Nutrition Data Infrastructure for the AI Era: Operationalizing FAIR for Agent-Mediated Research

Abstract

AI agents can accelerate nutrition research, but their analyses inherit the identity, semantic, and release ambiguities of the underlying data. We present Nutrition Data Service (NDS), source-preserving infrastructure that operationalizes FAIR for automated use: description resolution makes release-specific records findable; typed crosswalks connect independently released resources; machine-readable interfaces expose versioned sources and crosswalks, making analyses by AI agents replayable and auditable. On food-description benchmarks, NDS shows strong held-out accuracy and outperforms the best published language-model result on NutriBench. External and blinded crosswalk evaluations show that its typed contract favors defensible links and rejects unsupported mappings. In a person-level glycemic-index analysis, pinned NDS inputs produce identical outputs across models and repeated runs, while open-web reconstruction remains unstable. The central result is that agent-mediated nutrition research requires a new data infrastructure for data identity, search, and crosswalk.