Introduction

Replication vs Reproducible Research

A basic tenet in science is the ability to replicate the results of any experiment(1,2). Replication verifies results; however, research papers often lack the detail required for independent replication. Many attempts at replicating the results of well-known scientific studies have failed in a variety of disciplines (1).

Replicating studies with new data is expensive, for this reason computationally reproducible research is recommended as a way to assess the validity and rigor of scientific results(1,2). Research is considered reproducible when others can reproduce results of a study with only the original data, code, conditions and documentation(1).

Reproducible research has most of the advantages of replicating studies without the financial and time burden associated with collecting new data(2). It is the by-product of care and attention to detail throughout the research process and seen as a minimum standard that all researchers should strive for(1).

Video: Why reproducible research?

Benefits of reproducible research(1)

  • Easier to recall and explain work to collaborators, supervisors and reviewers
  • Faster to modify analyses and figures
  • Facilitates quick reconfiguration of research tasks for new projects with similar tasks
  • It can increase the quality and speed of peer review
  • Can increase citations and expanded research impact through the ability to access and cite the project code and data
  • Enhances opportunities for research methods training
  • Reproducible research is a strong indicator of rigour, trustworthiness and transparency

Some scenarios to consider

  • Could you continue your work?
  • Do you know where your data is stored?
  • Could you keep working effectively for 1 month? 1 year?

  • Could you continue your work?
  • Is your data backed up? Encrypted?

  • Can your provide your data and methods?

  • Can you show the steps taken and measures put in place to avoid this?

References

  1. Alston, J. M., & Rick, J. A. (2021). A beginner’s guide to conducting reproducible research. Bulletin of the Ecological Society of America, 102(2). https://doi.org/10.1002/bes2.1801

  2. Powers, S. M., & Hampton, S. E. (2019). Open science, reproducibility, and transparency in ecology. Ecological Applications, 29(1). https://doi.org/10.1002/eap.1822

1 - Data quality