SAS code I used more than 20 years ago still runs fine today!

https://www.nature.com/articles/d41586-020-02462-7

Reproducibility checklist

Although it’s impossible to guarantee computational reproducibility over time, these strategies can maximize your chances.

Code Workflows based on point-and-click interfaces, such as Excel, are not reproducible. Enshrine your computations and data manipulation in code.

Document Use comments, computational notebooks and README files to explain how your code works, and to define the expected parameters and the computational environment required.

Record Make a note of key parameters, such as the ‘seed’ values used to start a random-number generator. Such records allow you to reproduce runs, track down bugs and follow up on unexpected results.

Test Create a suite of test functions. Use positive and negative control data sets to ensure you get the expected results, and run those tests throughout development to squash bugs as they arise.

Guide Create a master script (for example, a ‘run.sh’ file) that downloads required data sets and variables, executes your workflow and provides an obvious entry point to the code.

Archive GitHub is a popular but impermanent online repository. Archiving services such as Zenodo, Figshare and Software Heritage promise long-term stability.

Track Use version-control tools such as Git to record your project’s history. Note which version you used to create each result.

Package Create ready-to-use computational environments using containerization tools (for example, Docker, Singularity), web services (Code Ocean, Gigantum, Binder) or virtual-environment managers (Conda).

Automate Use continuous-integration services (for example, Travis CI) to automatically test your code over time, and in various computational environments.

Simplify Avoid niche or hard-to-install third-party code libraries that can complicate reuse.

Verify Check your code’s portability by running it in a range of computing environments.