Act 1 — The problem

Data without structure is not knowledge

A scattered library contains the same information as a catalogued one — yet the knowledge is unreachable. Preclinical datasets face this problem at every scale, from a single lab to a multi-site consortium.

Accessibility requires structure

Even if the answer exists, it remains inaccessible without organisation

The data is there. The result was recorded. But without a way to find it, sort it, or compare it — it cannot be used. Structure is not presentation; it is access.

Metadata examples — a book

Four fields that make a book findable

Animal data analogy

Animals on shelves. Grouped by what makes them comparable.

Metadata makes evidence reusable

The fields that define comparability

Act 2 — The turn

Missing context makes interpretation fragile

A behavioral result without documented conditions — experimenter, protocol version, housing, time of year — cannot be replicated, compared, or included in a meta-analysis. The number is real. The evidence is not.

The same result — with and without provenance

Context + DOI = defensible evidence

Data versus metadata

The object and its descriptive map

Wilkinson et al. — Scientific Data 2016

The FAIR Guiding Principles

Four principles now central to funding body requirements, journal data policies, and reproducibility initiatives in preclinical research. Originally articulated for research data management broadly — now the standard for any dataset intended to outlast its originating experiment.

doi:10.1038/sdata.2016.18

FAIR components

Four properties. One goal.

Act 3 — Evidence

Metadata must be planned before the experiment, not reconstructed after

Post-hoc curation fails. By the time a dataset is analysed, experimental context has been overwritten, forgotten, or lost. FAIR must be designed into the protocol — at the same time as the hypothesis.

Ambition levels

Reporting is the floor. Reuse is the ceiling.

Introducing WellFAIR

From FAIR data to a WellFAIR research ecosystem

WellFAIR frames data stewardship as an ethical obligation, not a compliance requirement. Data welfare and animal welfare are the same problem — because data that cannot be reused means more animals must be used in the next study.

The ethical argument

Data welfare is animal welfare. FAIR principles overlap with the 3Rs.

Research article

WellFAIR — the paper

WellFAIR workflow

Four stages that connect intention to reusable evidence

Reference — Home Cage Monitoring

Metadata requirements for HCM data reuse

Strain, sex, age, housing conditions, device model and firmware version, cohort ID, operator identity, cage position in rack, light cycle timing — the fields that enable comparability across studies and sites. Documented in the Home Cage Monitoring metadata literature and the Neuronautix knowledge base.

Metadata gaps → 3Rs impact

Missing metadata flows to wasted evidence

Act 4 — What metadata protects

Method. Evidence. Animals.

Virtual control groups

Historical controls filtered by metadata into valid comparator pools

A virtual control group is not historical data — it is historical data whose comparability has been verified through metadata completeness, contextual matching, and statistical correction. EMA (2023) positions VCGs as innovative NAMs supporting the 3Rs.

Eligibility dimensions

Ten variables that define a valid comparator

The AI argument

AI cannot recover what was never captured

Language models and machine learning pipelines amplify available context — they cannot fabricate experimental conditions that were never recorded. Missing metadata is an irreversible information loss. Garbage in, garbage out remains true.

DVC® — Tecniplast · Neuronautix

Continuous home-cage context from the cage hardware

The Digital Ventilated Cage integrates RFID tracking, load cells, and sensor arrays to capture locomotion, feeding, and individual presence data automatically — providing continuous, structured metadata alongside the behavioral record. Neuronautix is a listed scientific partner of Tecniplast.

A critical distinction

Raw signal must be contextualised into structured descriptors

Close — Multi-site federation

Heterogeneous datasets, unified by metadata

Shared metadata labels — strain, sex, age, SOP version — enable cross-site comparability and VCG pool construction despite heterogeneous instrumentation.

Implementation — Metadatapp

A practical interface for structured metadata capture

Metadatapp provides the data-entry layer — schema-driven, controlled-vocabulary backed — that turns the WellFAIR workflow into daily practice for preclinical teams. Structured capture at source, from protocol design to file deposit.