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Reproducibility in Research

This guide provides resources, tips, and tools on making one's research reproducible

                           Figure depicting 4 domains to address

Figure 1: Recommendations to improve reproducibility and rigor of biomedical research organized across the 4 domains: teaching computational skills to produce reproducible research (“Teach”), development and distribution of data and software (“Develop and Distribute”), implementation of reproducible research (“Implement”), and incentivizing reproducible research (“Incentivize”). 

Gigascience, Volume 9, Issue 6, June 2020, giaa056,

Tips to Improve your Reproducibility

  • Create, implement, and enforce standard operating procedures for collecting, reporting, storing, sharing data and for onboarding new students

  • Store everything electronically and make a back-up; take photos if need-be

  • Work with a statistician consultant or company (ex: Experimental Design Assistant)

  • Train students on ideal reproducibility and and rigor practices and statistics

  • Publish and pre-register your protocols and clinical trials

  • Use version control software such as github, Box

  • Use LabArchives, an electronic lab notebook that helps with data sharing, storage and collaboration

  • Design your analyses so that they can be run automatically as a script

  • Celebrate no or negative results in addition to positive results

  • Include full transparency and compete methodological detail in lab notebooks and manuscripts

  • Attend talks or conferences, enroll in courses, and view tutorials on enhancing reproducibility

  • Use open source code, software, and tools and make your data open

  • Run periodic tests!  

  • Keep a clean and carefully labelled lab

How to make institutional change

  • Request the code for papers you are reviewing

  • Provide public access to scripts, runs, and results

  • Ask professional associations and societies to promote the importance of reproducible research (ex: editorials, official statements and guidelines,...)

  • Publish protocols and research outcomes, including negative and null results and replications studies 

  • Pre-register study design(s), primary outcome(s) and analysis plan(s)

  • Pressure publishers to require more detailed methodology sections 

  • Take reproducibility into consideration for promotion/tenure

  • Support incentives from publishers and funders (ex: badges or credits)

  • Make replication studies and testing common place

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