Data visualization is a common and effective tool for communicating science to both experts and non-experts in a variety of formats including scientific papers, presentations, and posters. How data is represented in visualizations can dramatically affect how science is understood. When creating visualizations, scientists should consider the type of data, the underlying research question, and the intended audience in order to best communicate their findings.
For more information on best practices in data visualization see:
The Visual Display of Quantitative Information by 
Data Visualization by 
R for Data Science: import, tidy, transform, visualize, and model data by The Digital Scholarship Lab (located on 2 West of the Main Library) provides support for visualization including software. Their workstations provide access to Python, R, Open Refine, and Tableau Desktop.
The types of charts and figures you choose should be dependent on the type of data you would like to visualize. From Data to Viz is a website that provides helpful classification charts based on data types to guide your visualization decisions. You may also want to explore the "Caveats" page to learn more about when a chart type may be misleading or misrepresent your data.
Color choices, captions, and formatting can all contribute to the accessibility or inaccessibility of data visualizations. The following resources give a brief overview of steps you can take to make your data visualizations more accessible.
Resources for Color Choices:
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