This is a free sample chapter from the book Building Science Graphics by Jen Christiansen, modified slightly for reading on digital devices. Subsections include The Science of Science Communication, Storytelling, SciArt and How Graphics Fit In.

Chapter 3

Science Communication Fundamentals

What pops to mind when you read the phrase “science communication”? My brain first flits back to around 1980, and memories of educational magazines, museums, and television shows. Specifically, flipping through collected copies of Zoobooks, gazing up at a wall of Ice Age animal skulls and a larger-than-life painting of a saber-toothed cat at the La Brea Tar Pits, and watching cinematic wildlife and space documentaries on television in my family’s living room. Then my thoughts jump forward to the 2016 digital comic “A Timeline of Earth’s Average Temperature” by xkcd (also known as Randall Munroe), journalist-initiated COVID graphics and infection rate charts, and the 2020 social media video sensation Antibodyody Antibody Song by Dr. Raven the Science Maven (also known as Dr. Raven Baxter).

Upon a quick read, the differences between those earlier and later associations may not seem significant—more of an indication of my age than anything else. But they also subtly reflect shifts in the broader practice of science communication over the last few decades. Public-facing “science communication” writ large in the US and UK was primarily considered to be an act of knowledge dissemination from an authoritative source (like a science institution or magazine), toward a broad and amorphous audience. More recently, a critical mass of science communication practitioners and researchers have been working toward recasting it as a conversation in the public sphere—an even exchange between many parties (not limited to scientists)—not a lecture by an institution.

That’s not to say that participatory science communication efforts didn’t exist at all earlier, or that the unidirectional approach (characterized by one-way transmission of information) is now extinct. Being part of a robust museum research apprenticeship program in high school certainly engaged me as an active participant in my youth. And some researchers argue that the deficit model—a philosophy that leans on the idea that people simply don’t know the facts, and that providing them with empirical evidence will impact their behavior and beliefs in a logical manner—is still alive and may work in some contexts. (Although evidence shows that it’s a futile form of unidirectional communication in most cases.)

Why is the state of the broader practice of science communication relevant to a book that’s specifically focused on science graphics? Because it informs who is expected and encouraged to make science graphics and provides a framework for thinking about who they are for, and to what ends.

In retrospect, I can see more clearly how those variables have interacted with—and impacted—my relationship with science communication over time. My early career unfolded during a time characterized by the unidirectional model of science communication. I double majored in geology and studio art in college, then went to graduate school for science communication, in the field of natural science illustration. I refined my illustration technique skills, learned conventions of the field, and made connections in the industry. Observing things in person (drawing from life) supplemented by research was a key part of the process. I was a cheerleader for science, with laser-like focus on the content. Not the audience.

Although I was definitely trained to observe objects that I was illustrating with a critical eye, it wasn’t until years later that I began to realize that I wasn’t always viewing the bigger picture through that critical eye. I was working at a magazine at the time, but I didn’t self-identify as a journalist. I considered myself a scientific illustrator and art director. I was working directly with scientists, using specialized skills to translate their findings into a visual language for a broader audience. Perhaps this is rooted in how I entered the field. I didn’t go to journalism school. At the time, journalism school didn’t feel like a good option for someone who wanted to draw about science.

Over time, I became more comfortable with applying a critical eye to the bigger picture, and with working as—and identifying as—a science journalist. As Teresa Carr states in an article about the role of science journalism within the science communication ecosystem:

“We use our skills to capture the beauty and complexity of scientific endeavor, to be sure, but also its shortcomings, its failings, its biases, and conflicts. We ask critical questions about sample-size and research design, and we communicate why those aspects of science matter, too. And we do so on behalf of readers, not scientists.”

That entails asking tough questions, and not defaulting to science cheerleading mode. (That said, the boundary is fuzzy. Can an article written by a scientist about their own area of specialty be considered a journalistic endeavor, even if they adhere to journalistic standards? As Carr reports, there’s not a consensus.) 

By the early 2010s it had become clear to me that the new wave of folks entering the scene weren’t contained by—or concerned with—specific roles within the structure of institutional science communication or science journalism. Magazine interns were emerging from journalism programs with data visualization and multimedia skills (not just reporting and writing chops). And scientists were routinely publishing blog posts on websites and networks hosted by news media companies.

