From Retrieval to Reuse: Provenance-Aware Scientific Data Discovery in AI-Mediated Digital Libraries

Abstract

Scientific data retrieval can identify potentially relevant datasets, but relevance alone does not establish whether those data are fit for reuse. Researchers may also need provenance and contextual information about data collection, methods, processing, prior use, and other conditions of data production to understand and evaluate candidate datasets. Although semantic, neural, and generative approaches are expanding possibilities for scientific data discovery, less is understood about how reuse-relevant evidence should be represented and surfaced as users move from retrieval to reuse assessment. My doctoral research investigates this transition by examining what provenance and contextual information scientific data reusers need, how scientific data repositories currently represent and surface such information during discovery and evaluation, and what design requirements follow for provenance-aware AI-mediated retrieval. The research combines comparative repository and interface analysis, semi- structured interviews primarily with scientific data reusers, with supplementary interviews with repository professionals, and evidence-to-requirements synthesis. The expected contribution is an empirically grounded set of design requirements for supporting the transition from scientific data discovery to informed reuse assessment.