Research Software Design by Example

When I first began working with scientists I was puzzled by how little they knew about programming. I soon realized that they knew far more about it than I did about galaxy formation, quantum chemistry, or protein folding, and that the principal reason they didn't know more was that people like me had never taught them.

It took me a longer to understand that many of the practices I was pushing them to adopt were a poor fit for their actual problems: branches in version control aren't designed to manage dozens of experimental variations on a machine learning algorithm, and it's hard to write unit tests when no-one in the world knows what the correct answer is

This tutorial is my latest attempt to teach a few of the things that I am sure are useful. If you have suggestions or would like to contribute, please get in touch.

1. Introduction
2. Parse Raw Data
3. A Plugin Architecture
4. Refactor
5. Unit Tests
6. Measure Performance
7. A Lazy Algorithm
8. Scale Up
9. A Python Package
10. Synthetic Data
11. Search
12. Laboratory Information Management System
13. A Static Web Site
14. Scrape Web Data
15. A Web Service
16. Conclusion

1. License
2. Code of Conduct
3. Contributing
4. Bibliography
5. Glossary
6. About the Author
7. Colophon