The role of metadata in reproducible computational research

TL;DR

This paper analyzes how metadata standards support reproducible computational research by providing context, provenance, and facilitating discovery and validation across various research components.

Contribution

It identifies key metadata standards that support RCR functions and offers recommendations for future development based on a functional content analysis.

Findings

Metadata standards are crucial for supporting RCR functions.

Gaps exist in current metadata standards for RCR.

Recommendations are provided for enhancing metadata support in RCR.

Abstract

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.