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Journal article
The role of metadata in reproducible computational research
Patterns (New York, N.Y.), v 2(9), pp 100322-100322
10 Sep 2021
PMID: 34553169
Featured in Collection : UN Sustainable Development Goals @ Drexel
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, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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Details
- Title
- The role of metadata in reproducible computational research
- Creators
- Jeremy Leipzig - Drexel UniversityDaniel Nuest - Univ Munster, Inst Geoinformat, Munster, GermanyCharles Tapley Hoyt - Harvard UniversityKarthik Ram - Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA.Jane Greenberg - Drexel University
- Publication Details
- Patterns (New York, N.Y.), v 2(9), pp 100322-100322
- Publisher
- Elsevier
- Number of pages
- 28
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science; Computer Science
- Web of Science ID
- WOS:000706777400009
- Scopus ID
- 2-s2.0-85120051431
- Other Identifier
- 991019168883004721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications