Authoring Data-Driven Videos with DataClips

Fereshteh Amini, Nathalie Henry Riche, Bongshin Lee, Andres Monroy-Hernandez, Pourang Irani

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

Original languageEnglish (US)
Article number7539370
Pages (from-to)501-510
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume23
Issue number1
DOIs
StatePublished - Jan 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Keywords

  • authoring tools
  • data storytelling
  • data video
  • narrative visualization
  • visualization systems

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