Checklist to Audit Data- and Computation-intensive Projects

There is quite some material to cover to make sure your workflows become efficient, reproducible, and well-structured.

Here's a checklist you can use to audit your progress.

data-preparation analysis paper ...
At the project level
Implement a consistent directory structure: data/src/gen
Include readme with project description and technical instruction how to run/build the project
Store any authentication credentials outside of the repository (e.g., in a JSON file), NOT clear-text in source code
Mirror your \data folder to a secure backup location; alternatively, store all raw data on a secure server and download relevant files to \data
At the level of each stage of your pipeline
File/directory structure
Create subdirectory for source code: \src\[pipeline-stage-name]\
Create subdirectories for generated files in \gen\[pipeline-stage-name]\: temp, output, and audit.
Make all file names relative, and not absolute (i.e., never refer to C:\mydata\myproject, but only use relative paths, e.g., ../output)
Create directory structure from within your source code, or use .gitkeep
Automation and Documentation
Have a makefile
Alternatively, include a readme with running instructions
Make dependencies between source code and files-to-be-built explicit, so that make automatically recognizes when a rule does not need to be run (properly define targets and source files)
Include function to delete temp, output files, and audit files in makefile
Versioning
Version all source code stored in \src (i.e., add to Git/GitHub)
Do not version any files in \data and \gen (i.e., do NOT add them to Git/GitHub)
Want to exclude additional files (e.g., files that (unintentionally) get written to \src? Use .gitignore for files/directories that need not to be versioned
Housekeeping
Have short and accessible variable names
Loop what can be looped
Break down "long" source code in subprograms/functions, or split script in multiple smaller scripts
Delete what can be deleted (including unnecessary comments, legacy calls to packages/libraries, variables)
Use of asserts (i.e., make your program crash if it encounters an error which is not recognized as an error)
Testing for portability
Tested on own computer (entirely wipe \gen, re-build the entire project using make)
Tested on own computer (first clone to new directory, then re-build the entire project using make)
Tested on different computer (Windows)
Tested on different computer (Mac)
Tested on different computer (Linux)

Versioned any sensitive data?

Before making a GitHub repository public, we recommend you check that you have not stored any sensitive information in it (such as any passwords). This tool has worked great for us: GitHub credentials scanner.