Accessible Text Descriptions for UpSet Plots

Data visualizations are typically not accessible to blind and low-vision users. The most widely used remedy for making data visualizations accessible is text descriptions. Yet, manually creating useful text descriptions is often omitted by visualization authors, either because of a lack of awareness or a perceived burden. Automatically generated text descriptions are a potential partial remedy. However, with current methods it is unfeasible to create text descriptions for complex scientific charts. In this paper, we describe our methods for generating text descriptions for one complex scientific visualization: the UpSet plot. UpSet is a widely used technique for the visualization and analysis of sets and their intersections. At the same time, UpSet is arguably unfamiliar to novices and used mostly in scientific contexts. Generating text descriptions for UpSet plots is challenging because the patterns observed in UpSet plots have not been studied. We first analyze patterns present in dozens of published UpSet plots. We then introduce software that generates text descriptions for UpSet plots based on the patterns present in the chart. Finally, we introduce a web service that generates text descriptions based on a specification of an UpSet plot, and demonstrate its use in both an interactive web-based implementation and a static Python implementation of UpSet.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/accessviz64636.2024.00005
Citations
1
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.35

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article

Accessible Text Descriptions for UpSet Plots

Daniel Hajas, Alexander Lex, J. B. Wagoner, Jack Wilburn et al.
1 citations
Natural Language Processing Techniques
0.35
article

Accessible Text Descriptions for UpSet Plots

Daniel Hajas, Alexander Lex, J. B. Wagoner, Jack Wilburn, N. Lanza, Ishrat Jahan Eliza
article en
1 citations

Abstract

Data visualizations are typically not accessible to blind and low-vision users. The most widely used remedy for making data visualizations accessible is text descriptions. Yet, manually creating useful text descriptions is often omitted by visualization authors, either because of a lack of awareness or a perceived burden. Automatically generated text descriptions are a potential partial remedy. However, with current methods it is unfeasible to create text descriptions for complex scientific charts. In this paper, we describe our methods for generating text descriptions for one complex scientific visualization: the UpSet plot. UpSet is a widely used technique for the visualization and analysis of sets and their intersections. At the same time, UpSet is arguably unfamiliar to novices and used mostly in scientific contexts. Generating text descriptions for UpSet plots is challenging because the patterns observed in UpSet plots have not been studied. We first analyze patterns present in dozens of published UpSet plots. We then introduce software that generates text descriptions for UpSet plots based on the patterns present in the chart. Finally, we introduce a web service that generates text descriptions based on a specification of an UpSet plot, and demonstrate its use in both an interactive web-based implementation and a static Python implementation of UpSet.

University of Utah (US), University College London (GB)
Chan Zuckerberg Initiative
Openalex Percentile: Top 32%
Natural Language Processing Techniques
0.35
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