论文标题
f ***工作流程：当不见了的部分时
F*** workflows: when parts of FAIR are missing
论文作者
论文摘要
科学数据的公平原则（可访问，可互操作，可重复使用）也与其他数字对象（例如研究软件和科学数据的科学工作流程）有关。公平的原则可以应用于科学工作流以及指定和执行工作流程所需的流程，软件和其他基础架构所处理的数据。公平的原则被设计为指南而不是规则，可以使不同社区和不同程度合规性的标准差异。有许多实际的考虑因素会影响真正可以达到的公平性水平，包括政策，传统和技术。由于这些考虑因素，在工作流程生命周期中经常遇到障碍，这些障碍直接追溯到实施公平原则的缺点。在这里，我们详细介绍了某些情况，没有命名名称，其中可以找到数据和工作流，但在现代公平方法，工具和用户通常所需和期望的领域缺乏其他情况。我们描述了这些问题中的一些（所有这些问题）如何成功地克服了这些问题，激励了我们推动完全公平的工作流程的系统和方法。
The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows.
