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Inclusive images and data visualizations

Use imagery to reflect the diversity of our world and promote inclusion

by Sabrina J. Ashwell
October 9, 2023 | A version of this story appeared in Volume 101, Issue 33


A triangle-shaped banner with a rainbow gradient. There is a bird in the background
Credit: ACS

Images are a powerful way to communicate a message. They can cause joy, inspire people to take action, and teach new concepts. But images can also cause distress, demotivate people, and perpetuate stereotypes. The ACS Inclusivity Style Guide—an American Chemical Society guide on inclusive language and images—aims to help people harness the power of images to send messages that reflect the diversity of our world and promote inclusion.

Inclusive images

The guide encourages varying the characteristics of people in images. Characteristics to consider include age, body size and shape, disability status, skin tone, gender expression, and hair style and texture. For example, an image accompanying a C&EN article about international students shows a wide range of people, which helps convey the diversity of students in the US educational system. But often, a single image can’t show the full breadth of human experience. So the guide recommends being thoughtful about who you choose to represent and avoiding showing the same combinations of characteristics across all your images.

Inclusive data visualizations

Related to images are data visualizations, such as graphs and charts. Many people consider data to be neutral—they’re just facts, after all. But as anyone who has adjusted the axis of a graph knows, the way data are presented can greatly skew the facts and create misconceptions. Each decision related to showing data affects the ultimate message that an audience gets. Which type of visualization will you use? What colors will you choose, and do those colors have connotations? What groups will you include? What will you call them, and how will you order them? What accompanying text is necessary to contextualize the visualization? How did you make the visualization accessible?

The ACS Inclusivity Style Guide aims to help take the guesswork out of inclusive data visualizations by providing guidance on these questions and more. A key piece of advice is to disaggregate data when possible so that more people are visible in the data. Aggregating groups—such as combining all Indigenous groups and multiracial people into an “Other” category—can contribute to their erasure in the data displayed. The guide also encourages communicators to ensure their data visualizations are accessible so that people with disabilities can access the information.

The top tips on making images and data visualizations inclusive are compiled in two tip sheets, shown here and at, where you can also find the full ACS Inclusivity Style Guide. We’re sharing all the guide’s tip sheets in a six-part series in C&EN. I invite you to share feedback on the guide and tip sheets at


Like what you’ve read? See the full guide and the most up-to-date tip sheets from the American Chemical Society at


For more context, review the “Diversity and inclusion in images” section of the Inclusivity Style Guide. Use this tip sheet in combination with the “General guidelines” tip sheet.

Use images that reflect diversity

Aim to use images that show the diversity of our world. Consider skin tone, gender expression, age, disability status, body size and shape, and hair texture. At the same time, ensure the images are authentic and don’t just take a “one of each” approach.

Don’t perpetuate stereotypes

Ensure depictions of people, including their positions in the image, roles, facial expressions, clothing, and props, do not reinforce stereotypes.

Be accurate

Ensure images accurately depict cultures, and avoid editing photos to artificially show more diversity.

Data visualization

For more context, review the “Data visualization” section of the Inclusivity Style Guide. Use this tip sheet in combination with the “General guidelines” tip sheet.

Identify biased data

Examine data for biases or gaps. Consider the data’s context and the potential harm or erasure that may result from how they are presented. Use caution with topics such as crime and public safety.

Design with empathy

Consider whether certain chart types or whether focusing on a smaller range of data might help people connect with the human element of the data.

Disaggregate when possible

Disaggregate data when groups experience dissimilar effects, there isn’t a shared history, or members of the community say analyzing these populations together is unreasonable (Urban Institute, 2022). If you lack data on specific subgroups, acknowledge that limitation. Also avoid using an “Other” category.

Handling small data samples

If the sample size for a particular group is very small, avoid omitting the data entirely or noting “not statistically significant” without additional context.

✓ Use: The C&EN article “What US Chemists Made in 2022” shows data for all identity groups surveyed, even those with low numbers. To ensure readers could make informed choices about how to interpret the data, the article includes the number of respondents for each group and the note “ACS considers data calculated from fewer than 50 responses unreliable. C&EN included small groups to help make all members visible.”

Choosing color in data visualizations

Avoid choosing colors at random without considering their meaning or cultural associations. Recognize when certain colors may perpetuate stereotypes, and prevent introducing distortions in the data.

Ordering groups in data visualizations

Carefully think about the order in which groups are presented and how it might imply a hierarchy. Consider starting with the particular group the study is focused on or sorting the groups alphabetically.



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