My reaction at the time was something along the lines of: That’s nice, but what’s their priority, who are they beholden to, and what will they choose to ultimately pursue? Writing or illustration? Practicing science or science communication? I was being narrow-minded. After all, many folks drawn to science communication—like me—already actively resist categorization. We have at least two clear interests: For me it’s one foot in science and one foot in the world of information design.

Before then, the tools of the publishing trade were expensive, and had steep learning curves. Drawing and design were just one part of the process. A whole other set of highly specific skills and bits of knowledge were needed when it came to preparing images for the printing press. I studied apprentice-style. The idea of learning desktop publishing skills and tools at the same time as juggling several other communication modes was daunting. This was reinforced and reflected by a hiring structure in the magazine world that rewarded medium specialization. Word people were reporters and editors—often with subject-matter areas of specialty—and image people were in the art department, working across all subject-matter areas.

But things are evolving. An example of those changes in journalism is The New York Times Climate Desk. Launched in 2017, it’s a capsule team of journalists within a larger organization united by a specific subject matter (climate), not divided by mode of communication.

The landscape is also evolving beyond the world of journalism. Tools are now more varied and accessible. Fellowships and residencies are nurturing cross-disciplinary experiences and relationships. Anyone with a smartphone can push digital content out into the public arena. Online crowdfunding has become an option for garnering financial support for science communication projects that may not fit into classic funding models. The internet has made a multi-directional exchange about science more possible, at scale.

My experiences as a science communicator underscore how easy it is to lose sight of the big picture when in the midst of practicing it. In this chapter, I take a step back and address science communication holistically. The frameworks that I describe in the pages that follow aren’t the only guiding science communication philosophies. Nor are they mutually exclusive. My hope is that this section provides you with a general lay of the land. The practical advice for building graphics in the chapters that follow should be thought of as strategies and techniques that can be used to help you build graphics that work within broader-scale science communication frameworks, like the ones described below.

The Science of Science Communication

If you are reading this book and interested in science and science communication, you’re likely invested in the idea of evidence-based decision making. So, naturally, you’re looking for best practices as guided by rigorous research experiments. But the field, by many accounts, is quite young. And the variables are tremendous. I hate to break it to you, but there’s not a single clear universal road map to science communication success rooted in empirical evidence. That said, there’s still so much to be learned from the folks who are studying it. (TLDR: know your audience, treat them with respect, and tell stories.)

As a practitioner, it can be frustrating to navigate research findings to help guide decisions related to specific projects, as things depend so heavily on context. I’m hoping that this chapter, the sources cited within, discussions in subsequent chapters on uncertainty and misinformation, and the recommended readings on page 43 will be informative and useful. But perhaps I’m getting ahead of myself. Let’s dive into what, exactly, science communication is.

Science communication encompasses a wide range of traditions and frameworks that vary across discipline, time, and space. At its broadest level, it generally refers to the idea of sharing information—and/or building knowledge with others—that is rooted in the practice or findings of science.* At a more granular level, it becomes complicated by who is—or who should be—participants in specific exchanges, the roles of the participants, and the goals of the exchanges.

* Admittedly, this “practices or findings of science” framing is a bit lazy in that most likely brings to mind modern so-called “Western” scientific methodology, and excludes a history and the full breadth of science communication traditions rooted in sharing information related to technology and science-related observations outside of that model. For really interesting discussions of the topic, see Lindy Orthia and Elizabeth Rasekoala’s post “Anti-Racist Science Communication Starts with Recognising Its Globally Diverse Historical Footprint,” and Stephanie Evergreen’s post “Decolonizing Data Viz.”

The sheer number of possible combinations of those variables make it complicated to sort out clear best practices. For example, the language, style, and multimedia aids used by one scientist to communicate the results of an experiment to a peer through a research journal will be reasonably different than that used by a museum educator to communicate related information to an elementary school group, which will in turn be reasonably different from a dialogue on the same topic in a town hall setting. And each of these scenarios will be influenced by the community in which exchange takes place.

Many contemporary researchers and practitioners have been converging upon what’s not effective, broadly speaking. Per a 2017 report by the National Academies of Sciences, Engineering, and Medicine: 

“A common assumption is that a lack of information or understanding of science fully explains why more people do not appear to accept scientific claims or engage in behaviors or support policies that are consistent with scientific evidence.”

