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.
翻译:模拟计算研究是硅分析科学方法的基石,将原始数据转换成已公布的结果,除了在研究完整性方面的作用外,还有能力大大加快评价和再利用,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了背景和来源,对发现和验证都至关重要。尽管与科学数据有这种共同的联系,但很少有研究明确描述元数据和RCR之间的关系。本文章采用功能内容分析,以确定支持RCR的元数据标准,在由投入数据、工具、笔记本、管道和出版物组成的分析堆中发挥作用。我们的文章提供了背景背景,探讨了差距,并发现了嵌入式和方法权重的构成趋势,我们从中为今后的工作提出建议。