Computational workflows represent major investments of effort and expertise. As first-class, publishable research objects of their own, they are key to sharing methodological know-how for reuse, reproducibility, and transparency. Thus, the application of the FAIR Principles to workflows is inevitable to enable them to be Findable, Accessible, Interoperable, and Reusable. Making workflows FAIR reduces duplication of effort, assists in the reuse of best practice approaches and community-supported standards, and ensures that workflows as digital objects can support reproducible, robust science. FAIR workflows draw from both FAIR data and software principles, and they help ensure and support data FAIRification. The FAIR Principles emphasize the association of persistent identifiers and machine-actionable metadata with workflows. Implementing the Principles requires a framework with appropriate programmatic protocols and an accompanying ecosystem of services, tools, policies, and best practices, as well the buy-in of existing workflow systems. The European EOSC-Life Workflow Collaboratory is an example of such a digital infrastructure for the Biosciences. It includes a metadata standards framework for describing workflows that is managed and used by dedicated new FAIR workflow services and programmatic APIs for interoperability and metadata access. It includes the WorkflowHub registry and LifeMonitor workflow testing service, and it incorporates existing workflow systems and packaging solutions. Here, we introduce the FAIR Principles for workflows and connect FAIR workflows with the FAIR ecosystems they inhabit with the EOSC-Life Collaboratory as a concrete example. We also introduce other community efforts that are easing the ways that workflows are shared and reused by others, and we discuss how the variations in different workflow settings impact their FAIR perspectives.

翻译：计算工作流凝聚了大量的专业投入与智力成果。作为具备独立发表价值的一流研究客体，它们是共享方法学知识以实现复用、可重复性与透明度的关键。因此，将FAIR原则应用于工作流，使其具备可发现性、可访问性、互操作性与可复用性，已成为必然趋势。实现工作流的FAIR化，既能减少重复劳动，促进最佳实践方法与社区支持标准的复用，又能确保工作流作为数字对象支撑可重复、稳健的科学研究。FAIR工作流融合了FAIR数据与软件原则，并有助于保障并推动数据的FAIR化进程。FAIR原则强调将持久标识符与机器可操作元数据关联至工作流。实施这些原则需要建立包含适当程序化协议的系统框架，以及配套的服务、工具、政策与最佳实践生态系统，并需获得现有工作流系统的支持。欧洲EOSC-Life工作流协作平台便是生物科学领域此类数字基础设施的一个范例。该平台包含用于描述工作流的元数据标准框架，该框架由专门的FAIR工作流服务与程序化API进行管理与调用，以实现互操作性与元数据访问。平台集成了WorkflowHub注册库与LifeMonitor工作流测试服务，并兼容现有的工作流系统与封装方案。本文中，我们介绍了适用于工作流的FAIR原则，并以EOSC-Life协作平台为具体实例，将FAIR工作流与其所处的FAIR生态系统相联结。同时，我们介绍了其他有助于简化工作流共享与复用方式的社区工作，并讨论了不同工作流环境中的差异性如何影响其FAIR化前景。