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  <title>VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository</title>
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     <h1>
      <b>VizNet</b><br>
      <span>Towards a Visualization Learning and Benchmarking Repository</span>
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    <img class="teaser" src="img/teaser.png" />
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  <section>
    <h2>Abstract</h2>
    <p>
      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.
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  <h2>Resources</h2>
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    <a href="img/2019-VizNet-CHI.pdf" target="_blank">Paper</a><span class="sep"></span><a href="https://github.com/mitmedialab/viznet" target="_blank">GitHub</a><span class="sep"></span><a href="https://www.youtube.com/watch?v=s7vKysUvQuI" target="_blank">Video</a>
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<section>
  <h2>Reference</h2>

  <div class="citation-container">
    <div class="citation">Kevin Hu, Neil Gaikwad, Michiel Bakker, Madelon Hulsebos, Emanuel Zgraggen, César Hidalgo, Tim Kraska, Guoliang Li, Arvind Satyanarayan, and Çağatay Demiralp. 2019. VizNet: Towards a large-scale visualization learning and bench-marking repository. In Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI). ACM.</div>
    <span class="citation-title">Plain Text</span>
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    <div class="citation">@inproceedings{viznet,
title={VizNet: {T}owards a large-scale visualization learning and benchmarking repository},
author={Hu, Kevin and Gaikwad, Neil and Bakker, Michiel and Hulsebos, Madelon and Zgraggen, Emanuel and Hidalgo, C\'{e}sar and Kraska, Tim and Li, Guoliang and Satyanarayan, Arvind and Demiralp, {\c{C}}a{\u{g}}atay},
booktitle={Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI)},
year={2019},
publisher={ACM}
    }</div>
    <span class="citation-title">BibteX</span>
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  <section>
    <h2>People</h2>
    <a href="http://kzh.space" target="_blank">Kevin Hu</a><span class="sep"></span><a href="http://web.media.mit.edu/~gaikwad/" target="_blank">Neil Gaikwad</a><span class="sep"></span><a href="https://www.media.mit.edu/people/bakker/overview" target="_blank">Michiel Bakker</a><span class="sep"></span><a href="https://github.com/madelonhulsebos" target="_blank">Madelon Hulsebos</a><span class="sep"></span><a href="http://emanuelzgraggen.com/" target="_blank">Emanuel Zgraggen</a><span class="sep"></span><a href="https://chidalgo.com/" target="_blank">César Hidalgo</a><span class="sep"></span><a href="http://people.csail.mit.edu/kraska/" target="_blank">Tim Kraska</a><span class="sep"></span><a href="http://dbgroup.cs.tsinghua.edu.cn/ligl/" target="_blank">Guoliang Li</a><span class="sep"></span><a href="https://arvindsatya.com/" target="_blank">Arvind Satyanarayan</a><span class="sep"></span><a href="https://hci.stanford.edu/~cagatay/" target="_blank">Çağatay Demiralp</a>
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