TL;DR
This paper systematically analyzes 100,000 Jupyter notebooks to identify accessibility barriers faced by blind and visually impaired users, offering recommendations for improving notebook accessibility and usability.
Contribution
It provides the first large-scale analysis of accessibility issues in data science notebooks and proposes practical solutions to enhance accessibility for BVI users.
Findings
Identified key accessibility barriers in published notebooks
Recommended authoring practices for accessible notebooks
Proposed infrastructure changes to improve accessibility
Abstract
Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We…
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