Jen Christiansen

Building Science Graphics: An illustrated guide to communicating science through diagrams and visualizations (published by CRC Press, AK Peters Visualization series) is a practical guide for anyone—regardless of previous design experience and preferred drawing tools—interested in creating scientific illustrated explanatory diagrams. Starting with a clear introduction to the concept of information graphics and their role in contemporary science communication, it then outlines a process for creating graphics using evidence-based design strategies. The heart of the book is composed of two step-by-step graphical worksheets, designed to help jump-start any new project. This is both a textbook and a practical reference for anyone that needs to convey scientific information in an illustrated form for articles, poster presentations, slide shows, press releases, blog posts, social media posts, and beyond.

For more details about the book, visit https://www.buildingsciencegraphics.com/

For a free sample chapter, visit
https://www.buildingsciencegraphics.com/science-communication-fundamentals

To order, visit the publisher's site, Bookshop.org, or your favorite bookseller.

If you’d like to receive a signed bookplate in the mail, please send the details of your request using the form at this link: https://www.jenchristiansen.com/contact

Working through the data for a Scientific American Graphic Science column got me thinking more about joint laureates. What does it mean to share a Nobel prize in science? And how do they split the money? I answer those questions in this short illustrated explainer.

How Does Sharing a Nobel Prize Work?
Joint Nobel laureates aren’t necessarily direct scientific collaborators, and the prize money isn’t always split evenly

Image credit: Jen Christiansen

This article for The Open Notebook includes strategies that I’ve developed for creating graphics in the service of science journalism, on deadline.

About the opening image: From initial exploratory sketches to the final product, creating a science graphic is a process. (See the story where this final graphic was published in the December 2015 issue of Scientific American.) Image credit: Jen Christiansen/Scientific American

More often than not, science-centric content is complex. Visuals are a powerful tool for helping your audiences make sense of complex stories. In creating or commissioning such visuals, your first instinct may be to simplify the information in order to make it more broadly accessible. Yet simplification may erase the latest key finding, distilling things in a way that doesn’t honor the cool new discovery you’re trying to highlight. I find it more productive to focus on clarifying, not simplifying (with a nod to the designer Nigel Holmes, the author Alberto Cairo, and many others who have spoken and written about this idea).

This chapter for the KSJ Science Editing Handbook (a project of the Knight Science Journalism Fellowship at MIT, supported by the Kavli Foundation and the Howard Hughes Medical Institute’s Department of Science Education) is graphics-centric, based on my own experience with input from others on photography, editorial illustration, and motion graphics. I focus on the process of determining when visuals might be a useful addition to a science-centric story, along with tips for producing and editing them.

Click here to read more from Chapter 11.

Click here for the KSJ Science Editing Handbook landing page. The book is free to download and redistribute.

I wrote a review of Edward Tufte's 2022 book Seeing with Fresh Eyes: Meaning, Space, Data, Truth for Design and Culture (The Journal of the Design Studies Forum). Review edited by Joshua Korenblat and Maggie Taft. Published online May 25, 2023.

An evolving sheet of selected organizations, conferences, videos, and readings that I've maintained in response to specific requests from students, scientists, and artists interested in learning more about scientific visualization—from illustration to data visualization—and the work I do as a science graphics editor.

Click here to visit the full sheet.

Starting with its first issue in 1845, Scientific American has faithfully reported on technology used to produce the magazine. Some advances have been shown directly on the cover, such as a digital letterform in May 1969. Echoes of other advances are visible in the cover color analysis here. After more than 75 years of weekly publishing—largely in black and white, with occasional washes of color—the magazine shifted to a rotary offset lithography press for covers in 1917, which allowed for the lush paintings that marked its transition to a monthly in 1921. In 1931 covers scaled back to two colors, likely to save money during the Great Depression. As the economy rebounded, a wider range of tones emerged in 1933 in the form of black-and-white photography. In 1948 the look changed again, this time because of a shift in ownership and editorial vision: color paintings returned, in a square image set on a solid background, which became white in 1952 and remained white for about 43 years. As desktop publishing arose at the end of the 20th century, designs became more experimental: images broke out of the box, ushering in a period of bold—primarily digitally crafted—illustrations.

