Title: From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience Show Chinese title

Title: 从数据到决策：协作eScience中可重现ML的数据中心基础设施

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. Show Chinese abstract

Abstract: 可重复性仍然是机器学习（ML）中的一个核心挑战，特别是在协作的电子科学研究项目中，团队需要反复迭代数据、特征和模型。 当前的机器学习工作流通常是动态且碎片化的，依赖于非正式的数据共享、临时脚本以及松散连接的工具。 这种碎片化阻碍了透明性、可重复性和实验随时间推移的适应性。 本文介绍了一个以生命周期为导向的可重复性数据驱动框架，围绕六个结构化的工件： 数据集、特征、工作流、执行、资产和受控词汇表。 这些工件形式化了数据、代码和决策之间的关系，使得机器学习实验能够随着时间的推移进行版本控制、可解释性和可追溯性。 通过青光眼检测的临床机器学习用例展示了这种方法，说明了该系统如何支持迭代探索、提高可重复性，并在整个机器学习生命周期中保留协作决策的出处。