Known as the deficit model, the solution under this assumption is to simply provide people with more information, in a one-way exchange. But, as the report goes on to describe, “people rarely make decisions based only on scientific information; they typically also take into account their own goals and needs, knowledge and skills, and values and beliefs.”

As it turns out, adhering to the deficit model of science communication is probably not your best option—especially if your goal is to change people’s minds—as exemplified by two projects related to vaccination behavior led by researcher Brendan Nyhan.

In a study published in 2014, Nyhan and his collaborators asked parents about intentions to have their children vaccinated for measles, mumps, and rubella (MMR). The parents then randomly received one of four interventions that either (1) corrected misinformation about vaccines, (2) presented information on disease risks, (3) used a dramatic narrative about the disease, or (4) employed visuals to make the risk of disease more tangible. Then they were asked about their intentions again.

You might expect that learning that a disease carries a higher risk than the vaccine would increase the probability of a parent having their kid vaccinated. Not so. “None of the pro-vaccine messages created by public health authorities increased intent to vaccinate with MMR.” (It should be said that the paper nods to a lot more nuance than that takeaway: Perhaps the construction of the educational materials themselves could’ve been improved. Notably, there were differences in the strength and type of reactions from parents that held different beliefs before the intervention.)

A subsequent study in 2015 on flu vaccines demonstrated that educational materials could help debunk the myth that the influenza vaccine can actually give people the flu. “However, the correction also significantly reduced intent to vaccinate among respondents with high levels of concern about vaccine side effects—a response that was not observed among those with low levels of concern.”

It’s not hard to find examples of this disconnect in action today. Despite the ever-growing proof that the available COVID-19 vaccines are effective and safe—and wide information campaigns to spread the word—many eligible folks in the US are refusing to be inoculated.

The erosion of confidence in the deficit model has occurred against a backdrop of large-scale changes in how lots of people relate to science, and how folks communicate with each other across all topics, as explored in a 2021 special issue of the Journal of Science Communication. In the introduction to that issue, Frank Kupper, Carolina Moreno-Castro, and Alessandra Fornetti assert that “the network of connections between science and society is becoming ever more complex, fragmented, heterogeneous and context-specific.” They nod to the digitalization of the public sphere, and its impact on how the public interacts with scientists and science communicators.

Some (including the authors mentioned above) write of an increasingly blurred boundary between science and society. But many view science and society as fundamentally interconnected, and have been shaping their communication efforts accordingly all along. Take African Gong, a “Pan-African Network for the Popularization of Science & Technology and Science Communication,” founded in 2014. Their vision statement is “to realise a scientifically literate African citizenry driven and powered by its ownership of scientific knowledge.”

Elizabeth Rasekoala, president of the network, expands upon that idea in an interview by Cristina Sáez for “la Caixa” Foundation:

“At African Gong we talk about social literacy, which is a synthesis of scientific and other types of literacy, like layers of knowledge. A person with basic scientific literacy is someone who can think critically that ‘A causes B and so C.’ Thus, if a politician or political party says ‘No, first comes C and then A,’ the person can question that message. If you are capable of equipping citizens with analytical, rational thought, you increase the country’s democratic quality. And that’s the link between scientific and social literacy.” (Quote reproduced with permission from Elizabeth Rasekoala and “la Caixa” Foundation.)

How do they work toward that vision? In part by empowering people through folding Indigenous knowledge systems into the practice of science communication. It doesn’t stop at simply considering the audience. It also reflects and actively involves everyone. As Summer May Finlay, Sujatha Raman, Elizabeth Rasekoala, Vanessa Mignan, Emily Dawson, Liz Neeley and Lindy A. Orthia assert, a respectful and level exchange of expertise between all parties should be prioritized, as opposed to an “us” to “them” flow of information:

“...we hope to undermine models of inclusion that picture ‘science communicators’ on one side and racialised, or otherwise-othered, ‘communities’ on the other. Such models risk sidelining the wealth of science communication practices occurring outside the mainstream, and can falsely characterise minoritised communities as resource poor, as if having nothing to offer, when in fact such communities produce relevant resources and are not in science or behavioural ‘deficit.’” (Quote reproduced with permission from the authors.)

