Open-source scientific digital twins and reproducible research baselines — built with Vicena.
Vicena is building a cloud laboratory for end-to-end R&D: one workspace where researchers can search papers and patents, develop ideas, build computational models, design experiments, analyze experimental results, and preserve the complete project record in one continuous workflow.
Instead of assembling separate subscriptions, payment agreements, file stores, notebooks, and scientific services for every project, Vicena integrates research software and external scientific providers behind a common workspace. The goal is a low-barrier, pay-for-what-you-use environment where the tools, compute, and services needed for the next research step become available when they are relevant.
Vicena Labs publishes open-source digital twins and reproducible research baselines created with this approach. They help researchers begin from a verified, extensible foundation rather than an empty project. Each repository combines domain models, executable examples, validation rules, provenance, contribution contracts, and agent instructions so both people and scientific AI systems can work with it responsibly.
- Reproducible: versioned configuration, runnable examples, tests, and portable results.
- Scientifically explicit: assumptions, units, provenance, validation level, and limitations are documented.
- Agent-ready: root
AGENTS.mdand repository-specific skills guide safe, consistent work. - Extensible: researchers can replace data, models, parameters, experiments, and workflows without rebuilding everything.
- Compute-aware: projects use local tools first and route to protected remote scientific compute only when appropriate.
- Open for reuse: repository-owned code is released under permissive open-source licenses; third-party material retains its own license.
- Open Vicena.ai.
- Start a new research conversation.
- Paste a repository URL, for example:
https://github.com/vicena-labs/battery-research-twin.git
- Ask Vicena to clone the repository, read its agent instructions, run the verification tests, and adapt it to your data while keeping scientific assumptions explicit.
Battery modeling • electrochemistry • optical simulation • fluorescence imaging • thermal modeling • scientific computing • validation • digital twins • reproducible research • AI-assisted science
Researchers, engineers, and maintainers are welcome to open issues, propose validated reference datasets, improve models, add benchmarks, and build new scientific digital twins. Contributions should include provenance, tests, applicability limits, and evidence appropriate to the scientific claim.