Designing Figures for Colorblind Viewers

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Choose colorblind-safe palettes like Okabe-Ito or ColorBrewer alternatives that avoid red-green combinations, which are problematic for the most common forms of color vision deficiency.
• Test every figure with a colorblind simulator before submission or presentation, since up to 8% of male viewers may perceive your data differently than you do.
• Add redundant encoding such as patterns, shapes, labels, or direct data values so information remains accessible even when color perception differs.

Understanding Color Vision Deficiency in Your Audience

Color vision deficiency (CVD) affects a substantial portion of the population, with estimates indicating that up to 8% of men and about 0.5% of women experience some form of reduced color discrimination. The most common types involve difficulty distinguishing between red and green hues, which creates immediate problems for scientific figures that rely on default red-green color schemes. When you present research findings, your audience may include individuals who cannot perceive the distinctions you intend to convey through color alone.

The practical consequence is straightforward: a figure that appears clear and informative to you may be confusing or entirely misleading to a portion of your viewers. This matters in scientific presentations, thesis defenses, journal submissions, and laboratory meetings where accurate data interpretation is essential. A reviewer or committee member with CVD may misinterpret your results, question your methodology, or fail to grasp the significance of your findings simply because the visual encoding is inaccessible.

Color perception exists on a spectrum, and the severity of deficiency varies widely among individuals. Some people have mild difficulty distinguishing certain shades, while others cannot perceive particular hues at all. This variability means that designing for colorblind accessibility is not about accommodating a rare edge case but about ensuring your figures communicate effectively to the broadest possible audience. The design choices you make affect real people making real judgments about your work.

Core Principles of Colorblind-Safe Figure Design

Prioritize Luminance and Contrast Over Hue

The most reliable way to make figures accessible to viewers with CVD is to ensure that different data categories differ in lightness or brightness, beyond in hue. When two colors have similar luminance values, they appear nearly identical to someone with CVD even if they look quite different to someone with normal color vision. By contrast, colors that differ substantially in lightness remain distinguishable regardless of the viewer's color perception.

This principle applies across all figure types. In line charts, use line styles such as solid, dashed, and dotted in addition to color. In bar charts, vary the fill lightness from dark to light. In scatter plots, use different marker shapes alongside color. These redundant encoding strategies ensure that even if color information is lost, the structural information remains intact.

Choose Palettes Designed for Colorblind Accessibility

Several well-tested color palettes have been developed specifically to remain distinguishable for viewers with common forms of CVD. The Okabe-Ito palette, named for its creators, provides eight colors that are designed to be distinguishable by individuals with protanopia, deuteranopia, and tritanopia. This palette includes colors such as vermillion, blue, and sky blue, which maintain their distinctiveness across different types of color vision.

ColorBrewer offers sequential, diverging, and qualitative palettes that have been evaluated for colorblind safety. When selecting a palette, look for those labeled as colorblind-safe or check the accompanying documentation for accessibility notes. Many scientific plotting libraries include these palettes as built-in options, making implementation straightforward.

Avoid Red-Green Combinations Entirely

The most common forms of CVD affect the red-green axis of color perception. This means that any figure relying on red-green distinctions to convey meaning will fail for a significant portion of your audience. Common examples include red-green heatmaps, red-green scatter plots, and figures that use red to indicate one condition and green to indicate another.

The solution is not to find the perfect shade of red or green but to eliminate the reliance on this color axis altogether. Replace red-green schemes with blue-orange, blue-red, or other combinations that remain distinguishable across CVD types. If you must use red and green, add redundant encoding such as different shapes, patterns, or direct labels to ensure the information is accessible through other channels.

Selecting Appropriate Color Palettes

Qualitative Palettes for Categorical Data

When your figure displays distinct categories such as treatment groups, experimental conditions, or sample types, use a qualitative palette designed for colorblind accessibility. The Okabe-Ito palette provides eight colors that work well for categorical data. For more than eight categories, consider whether your figure is trying to convey too much information at once and whether grouping or faceting might improve clarity.

The ColorBrewer qualitative palettes labeled as colorblind-safe offer additional options. These palettes have been tested for accessibility and are available in most scientific plotting software. When selecting a qualitative palette, verify that adjacent categories differ in both hue and luminance to provide maximum distinguishability.

