Data Visualization

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Data visualization turns data into charts, maps, diagrams, and interactive views so people can find patterns, compare values, and communicate evidence. Research develops visual encodings and analysis tools, including dimensionality-reduction methods, scientific visualization workflows, and accessible alternatives such as sonification. A growing line of work uses language and vision models to generate, edit, explain, and interpret charts, while benchmarks test these systems on chart questions, code generation, dashboards, and multi-step scientific tasks. A central challenge is making outputs faithful and useful: models can produce plausible visuals yet misread data, miss design or perceptual rules, and struggle with complex interactions, so evaluation must consider both machine metrics and human interpretation.

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121 papers9 papers in the last four weeks, up 80% on the four weeks before. 0.1% of all new papers.

A General Approach to Visualizing Uncertainty in Statistical Graphics
Jul 31, 2025Bernarda Petek, David Nabergoj, Erik ŠtrumbeljScientific Visualization Data Visualization
University of Ljubljana Faculty of Computer and Information Science Veˇcna pot 113, 1000 Ljubljana, Slovenia

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A General Approach to Visualizing Uncertainty in Statistical Graphics

Bernarda Petek, David Nabergoj, Erik Štrumbelj

University of Ljubljana Faculty of Computer and Information Science Veˇcna pot 113, 1000 Ljubljana, Slovenia

Scientific Visualization Data Visualization Uncertainty Quantification

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We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees without being explicitly quantified or visualized. As a proof of concept, we implement our approach in the IID setting using resampling, provided as an open-source Python library. Because the approach operates directly on images, the user needs only to supply the data and the code for visualizing the quantities of interest without uncertainty. Through several examples, we show how both familiar and novel forms of uncertainty visualization can be created. The implementation is not only a practical validation of the underlying theory but also an immediately usable tool that can complement existing uncertainty-visualization libraries.