Labora
Uncertainty Visualization in Scientific Data
Methods and tools for communicating data uncertainty in large-scale scientific visualizations, from climate models to genomic data.
Lead: Jane Doe
Uncertainty is inherent in scientific data — from measurement error to model variance — yet most visualization tools treat data as ground truth. This project investigates how to faithfully represent uncertainty in interactive visual systems without overwhelming users or obscuring the underlying signal.
We develop encoding techniques, perceptual studies, and open-source tools that make uncertainty a first-class citizen in scientific visualization workflows.
Related Publications
Related Software
A Python library for building interactive scientific visualizations with built-in uncertainty encoding and perceptually uniform color scales.