Sequential Palettes for Ordered Data

Sequential palettes display data that ranges from low to high values, such as expression levels, concentrations, or time points. These palettes typically use a single hue with varying lightness, which naturally provides the luminance contrast that supports colorblind accessibility. Choose a sequential palette that progresses from light to dark or from dark to light in a way that remains perceptible when color information is reduced.

ColorBrewer's sequential palettes include options that are specifically designed to be perceptually uniform, meaning that equal steps in data values correspond to equal steps in perceived color difference. This property supports accurate interpretation for all viewers, including those with CVD.

Diverging Palettes for Data With a Meaningful Midpoint

Diverging palettes display data that has a meaningful central value, such as fold changes where zero represents no change, or correlation values where zero represents no relationship. These palettes typically use two hues that meet at a neutral midpoint. For colorblind accessibility, choose diverging palettes that use hues distinguishable across CVD types, such as blue-orange or blue-red, instead of red-green.

The ColorBrewer diverging palettes labeled as colorblind-safe provide tested options. Ensure that the midpoint is visually distinct from both endpoints and that the progression of lightness supports interpretation even when hue information is partially lost.

Implementing Redundant Encoding Strategies

Use Patterns and Textures in Addition to Color

Patterns and textures provide a powerful way to encode categorical information that remains accessible to viewers with CVD. In bar charts and area charts, use hatching, stippling, or other fill patterns to distinguish categories. In maps and heatmaps, use different symbol types or line patterns to supplement color information.

When implementing patterns, ensure that the patterns are visually distinct and do not create moire effects or visual artifacts that distract from the data. Test your figures at the size they will be displayed to verify that patterns remain discernible.

Incorporate Shapes and Marker Styles in Plots

In scatter plots and line charts, use different marker shapes to distinguish data series. Common options include circles, squares, triangles, diamonds, and crosses. Combine shape differences with color differences to provide redundant encoding that supports interpretation for all viewers.

For line charts, vary line style using solid, dashed, dotted, and dash-dot patterns. This approach ensures that individual lines remain distinguishable even when printed in grayscale or viewed by someone with CVD.

Add Direct Labels and Data Values

The most robust form of redundant encoding is direct labeling. Instead of relying solely on a legend, place labels directly on or near the data elements they describe. This approach eliminates the need for viewers to match colors between the plot area and the legend, which is a common source of confusion for individuals with CVD.

For figures where precise values matter, consider adding data labels directly to points, bars, or lines. This practice supports accurate interpretation and reduces the cognitive load required to extract information from the figure.

Testing Your Figures for Colorblind Accessibility

Use Colorblind Simulators

Several software tools simulate how figures appear to individuals with different types of CVD. These simulators apply mathematical transformations that approximate the color perception of individuals with protanopia, deuteranopia, and tritanopia. Running your figures through these simulators reveals whether your design choices remain effective when color information is reduced.

Many image editing programs include colorblind simulation filters, and several web-based tools allow you to upload images for analysis. Some scientific plotting libraries include simulation functions that let you preview figures before export. Make simulator testing a routine part of your figure creation workflow.

Print in Grayscale as a Quick Check

Printing your figure in grayscale provides a quick approximation of how it appears to someone with severe CVD. If the figure remains interpretable in grayscale, it will likely remain interpretable for most viewers with CVD. This check is particularly useful for verifying that your design relies on luminance contrast and redundant encoding instead of hue alone.

Keep in mind that grayscale printing is a conservative test. Some figures that fail this check may still be accessible to individuals with mild CVD, but designing for grayscale readability ensures maximum accessibility.

Seek Feedback From Colleagues With CVD

If you have colleagues or collaborators with CVD, ask them to review your figures and provide feedback. Their direct experience can reveal issues that simulators may not fully capture. This feedback is valuable for understanding how real viewers perceive your figures and for identifying design choices that cause confusion.

When seeking feedback, ask specific questions about whether they can distinguish data categories, follow trends, and extract the intended information from the figure. Their responses will guide refinements that improve accessibility for all viewers.

Practical Workflow for Creating Accessible Figures

Step 1: Plan Your Color Strategy Before Creating the Figure

Decide on your color palette and redundant encoding strategy before you begin plotting. This planning prevents the common failure of creating a figure with default colors and then attempting to retrofit accessibility. Consider the type of data you are displaying, the number of categories or series, and the medium where the figure will appear.

