Example Videos Recreated based on online data videos
Video generated with a financial analyst
Video generated with a TV news anchor based on data on recent teacher strike
Video generated with a health analytics company based on data gathered by wearable devices
To compose a library of clips that would enable the creation of a broad range of data videos, we examined a corpus of 70 data videos available on news media, government and research center websites, visualization blogs, and online video portals. We selected videos that presented arguments supported by data and that included at least one data visualization.
We proceeded to segment each video from this corpus into short clips. We then counted the occurrences of each type of clip. A re-peating occurrence exists when the same visualization on the same dataset reappears in the video. For example, the data video in  includes animated an bar chart with growing bars three different times throughout the video but it reuses the same dataset, hence, we counted the bar chart of creation clip type a single time.
Our analysis led to 7 types of clips we describe below, each ap-plied to 8 types of visualizations most commonly found in the vide-os. Figure 4 gives an overview of the visualization type by type of clip, including the number of occurrences in our corpus. It is im-portant to note that each clip ×visualization combination has differ-ent variations. For example, a line chart can be created by drawing both axes and lines together or one after the other. To give an idea of the content of the library, we colored cells of Figure 4 in orange when at least one implementation is available in DataClips. Darker shades indicate that the DataClips’s library contains several varia-tions of this clip.
We conducted a user study to evaluate if non-experts could create data videos using DataClips, and gain insights on how the authoring experience and output would compare to videos created with profes-sional tools. Our study was a between-subjects design with two conditions: one group of participants used DataClips; the other group used Adobe Illustrator and After Effects, commonly used to create data videos. We asked participants to generate data-driven clips based on a list of insights and accompanying dataset we provided. We report our qualitative observations during this process and pro-vide insights on the quality of the videos generated from both condi-tions by asking 40 different volunteers to rate them.
A copy of insights given to the participants can be found HERE