Scientific rigor
Ontologies, standards, and interoperable metadata boost statistical power and translational probability.
Maximize scientific insight. Minimize animal use.
MAAP turns every preclinical animal into a rich, reusable research asset—linking FAIR metadata, 3Rs, and AI so teams run fewer repeats and make earlier go/no-go decisions.
What is MAAP?
Vision: Extract maximum scientific value from every animal used.
Problem: scattered spreadsheets and siloed systems force repeat studies and missed phenotypes.
Solution: a FAIR-by-design platform that structures metadata, monitors outcomes, and integrates with your lab stack—so studies are powered, traceable, and reusable.
MAAP is not a digital notebook or database plumbing. It turns animal experiments into structured, interoperable scientific assets that can be combined, compared, and reanalyzed without re-running cohorts.
Key pillars
Ethical responsibility (3Rs)
Reduction via reuse; refinement via better monitoring; replacement via better predictions.
Operational performance
Less reinventing data, accelerated timelines, and AI-ready assets for multi-site programs.
FAIR by design
MAAP isn't merely "compatible" with FAIR; it makes FAIR measurable:
- Run multi-site studies without chaos.
- Statistical power from better metadata, fewer repeats.
- Traceable assets that feed AI/ML and regulatory packages.
Why Teams Choose MAAP
Accelerate and Simplify Research
Capture once, reuse everywhere—centralize and link data, metadata, and protocols to cut repeats.
Ensure Trust and Compliance
Ontology-driven, FAIR-by-design architecture with audit trails and SEND/ARRIVE-ready outputs.
Collaborate, Integrate, Innovate
Secure sharing across sites; LIMS/ELN/HCM integrations; assets ready for AI and regulators.
Workflows made visible
01
Capture
Investigations → Studies → Assays. Ingest from LIMS/ELN/AMS with lineage intact.
02
Standardize
JSON-LD + vocab validation so every subject and assay stays comparable. See <a href="/fair-by-design">FAIR</a>.
03
Reuse & Decide
Assets, not files: export, reanalyze, and compare across cohorts. Browse the <a href="/use-cases">use cases timeline</a>.
ROI and reduction
Fewer repeats
Better metadata → higher statistical power → fewer repeated experiments.
Earlier go/no-go
Better monitoring → fewer missed phenotypes → earlier decisions.
Less waste
Interoperability across sites → reduced duplication and cleaner transfers.
Quantify it
Ask: “How many mice could we reduce with 30% reuse of behavioral metadata?” Talk to us.
Q & A
Does MAAP replace my ELN or LIMS?
No. MAAP connects with your existing systems—ELNs, LIMS, HCM platforms—to avoid duplicate entry. It serves as a metadata hub that enriches, links, and harmonizes what you already capture.
What kind of data and standards does MAAP support?
MAAP manages preclinical metadata—subjects, protocols, devices, and outcomes—using ontology-based standards (JSON-LD, ARRIVE, FAIR, NWB soon) for full semantic consistency. Learn more in <a href="/fair-by-design">FAIR-by-Design</a>.
How does MAAP ensure FAIR, transparent, and ethical research?
MAAP records all subject and experiment life events, enabling traceability, reproducibility, and transparency aligned with ethical best practices.
What export and integration options are available?
Export to CSV, JSON, JSON-LD, and RO-CRATE (NWB coming soon), and integrate with analytics tools, publications, or repositories like OSF.
Who is MAAP for?
MAAP serves academic labs, CROs, and pharma teams. Its modular architecture scales from small studies to enterprise programs.
The Team
Damien Huzard
Founder, CEO & CSO
15 years in preclinical research and behavioral neuroscience; founded Ontaya, NeuroNautix, and built MAAP to improve metadata management.
Laurent Huzard
Backend Backbone
API Platform specialist; reviews architecture, hardens code, and crafts reusable modules.
Frédéric Deverre
CCO
Business development leader with 20+ years in MedTech across hematology, cell therapy, and osteoarthritis. Shapes GTM, builds CRO/biopharma/academic partnerships, and drives commercialization that keeps preclinical metadata FAIR and usable. Native French; fluent in English and Spanish.
Have questions or want a demo?
Get in touch today.
Latest updates
View all11/1/2024
Welcome to Metadatapp
How we make preclinical metadata FAIR-by-design.