Document your color choices and the rationale behind them. This documentation supports consistency across figures in a presentation or manuscript and helps collaborators understand your design decisions.

Step 2: Select a Colorblind-Safe Palette From a Tested Source

Choose your palette from a source that has evaluated colorblind accessibility, such as ColorBrewer or the Okabe-Ito palette. Verify that the palette includes sufficient luminance contrast between categories and that the hues remain distinguishable across CVD types. Avoid mixing palettes or introducing custom colors unless you have tested them for accessibility.

Step 3: Apply Redundant Encoding Throughout the Figure

Add patterns, shapes, line styles, or labels to supplement color information. Apply these encoding strategies consistently across all elements of the figure. For example, if you use circle markers for one condition and square markers for another, maintain this distinction throughout the figure and in any related figures.

Step 4: Test With Simulators and Grayscale

Run your completed figure through colorblind simulators for protanopia, deuteranopia, and tritanopia. Print or view the figure in grayscale to verify that luminance contrast supports interpretation. Review the figure at the size it will be displayed to ensure that patterns and labels remain legible.

Step 5: Refine Based on Test Results

If the figure fails any accessibility test, refine the design and retest. Common refinements include increasing luminance contrast, adding or adjusting patterns, and placing direct labels on data elements. Repeat the testing cycle until the figure passes all checks.

At a Glance: Colorblind-Safe Design Decisions

Common Failure Patterns in Scientific Figures

Default Software Color Schemes

Most scientific plotting software defaults to color schemes that are not colorblind-safe. These defaults often include red-green distinctions that fail for a significant portion of viewers. Failing to change these defaults is the most common accessibility error in scientific figures.

The solution is to explicitly set your color palette instead of accepting defaults. Most plotting libraries allow you to specify custom palettes, and many include colorblind-safe options as presets. Make it a habit to override default colors in every figure you create.

Rainbow and Jet Colormaps

Rainbow and jet colormaps are widely used for heatmaps and surface plots, but they are problematic for colorblind accessibility. These colormaps cycle through many hues, making it difficult for viewers with CVD to map colors to values. The perceptual spacing of these colormaps is also uneven, which can distort the apparent magnitude of differences in your data.

Replace rainbow and jet colormaps with perceptually uniform sequential or diverging palettes. These alternatives provide consistent perceptual spacing and remain interpretable for viewers with CVD.

Overreliance on Legends

Legends require viewers to match colors between the plot area and the legend key. This matching process is difficult for individuals with CVD, particularly when the figure contains many categories or when colors are similar. Overreliance on legends is a common accessibility failure even when the chosen palette is colorblind-safe.

Reduce reliance on legends by placing direct labels on data elements, using annotations to identify key features, and keeping the number of categories manageable. These practices support interpretation for all viewers and reduce the cognitive load required to extract information.

Insufficient Luminance Contrast

Figures that use colors with similar lightness values fail for viewers with CVD even when the hues are technically different. This failure occurs because hue information is lost or reduced in CVD, leaving only luminance differences to distinguish categories. When luminance differences are minimal, categories become indistinguishable.

Ensure that your chosen palette provides sufficient luminance contrast between categories. Test your figures in grayscale to verify that categories remain distinguishable when hue information is removed.

Tools and Software for Colorblind-Safe Design

ColorBrewer for Palette Selection

ColorBrewer provides tested color palettes for sequential, diverging, and qualitative data. The palettes are labeled with accessibility information, and the website allows you to preview how palettes appear under different types of CVD. ColorBrewer palettes are available in most scientific plotting libraries, making implementation straightforward.

When using ColorBrewer, select palettes labeled as colorblind-safe and verify that the number of colors matches your data categories. Using more colors than necessary reduces distinguishability and increases the risk of confusion.

Okabe-Ito Palette for Categorical Data

The Okabe-Ito palette provides eight colors specifically designed to be distinguishable by individuals with common forms of CVD. This palette is widely used in scientific publishing and is available in many plotting libraries. The palette includes vermillion, blue, sky blue, and other colors that maintain distinctiveness across CVD types.

For figures with more than eight categories, consider whether the figure is trying to convey too much information. Grouping related categories, using faceting, or splitting the figure into multiple panels may improve clarity and accessibility.

