ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML

The Journal of Open Source Software 2026 Open access

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Authors: Robin P. van de Water, Hendrik Schmidt, Patrick Rockenschaub
DOI: 10.21105/joss.09261
Cited by: 1
References: 13

Abstract

Machine Learning (ML) workflows live or die by their data-preprocessing steps.In Python, these steps are often scattered across ad-hoc scripts or opaque scikit-learn snippets that are hard to read, audit, or reuse.ReciPies provides a concise, human-readable, and reproducible way to declare, execute, and share preprocessing pipelines following configuration-as-code principles.It lowers the cognitive load of feature engineering, improves reproducibility, and makes methodological choices explicit for researchers, engineering teams, and peer reviewers.

Fields of Study & Subjects

Parallel Computing and Optimization Techniques Model-Driven Software Engineering Techniques Software Testing and Debugging Techniques

Publication details

Journal / source
The Journal of Open Source Software
Publisher
The Open Journal
Volume
11
Issue
117
Pages
9261–9261
Publication date
5 January 2026