From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911013719244800 |
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| author | Li, Zhiwei Kesselman, Carl Nguyen, Tran Huy Xu, Benjamin Yixing Bolo, Kyle Yu, Kimberley |
| author_facet | Li, Zhiwei Kesselman, Carl Nguyen, Tran Huy Xu, Benjamin Yixing Bolo, Kyle Yu, Kimberley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience Li, Zhiwei Kesselman, Carl Nguyen, Tran Huy Xu, Benjamin Yixing Bolo, Kyle Yu, Kimberley Machine Learning Databases Digital Libraries Human-Computer Interaction 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. |
| title | From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience |
| topic | Machine Learning Databases Digital Libraries Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.16051 |