A thoughtful approach to model-informed science.

Our direction

We aim to make complex scientific questions easier to investigate through structured analysis, mechanistic modeling and transparent communication of assumptions and uncertainty.

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Principles that guide the work

• Begin with the development decision and intended context of use.
• Assess data quality and model suitability before drawing conclusions.
• Distinguish evidence, assumptions and uncertainty.
• Communicate findings clearly and reproducibly.