Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations.

ABSTRACT: Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. F

SUBMITTER: Zhuang H 

PROVIDER: S-EPMC8700024 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature