About

We are a team of visualization researchers at the University of Utah. Our interests include the process of designing and developing visualizations, visualization for biology, visualization frameworks, and, more generally, visualization of big, heterogeneous, and complex datasets.

VDL is part of the Scientific Computing and Imaging Institute and the School of Computing.

Blog And News

Blog Post: Ferret — Catching Scientific Fraud with Data Visualizations 15 Sep 2023
Ferrets search data
Alas, artifacts abound
Manipulation

A Ferret cartoon questioning what we can do as visualization researchers to help prevent scientific fraud.
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News: Haihan Lin Successfully Defends Dissertation 19 Jul 2023
Congratulations to Haihan!

Haihan celebrating that she passed her defense!

Haihan successfully defended her dissertation on “Data Hunches: Expressing Personal Knowledge in Data Visualizations”. Haihan was advised by Alex Lex, with Miriah Meyer, Marina Kogan, Jason Wiese, and Eytan Adar serving on the committee.

Haihan is staying in Utah and will join other VDL Alumni at Lucid Software. Congrats, and good luck with your next steps!

Haihan and Alex Haihan opening some Champagne Haihan and her committee

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Blog Post: How People Actually Lie With Charts 17 Apr 2023
Online audiences and visualization researchers often share, discuss, and critique misleading visualizations. Existing critiques typically point out suboptimal choice of visual encoding or violations of common design guidelines. But is this how charts are used to deceive audiences and spread misinformation in practice? This blog post discusses the findings from our paper on misleading visualizations.

Figure shows an overlapping pile of screenshots of Twitter posts that include data visualizations.
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Blog Post: Reusing Operations In Interactive Visualizations and Computational Notebooks 3 Jan 2023
Interactive data analysis leverages human perception to enable various analysis tasks; however, a prior analysis can rarely be used when the dataset updates or is transferred to a different analysis environment, like a computational notebook. In this post, we discuss how we can capture reusable interactive workflows.

Figure shows a scatterplot with a cluster dataset. One cluster is selected. Next to the scatterplot is a provenance graph with three steps - adding scatterplot, select 61 points, and apply cluster selections. The caption reads 'Curate workflow from analysis provenance' There are two arrows originating from the scatterplot. One points to another scatterplot, which shows the selected cluster moving along Y-axis. Polygons indicate selected cluster. The caption reads 'Reapply the workflows on updated datasets' The other arrow points to a screenshot of jupyter notebook which demonstrate use of the Reapply library. The caption reads 'Apply the workflow in different environment'
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