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ARE: Accessability, Readability, and Explainability

Figures are an essential part of scientific communication. Yet limited 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--i.e., ARE issues. We develop computational techniques to measure these features and analyze a large sample from open access publications. Our method combines principles from computer and human vision research, achieving high accuracy. Overall, we found that around 5% of all figures contain some issue. We found that readability and accessibility are lower for lower-ranked journals and smaller author’s h-index . We release our analysis as a dataset and method for further examination by the scientific community.

Requirement

  1. YoloV4 (Bochkovskiy et al., 2020)
git clone https://github.com/AlexeyAB/darknet.git

For Yolo wegihts and configuration file, please download from ARE_explainability

  1. Tensorflow Please download the legend neediness model from ARE_explainability

Method

  • Image preprocessing:

  • Accessability: Inaccessible to color-blind readers

    1. Obtain figures in color-blind vision systems through a simulation method (Machado et al., 2009)
    2. Colorblind unsafe: examine if a figures contains red and green areas at the same time and if the red area disappears in the simulated figure.

  • Readability:

    1. Low light image classification: Fined-tuned a convolutional neural network to classify.
    2. Computed RMS constrast and spatial frequency.
    3. Image with issues: the proportion of high spatial frequcney > 0.5 in low contrast and low light images.

  • Explainability: Contain legends and appropriate captions in line charts.

    1. Image classification: Fine-tuned YOLOv4 to classify charts into different categories (line charts, bar charts, ...). (Please refer https://github.com/sciosci/graph_check)
    2. Legend detection on suplots: Fine-tuned YOLOv4 (pre-trained on MS COCO dataset) with around 1400 charts to detect legend in graph. We also detect corresponding compound figures to check if legend exists.
    3. Legend neediness classification: Fine-tuned ResNet152v2 (pre-trained on ImageNet) with arond 1500 charts. If a charts has more than one lines and symbols, we annotated as needed.
    4. Legend descriptive words in caption: Analyzed and counted descriptive words in corresponding caption. (e.g. color words(red, blue, etc.), dashed line, solid line, triangle, square)

Method Flowchart

drawing

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
(c) Han Zhuang, Tzu-Yang Huang, and Daniel Acuna 2021 - 2022 Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Science of Science + Computational Discovery Lab in the School of Information Studies at Syracuse Univeristy.

Reference

  • Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv:2004.10934 [Cs, Eess]. http://arxiv.org/abs/2004.10934
  • Jambor, H., Antonietti, A., Alicea, B., Audisio, T. L., Auer, S., Bhardwaj, V., Burgess, S. J., Ferling, I., Gazda, M. A., & Hoeppner, L. H. (2021). Creating clear and informative image-based figures for scientific publications. PLoS Biology, 19(3), e3001161.