Reproducibility in Science

A Guide to enhancing reproducibility in scientific results and writing


In every field of science, scientists are increasingly using electronic tools, from collecting their data to publishing their results. With the increase in computational tools comes the advantage of reproducibility at an extent not previously possible. Unfortunately, reproducibility of results is actually becoming increasingly more difficult, owing to the variety of ways of approaching analysis and incapability of data structures and file types. This is a guide to make scientific research more easily communicated and performed by using tools that promote reproducibility.

While technology increasingly evolves, so do best practices in using these technologies. If you have already written something that you believe fits well here, consider submitting to part of a section or creating an entirely new section for your work. Contribution to the guide is highly encouraged by collaboration through Github.




Questions Checklist

How to Share Data with a Statistician

Project Metadata

Guidelines to Writing Code

Work Flows

Data Storage

Copyright Licensing

Further Reading

Want to contribute, but are new to collaborating through Github? Try our guide!

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