A project of the Open Science Institute
OpenTwin Lab
Build and validate human digital twins on open standards
OpenTwin Lab provides standards, protocols, and reference pipelines to build, validate, and apply digital twins without centralizing personal health data.
Problem
Healthcare is fragmented.
Data infrastructure lacks interoperability.
Digital twin implementations are built in isolation.
OpenTwin Lab provides the missing foundation.
Positioning
What OpenTwin Lab is (and is not)
Not a product
→ provides open standards and protocols
Not a data platform
→ provides validation layer
Not a healthcare application
→ provides reference implementations
How it works
01
Data Sources
Data remains where it is generated: in clinical, research, or pilot environments. OpenTwin Lab does not centralize or store raw health data.
02
Open Building Blocks
Standards, protocols, and reference pipelines define how digital twins are built, executed, and compared in a transparent and reproducible way.
03
Validated Outputs
Digital twins are validated against shared benchmarks, enabling comparability, reproducibility, and real-world deployment.
Get started
Explore the OpenTwin Lab ecosystem and bring your first digital twin to life.
- 01
Define your data interfaces
- 02
Run a reference pipeline
- 03
Validate against shared benchmarks
- 04
Generate interoperable outputs
Implementations
Digital twins in action
OpenTwin Lab enables validated digital twin implementations across research and clinical environments.
Active
VuseXR
Project description coming soon.
↗ Visit projectComing soon
OpenTwin Hub @ Etherlaken
Coming soon.
To be added
More pilots
Real-world examples and pilots to be added.
Contribute
OpenTwin Lab is developed openly and collaboratively.
Contribute to standards, pipelines, and validation methods.
Open Source
MIT-licensed. Maintained under the Open Science Foundation on GitHub.
Standards · Protocols · Reference pipelines · Validation
About
OpenTwin Lab is grounded in the concept of an open twin ecosystem, as outlined in Wilkening & Etzrodt 2026, which connects data infrastructures, models, and governance frameworks to enable scalable and trustworthy digital twins in medicine.
The initiative is supported by the Open Science Foundation, advancing open science standards for predictive, preventive, personalized, and participatory healthcare.