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: 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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Blog Post: Can We Guess Why you Selected Something in a Scatterplot? 28 Oct 2022
Selections are a key interaction in data visualization. They are used for highlighting and as the starting point for subsequent actions, including filters, group assignments, etc. Capturing the intent – WHY were these items selected? – can be used to help users refine their selections and to keep a meaningful history of the analysis process. This post discusses our paper on techniques to capture such intents.

Figure shows the techinque for predicting intent. First step shows a scatterplot with a brush selection, captioned 'Selection' with a small human silhouette. Second step shows stacked scatterplots with a cluster selected. The caption reads 'Predictions' with small robot face. Third step shows list of three patters - Range, cluster and outlier with bars beside each to show ranking. Cluster pattern is selected with orange background. The caption reads 'Ranking' and shows a small robot face. The fourth step shows a scatterplot with a cluster selected. One of the points is highlighted in orange. The caption reads 'Confirming intent & annotation' with a human silhouette.
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News: VDL at IEEE VIS! 12 Oct 2022


Collage of VDL Contributions

Seven VDL lab members are back in person at IEEE VIS this year! We especially look forward to co-host the Utah Party together with other SCI members on Tuesday night at Social Capital! Make sure to say hi, come to our talks and to the party.

Our activities are:

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Blog Post: Data Hunches – Recording and Communicating Personal Knowledge in Visualizations 11 Oct 2022
Have you ever looked at data visualization and thought: that doesn't look right. Maybe you knew more about the data than is actually contained in the dataset. Did you then remember that hunch throughout your data analysis process, impacting your judgment and interpretation of the data? That thought, whether you were aware of it or not, possibly impacted your interpretation. Especially if that hunch is based on knowledge you have about the data, it would be useful to externalize that hunch, so others can learn about it and also consider it in their analysis. However, current visualization methods do not support this. In this blog post, we dive into how we came up with data hunches to describe personal knowledge brought to data analysis. We explore methods and designs to record and communicate data hunches through visualizations explicitly.

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