Abstract:
Reproducibility is a foundational tenet of science. As artificial
intelligence (AI) becomes increasingly embedded across science, the need to
accurately document the provenance, structure, and behavior of training
data, models, and workflows grows correspondingly. Metadata, understood as
explicit and structured knowledge about data and related entities, is a
critical yet often underexamined component of AI systems that helps address
this need. High-quality metadata describing datasets, models, and workflows
supports the FAIR (Findable, Accessible, Interoperable, Reusable)
principles, strengthens reproducibility, and enables evaluation of
AI-readiness by making data and models interpretable, traceable, and
structurally consistent. Despite its central importance, sustained
discussion of metadata as a core component of AI infrastructure remains
limited. This article examines the role of metadata in advancing AI-enabled
research, focusing on the design, implementation, and operationalization of
metadata standards within an evolving metadata ecosystem. We first present a
conceptual view of the metadata ecosystem, framed by data structure, data
value, data encoding, and syntax standards, as a foundation for
understanding how metadata enables AI and how AI contributes to metadata
generation and refinement. We then introduce four case studies that
illustrate how metadata can be generated, refined, and leveraged within AI
workflows. The discussion synthesizes the cases, highlights limitations,
including metadata quality challenges and the role of structured constraints
in addressing AI errors, and relates each case to the metadata ecosystem
dimensions it engages. Taken together, these cases illustrate that metadata
is not a peripheral add-on but an essential component of AI-ready,
FAIR-aligned, transparent, and reproducible research systems.
Last modified on June 24, 2026.