That approach is more in line with modern science communication models that emphasize dialogue or participation, as opposed to deficiencies. All of these models are reflected/used to differing degrees in the mainstream science communication frameworks described below. (Note that the framework titles are, in some cases, my own characterizations. It is not an exhaustive, formal, or ubiquitous list.) When considering this selection of frameworks, it is important to keep in mind that—as highlighted by Finlay and co-authors—many of the concepts related to inclusivity have been authentically in play all along. As they put it (my emphasis), “Our examples show that outside the mainstream, this configuration is routine, because communicators are working within their own communities.”

MODE-CENTRIC FRAMEWORK • The idea of a multi-directional exchange is baked into a concise definition by Massimiano Bucchi and Brian Trench, published in 2021: “science communication is the social conversation around science.”  They continue:

“Two related usages of conversation are in play here: a mode of interactive communication that is set in contrast with dissemination or other hierarchical modes, and a concept that embraces all that is being said on a certain matter in society.  ...The conversation we speak of is both singular—the social conversation—and plural—the dispersed conversations of communities and colleagues, including the behind-the-scenes conversations of scientists that come increasingly into public view through social networks.” (Quote reproduced with permission from the authors.)

This framework—broken down in the table below—provides a few concrete ways to think about how science graphics can serve the social conversation around science. Some of those ways are familiar to me, like the concept of creating graphics by scientists for scientists in the context of academic journals. But it also leaves room for innovation in terms of thinking about how to create graphics that become part of larger-scale conversations and popular culture, as well as graphics that are designed to participate in specific conversations within more intimate groups of people.

The table folds in the idea of co-creation, which lies at the very heart of many writings on truly inclusive science communication efforts. Although, those writings also lead me to believe that the idea of co-creation should be prioritized across the full framework, and not relegated to the more “informal” end of the spectrum. Candidly, I’m not practiced in doing this myself. More on the topic of co-creation in the context of information design is on pages 164–165 and 304–306.

COMMUNITY-CENTRIC FRAMEWORK • Bucchi and Trench certainly nod to the idea of conversations within communities in their work, but community orbs aren’t overtly referenced in their table. Lindy A. Orthia, Merryn McKinnon, John Noel Viana, and Graham J. Walker address it head-on, with a way of thinking about the topic that can be used in tandem with other frameworks:

“...we identified three models of existing community-oriented science communication, which we labelled neighbourly, problem-solving and brokering. The models complement rather than overlap familiar concepts such as one-way, dialogue or participatory science communication, any of which may be incorporated into community-oriented activities at different points. ...The primary difference between our three models is their priority: brokering science communication is primarily concerned with serving the community’s interests, while neighbourly approaches tend to serve the interests of science, and problem-solving approaches prioritise practical solutions to a problem.” (Quote reproduced with permission from the authors.)

They maintain that co-creation, relationship building, and relationship maintenance is key, and acknowledge that there isn’t a clear, singular path to success.

GOAL-CENTRIC FRAMEWORK • Yet another complementary framework is presented in the National Academies of Sciences, Engineering, and Medicine’s research agenda on the topic. This one argues that the end goal should drive the communication method. The five primary goals are (paraphrased by me):

• Share the findings and excitement of science.

• Increase appreciation for science in general, to enhance peoples’ understanding of—and ability to navigate—the modern world.

• Increase knowledge and understanding of the science related to a specific issue, with an eye to personal decision making.

• Influence peoples’ opinions, behavior, and policy preferences.

• Expand engagement so that a diverse set of perspectives about issues related to science and society can properly be considered when solving society-wide problems.

Clear best practices are not included, as the list of goals was presented as a prompt in 2017 for more research to help identify what communication methods best serve what goal(s).

