Best practices for scientific computing

Material for a training on best practices for scientific computing.

Programming languages

Although this training aims to be programming language-agnostic, the repository also list a number of tools that are programming language-specific. Obviously, this can not be exhaustive, so feel free to suggest additional tools if you are aware of any.

Programming languages covered:

• C
• C++
• Fortran
• Python
• R

Table of contents

1. Syntax versus semantics
2. Code style and conventions
3. Version control & collaboration
4. Code documentation
5. Testing
   • Testing as experiments
   • Unit testing
   • Functional testing
   • Code coverage
6. Optimization
7. Deployment
8. Continuous integration
9. Reproducibility
10. References
11. Tools
    • C
    • C++
    • Fortran
    • Python
    • R
12. Training

Acknowledgments

I've "borrowed" much of the table of contents from a training given by the Netherlands eScience Center, although no actual contents of that training was used for the development of this material.

Thanks to the following people for their suggestions and comments: * Ilaria Misuri: pointed out the rpy2 package for using R from Python.