A Survey on Metadata for Machine Learning Models and Datasets: Standards, Practices, and Harmonization Challenges

The growing availability of machine learning (ML) models, datasets, and related artifacts across platforms,
such as Hugging Face, GitHub, and Zenodo, has amplified the need for structured and standardized metadata.
However, metadata practices remain highly heterogeneous, differing in schema design, vocabulary usage, and
semantic expressiveness, posing significant challenges for tasks such as representation, extraction, alignment, and
integration. This fragmentation impedes the development of infrastructures that depend on machine-actionable
metadata to support discovery, provenance tracking, or cross-platform interoperability. While metadata is also
foundational to enabling FAIR (Findable, Accessible, Interoperable, and Reusable) principles in ML, there is a lack
of consolidated understanding of how existing standards support interoperability and alignment across platforms.
In this survey, we review and compare a range of general-purpose and ML-specific metadata standards, evaluating
their suitability for cross-platform alignment, discoverability, extensibility, and interoperability. We assess these
standards based on defined criteria and analyze their potential to support unified, FAIR-compliant metadata
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infrastructures for ML, laying the groundwork for scalable and interoperable tooling in future ML ecosystems.