INCLUSION-CENTRIC FRAMEWORK • Many of the frameworks above flirt with the idea of inclusivity, but it’s worth stating things overtly, along with recommendations to help people work actively toward it. As Elizabeth Rasekoala put it in an interview with Stefan Skupien during the SciCOM 100 Conference in 2018:

“The visioning...was that these issues of diversity, equity and social inclusion would also grow with the movement over time. However,...as the international science communication field/movement grew, it just seemed to carry on reinforcing a very Euro-centric and male hegemony. These issues somehow—once in a while they get discussed in conferences—in panel sessions, but are not really addressed as a mainstream drive, and are not really embedded into the consciousness of the movement.” (Quote reproduced with permission from Elizabeth Rasekoala. From “Elizabeth Rasekoala on Science and Ownership,” Sustainable Research Cooperation – A review, January 17, 2019, accessed May 31, 2021. Original link is currently broken.)

Katherine Canfield and co-authors formalize a call for action on this front in their 2020 paper “Science Communication Demands a Critical Approach That Centers Inclusion, Equity, and Intersectionality.” They center these eight points of reflection:

EXCERPT FROM INTRODUCTION: “We envision a fundamental shift in science communication whereby inclusion, equity, and intersectionality ground all research and practice. Eventually, we hope the term ‘inclusive science communication’ will be redundant. For now, however, the ‘inclusive’ descriptor is a valuable framing device to clarify objectives and speed this transition. To this end, we define ISC as an intentional and reflexive practice and research approach that:

•  Recognizes historical oppressions, discrimination, and inequities and centers the voices, knowledge, and experiences of marginalized individuals and communities in STEMM dialogue.

•  Acknowledges that each person’s individual characteristics (e.g., gender, race, physical ability) overlap with one another (defined as “intersectionality” by Crenshaw, 1989) and that these intersectional identities affect their status in the world (Shimmin et al., 2017).

•  Further acknowledges that explicit and implicit biases (historical, cultural, experiential) of science communication practitioners and scholars influence the design and implementation of their work (Reich et al., 2010; Dawson, 2014c).

•  Rejects the oversimplifications of the deficit model (Trench, 2008; Simis et al., 2016), in which science communicators treat public audiences as lacking relevant knowledge or experience.

•  Incorporates asset-based methods that respect and value the ideas, experiences, questions, and criticisms that diverse publics bring to conversations about STEMM (Banks et al., 2007).

•  Aims to cultivate belonging and engagement of audience and collaborator perspectives (Wynne, 1992; Cheryan et al., 2013; Haywood and Besley, 2014; Leggett-Robinson et al., 2018).

•  Offers a multi-scaled approach to shift organizational cultures and structures and redress the systemic problems of inequitable access to and engagement with STEMM (Anila, 2017; Bevan et al., 2018).

•  Is relevant across formal and informal learning and engagement settings.

In summary, we urge a paradigmatic shift in science communication toward an overarching objective of expanding a sense of belonging in STEMM and approaches that embrace varied forms of expertise and ways of knowing.”

—From “Science Communication Demands a Critical Approach That Centers Inclusion, Equity, and Intersectionality,” by Katherine N. Canfield, Sunshine Menezes, Shayle B. Matsuda, Amelia Moore, Alycia N. Mosley Austin, Bryan M. Dewsbury, Mónica I. Feliú-Mójer, Katharine W. B. McDuffie, Kendall Moore, Christine A. Reich, Hollie M. Smith, and Cynthia Taylor, in Frontiers in Communication, Vol. 5 (2020). Excerpt is from an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) . © 2020 Canfield, Menezes, Matsuda, Moore, Mosley Austin, Dewsbury, Feliú-Mójer, McDuffie, Moore, Reich, Smith, and Taylor.

These are not the only structured ways of thinking about science communication. And they don’t necessarily dictate how strategies related to different models—like deficit, dialogue and participation—should be enacted within each structure. I present these four frameworks simply in the hopes that they’ll help you (and me!) think through our collective science communication efforts more critically. And—particularly when taken together—they address many pertinent topics and open questions in the field. Looking for something more conclusive to dig your teeth into? Let’s move on to a specific strategy: storytelling.

Storytelling

The last decade or so has been characterized by a resurgence in the popularity of narrative storytelling. Many folks—including data designers and scientific illustrators—have been exploring its potential, although research on its effectiveness in the service of science communication is still a work in progress.

