Uncertainty Visualization in Scientific Data
Methods and tools for communicating data uncertainty in large-scale scientific visualizations, from climate models to genomic data.
Lead: Felipe Adeildo
Uncertainty is inherent in scientific data, from measurement error to model variance, yet most visualization tools treat data as ground truth.
We develop encoding techniques, perceptual studies, and open-source tools that make uncertainty a first-class citizen in scientific visualization workflows.