Full Text Available

Note: Clicking the button above will open the full text document at the original institutional repository in a new window.

ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML

Saved in:
Bibliographic Details
Published in:Journal of Open Source Software
Format: Online Article RSS Article
Published: 2026
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1864030190087176197
collection WordPress RSS
FRELIP Feed Integration
container_title Journal of Open Source Software
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:5127
institution FRELIP
journal_source_facet Journal of Open Source Software
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
title_auth ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
title_full ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
title_fullStr ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
title_full_unstemmed ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
title_short ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
title_sort recipies: a lightweight data transformation pipeline for reproducible ml
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url https://joss.theoj.org/papers/10.21105/joss.09261