Organizing your projects

Objectives

Get an overview on how to organize research projects

Instructor note

10 min teaching incl. discussions

One of the first steps to make your work reproducible is to organize your projects well. Let’s go over some of the basic things which people have found to work (and not to work).

Directory structure for projects

Project files in a single folder
Different projects should have separate folders
Use consistent and informative directory structure
- Avoid spaces in directory and file names – it is uglier for humans but handy for computers.
If you need to separate public/private, you can put them in public and private Git repos
- If you need to separate public/secret, use .gitignore or a separate folder that’s not in Git
Add a README file to describe the project and instructions on reproducing the results
If a software is reused in several projects it can make sense to put them in own repo

A project directory can look something like this:

project_name/
├── README.md             # overview of the project
├── data/                 # data files used in the project
│   ├── README.md         # describes where data came from
│   └── sub-folder/       # may contain subdirectories
├── processed_data/       # intermediate files from the analysis
├── manuscript/           # manuscript describing the results
├── results/              # results of the analysis (data, tables, figures)
├── src/                  # contains all code in the project
│   ├── LICENSE           # license for your code
│   ├── requirements.txt  # software requirements and dependencies
│   └── ...
└── doc/                  # documentation for your project
    ├── index.rst
    └── ...

Tracking source code, data, and results

All code is version controlled and goes in the src/ or source/ directory
Include appropriate LICENSE file and information on software requirements
You can also version control data files or input files under data/
If data files are too large (or sensitive) to track, untrack them using .gitignore
Intermediate files from the analysis are kept in processed_data/
Consider using Git tags to mark specific versions of results (version submitted to a journal, dissertation version, poster version, etc.):
```
$ git tag -a thesis-submitted -m "this is the submitted version of my thesis"
```

Check the Git-intro lesson for a reminder.

Discussion on reproducibility

Discuss in the collaborative document:

How do you collaborate on writing academic papers?

- Are you using version control for academic papers?
  - ...
  - ...
  - (share your experience)

- How do you handle collaborative issues e.g. conflicting changes?
  - ...
  - ...
  - (share your experience)

Please write or discuss your ideas before opening solution!

Solution

Consider using version control for manuscripts as well. It may help you when keeping track of edits + if you sync it online then you don’t have to worry about losing your work.
Collaboration can be done efficiently by
- real time collaboration tools like HackMD/HedgeDoc where conflicts are resolved on the fly
- version control where conflicts are detected and shown – and solved manually

Some tools and templates

R devtools
Python cookiecutter template
Reproducible research template by the Turing Way

More tools and templates in Heidi Seibolds blog.

Reproducible publications

Git can be used to collaborate on manuscripts written in, e.g., LaTeX and other text-based formats but other tools exist, some with git integration:
- Overleaf or Typst: online, collaborative LaTeX editor
- Authorea: collaborative platform for preprints
- HackMD or HedgeDoc: online collaborative Markdown editors
- Manuscripts.io: a collaborative authoring tool that support scientific content and reproducibility.
- Google Docs can be a good alternative
Many tools exist to assist in making scholarly output reproducible:
- rrtools: instructions, templates, and functions for writing a reproducible article or report with R.
- Jupyter Notebooks: web-based computational environment for creating code and text based notebooks that can be used as, see also our Jupyter lesson later in this workshop. supplementary material for articles.
- Binder: makes a repository with Jupyter notebooks available in an executable environment (discussed later in the Jupyter lesson).
- “Research compendia”: a set of good practices for reproducible data analysis in R, but much is transferable to other languages.