FAIR”提出的FAIRification流程建立的开放技术解决方案的体系结构设计，该流程解决了在处理健康数据集时已确定的差距。

方法 通过对现有步骤施加限制并针对健康数据的特定要求引入新步骤来开发通用的FAIRification工作流程。这些要求是在从不同角度分析FAIRification工作流程后得出的：技术障碍，道德影响和法律框架。在将GO FAIR提出的FAIRification流程应用于健康研究数据管理时，该分析发现了在数据管理，验证，去标识，版本控制和索引编制方面的差距。

结果提出了 一种基于国际七级卫生（HL7）FHIR（快速医疗保健互操作性资源）资源的技术架构，以支持经过修订的FAIRification工作流程。

 世界各地的讨论研究资助机构越来越多地要求将FAIR指导原则应用于健康研究成果。现有工具不能完全满足已确定的健康数据管理需求。因此，在未来几年中，研究人员可能会受益于一个通用框架，该框架支持将建议的FAIRification工作流程应用于健康数据集。

结论 遵循建议的FAIRification工作流程并实施技术架构，可以在健康研究界内部对来自健康研究的常规保健数据集或数据进行公平化，共享和重用。