2025-12-08 –, Horace Mann
The default color space for computers includes over 16 million colors—an embarrassment of riches that is also a potential quagmire to anyone considering how to best choose colors for visualizations. In this workshop, we will provide a practical framework for working with color. We will start by developing an understanding of color models and color theory, building from these to provide simple but powerful heuristics for color selection that will enable creators of data visualization to enhance the clarity, power, and storytelling of their visualizations. We will conclude with the introduction of tools for working with and selecting color, followed by hands-on activities using these tools. No prior knowledge is needed or assumed, and the only tools you will need is a computer with a web browser and an internet connection.
We propose a workshop targeting anyone who finds themselves making data visualizations (e.g., data scientists, engineers, programmers, etc). The goal of the workshop is to help such people understand color and how choices in color impact the perception of their data visualizations. Though color is a ubiquitous aspect of the design of data visualizations, few people have been trained in its use. In other words, a great deal of time is spent training people to understand how to obtain, clean, and analyze data, as well as the basic mechanisms of making data visualizations, but comparatively little guidance is given regarding how to leverage tools like color for the most effective communication of data-based insights.
For the past 5 years, we have been involved in teaching a course at Penn State (SC103N: When Data Meets Design) aimed at exploring the intersection between data visualizations and graphic design. Having taught hundreds of students within this course, we have found that within a 50 minute lecture period, we can provide a short introduction to color that produces a noticeable benefit how color is used to emphasize the stories they are trying to tell with their data. Thus, we are confident that, within a 90 minute workshop, we can provide participants with the same introduction, followed by practical applications. The overall result will be simple, yet powerful and enduring, tool to inform the design of your own data visualizations.
The proposed workshop will start with a 15 minute introduction to color theory and color models. We will then spend the next 30 minutes in an interactive exploration of various heuristics that can be used to enhance storytelling with data. This will be done by considering existing data visualizations and discussion of how color is used, and how this use might be improved. The next 30 minutes will involve guided, hands-on activities in which participants will explore tools for building effective color palettes and evaluating the accessibility of their color choices. This hands-on session assumes no prior knowledge, and will use no tools other than a computer with a web browser and access to the internet. The last 15 minutes will be used for a round-table on the topic, where participants can ask and discuss any remaining questions they might have, including discussion of any data visualizations they have created. The goal is to emerge with a practical framework for working with color, as well as the tools and confidence needed to immediately use and improve your own data visualizations.
Benjamin Lear is a professor of chemistry at the Pennsylvania State University in University Park, PA. There he runs a research group focused on understanding the interactions between nanoscale materials and their chemical environment. In addition to running this research group, he teaches a course on the design of data visualizations---a topic on which he has delivered numerous international presentations and workshops. He is a regular user of Python and has co-authored a forthcoming book from MIT press that teaches Python to experimental chemists.
I’m a Ph.D. candidate in Chemistry at Penn State University with a passion for teaching and educational innovation. My work centers on integrating AI tools into chemistry instruction, designing research-based learning activities, and exploring how students develop conceptual understanding in science. I’m also involved in global agricultural education through the Global Teach Ag Network, where I help connect food science, sustainability, and classroom practice.