Authoring Data-Driven Videos with DataClips - , Fereshteh Amini; Henry Riche, Nathalie; Lee, Bongshin; Monroy-Hernández, Andrés; Irani, Pourang (IEEE Transactions on Visualization & Computer Graphics, 2016)
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 barrier to craft 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 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 DataClip with a short training. In the span of 1 hour, they could produce more videos than experts using a professional editing tool and their clips were rated similarly by an independent audience.