Reproducible manuscript preparation with RMarkdown application to JMSACL and other Elsevier Journals

Abstract

INTRODUCTION: With the rising complexity of modern multimarker analytical techniques and notable scientific publication retractions required for erroneous statistical analysis, there is increasing awareness of the importance of research transparency and reproducibility. The development of mature open-source tools for literate programming in multiple langauge paradigms has made fully-reproducible authorship possible. OBJECTIVES: We describe the procedure for manuscript preparation using RMarkdown and the R statistical programming language with application to JMSACL or any other Elsevier journal. METHODS: An instructional manuscript has been prepared in the RMarkdown markup language with stepwise directions on preparing sections, subsections, lists, tables, figures and reference management in an entirely reproducible format. RESULTS: From RMarkdown code, a submission-ready PDF is generated and JMSACL-compatible LaTeX code is generated. These can be uploaded to the Editorial Manager. CONCLUSION: A completely reproducible manuscript preparation pipeline using the R and RMarkdown is described.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 5 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.