IEEE VIS 2024 Content: UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox

UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox

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Patrick Paetzold - University of Konstanz, Konstanz, Germany

 David Hägele - University of Stuttgart, Stuttgart, Germany

 Marina Evers - University of Stuttgart, Stuttgart, Germany

 Daniel Weiskopf - University of Stuttgart, Stuttgart, Germany

 Oliver Deussen - University of Konstanz, Konstanz, Germany

 Room: Bayshore VI

2024-10-14T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T12:30:00Z

Exemplar figure, described by caption below — The UADAPy software package is a toolbox providing high-dimensional uncertain sample data sets, uncertainty-aware data transformations and analysis methods, and visualization methods tailored to show uni- and multivariate sets of probability distributions.

Abstract

Current research provides methods to communicate uncertainty and adapts classical algorithms of the visualization pipeline to take the uncertainty into account. Various existing visualization frameworks include methods to present uncertain data but do not offer transformation techniques tailored to uncertain data. Therefore, we propose a software package for uncertainty-aware data analysis in Python (UADAPy) offering methods for uncertain data along the visualization pipeline.We aim to provide a platform that is the foundation for further integration of uncertainty algorithms and visualizations. It provides common utility functionality to support research in uncertainty-aware visualization algorithms and makes state-of-the-art research results accessible to the end user. The project is available at https://github.com/UniStuttgart-VISUS/uadapy.