Before Metadatapp
- Scattered files and inconsistent formats
- Manual handoffs and weak provenance
- Rework to make studies reusable
Open Source
Timeline demonstrating how Metadatapp accelerates reproducibility.
✦FAIR by design✦JSON-LD / RO-Crate✦Self-hostable AGPL-3.0
After Metadatapp
- Standardized metadata and shared context
- Faster retrieval and reuse across cohorts
- Clear lineage for audits and analysis
From Problem to Pipeline
01
The Problem
Data scattered across software/servers in heterogeneous formats; provenance is hard to track, wasting time and money.
02
The Solution
Metadatapp connects to existing lab systems via APIs, standardizes (meta)data, and keeps it secure and reusable so you focus on research.
03
Example
Example workflows show faster retrieval, fewer repeats, and cleaner lineage for downstream analysis.
04
FAIR‑by‑Design Architecture
API-first, JSON-LD, RBAC, auditability, export recipes.
05
Pipeline Overview
Capture → Standardize → Validate → Export → Reuse.
