Tools of reproducibility

Reference

Florian Markowetz. Five selfish reasons to work reproducibly. Genome Biology, (2015) 16:274 - 277.

Stephen R Piccolo, Adam B Lee, Michael B Frampton. Tools and techniques for computational reproducibility. Gigascience, Volume 5, Issue 1, 1 December 2016, Pages 1 – 13.

Tools of reproducibility

• Keep your project organized, name your files and directories in some informative way, store your data and code at a single backed-up location.

• Learn some tools of computational reproducibility:

  • Do your analysis in R or Python.

  • Document your analysis using knitR or IPython notebooks.

  • Merge descriptive text with analysis code into dynamic documents that can be automatically updated every time the data or code change.

• Provide a detailed, written narrative description of the process to enable others to reproduce a computation analysis.

• Learn to use a version-control system like git on a collaborative platform such as GitHub.

• For professionals, learn to use docker, which will make the analysis self-contained and easily transportable to different systems.

Reasons to work reproducibly

• Reproducibility helps to avoid disaster.

• Reproducibility makes it easier to write papers.

• Reproducibility helps reviewers see it your way.

• Reproducibility enables continuity of your work.

• Reproducibility helps to build your reputation.