Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, RCR has the capacity to significantly accelerate evaluation and reuse. This potential and wide-support for the FAIR principles have motivated interest in metadata standards supporting RCR. Metadata provides context and provenance to raw data and methods and is essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described the relationship between metadata and RCR. This article employs a functional content analysis to identify metadata standards that support RCR functions across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our article provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

中文翻译：

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元数据在可重复计算研究中的作用

可重复计算研究 (RCR) 是计算机分析科学方法的基石，可将原始数据转换为已发表的结果。除了在研究诚信方面的作用外，RCR 还能够显着加速评估和再利用。这种对 FAIR 原则的潜在和广泛支持激发了对支持 RCR 的元数据标准的兴趣。元数据为原始数据和方法提供背景和来源，对于发现和验证都至关重要。尽管与科学数据存在这种共享关系，但很少有研究明确描述元数据与 RCR 之间的关系。本文采用功能内容分析来确定支持跨分析堆栈的 RCR 功能的元数据标准，该分析堆栈由输入数据、工具、笔记本、管道、和出版物。我们的文章提供了背景背景，探索了差距，并发现了嵌入性和方法权重的组成趋势，我们从中得出了对未来工作的建议。