What is narrative storytelling? As described by science communication researcher Michael Dahlstrom in 2014, “narratives follow a particular structure that describes the cause-and-effect relationships between events that take place over a particular time period that impact particular characters.” Liz Neeley and colleagues from The Story Collider emphasize their role as sensemaking devices. “They are means by which groups of people collectively reduce their uncertainty, resolve ambiguity, attribute consequences, and assign blame, among other things.” As story coach and author Lisa Cron says, “You can’t change how someone thinks about something, without first changing how they feel about it.”  And, simply put, stories impact how people feel about things.

The general consensus seems to be that thoughtfully crafted stories that feature the trajectory of a character (or characters) over time have the potential to encourage more people to care about—and connect with—topics related to science. But is that potential being realized? Some research indicates that it is, although the results are focused on very specific case studies, or are extrapolated from storytelling research that isn’t necessarily limited to science communication applications. A few key evidence-supported findings on stories in general (not necessarily ones crafted specifically in service of science communication) are:

• Communications that absorb folks into a constructed world using descriptive language, characters, and a story arc are more engaging and are more likely to change people’s behavior than those that do not.

• This effect is particularly strong if the audience can identify or connect on some level with a character or social situation in the story.

• Information is more memorable when served up in the form of a narrative story as opposed to expository forms.

For more on storytelling (both narrative and expository) and design strategies for telling stories with—and about—graphics, see Chapter 11.

SciArt

If one of the strengths of stories—and their core power as a science communication strategy—lies in the ability to get people to feel things, then the same can be said of the arts more broadly. Works of art and performances* invite introspection and conversation. It follows that they can be used in the service of science communication.

* See Jamē McCray’s 2017 SciVizNYC talk “Science on Stage” for more on how Superhero Clubhouse uses the performing arts as a way to bridge the gap between scientific facts and the public consciousness.

“SciArt”—as well as the addition of “A” in the classic STEM acronym, creating STEAM; Science, Technology, Engineering, Art and Mathematics—are related to the idea of integrating the arts with sciences in fundamental ways. Not just as add-ons or exclusively as expository communication tools. In some cases, the goal may be to infuse creative-arts thinking into the process of scientific research. For example, artist/scientist collaborations in the form of residencies. In other cases it may refer to science-inspired art. (This can take the form of a graphic, but also many things that are out of the scope of this book, like fashion.)

More often than not, the efforts result in visual artifacts or experiences that inform or instigate conversations about science. But the artifact shouldn’t be thought of as the only act of “science communication” output. Co-creating the artwork opens up conversations between the collaborators: Those conversations are just as critical as the conversations sparked by the artwork ultimately shared with others.*

*See “Art–Science Collaborations, Complexities and Challenges,” by Megan K. Halpern and Hannah Star Rogers, in Routledge Handbook of Public Communication of Science and Technology, 3rd ed. Routledge, 2021.)

How do Graphics Fit in?

It’s tempting to proclaim that visual languages are more universal than spoken and written languages, and that the very act of presenting information in the form of a drawing instead of words makes it more accessible. But that’s not necessarily the case.* Visual jargon, for example, is just as prevalent as written jargon. Symbols that carry highly specific information within a specific context can be a really efficient way to communicate with others that are fluent in that visual language, like a peer group of scientists. But they simultaneously act as a brick wall to outsiders.

* This is thoroughly explored by Neil Cohn in the context of sequential images in the book Who Understands Comics? Questioning the Universality of Visual Language Comprehension.

That said, think about how you experience a print newspaper or magazine. Do you flip through the pages first, scanning the images? What about social media? Once you start scrolling quickly down your timeline, what has the power to make you stop? Chances are, it’s an image. There’s generally a low initial barrier to entry when faced with an image. Color, form, and composition can trigger a reaction from the viewer without significant conscious effort. As perception researcher Colin Ware wrote, “visual media can support the perception of almost instantaneous scene gist, rapid explorations of spatial structure and relationships between objects, as well as emotions and motivations.”

This ability to communicate quickly—before asking too much of the audience—is a powerful thing when vying for eyeballs. Especially if you prescribe to the idea of peoples’ attention as being a limited resource. In science communication, a wide range of image types serve the purpose of engagement. Photographs, editorial illustrations, fine art, and graphics all have the potential to quickly capture the attention of people in different ways.

Under the “science communication is the social conversation around science” framework discussed earlier, each image type has a different role to play in nurturing those conversations. But to my mind, science graphics are uniquely positioned as visual aids that have the power to both beckon folks in, and to provide concrete information to influence the conversations that follow. At its best, engagement is followed by learning which then leads to continued engagement. All within the same frame.