Simulation Tools for Testing

Several tools allow you to simulate how figures appear to individuals with CVD. These include browser-based simulators, image editing filters, and functions within scientific plotting libraries. Use these tools to test every figure before submission or presentation.

Simulation tools provide an approximation of CVD perception and are not perfect representations of individual experience. Combine simulator testing with grayscale checks and feedback from colleagues with CVD to ensure comprehensive accessibility.

Records and Documentation for Figure Accessibility

Maintain a Style Guide for Your Research Group

Document your colorblind-safe design choices in a style guide that all group members can reference. Include the specific palettes you use, the redundant encoding strategies you apply, and the testing procedures you follow. This documentation supports consistency across figures and reduces the learning curve for new group members.

Update the style guide as you discover new tools, palettes, or techniques that improve accessibility. Review the guide periodically to ensure it remains current and useful.

Record Testing Results for Each Figure

Keep a record of the accessibility testing performed for each figure, including the simulation tools used and the results of grayscale checks. This documentation supports quality control and provides evidence that accessibility was considered during figure creation.

For figures intended for publication or formal presentation, include accessibility testing in your workflow documentation. This practice supports transparency and demonstrates attention to audience needs.

Track Feedback and Refinements

When colleagues with CVD provide feedback on your figures, record their observations and the refinements you made in response. This feedback is valuable for improving your design practices and for identifying patterns that cause confusion across different figures.

Review feedback across multiple figures to identify recurring issues and address them in your style guide. This continuous improvement process supports increasingly accessible figure design over time.

Welfare and Ethical Considerations in Figure Design

Accessibility as a Professional Responsibility

Designing accessible figures is a professional responsibility for researchers and educators. When your figures are inaccessible to viewers with CVD, you exclude a portion of your audience from full participation in scientific discourse. This exclusion has ethical implications for the dissemination of research findings and the education of students.

The National Library of Medicine provides access to research methods resources that emphasize rigorous and transparent research practices. Accessible figure design supports these goals by ensuring that research findings are communicated effectively to the broadest possible audience.

Transparency in Research Communication

Transparent research communication requires that your figures accurately convey your findings to all viewers. When figures rely on color distinctions that some viewers cannot perceive, the communication is incomplete and potentially misleading. Accessible design supports the transparency that is fundamental to scientific integrity.

The Committee on Publication Ethics outlines core practices for ethical research conduct, including transparency in reporting. Accessible figure design aligns with these practices by ensuring that visual information is available to all readers and reviewers.

Supporting Inclusive Scientific Environments

Creating accessible figures supports inclusive scientific environments where all participants can engage fully with research content. This inclusivity extends beyond colorblind accessibility to encompass other accessibility considerations, such as legible fonts, sufficient contrast, and clear labeling.

Inclusive design practices benefit all viewers, also those with specific accessibility needs. Figures that are clear, well-labeled, and easy to interpret support effective communication for everyone.

Limitations and Considerations

Individual Variation in Color Perception

Color vision deficiency varies widely among individuals, and simulation tools provide only approximations of how figures appear to specific viewers. Some individuals with CVD may perceive figures differently than simulators predict, and individual preferences for color combinations vary.

Design for the most common forms of CVD while recognizing that no design can be perfectly accessible to every individual. Redundant encoding strategies provide the best approach for maximizing accessibility across the spectrum of color perception.

Medium-Specific Considerations

The medium where your figure appears affects accessibility considerations. Figures displayed on screens may use backlit displays that render colors differently than printed figures. Projected figures in presentation rooms may be affected by ambient lighting and projector calibration.

Test your figures in the medium where they will be presented whenever possible. For presentations, review your slides on the actual projection equipment before your talk. For publications, review proofs carefully to verify that colors render as intended.

Balancing Aesthetics and Accessibility

Some researchers resist colorblind-safe design because they perceive accessible palettes as less visually appealing than default color schemes. This perception is often based on familiarity instead of objective aesthetic quality. Colorblind-safe palettes can produce visually striking and effective figures when applied thoughtfully.

Focus on the communicative function of your figures instead of aesthetic preferences. A figure that effectively conveys your data to all viewers is more valuable than one that is visually appealing but inaccessible to a portion of your audience.

