Reproducibility Curriculum
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Home
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Sessions
Introductions and motivation
Useful tools: Why you should learn (and love) the command line
Day 1 reproducibility
Day N-1 reproducibility
Working with confidential data, when to share data and when no to
Documenting it all with a README
How to run code reproducibly
API and AI
The data lifecycle: preserving raw data
Writing articles that combine text and code
High-performance computing and why you should care about it
Reproducibility, transparency, and data ethics: How and when to share data, and when not to
Extras
Writing a referee report
Writing a grant proposal
Guide for good presentations
Hands-on: A very imperfect example
Sample syllabi
Econ 7850 Cornell
Predoc sequence
A Two-Day Reproducibility Curriculum
Acknowledgements
Funding by Sloan grant (TBD).