Researchers have been chipping away at questions related to how graphics work—or don’t work—in the service of science communication. But there are limitations. Many of the studies I highlight here focus on graphics that are created by scientists for an audience of their peers. Admittedly, that’s a pretty narrow slice, and doesn’t really speak directly to many of the points made earlier in this chapter about public-facing models and frameworks. But the results are still telling.

Several studies focus on graphical abstracts (also known as visual abstracts) from research journals. Graphical abstracts are summary figures that serve as a concise, image-driven preview of the paper. They’re a handy subject. Many journals are starting to request them from paper authors, they generally adhere to a standard size (which also happens to fit well into many social media formats), and their goal is clear: Provide an easy-to-digest overview of the paper’s findings to engage individuals who are scrolling through a lot of content.

According to a 2016 study by Eva-Maria Pferschy-Wenzig and colleagues, the mere presence of a graphical abstract in a paper is not associated with a higher rate of article downloads, abstract views, or total citations. (Citations are a nod in a paper to an earlier work—like many of the footnotes I use in this book [most of those citations are translated into links for this digital version of the chapter]—and are often used as a way to measure impact of an original article. The idea being that if a paper is cited in other papers quite a lot, it contains information notable enough to cause ripple effects in the field and impact the scholarship of others.) At least that was the case for 1,326 articles published from March 2014 through March 2015 in the journal Molecules.

A 2017 study by Andrew Ibrahim and colleagues paints a more complex picture. They found that social media engagement was higher with posts that included graphical abstracts, as opposed to posts about the articles that didn’t include imagery. Specifically, tweets with graphical abstracts had about eight times as many impressions and retweets on Twitter, and 2.7 times as many click-throughs to the article. (Their study included 44 journal papers, and a set of graphical abstracts that were all created by a single designer—not the article authors themselves—using a consistent aesthetic.)

Sandra Oska and colleagues built on this study by introducing another variable: posts on Twitter that included a figure from the paper other than the graphical abstract. Tweets that contained a graphical abstract had more than twice as many views as both citation-only tweets and non-abstract figure tweets, five times the engagements of citation-only tweets and more than 3.5 times the engagements of non-abstract figure tweets.

What can we glean from that series of studies? Graphical abstracts don’t necessarily boost engagement by simply existing. A strategy for using those graphics is also critical.

What about the impact of graphics on the perception of quality? Karen Cheng, Yeechi Chen, Kevin Larson, and Marco Rolandi investigated how graphical abstracts influenced reader impressions of the research paper and the authors that wrote it. They identified existing graphical abstracts that didn’t adhere to design best-practices (in terms of making the best use of color, contrast, composition, and scale to boost legibility), gave them makeovers, and conducted a survey to see how people reacted to them. They found that the made-over graphical abstracts had a positive impact. The associated paper was perceived as “more interesting, more clearly written and more scientifically rigorous.”

Here’s an example of one of the graphical abstracts, before the redesign:

Here’s the redesigned graphical abstract, for the same article:

And here are results rooted in all 10 original and 10 redesigned graphical abstracts, according to 50 survey participants.

Although the presence of a graphical abstract in a paper isn’t associated with higher citation rates (per the study discussed above), it seems that another measure related to graphics is. In a 2018 study, Po-shen Lee, Jevin D. West, and Bill Howe used computer vision and machine learning to count and classify nearly 7 million figures from 494,663 biomedical and life science research papers in the PubMed Central archive. They found that highly cited papers tend to have a larger number of diagrams and schematics per page than less-cited papers (by a greater measure than photographs, data visualizations, and tables). Although they didn’t test for why that might be the case, they proposed a few possible explanations. Perhaps “visual information improves clarity of the paper, leading to more citations and higher impact, or that high-impact papers naturally tend to include new, complex ideas that require visual explanation.”

Large-scale studies like this one are possible in part due to the sharply focused and common primary goal of research papers—conveying experimental results to other researchers. They are also possible due to well-established traditions in scholarly scientific publishing—including routine use of citations—which provide a measure of the paper authors’ success in their goal of reaching and influencing other researchers. I think it’s fair to say that things get more complex when you step away from the clearly defined framework of the world of research journals. More complex, but not impossible.