Professional Escalation Criteria

When to Seek Additional Guidance

If you are unsure whether your figures meet accessibility standards, seek guidance from colleagues with expertise in data visualization or from your institution's accessibility office. These resources can review your figures and provide specific recommendations for improvement.

For figures intended for publication, consult the journal's author guidelines for any accessibility requirements. Some journals have specific requirements for color use and figure accessibility, and following these requirements supports successful submission.

When to Revise Existing Figures

If you discover that figures in a submitted manuscript or scheduled presentation are not colorblind-safe, revise them before final submission or presentation. The effort required to revise figures is small compared to the cost of miscommunication or negative reviewer feedback.

For published figures that are not accessible, consider whether corrections or supplementary accessible versions are appropriate. Consult with coauthors and follow relevant publication ethics practices when addressing accessibility issues in published work.

When to Update Institutional Practices

If you identify systemic accessibility issues in your research group's figure production, advocate for updated practices and documentation. Propose updates to your style guide, recommend tools for accessibility testing, and share your findings with colleagues.

Institutional change requires sustained effort and collaboration. Start by documenting the issues you observe, proposing specific solutions, and building support among colleagues who share your commitment to accessible research communication.

Frequently Asked Questions

What percentage of viewers have color vision deficiency?

Up to 8% of men and about 0.5% of women have some form of color vision deficiency. In a typical scientific audience, this means that several people in a room of 100 may perceive your figures differently than you do. The most common forms affect red-green color discrimination, which is why red-green color schemes are particularly problematic.

Are all colorblind people unable to see any colors?

No, color vision deficiency exists on a spectrum. Most individuals with CVD can see many colors but have difficulty distinguishing specific hues, particularly along the red-green axis. Some individuals have difficulty with blue-yellow distinctions, and a small number have more severe limitations. This variability is why redundant encoding strategies are important for accessibility.

Can I use red and green if I add labels to my figure?

Adding labels or other redundant encoding can make a red-green figure accessible, but it is generally better to avoid red-green combinations entirely. Labels and other encoding strategies add visual clutter and require viewers to read text to understand the data. Choosing a colorblind-safe palette with sufficient luminance contrast is a cleaner and more effective approach.

What is the Okabe-Ito palette?

The Okabe-Ito palette is a set of eight colors designed to be distinguishable by individuals with common forms of color vision deficiency. The palette includes vermillion, blue, sky blue, and other colors that maintain their distinctiveness across CVD types. It is widely available in scientific plotting libraries and is a good default choice for categorical data.

How do I test my figures for colorblind accessibility?

You can test figures using colorblind simulators, which apply mathematical transformations to approximate CVD perception. Many image editing programs include simulation filters, and several web-based tools allow you to upload images for analysis. Printing in grayscale provides a quick check of luminance contrast, and feedback from colleagues with CVD offers direct insight into accessibility.

Are ColorBrewer palettes colorblind-safe?

ColorBrewer provides palettes that have been evaluated for colorblind accessibility, and palettes labeled as colorblind-safe are designed to remain distinguishable for viewers with common forms of CVD. When using ColorBrewer, select palettes with the colorblind-safe label and verify that the number of colors matches your data categories.

What should I do if my figures are already created with inaccessible colors?

Revise existing figures before final submission or presentation. Replace inaccessible color schemes with colorblind-safe palettes, add redundant encoding such as patterns or shapes, and test the revised figures with simulators and grayscale checks. The revision process is straightforward and significantly improves accessibility.

Do journals require colorblind-safe figures?

Some journals have specific requirements for figure accessibility, and many journals provide author guidelines that address color use. Even when not explicitly required, colorblind-safe figures support successful review by ensuring that all reviewers can accurately interpret your data. Consult the journal's author guidelines for specific requirements.

Using the Evidence

Related Articles

• Scientific Data Provenance For Reproducible Research
• Data Management Basics: Principles, Processes, and Best Practices
• What Is a Data Lake? Architecture, Use Cases, and Best Practices
• Data Presentation in Research: Tables, Figures, and Text
• Ethical Data Collection in Research: Principles and Best Practices

References and Further Reading

• Research Methods Resources. National Library of Medicine.
• EQUATOR Network. EQUATOR Network.
• Core Practices. Committee on Publication Ethics.
• NIH Grants and Funding. National Institutes of Health.
• ORCID for Researchers. ORCID.
• Data Management and Sharing Policy. National Institutes of Health.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.