StoryFacets: A design study on storytelling with visualizations for collaborative data analysis

  author   = {Deokgun Park and Mohamed Suhail and Minsheng Zheng and Cody Dunne and Eric Ragan and Niklas Elmqvist},
  journal  = {Information Visualization},
  title    = {{StoryFacets}: A design study on storytelling with visualizations for collaborative data analysis},
  year     = {2022},
  note     = {Preprint: \url{}},
  number   = {0},
  pages    = {3--16},
  volume   = {21},
  abstract = {Tracking the sensemaking process is a well-established practice in many data analysis tools, and many visualization tools facilitate overview and recall during and after exploration. However, the resulting communication materials such as presentations or infographics often omit provenance information for the sake of simplicity. This unfortunately limits later viewers from engaging in further collaborative sensemaking or discussion about the analysis. We present a design study where we introduced visual provenance and analytics to urban transportation planning. Maintaining the provenance of all analyses was critical to support collaborative sensemaking among the many and diverse stakeholders. Our system, STORYFACETS, exposes several different views of the same analysis session, each view designed for a specific audience: (1) the trail view provides a data flow canvas that supports in-depth exploration + provenance (expert analysts); (2) the dashboard view organizes visualizations and other content into a space-filling layout to support high-level analysis (managers); and (3) the slideshow view supports linear storytelling via interactive step-by-step presentations (laypersons). Views are linked so that when one is changed, provenance is maintained. Visual provenance is available on demand to support iterative sensemaking for any team member.},
  doi      = {10.1177/14738716211032653},
  series   = {IVI},

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