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

arXiv: https://arxiv.org/abs/2506.16051 · HTML: https://mlsanity.com/paper/2506.16051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)
- [The Fundamental Principles of Reproducibility](https://mlsanity.com/paper/2011.10098/) (2020)
- [Sharing Begins at Home](https://mlsanity.com/paper/2201.06564/) (2022)
- [Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models](https://mlsanity.com/paper/2407.01608/) (2024)

## Abstract (arXiv)

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.