CITATION.cff

cff-version: 1.2.0
message: "See also McLevey, Browne, and Crick. Reproducibility and principled data processing. in Engel, Quan-Haase, and Lyberg (eds) Handbook of Computational Social Science, Volume 2. Routledge, 2021. 108-124."
title: "Principled Data Processing with Python (pdpp)"
version: 0.5.0
date-released: 2024-08-04
authors:
- family-names: "Browne"
  given-names: "Pierson"
  affiliation: "University of Waterloo"
- family-names: "McLevey"
  given-names: "John"
  affiliation: "University of Waterloo"
- family-names: "Crick"
  given-names: "Tyler"
  affiliation: "University of Waterloo"
- family-names: "Wood"
  given-names: "Rachel"
  affiliation: "University of Waterloo"
url: "https://github.com/UWNETLAB/pdpp"
repository-code: "https://github.com/UWNETLAB/pdpp"
license: "MIT"
keywords:
  - data processing
  - reproducibility
  - Python
  - automation
  - transparency
abstract: >
  pdpp is a command-line interface enabling transparent and reproducible data science workflows.
  It's designed around the principles espoused by Patrick Ball (Human Rights Data Analysis Group) in his 2016 Data & Society talk 'Principled Data Processing'.