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. VizNet provides the necessary common
baseline for comparing visualization design techniques, and developing
benchmark models and algorithms for automating visual analysis. To demonstrate
VizNet's utility as a platform for conducting online crowdsourced experiments
at scale, we replicate a prior study assessing the influence of user task and
data distribution on visual encoding effectiveness, and extend it by
considering an additional task: outlier detection. To contend with running such
studies at scale, we demonstrate how a metric of perceptual effectiveness can
be learned from experimental results, and show its predictive power across test
datasets.

本文介绍了 VizNet，它是一个包含了超过 3100 万个数据集的大规模语料库，从开放数据库和在线可视化画廊中编译而来。它提供了一个必要的公共基线，用于比较可视化的设计技术，并为自动化可视化分析开发基准模型和算法。我们展示了如何从实验结果中学习知觉有效性度量，并展示了它在测试数据集上的预测能力。