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Lectures in Data Visualization: Jen Christiansen, Scientific American

April 20, 2023 @ 12:00 pm - 1:30 pm EDT

Lectures in Data Viz Promo, Session 4

Special Considerations for Science Graphics
Jen Christiansen, Scientific American

Science graphics are beholden to the same design principles as other types of graphics. But the information they hold is often the product of a process that a lot of people in your audience may not be familiar with. It’s important to honor the fact that the data you’re presenting is both the product of a rigorous study that builds upon past studies, and that interpretations may eventually shift a bit as future research sheds more light on the topic. This session provides you with some strategies for addressing those challenges. In particular, three overarching themes that are particularly pertinent to science graphics: honoring complexity, avoiding misinformation pitfalls, and visualizing uncertainty.

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About the Lecture Series
The Brown Institute at Columbia Journalism School, in partnership with the Data Science Institute and the Department of Computer Science, is excited to present a lecture series that delves into the art and science of data visualization. This dynamic interdisciplinary series will explore the ways in which technology is transforming how we encounter, comprehend, and create data-driven narratives. The series will take place every other Thursday over the lunch hour from March to May, and will feature esteemed experts in these fields. Over the course of a few months, we will explore the profound impact that the tools and techniques utilized in data visualization have on the stories we can tell.

The series will include five lectures, led by renowned experts including Cindy Xiong, Dom Moritz, Arvind Satyanarayan, Jen Christiansen, and Gurman Bhatia. The topics to be covered in the series are diverse and thought-provoking, encompassing the role of ML in data visualization, the design process for best representing the stories behind the data, the future of interactive visualization, and the very role tools play in our approaches to graphics. Whether you’re a data scientist, a journalist, a technologist, a storyteller, or a combination thereof, this series will explore a practice that spans all disciplines. Join us as we hear from these experts and engage in interactive discussions exploring the latest advancements in data visualization and technology.

Join us for a lecture followed by a small reception, all held in the Brown Institute for Media Innovation on the entry floor of Pulitzer Hall (Journalism School). Registration required.

About the Speaker

Jen Christiansen

Photograph by Liz Tormes

Jen Christiansen is a science communicator specializing in visual media, the individual produces explanatory diagrams and data visualizations. She is the author of Building Science Graphics, a publication by A K Peters/CRC Press, and holds the position of senior graphics editor at Scientific American. Jen strives to create engaging and informative images catering to specialist and non-specialist readers. She possesses the ability to comprehend, interpret, and communicate scientific concepts visually, whether it involves illustrating complex processes or assisting readers in navigating a story. Although she is capable of producing final renderings, they also collaborate with freelance illustrators, data designers, and researchers on a project-by-project basis, as demonstrated by their work on this site.

Since 2007, Christiansen has held the position of graphics editor at Scientific American. However, their association with the magazine dates back to 1996 when they were hired by art director Ed Bell as an intern, straight out of the science illustration graduate program at U.C. Santa Cruz. With a double major in geology and studio art from Smith College, Jen started as an assistant art director and later moved on to work for National Geographic magazine as a hybrid assistant art director/researcher and then as a designer.


April 20, 2023
12:00 pm - 1:30 pm EDT
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Brown Institute @ Columbia
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