REVIEW 2 cited by
Accessible Color Sequences for Data Visualization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Color sequences, ordered sets of colors for data visualization, that balance aesthetics with accessibility considerations are presented. In order to model aesthetic preference, data were collected with an online survey, and the results were used to train a machine-learning model. To ensure accessibility, this model was combined with minimum-perceptual-distance constraints, including for simulated color-vision deficiencies, as well as with minimum-lightness-distance constraints for grayscale printing, maximum-lightness constraints for maintaining contrast with a white background, and scores from a color-saliency model for ease of use of the colors in verbal and written descriptions. Optimal color sequences containing six, eight, and ten colors were generated using the data-driven aesthetic-preference model and accessibility constraints. Due to the balance of aesthetics and accessibility considerations, the resulting color sequences can serve as reasonable defaults in data-plotting codes, e.g., for use in scatter plots and line plots.
Forward citations
Cited by 2 Pith papers
-
The completeness of the open cluster census towards the Galactic anticentre
Open clusters in the outer Milky Way are more likely to be old than young even after correcting for Gaia detection biases, implying young massive clusters are rare in the outer disc.
-
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Flow Annealed Importance Sampling Bootstrap, applied with replay buffering to differentiable matrix elements, achieves higher importance sampling efficiency per target evaluation than rKLD or fKLD in high-dimensional ...
Discussion (0). Continue with ORCID to comment.