From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience

TL;DR

This paper presents a data-centric framework for improving reproducibility and transparency in collaborative machine learning projects by formalizing data, features, workflows, and decisions, demonstrated through a clinical glaucoma detection case study.

Contribution

It introduces a structured, lifecycle-aware approach with six artifacts to formalize relationships and enhance reproducibility in collaborative ML workflows.

Findings

Supports iterative exploration and decision tracking

Enhances reproducibility and provenance preservation

Demonstrated effectiveness in a clinical ML use case

Abstract

Reproducibility remains a central challenge in machine learning (ML), especially in collaborative eScience projects where teams iterate over data, features, and models. Current ML workflows are often dynamic yet fragmented, relying on informal data sharing, ad hoc scripts, and loosely connected tools. This fragmentation impedes transparency, reproducibility, and the adaptability of experiments over time. This paper introduces a data-centric framework for lifecycle-aware reproducibility, centered around six structured artifacts: Dataset, Feature, Workflow, Execution, Asset, and Controlled Vocabulary. These artifacts formalize the relationships between data, code, and decisions, enabling ML experiments to be versioned, interpretable, and traceable over time. The approach is demonstrated through a clinical ML use case of glaucoma detection, illustrating how the system supports iterative exploration, improves reproducibility, and preserves the provenance of collaborative decisions across the ML lifecycle.