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Research Reproducibility workshop: Home

Materials for half day research reproducibility workshop taught to doctoral students in the G-RISE program.

Workshop background

The Research Reproducibility workshop was planned as part of a grant proposal submitted to NIH by the OSU Graduate College. The workshop content is intended to be a high level introduction to new doctoral students in the health sciences.  Reproducibility for Everyone (R4E) is a community-led education initiative to increase adoption of open research practices at scale.  More information about the community is available at https://www.repro4everyone.org/.  The materials presented in the first unit of the workshop are adapted from those provided by R4E.  

Workshop description

 
The Research Reproducibility workshop is intended to be a high level introduction for new doctoral students in the health sciences.  The workshop will be taught in three sections with the first an introduction to tools and research practices that improve reproducibility, the second a review of the role of funders and publishers in advancing reproducibility through open science and the final unit will be a discussion of FAIR data principles and how they contribute to data sharing.   

Workshop content outline

Unit 1 - Use the R4E materials to cover a practical introduction to reproducibility concepts

~1 1/2 hours - Prerecord some/all modules?

  • Introduction, 15 mins -
  • Data Management, 10 mins -
  • ELNs, 10 mins -
  • Protocol Sharing - 12 mins,
  • Includes interactive drawing exercise
  • Reagent sharing, 12 mins -
  • Bioinformatics, 10 mins -
  • Data and code sharing, 8 mins -
  • Data analysis and visualization, 10 mins -
  • Images, 10 mins -
  • Summary, 3 mins -

Unit 2 - The role of publishing and funders in reproducibility

i. Funder expectations

j. Publishing paradigms – incentives, no bad results

k. Open Access vs paywall publishing

l. Licensing and registration of research plan

Unit 3 – Data is the foundation of reproducibility

a. A closer look at reproducibility

b. How open science and data sharing improve reproducibility

c. FAIR Data – accessible isn’t always open and open isn’t always accessible