Click here to read more, and to view an interactive cover color analysis by Nicholas Rougeux.

Image credit: Graphic by Nicholas Rougeux; May 1948 cover image by Stanley Meltzoff

Where does the illustrator end and the infographer begin? How does data visualization fit in? And what does science have to say about the design decisions we make?

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Sure, I was familiar with the graphic—and I’m not alone. Joy Division’s 1979 Unknown Pleasures album cover leaned entirely on a small mysterious data display, printed in white on black. No band name, album title or other identifiers. An interesting move for a debut studio album.

The cover image became an icon but remained mysterious. Even as knowledge spread about the band’s inspiration point–a preexisting pulsar data visualization—the true origin of that visualization continued to be a bit of a riddle. Somewhere along the way, I became obsessed with the narratives behind pulsar discovery and stacked plots, along with a growing desire to learn all that I could about the image and the research it was connected to. This post is an abridged story borne of that obsession, starting with a video screened at a data visualization conference and ending with an interview with Harold (Hal) Craft, the radio astronomer who created the plot from data collected at the Arecibo Radio Observatory.

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Image credit: Arecibo Radio Observatory, From “The Nature of Pulsars,” by Jeremiah P. Ostriker, Scientific American, January 1971.

In an earlier post, I described my quest to uncover the story behind the story of Joy Division’s Unknown Pleasures album cover. The stacked plot featured on the record was originally created by radio astronomer Harold D. Craft, Jr. in the course of working on his PhD dissertation, “Radio Observations of the Pulse Profiles and Dispersion Measures of Twelve Pulsars” (September, 1970).

When folks refer to the data visualization, they generally just say that it shows a series of radio frequency periods from the first pulsar discovered. But what does that mean? What is a pulsar, how do you read the stacked plot, and how was the data collected?

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Winter Storm Stella demonstrated the fuzzy edges, shifting nature, and uncertainty inherent to storm forecasting. Early predictions had New York City prepping for a record-breaking—well, compared to past storms in March, anyway—20+ inches of snowfall. But Stella delivered less than eight inches of slush in Central Park. That said, the numbers weren’t too surprising for folks watching meteorologists hedge their bets with talk of shifting snowfall bands along the coast 24 hours before the precipitation began. (Even as the Weather Prediction Center cautiously held to their maximum predictions for NYC). And the storm lived up to the hype in places such as Binghamton, NY, with a 24-hour record-breaking snowfall of 31.3 inches.

Interestingly, though, in the days following the storm I didn’t see folks critiquing visualizations of precipitation uncertainty related to Stella. And when I think of Nor'easters, I think of daily updates of fairly standard radar images and maps with bands of precipitation projections, often in the form of low-to-high ranges. Hurricane projections, on the other hand, are nearly always accompanied by problematic “cone of uncertainty” visualizations.

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Image credit: National Weather Service

What is visualization? When asked of data and information visualization professionals, answers will generally swirl around one of two answers…a visual tool that aids in (1) analysis or (2) communication of information. At the 2015 Chicago IEEE VIS meeting—an annual conference focused on the tools and technology of visualizing information—Purdue Polytechnic professor Vetria Byrd reminded me that visualization is also a process, one that requires us to think critically and sequentially though the information at-hand.

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At Scientific American, we specialize in making advances in science and technology accessible to non-specialist audiences. That includes developing illustrated explanatory diagrams and data visualizations that both explain the latest research findings in depth, and place those findings in context of the larger research arc. That may sound straightforward, but many newsworthy topics in science stand on a foundation of lots of incremental research findings, most of which we can’t assume that our readers already know. And some of which haven’t been illustrated clearly before. For an article on the placenta (in the October 2017 issue), for example, we were attempting to show newly emerging details on placental development for which very little visual reference material already existed.

