A computational analysis of accessibility, readability, and explainability of figures in open access publications

Abstract

Figures are an essential part of scientific communication. Yet little is understood about how accessible (e.g., color-blind safe), readable (e.g., good contrast), and explainable (e.g., contain captions and legends) they are. We develop computational techniques to measure these features and analyze a large sample of them from open access publications. Our method combines computer and human vision research principles, achieving high accuracy in detecting problems. In our sample, we estimated that around 20.6% of publications contain either accessibility, readability, or explainability issues (around 2% of all figures contain accessibility issues, 3% of diagnostic figures contain readability issues, and 23% of line charts contain explainability issues). We release our analysis as a dataset and methods for further examination by the scientific community.