Despite the critical importance of successful visual communication in all research fields, the focus on creating effective and powerful science visualizations remains relatively new. With the growing amount of knowledge, and denial, concerning major scientific issues like climate change and the COVID pandemic, effective scientific communication has become absolutely crucial to convincing the general public to pay attention to proven facts. This first semester, I have worked towards answering my essential question: how can storytelling, animation, and computer-generated visualizations be used to more effectively communicate scientific findings? I found and read books on the topics of effective data visualization and storytelling, and through these books, I learned methods and strategies for creating engaging visualizations.
Edmund Tufte is a statistician and pioneer in data visualization. In his books The Visual Display of Quantitative Information, Envisioning Information, and Visual Explanations, Tufte carefully details how exactly data visualizations can be deemed effective or ineffective. Tufte claims effective graphs usually come from emphasizing a few key qualities. First, he asserts that large, well-layered and organized quantities of data are often the most effective. He argues that removing data to simplify the visualization shortchanges the viewer. Along those lines, he also argues that presenting data in multiples or parallels offers more areas of comparison. By providing large amounts of data or parallels, the graphs bring the viewer into the interpretation of its patterns, making it more compelling and interesting. Tufte lists several traits as signs of ineffective communication, the most recurrent being what he refers to as “chartjunk.” Chartjunk to Tufte is any design that distracts the reader, obscures the data, or is irrelevant to the graphic. This can include patterns that create a vibrating optical illusion, grids that over power the data, or any other design that clutters the visualization. Data distortion, where the graphic conveys something that doesn’t agree with the data, is another mistake Tufte warns about. Distortion can come from not putting the data in context, or warping the display to a magnitude not consistent with the actual data. Tufte’s points on effective data communication helped me to make a base of knowledge that is useful for the purely scientific side of my essential question. Even though I don’t see myself creating actual graphs like the ones Tufte critiques in his books, the fundamentals of data visualization he discusses will still be relevant. I will use his various points to critique my own work. Randy Olson is a marine biologist who decided to go film school and work in Hollywood. His experience, both with the scientific world and the cinematic world, give him a unique perspective on scientific communication. He has written a number of books on how scientists can improve their communication skills through storytelling. Although Olson spends most of his book detailing arguably loosely-related anecdotes about his experiences in Hollywood, he does arrive at some useful points. In his two books, Don’t Be Such a Scientist and Houston, We Have a Narrative, Olson makes the case for why scientific communication needs storytelling to excite an audience. The largest takeaways are his two proposals for templates for scientists. First, Olson offers the “word template,” where he recommends you summarize the central theme of your work with a single word, substituting into a given phrase. The template he provides for this section is a quote from Theodosius Dobzhansky about studying biology: “Nothing in biology makes sense, except in the light of evolution.” Olson proposes that if you remove the words “biology” and “evolution” and swap them with words that pertain to your work, you will be left with your central theme. The second suggestion is the “And But Therefore” (ABT) template. This template is where you make sure you don’t bore your audience, and that your work has a streamlined narrative structure. Similar to the concept of structuring a narrative with a thesis, antithesis, and synthesis, the ABT method is a simple way to make scientific findings compelling. For example, Olson breaks down and paraphrases the work of Watson and Crick into this template: a structure has been proposed by others AND their model has three chains BUT we disagree THEREFORE here is our new data. Another important point he makes in the beginning of the book is knowing where to draw the line with adding narrative. Too little narrative makes the data boring and unlikely to gain public interest, while too much makes the data convoluted and may be prone to deceiving people. These templates will be immensely helpful as a starting point for my visualizations. I will use them to make sure I stay true to the main point of the data and create a compelling narrative along with it. My work during the first two quarters has helped me to build a solid foundation of knowledge in both pure data visualization and storytelling’s relationship to scientific communication.
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