From the course: Data Visualization: A Lesson and Listen Series

Lesson: Science visualizations

From the course: Data Visualization: A Lesson and Listen Series

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Lesson: Science visualizations

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(upbeat music) - Visualization of scientific concepts has long been considered a subtopic within or a sister topic to data visualization. I think there are these two reasons for this. For one, scientific visualization includes things like anatomical drawings and visualizations of complex scientific processes and concepts, including like cell mutations or chemical processes, neither of which you might normally think of as data-driven. Another is because even when the topic is data-driven, scientific data often brings with it nuance and complexity that should give you pause before trying to explain it. The best recent example of this that everyone can probably relate to is epidemiological data, especially around COVID-19 statistics. Case counts, case positivity rates, infection rates per population, even a simple idea like when you start counting things. These all became hot topics around disease and disease prevention in 2020. Science visualization is its own very special thing and I'd love to share a few tips and tricks around it with you. The number one most important tip is to call on an expert. Unless you're an epidemiologist, don't try to visualize epidemiological data. Unless you're a biostatistician, don't try to, you know what I'm trying to say here. The level of nuance and complexity in some of these subjects, and perhaps more importantly, the risks when you get it wrong, require a very careful and expertise-driven approach. So lean on expertise. Another tip I can give you is that you should make sure that you study up on the subject as best you can, even though you will be working with experts. This is because experts often aren't communicators and they frequently suffer from the curse of knowledge. So while they can explain what's important about the data, they may get stuck in their own ways of communicating the information for expert audiences. And you need to know something about the subject to help translate from their world to a more audience-friendly world. Some familiarity with the subject will go a long way toward making your work more meaningful. Finally, you should look for examples in the field to inspire you. Scientific American, for example, has an endless supply of scientific visualizations from across all of science. And while you don't want to get stuck with existing ideas that you might find in Scientific American or elsewhere, and you certainly don't want to copy others' work, you may find norms, best practices, standards, and creative concepts that you can incorporate into your work. For example, going back to COVID, the Financial Times was doing some really amazing work visualizing pandemic data in a bunch of interesting ways throughout 2020. And I saw many examples of work later on that were reminiscent of things FT was doing on day one, but that maybe took a new spin on it, either in terms of the visual design and/or how the data was organized. I just mentioned Scientific American and that was no coincidence. Next up, I'll be chatting with Jen Christiansen, Senior Graphics Editor at Scientific American, about her work at the magazine. Join me.

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