The studies above highlight how science graphics can boost engagement and influence perceptions of the research and its authors. Tracie Curry and Ellen Lopez demonstrate how images can also provide context. They investigated the impact of including context-rich images—in this case, graphics and other visuals that reflected local and Indigenous knowledge—in reports designed for consideration by public sector resource management practitioners.

Their pilot study, conducted in collaboration with the Native Village of Wainwright (traditionally Ul˙guniq), focused on adaptation to climate change in northern Alaska. Interviews with environmental management agency employees made it clear that administrators would likely prefer summary images or tables over text-heavy documents, “due to time constraints.” Curry and Lopez subsequently built three versions of a two-page informational report:

A. Standard with text that summarizes knowledge from the natural sciences, along with a few conventional graphics, including a sea ice extent timeline, and a pair of maps.

B.  Same content as report A, with the addition of quotes from local residents.

C.  Same content as report B, with the addition of context-rich images.

The survey participants rated the reports that included quotes (B) and context-rich imagery (C) equally or more credible than reports that did not (A). Interviews with the study participants revealed some hesitations about the idea of including “context-rich” graphical content, as it was a bit outside of the norm within their agency. But they also acknowledged that ultimately the additional imagery and quotes helped underscore “the societal relevance of the science and [helped] readers understand what environmental conditions mean to people who live and work in the Arctic.”

Graphics allow for quantitative and qualitative presentation in a single image. And, as in the case of the figure shown here, they can provide context by presenting information in a way that nods to perspectives that may not otherwise be suitably represented in the text.

I should admit that I’m a bit conflicted about how, exactly, science graphics fit in moving forward. The mainstream science communication mindset has clearly been shifting away from one-way communication with the public, and toward approaches centered on conversation, collaboration, and interaction. (Although I should be clear, the practice of science itself has long been a conversation between scientists, in which researchers build on each others’ work, document and repeat compelling observations by replicating experiments, and challenge each other with counter-arguments.) But what does that mean for static imagery that is often designed to be handed over to an audience, and digested in solitude (presentation aids and public exhibits notwithstanding)? Perhaps the answer lies in thinking of these sorts of static graphics as engaging and digestible content to help inform and spark conversations. Or perhaps they serve to document the conversations. (Compelling meta-nods to the idea of documenting the conversation as a science communication tool include The Dialogues: Conversations about the Nature of the Universe by Clifford V. Johnson, and Equity, Exclusion and Everyday Science Learning (Zine Edition) by Emily Dawson and Sophie Wang.) Or—more in line with what lies at the heart of the paradigm shift—it’s time to grapple with how to flip things so that conversation, collaboration, and interaction with the intended audience can inform the graphic. Many folks are already doing this work. I’m still struggling with figuring out how to apply those lessons in a sustainable and meaningful way in the context of my own workflow and deadline schedules.

More to Explore

Getting to the Heart of Science Communication: A Guide to Effective Engagement, by Faith Kearns (Island Press, 2021)

Routledge Handbook of Public Communication of Science and Technology, Third Edition, edited by Massimiano Bucchi and Brian Trench (Routledge, 2021)

Communicating Science Effectively: A Research Agenda, by National Academies of Sciences, Engineering, and Medicine (The National Academies Press, 2017)

Equity, Exclusion and Everyday Science Learning: The Experiences of Minoritised Groups, by Emily Dawson (Routledge, 2020)

The Open Notebook: The story behind the best science stories • “A non-profit organization that provides tools and resources to help science, environmental, and health journalists at all experience levels sharpen their skills.”

Lifeology University SciComm Program: Free series of online flashcard courses.

A Tactical Guide to Science Journalism: Lessons From the Front Lines, edited by Deborah Blum, Ashley Smart, and Tom Zeller Jr., (Oxford University Press, 2022)

Race and Sociocultural Inclusion in Science Communication: Innovation, Decolonisation, and Transformation, edited by Elizabeth Rasekoala (Bristol University Press, 2023) (Reference added to this list after Building Science Graphics was published)

This is a free sample chapter from the book Building Science Graphics by Jen Christiansen, modified slightly for reading on digital devices.