14 Session 14: Reproducibility and Provenance

14.1 Reproducibility and Provenance

14.1.1 Learning Objectives

In this lesson, you will learn:

  • About the importance of computational reproducibility
  • The role of provenance metadata
  • Tools and techniques for reproducibility supportred by the Arctic Data Center
  • How to build a reproducible paper in RMarkdown

A great overview of this approach to reproducible papers comes from:

  • Ben Marwick, Carl Boettiger & Lincoln Mullen (2018) Packaging Data Analytical Work Reproducibly Using R (and Friends), The American Statistician, 72:1, 80-88, doi:10.1080/00031305.2017.1375986

This lesson will draw from existing materials:

To start a reproducible paper with rrtools, run:

Then, add some more structure to the package:

Now write a reproducible paper!

Borer, Elizabeth, Eric Seabloom, Matthew B. Jones, and Mark Schildhauer. 2009. “Some Simple Guidelines for Effective Data Management.” Bulletin of the Ecological Society of America 90: 205–14. https://doi.org/10.1890/0012-9623-90.2.205.

Hampton, Stephanie E, Sean Anderson, Sarah C Bagby, Corinna Gries, Xueying Han, Edmund Hart, Matthew B Jones, et al. 2015. “The Tao of Open Science for Ecology.” Ecosphere 6 (July). https://doi.org/http://dx.doi.org/10.1890/ES14-00402.1.

Munafò, Marcus R., 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, and John P. A. Ioannidis. 2017. “A Manifesto for Reproducible Science.” Nature Human Behaviour 1 (1): 0021. https://doi.org/10.1038/s41562-016-0021.