How can you create accessible data visualizations for your lab team?

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Data visualization is a powerful tool to communicate complex and nuanced information in a clear and engaging way. However, not all data visualizations are equally accessible to everyone. If you want to create data visualizations that your lab team can easily understand and use, you need to consider some key aspects of design, format, and accessibility. In this article, you will learn how to create accessible data visualizations for your lab team by following these six steps:

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Elijah Meeks

Data Visualization Expert

1 Choose the right type of chart

The first step is to choose the type of chart that best suits your data and your message. Different types of charts have different strengths and weaknesses, and some are more accessible than others. For example, bar charts and line charts are generally easy to read and compare, while pie charts and scatter plots can be confusing and cluttered. You should also avoid using 3D effects, fancy animations, or unnecessary embellishments that can distract from the data.

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Elijah Meeks

Data Visualization Expert
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You've probably learned about the most effective ways to encode categorical and numerical data. Like how length is better than angle so that's why bar charts are better. All of those rules are valid but if you want your charts to be effective you should ensure that you are making the charts that your audience expects. If the audience is you, then use charts and styles optimized for exploration and invested work (complex charts and "cluttered" charts are okay) but if your audience is other people, make sure you understand what they expect and build within that restriction. You can still help shepherd them away from pie charts and rainbow charts but only after you establish your credibility.

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Hammad Ali Q.

Corporate project management | Strategic ecommerce consultant | Innovative business development | Driving growth & operational excellence | Clinical laboratory scientist | Quality assurance specialist
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To create accessible data visualizations for your lab team: Choose simple, easy-to-understand formats. Ensure clear labels and titles for all elements. Provide alternative text for screen readers. Use high-contrast colors and patterns. Consider interactive features for customization. Test with diverse users for accessibility. Offer training and support for interpretation.

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2 Use clear and consistent labels

The second step is to use clear and consistent labels for your axes, legends, titles, and captions. Labels should be concise, descriptive, and accurate, and use the same terminology and units throughout the chart. You should also avoid using abbreviations, acronyms, or jargon that might not be familiar to your audience. Labels should be legible and contrast well with the background, and use a font size and style that is easy to read.

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3 Choose appropriate colors and symbols

The third step is to choose appropriate colors and symbols for your data points, categories, and trends. Colors and symbols should be meaningful, relevant, and consistent, and help to highlight the most important information. You should also avoid using too many colors or symbols that can make the chart look busy or confusing. Moreover, you should consider the colorblindness of your audience, and use colors that are distinguishable by people with different types of color vision. You can use tools like ColorBrewer or Color Oracle to test your color choices.

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Elijah Meeks

Data Visualization Expert
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Remember that color distinctiveness depends on the size of the shapes being encoded with color. You can distinguish colors more easily in areas (like the bars of bar charts) than in lines and in lines more than in points. Keep that in mind with color resources like ColorBrewer, which are designed for area-based viz like choropleth maps and may not be suitable for point-based viz like scatterplots. Try not to use symbols.

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4 Provide alternative text and captions

The fourth step is to provide alternative text and captions for your data visualizations, especially if you are sharing them online or in a document. Alternative text is a brief description of the chart that can be read by screen readers or other assistive technologies. Captions are longer explanations of the chart that can provide additional context, insights, or limitations. Both alternative text and captions should summarize the main message, data, and features of the chart, and use plain language.

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5 Test your data visualizations

The fifth step is to test your data visualizations with your lab team and get feedback. Evaluating your charts can help you identify and correct any errors in your data, design, or accessibility. Ask your team members to review the charts and provide answers to questions such as what the main message or purpose of the chart is, what the key data points or trends are, how easy it is to read and understand, if it is visually appealing and engaging, if there are any confusing or misleading elements, and how the chart can be improved.

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6 Update and refine your data visualizations

The sixth and final step is to update and refine your data visualizations based on the feedback and testing results. You should make any necessary changes or adjustments to your data, design, or accessibility, and ensure that your charts are clear, accurate, and effective. You should also document your data sources, methods, and assumptions, and provide any references or citations if needed. Finally, you should share your data visualizations with your lab team and invite them to use them for their own analysis, communication, or decision making.

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Elijah Meeks

Data Visualization Expert
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Always invited feedback and integrate that into your work to build trust with your stakeholders. Trust is key to developing more sophisticated information displays over the long term and more sophisticated information displays are key for making better decisions.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you create accessible data visualizations for your lab team?

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1 Choose the right type of chart

2 Use clear and consistent labels

3 Choose appropriate colors and symbols

4 Provide alternative text and captions

5 Test your data visualizations

6 Update and refine your data visualizations

7 Here’s what else to consider

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