Metadata as a NAM (MAN) — why data context belongs in the 3Rs toolbox

The data-context problem behind every NAM claim

Preclinical datasets are rich in numbers and poor in context. The same readout — say, a wheel-running curve or a t-maze score — can mean different things depending on strain, sex, housing temperature, light cycle, age at testing, prior handling, and dozens of other variables that rarely travel with the file. The FAIR Guiding Principles describe this gap precisely: data that is not findable, accessible, interoperable, and reusable by machines fails as scientific infrastructure, regardless of how it was generated [1]. A NAM that produces beautifully measured but context-free data is still a black box to the next reviewer.

Why metadata earns the NAM label

The European NAM definition is deliberately broad: any technology, methodology, approach, or combination that can inform regulatory and scientific decisions without using more animals. Structured metadata fits that definition directly. When experiment context is captured at source in a harmonised schema, three things become possible that no purely biological NAM can deliver on its own: (i) cross-study comparison without re-running animals, (ii) credible virtual control groups assembled from historical data, and (iii) machine-readable evidence packages that downstream AI agents and regulators can actually audit. The biology is necessary; the metadata is what makes the biology reusable.

From metadata to virtual control groups

Virtual control groups (VCGs) are the most concrete 3Rs payoff of treating metadata as a NAM. A VCG only works if historical control animals can be matched to a new study on the variables that actually drive variance — strain, sex, age, vendor, housing, circadian phase, baseline phenotype. Without harmonised metadata those matches collapse into apples-to-oranges pooling. With harmonised metadata, every well-curated past control becomes a candidate comparator, which directly reduces the number of new control animals required. Home-cage monitoring platforms make this even sharper, because they generate the longitudinal phenotyping metadata that historical controls usually lacked [2].

What it takes to operationalise MAN

Treating metadata as a NAM is not a documentation exercise; it is an infrastructure commitment. In practice it means defining a domain schema before data collection, capturing fields at the point of measurement rather than retro-fitting them, validating against community ontologies, and shipping the resulting evidence with provenance attached. The SCAND-LAS 2026 deck walks through this end-to-end, from the "Title / Author / ISBN / DOI" analogy of a library catalogue all the way to a working VCG workflow.

• Schema-first capture — define the fields before the experiment, not after
• Standard vocabularies — strain, intervention, housing condition, endpoint definitions
• Provenance by default — who, what instrument, which protocol version, which software
• Machine-actionable export — JSON-LD or equivalent so downstream tools can validate

Companion presentation

This note is the written companion to the invited SCAND-LAS 2026 talk Metadata as a NAM — From Data Context to Virtual Control Groups (46 slides, PDF).