For a book that attempts to explain how to understand visuals in life sciences, it seems prudent to first explain what we mean by “visual,” even if it may seem quite a common word.

In everyday conversation, “visual” is often used as an adjective and means “relating to seeing or sight,” as in “visual impression” or “visual effect.” In the context of this book, “visual” is used similarly as an adjective, but in addition, and more often, it is used as a noun. As a noun, it refers to the variety of images used in life science communication. For example, photographs are a type of visual commonly used in life science communication, and so are drawings.

Illustrations are a visual staple in life science communication. Despite being commonplace, they are in many ways a blackbox. They mask the creative – and scientific – decisions that go into making them. They present an end product that says, as it were, “this is how you look through life to its essence.” The use of precise lines and explicit shapes helps to convey this scientific authority. In contemporary illustrations, pseudo-details such as colors and dimensions further prove that “this is what life looks like.”

Micrographs, like the little (pun intended) cousin of photographs, are considered by some as an objective portrayal of nature. Why, they are photographs of the microscopic world invisible to the naked human eye. As such, what you see is what you get, and what you get is nature unveiled.

Particularly because the microscopic world is invisible to us in everyday life, we find it even more urgent to behold that world. We assume that if and when we see, we will automatically understand. If and when we observe microorganisms in their smallest components, we will be able to “get” them and conquer them.

Contemporary life sciences are big data sciences. The human genome, for example, contains about three billion DNA base pairs and an estimated 20,000 protein-coding genes. Public health data, as another example, are endlessly enormous and encompass electronic medical records, health monitoring data, environmental data, and more. When it comes to analyzing and presenting these big data, interactive online visuals – maps, graphs, three-dimensional models, even computer games – have inherent advantages. They are dynamic and easily updated. They support user interaction and allow users to create displays that make sense to them. Being “hands-on” also makes these visual displays more interesting. As computer visualization technologies continue to advance, we are guaranteed to see faster, more fluid, more ingenious interactive displays.

As we have seen throughout this book, standalone visuals like photographs and illustrations are promising ways to communicate science to the public – and they carry their fair share of misconceptions and complications. These promises – as well as challenges – are multiplied in infographics.

The word “infographic” comes from the phrase “information graphic.” Originally, the term referred to the production of graphics for print media such as newspapers and magazines. Today it refers to a unique multimodal genre that combines data visualizations (i.e., graphs such as lines, pies, bars, and pictographs), illustrations (such as icons and drawings), photographs, and small amounts of text. When designed for online use, infographics can also have interactive components. For example, putting the mouse cursor somewhere on the infographic may reveal a small pop-up window with additional information. Some infographics are also animated: bars in a bar chart may grow, colors may change, or characters may move. This is often achieved by using animated GIF files that display a sequence of static images in a repeating loop, which creates the illusion of motion.

Graphs – such as line graphs or bar graphs – convey numerical data. They are commonly used in life science communication as well as other communication contexts, such as when conveying stock market data, crime statistics, or real estate trends. The prevalence of these graphs doesn’t mean, as some may assume, that they are always easy to understand. Depending on design choices, some graphs will be able to shed light on important numerical data for public understanding of science, while others are likely to confuse or leave readers with a heightened conviction that science is an inaccessible enterprise.

Photographs are often considered an “easy” and accessible type of scientific visual. After all, they are commonplace in everyday life and not exclusive to scientific research. Everyone takes photographs and knows what photographs are. As long as one can physically see, one (so it is thought) can get what a photograph is about. Unfortunately, when it comes to life science photographs, much of this is misconception. This chapter explains why.