A template for reproducible academic projects. The idea of this template is to provide a structure and way of working that eases those wanting to work more reproducibly.
Important
This is work in progress, as I slowly add material.
A large part of working reproducibly is building habits. Learning everything at once will also be daunting and overwhelming and can hinder your adoption of reproducible practices as a result. Building comfort with tools and practices over time and maintaing a reasonable level of challenge, is a better recipe to succeed.
There are two key tenets:
- Codify everything.
- Document everything else.
Before dumping all your files in a copy of this template, decide on a folder structure you want to maintain. Keeping it as simple as possible will help maintain it over time, and provide you with some flexibility.
This repository implements the following suggestion:
/
|- analyses/ // Contains scripts that launch your workflows. Codify everything!
|- data/ // A folder to organise your data.
|- docs/ // Website, Slides, Reports, Articles, Thesis that use literate programming for reproducibility.
|- workflows/ // Workflows for large scale analyses, often executed on a HPC or other non-local system.
See the README.md's in each directory for further description of the folder's purpose.
Github can be used to make a template. The idea is that you start a new project from this template, and it provides you with the folder structure you want already set up. You can populate it with useful scripts and templates that can help you work more effectively, and always have them ready in your projects. It's a good idea to have README in each folder too to tell future you how you're using these folders.
See Creating a template repo on Github for more information on how to set one up.
Note
Setting up a template is there to help enforce your folder structure usage. Integrating git into
your process for version control is another step.
- Folder structure, file names, and versioning
- Easing Into Open Science: A Guide for Graduate Students and Their Advisors
- A Quick Guide to Organizing Computational Biology Projects
- Ten Simple Rules for Reproducible Computational Research
- Ten simple rules for biologists learning to program
- Best Practices for Scientific Computing
- Good enough practices in scientific computing
- Streamlining data-intensive biology with workflow systems