MatplotAlt is an open-source Python package for easily adding alternative text to matplotlib figures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs.
Our evaluation indicates that MatplotAlt’s heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib figures. We find that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib figure.
Here is some example ALT text generated for the pie chart shown below. A variety of examples can be found in the MatPlotAlt documentation.
A pie chart titled ’percentage of annual sunshine’. There are 12 slices: jan (3.19%), feb (4.993%), mar (8.229%), apr (9.57%), may (11.7%), june (12.39%), july (14.42%), aug (12.99%), sep (10.22%), oct (6.565%), nov (3.329%), and dec (2.404%). The data has a standard deviation of x=4.006, an average of x=8.333, a maximum value of x=14.42, and a minimum value of x=2.404. The data strictly increase up to their max at x=14.42, then strictly decrease.
Stacy Hsueh, Beatrice Vincenzi, Akshata Murdeshwar, and Marianela Ciolf Felice. 2023. Cripping Data Visualizations: Crip Technoscience as a Critical Lensfor Designing Digital Access. In The 25th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ’23), October 22–25, 2023, New York, NY, USA. ACM, New York, NY, USA, 16 pages. https://doi.org/10. 1145/3597638.3608427
Data visualizations have become the primary mechanism for engaging with quantitative information. However, many of these visualizations are inaccessible to blind and low vision people. This paper investigates the challenge of designing accessible data visualizations through the lens of crip technoscience. We present four speculative design case studies that conceptually explore four qualities of access built on crip wisdom: access as an ongoing process, a frictional practice, an aesthetic experience, and transformation. Each speculative study embodies inquiry and futuring, making visible common assumptions about access and exploring how an alternative crip-informed framework can shape designs that foreground the creativity of disabled people. We end by presenting tactics for designing digital access that de-centers the innovation discourse.
Dashboards are frequently used to monitor and share data across a breadth of domains including business, finance, sports, public policy, and healthcare, just to name a few. The combination of different components (e.g., key performance indicators, charts, filtering widgets) and the interactivity between components makes dashboards powerful interfaces for data monitoring and analysis. However, these very characteristics also often make dashboards inaccessible to blind and low vision (BLV) users. Through a co-design study with two screen reader users, we investigate challenges faced by BLV users and identify design goals to support effective screen reader-based interactions with dashboards. Operationalizing the findings from the co-design process, we present a prototype system, Azimuth, that generates dashboards optimized for screen reader-based navigation along with complementary descriptions to support dashboard comprehension and interaction. Based on a follow-up study with five BLV participants, we showcase how our generated dashboards support BLV users and enable them to perform both targeted and open-ended analysis. Reflecting on our design process and study feedback, we discuss opportunities for future work on supporting interactive data analysis, understanding dashboard accessibility at scale, and investigating alternative devices and modalities for designing accessible visualization dashboards.
Arjun Srinivasan, Tim Harshbarger, Darrell Hilliker and Jennifer Mankoff: University of Washington (2023): “Azimuth: Designing Accessible Dashboards for Screen Reader Users” ASSETS 2023.
Hospitalized children on continuous oxygen monitors generate >40,000 data points per patient each day. These data do not show context or reveal trends over time, techniques proven to improve comprehension and use. Management of oxygen in hospitalized patients is suboptimal—premature infants spend >40% of each day outside of evidence-based oxygen saturation ranges and weaning oxygen is delayed in infants with bronchiolitis who are physiologically ready. Data visualizations may improve user knowledge of data trends and inform better decisions in managing supplemental oxygen delivery.
First, we studied the workflows and breakdowns for nurses and respiratory therapists (RTs) in the supplemental oxygen delivery of infants with respiratory disease. Secondly, using end-user design we developed a data display that informed decision-making in this context. Our ultimate goal is to improve the overall work process using a combination of visualization and machine learning.
Visualization mockup for displaying O2 saturation over time to nurses.
