FAIR compliance, AI-readiness, and ontology maturity of garment datasets: a systematic assessment
TL;DR
This systematic review assessed 64 fashion datasets identified through a PRISMA 2020-guided two-stream search, evaluating each on FAIR compliance, a composite AI-Readiness Score, and a five-level ontology maturity model, finding that new fashion datasets should adopt machine-readable open licenses with persistent identifiers and structure annotations at least at the Taxonomy level of the proposed maturity model.
Abstract
This systematic review assessed 64 fashion datasets identified through a PRISMA 2020-guided two-stream search, evaluating each on FAIR compliance, a composite AI-Readiness Score, and a five-level ontology maturity model. The AI-Readiness assessment yielded a grade distribution concentrated in the middle tiers (B: 60.3%, C: 36.5%), while 79.7% of datasets provided only flat attribute annotations (Level 2 or below) on the ontology maturity scale. Beyond diagnosis, the review distills its findings into evidence-derived recommendations for future dataset construction — most consequentially, that new fashion datasets should adopt machine-readable open licenses with persistent identifiers, and should structure annotations at least at the Taxonomy level (Level 3) of the proposed maturity model. Together, the three assessment perspectives — FAIR compliance, AI-readiness, and ontology maturity — and these recommendations provide actionable tools for evaluating future data investments and guiding the development of next-generation fashion AI datasets.