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Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalability issues, since growing data sizes eventually lead to overplotting and visual clutter on a screen with a fixed resolution, which hinders the data analysis process. We propose an algorithm that compensates for irregular sample distributions by a smooth transformation of the scatterplot's visual domain.
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The dataset contains the stress tensor field data used to test various glyph placement strategy in the upcoming paper, at time when this dataset was uploaded - “Visualization of 3D stress tensor fields using superquadric glyphs on displacement streamlines.” The tensor field generation procedure is described in the supplementary information of the paper. Here, we provide the actual stress tensor fields that were generated for the glyph placement comparison study.
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