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Supplemental Material for the paper : Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures

, , , , , , , and . Dataset, (2022)Related to: Katrin Angerbauer, Nils Rodrigues, Rene Cutura, Seyda Öney, Nelusa Pathmanathan, Cristina Morariu, Daniel Weiskopf, and Michael Sedlmair. 2022. Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures. In CHI Conference on Human Factors in Computing Systems (CHI '22). Association for Computing Machinery, New York, NY, USA, Article 134, 1-23. doi: 10.1145/3491102.3502133.
DOI: 10.18419/darus-2608

Abstract

Study data and supplemental material for the paper- Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures presented at CHI 2022.We performed a large scale data study on the color vision deficiency (CVDs) accessibility of paper figures, considering four CVDs. As images for our study we used the Vis30K image dataset (http://ieee-dataport.org/2494).Here, we selected subset of images that were analyzed by four researchers as well as 200 crowdworkers on Amazon Mechanical Turk. Accessibility ratings, issues, helpful aspects and optional comments were provided for each image. Each image was rated in each CVD condition and by multiple crowdworkers.This dataset contains the anonymized raw data of the crowdsourcing study as well as other aggregated evaluation data such as correlation computations and comment analysis. Further, it comprises supplemental image and video files that could not be included into the paper.For more information please consult the paper and the README.html

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