Daniel T Holmes1,2, Mahdi Mobini1,3, Christopher R McCudden4,5,6. 1. St. Paul's Hospital, Department of Pathology and Laboratory Medicine, 1081 Burrard St., Vancouver, BC V6Z 1Y6, Canada. 2. University of British, Columbia Department of Pathology and Laboratory Medicine, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z7, Canada. 3. Providence Health Digital Products, 1190 Hornby St., Vancouver, BC V6Z 2K5, Canada. 4. Department of Pathology and Laboratory Medicine, University of Ottawa, Canada. 5. Department of Pathology and Laboratory Medicine, Ottawa Hospital, General Campus, 501 Smyth Road, Ottawa, ON K1H 8L6, Canada. 6. Eastern Ontario Regional Laboratory Association, Canada.
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.
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.
Authors: Marcus R Munafò; Brian A Nosek; Dorothy V M Bishop; Katherine S Button; Christopher D Chambers; Nathalie Percie du Sert; Uri Simonsohn; Eric-Jan Wagenmakers; Jennifer J Ware; John P A Ioannidis Journal: Nat Hum Behav Date: 2017-01-10
Authors: Barry R Zeeberg; Joseph Riss; David W Kane; Kimberly J Bussey; Edward Uchio; W Marston Linehan; J Carl Barrett; John N Weinstein Journal: BMC Bioinformatics Date: 2004-06-23 Impact factor: 3.169