Technology Category
Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustApplication Domains: Humanities & Computational Social ScienceSearch and Optimization: Metareasoning and Metaheuristics
Experiversum: an Ecosystem for Curating and Enhancing Data-Driven Experimental Science
🤖 AI Summary
In data-driven experimental science, challenges persist regarding disorganized data curation, insufficient documentation, and poor reproducibility. To address these, this paper proposes a lakehouse-based scientific data governance ecosystem. The system integrates automated metadata capture, joint data-and-code versioning, and collaborative, auditable decision logging, establishing an iterative closed loop spanning data acquisition, processing, analysis, and interpretation—thereby harmonizing exploratory research with reproducibility requirements. Crucially, it embeds dynamic, context-aware governance directly into the research workflow. Its cross-disciplinary applicability is empirically validated across heterogeneous domains: Earth science, life science, and political science. Results demonstrate significant improvements in process transparency, auditability, and result interpretability. The ecosystem provides a scalable, verifiable infrastructure for multi-disciplinary collaborative research.
Technology Category
Application Category
📝 Abstract
This paper introduces Experiversum, a lakehouse-based ecosystem that supports the curation, documentation and reproducibility of exploratory experiments. Experiversum enables structured research through iterative data cycles, while capturing metadata and collaborative decisions. Demonstrated through case studies in Earth, Life and Political Sciences, Experiversum promotes transparent workflows and multi-perspective result interpretation. Experiversum bridges exploratory and reproducible research, encouraging accountable and robust data-driven practices across disciplines.
Problem
Research questions and friction points this paper is trying to address.
Facilitates curation and reproducibility of exploratory experiments
Enables structured research through iterative data cycles
Bridges exploratory and reproducible research across disciplines
Innovation
Methods, ideas, or system contributions that make the work stand out.
Lakehouse-based ecosystem for experimental science
Enables structured research via iterative data cycles
Captures metadata and collaborative decision documentation
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