Foster Ian, Kesselman Carl
Argonne National Laboratory & The University of Chicago.
University of Southern California.
Computer (Long Beach Calif). 2022 Aug;55(8):20-30. doi: 10.1109/MC.2022.3160876. Epub 2022 Aug 2.
Despite much creative work on methods and tools, reproducibility-the ability to repeat the computational steps used to obtain a research result-remains elusive. One reason for these difficulties is that extant tools for capturing research processes, while powerful, often fail to capture vital connections as research projects grow in extent and complexity. We explain here how these interstitial connections can be preserved via simple methods that integrate easily with current work practices to capture basic information about every data product consumed or produced in a project. By thus extending the scope of findable, accessible, interoperable, and reusable (FAIR) data in both time and space to enable the creation of a continuous chain of Continuous and Ubiquitous FAIRness linkages (CUF-links) from inputs to outputs, such mechanisms can facilitate capture of the provenance linkages that are essential to reproducible research. We give examples of mechanisms that can facilitate the use of these methods, and review how they have been applied in practice.
尽管在方法和工具方面进行了大量创造性工作，但可重复性（即重复用于获得研究结果的计算步骤的能力）仍然难以实现。造成这些困难的一个原因是，现有的用于捕获研究过程的工具虽然功能强大，但随着研究项目规模和复杂性的增加，往往无法捕获至关重要的联系。我们在此解释如何通过简单的方法来保留这些间隙性联系，这些方法可以轻松地与当前的工作实践相结合，以捕获项目中消耗或产生的每个数据产品的基本信息。通过在时间和空间上扩展可查找、可访问、可互操作和可重用（FAIR）数据的范围，从而能够创建从输入到输出的连续且无处不在的FAIR性链接（CUF链接）的连续链，这样的机制可以促进捕获对可重复研究至关重要的溯源链接。我们给出了能够促进这些方法使用的机制示例，并回顾了它们在实践中的应用情况。
