Verifiability and reproducibility are among the cornerstones of the scientific process. They are what allows scientists to "stand on the shoulder of giants". Maintaining reproducibility requires that all data management, analysis, and visualization steps behind the results presented in a paper are documented and available in full detail. Reproducibility here means that someone else should either be able to obtain the same results given all the documented inputs and the published instructions for processing them, or if not, the reasons why should be apparent. From Reproducible Science Curriculum
- Summarize the four facets of reproducibility.
- Describe several ways that reproducible workflows can improve your workflow and research.
- Explain several ways you can incorporate reproducible science techniques into your own research.
Getting Started with Reproducible Science
Please view the online slide-show below which summarizes concepts taught in the Reproducible Science Curriculum.
A Gap In Understanding
Reproducibility and Your Research
How reproducible is your current research?
- Do you currently apply any of the items in the checklist to your research?
- Are there elements in the list that you are interested in incorporating into your workflow? If so, which ones?
Additional Readings (optional)
- Nature has collated and published (with open-access) a special archive on the Challenges of Irreproducible Science .
- The Nature Publishing group has also created a Reporting Checklist for its authors that focuses primaily on reporting issues but also includes sections for sharing code.
- Recent open-access issue of Ecography focusing on reproducible ecology and software packages available for use.
- A nice short blog post with an annotated bibliography of "Top 10 papers discussing reproducible research in computational science" from Lorena Barba: Barba group reproducibility syllabus.
If you have questions or comments on this content, please contact us.