Best Practices for Reproducibility and Testing in Scientific Software

Program Overview

The goal of this project is to promote continuous learning, encourage the integration of best practices in scientific software development, and build a collaborative community of scientists through a comprehensive training program.

The individual courses are designed to be self-paced, spanning 3-5 weeks, with a relatively small time commitment of 1-2 hours per week.

You can complete the courses at your own pace, but I will be running multiple editions during which I will provide more structured experience and office hours. I will be providing updated schedule on this webpage.

• If you want to join the session starting February 2025, please sign up for updates by filling this form.

Course Prerequisite

We will be using GitHub and GitHub Skills templates during the course, so you should create a GitHub account (go to https://github.com and follow the “Sign up” link) and I strongly suggest completing two GitHub skills courses:

• Introduction to GitHub
• Code with Codespace (the first step is enough for this course)

Don’t worry, you will learn more about the Git during the course!

Reproducibility in Scientific Software

• Introduction to Shell
• Introduction to Version Control Systems and Git

Testing and Debugging with Python

• Introduction to pytest
• Introduction to debugging in Python

About me and BSSw

This work is done as a part of the Better Scientific Software (BSSw) Fellowship Program.

You can find my profile on the BSSw website

You can also read about my experience with the fellowship.

Acknowledgment

This work was supported by the Better Scientific Software Fellowship Program, a collaborative effort of the U.S. Department of Energy (DOE), Office of Advanced Scientific Research via ANL under Contract DE-AC02-06CH11357 and the National Nuclear Security Administration Advanced Simulation and Computing Program via LLNL under Contract DE-AC52-07NA27344; and by the National Science Foundation (NSF) via SHI under Grant No. 2327079.