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Image credit: AXS Studio (Contributors: Man-San Ma, Sonya Amin, Eddy Xuan, Joyce Hui, and Susan Park)

I'm a graphics editor here at Scientific American, so it may not surprise you that some of my favorite articles from the archive are rooted in the process of making and printing images. Lucky for me, the topic is built into the very core of the magazine. As stated in the first issue by founding editor Rufus Porter, not only would each issue “be furnished with from two to five original Engravings,” but the content would be dedicated, among other things, to “useful information and instruction in various Arts and Trades.”

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Image credit: “From Easel to Cover—Offset Lithography as Applied to the Scientific American Covers,” by Austin C. Lescarboura. Scientific American, November 1921

Yep, that's Jerry Garcia and Howard Wales holding up an issue of Scientific American.

I did a double take, too, when I received a photo of the inside gatefold cover of the album Hooteroll? via e-mail from Eric Klotz, founder of Visualized. (Incidentally, Klotz was also responsible for my last album cover obsession—and the pulsar data visualization it was based on). I had to learn more. So I bought a second-hand vinyl (released in 1971, Douglas 5 record label) and CD (released in 1987, Rykodisk and Douglas Communications), and settled in for some internet research.

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Image credit: Photo featured on inside gatefold of Hooteroll? album cover by Ron Rakow

The title of this post borrows from ideas presented by Sha Hwang at the Visualized conference in New York City: He kicked off the data-visualization event with a talk that—in effect—challenged the audience to take a step back. Way back. And then to look again, with fresh unblinking eyes. What does a long take of the data look like? And what is the view from afar? Citing Frank White’s book The Overview Effect: Space Exploration and Human Evolution, in which astronauts refer to a profound shift in their perception of self and our world on viewing Earth from space, Hwang got me thinking. Can you really make the shift from visible to visceral in a data visualization simply by changing the readers’ point of view, or including a broader context for the data set?

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Image credit: Courtesy of the Earth Science and Remote Sensing Unit, NASA Johnson Space Center (Astronaut photograph AS17-148-22727)

Perhaps you saw this graphic on the front page of The New York Times last week, leading into Amy Harmon's article about scientists from a variety of labs banding together in the fight against the Zika virus. The researchers' shared goal; sequence the genome of the virus' mosquito vector, Aedes aegypti, in the hope that a more complete knowledge of the insect’s genetic makeup will lead to ideas on how to prevent it from transmitting the virus that causes disease in humans. (The last major—although incomplete—sequencing effort was published in 2007).

The New York Times caption (as it appears online) states that you're looking at "A visualization of the recently sequenced Aedes aegypti genome. Each of the 3,752 colored lines is a fragment of its three chromosomes..."

But what does that mean? How do you read the graphic, and how was it built? To find out, I reached out to Mark Kunitomi, author of the chart and postdoctoral fellow in the Andino Lab at University of California, San Francisco.

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Image credit: Graphic by Mark Kunitomi (as printed in the New York Times, April 1, 2016)

In college in the 1990s, I suffered an identity crisis. Was I a scientist or an artist? I loved the clarity and order inherent to the scientific process; ask questions, set up methodologies, collect data, analyze. Research projects and papers I co-authored on the topics of trace fossils and hydrothermal vent species were immensely satisfying. No matter the result of the study–don’t even ask about the punch line of my college honors thesis–it gave me the chance to contribute a rigorously-produced bit of knowledge.

But I also loved the idea of communicating through visuals rather than words. My studio arts classes encouraged me to question and morph methodologies. I couldn’t stop making images.

Nor could I imagine choosing one discipline at the expense of the other. Scientific illustration, however, allowed me to merge those two identities. Finding the similarities between art and science, I honed in on observation and interpretation. Instead of contributing new findings to the world, I’d help make sure that other peoples’ findings were accessible to wider audiences through a visual language.

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