The shift we are enabling
- FAIR metadata as infrastructure, not an afterthought
- Experimental data that travels across labs, time, and collaborators
- Fewer repeated experiments through genuinely reusable data
- AI-ready datasets from day one of a study
Open Source
Vision & Impact
Why structured, FAIR-by-design research metadata is the most direct path to better science and meaningful animal welfare.
✦FAIR by design✦JSON-LD / RO-Crate✦Self-hostable AGPL-3.0
Why it matters now
- AI and ML pipelines need structured, comparable research data
- Open science mandates require FAIR outputs from funded research
- Animal welfare: reusable data directly reduces experiment repetition
- Reproducibility crisis demands better experimental descriptions
Vision
Metadatapp is building the open-source FAIR metadata infrastructure that turns biomedical experiments into reusable scientific assets — so the data from one lab can answer questions in another, and animals already used contribute evidence that keeps giving.
This is not a data warehouse or an ELN. It is a semantic interoperability layer — built on JSON-LD, RO-Crate, and ontology-based modelling — that preserves context, provenance, and structure so research data can travel across teams, institutions, and time without losing meaning.
As an open source project (AGPL-3.0), Metadatapp invites researchers, engineers, and institutions to contribute, self-host, adapt, and build on this infrastructure.
Mission
Make experimental metadata share-ready by default — structured at capture time with controlled vocabularies, ontology-based schemas, and open standards (JSON-LD, RO-Crate, ARRIVE) so research data is reusable without manual cleanup.
We connect the systems researchers already use — ELN, LIMS, animal management, analytics — and keep metadata coherent, validated, and semantically linked from study design to publication or repository deposit.
Impact
Scientific reuse: Structured, FAIR experiments become reusable assets — enabling cross-cohort comparison, longitudinal insight, and AI-ready analytics without re-running studies.
Animal welfare through data quality: When metadata is structured to travel, experiments don't have to be repeated to be understood. Fewer duplicated studies is a direct consequence of better data — not a reporting checkbox.
Interoperability: JSON-LD and RO-Crate outputs connect directly with repositories, AI pipelines, and collaborative platforms — without translation steps or manual preprocessing.
Open standards at the core
Metadatapp is built on open, community-driven standards: JSON-LD for semantic markup, RO-Crate for research object packaging, ARRIVE for experimental reporting, and FAIR principles throughout. These choices ensure data stays portable, machine-readable, and repository-ready — independent of any single platform or vendor.