VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository

Understand

Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs.

• These exemplars often lack the characteristics of real-world datasets, and their one-off nature makes it difficult to compare different techniques.
• In this paper, we present VizNet: a large-scale corpus of over 31 million datasets compiled from open data repositories and online visualization galleries.
• On average, these datasets comprise 17 records over 3 dimensions and across the corpus, we find 51% of the dimensions record categorical data, 44% quantitative, and only 5% temporal.