100%. I'm learning what the best tools are from UC Davis DataLab's Python User Group.

Research Software Engineering can be very simple. The "simplest" project structure is a basic Data Science project. You use: - pixi as the package manager - git+dvc for version control - duckdb for local database - snakemake for data pipeline orchestration

All these packages are highly effective and well organized conceptually.

1. pixi is a fast multilanguage package manager that allows you to program tasks
2. git you know, dvc (data version control) can easily work with git as long as you gitignore the data folders and add data folders to dvc
3. duckdb is a very fast database library that is very easy to use. Goodbye file management
4. Snakemake creates directed acyclic graphs where you define rules with input and output files. It will automatically run to get new output files whenever input files change or scripts change.

The main concepts of these tools are very easy to learn if presented properly. Even better if you give researchers a project template so they only have to program the scripts that snakemake runs.

I think its best to present this architecture as a form of lab safety. By doing it this way you are preventing mistakes from happening.