Neuronautix
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FAIR metadata management

From standardization to virtual control groups

Making preclinical reuse computable, reviewable, and scientifically defensible.

Damien Huzard, PhD · Neuronautix

· 12 min

Illustration of home-cage monitoring data flowing into FAIR metadata infrastructure

01 · The bottleneck

Most datasets fail reuse before analysis starts.

Raw files can exist, be backed up, and still be unusable because the experimental context is ambiguous, hidden, or not computable.

Ambiguous context Strain, age, housing, and protocol details are trapped in prose or memory.
Hidden heterogeneity Device, rack, firmware, and software differences are not queryable.
No computable matching Historical cohorts cannot be filtered by explicit eligibility criteria.

02 · Translation

FAIR becomes useful when it becomes operational.

Findable Accessible Interoperable Reusable
IdentifiersPersistent IDs and indexed metadata
Access rulesRetrieval protocols and permissions
Shared semanticsControlled terms, schemas, units
ProvenanceLicense, processing history, audit trail

03 · Standardization target

Capture the context that changes interpretation.

Illustration of heterogeneous scientific sources being standardized into metadata records
Animal Facility Device Environment Protocol Endpoint Analysis Provenance

04 · Minimum viable standardization

Start with a core metadata contract, then enrich progressively.

Required at day one

  • Study and cohort identifiers
  • Species, strain, sex, age, supplier
  • Housing and cage context
  • Device model, firmware, calibration
  • Endpoint definitions and units

Enrich progressively

  • JSON-LD context
  • Ontology mappings
  • SHACL or LinkML validation
  • Provenance packages
  • Catalog and warehouse exports

05 · Architecture pattern

Separate capture, validation, storage, and federation.

01 Capture forms, importers, ELN/LIMS
02 Validate schema, units, terms
03 Store versioned records
04 Federate catalogs and APIs

A reusable catalog starts by rejecting records that do not meet the contract.

06 · Machine-readable context

A small manifest can carry high-value context.

{
  "@context": "https://neuronautix.com/context/hcm-study.jsonld",
  "study_id": "HCM-2026-014",
  "cohort": {
    "species": "NCBITaxon:10090",
    "strain": "C57BL/6J",
    "sex": "female",
    "age_weeks": 10
  },
  "device": {
    "system": "Digital ventilated cage",
    "firmware": "4.2.1",
    "calibration_date": "2026-04-29"
  },
  "provenance": {
    "analysis_software": "pipeline-0.9.3",
    "data_freeze": "2026-05-11"
  }
}

07 · Validation gates

Trust comes from rejecting bad metadata early.

Reject

Machine gate

  • Missing required fields
  • Invalid controlled terms
  • Unit conflicts
  • Incomplete provenance
Review

Scientific gate

  • Protocol deviations
  • Biological comparability
  • Endpoint eligibility
  • Reuse limits and uncertainty

08 · Internal reuse

Internal reuse comes before virtual controls.

Before standardization

  • Files are searchable by filename
  • Context lives in reports or memory
  • Exclusion rules are implicit
  • Reuse is slow and ad hoc
Illustration of mouse behavioral data moving through structured data pipelines

After standardization

  • Cohorts are queryable by criteria
  • Context is machine-readable
  • Rejection reasons are documented
  • Reuse can be audited

09 · Virtual control groups

VCGs are controlled reuse, not AI magic.

Curated historical control data are selected by explicit matching criteria, assessed statistically, and reviewed by experts.

Species / strain Sex / age / supplier Vehicle / protocol Facility / environment Endpoint / time window Distributional similarity

EMA consultation page and VICT3R SOP, 2026

10 · Agent operating model

Agents accelerate curation, not approval.

Agent Proposes draft fields, term candidates, missing metadata
Validator Enforces schema, units, terms, provenance
Expert Approves eligibility, exclusions, uncertainty
Catalog Publishes reviewed records and reuse reports

11 · Implementation path

The 90-day path is narrow, validated, and reviewable.

  1. Days 0-15Choose one dataset family and reuse question.
  2. Days 15-30Define the minimal metadata contract.
  3. Days 30-60Add schema and semantic validation gates.
  4. Days 60-90Test retrieval, matching, exclusion, and review reports.

neuronautix.com/contact  ·  metadatapp.net