Accessible Data

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Accessibility


Accessible Icon. Blue circle with a white stick figure inside.

The Office for Civil Rights (OCR) at the U.S. Department of Education defines accessibility as meaning “when a person with a disability is afforded the opportunity to acquire the same information, engage in the same interactions, and enjoy the same services as a person without a disability in an equally integrated and equally effective manner, with substantially equivalent ease of use.”

Plain and Accessible Language


Plain and accessible language not only supports understanding but belonging. Plain language is language that is understood the first time a person reads it. 

Below are some resources generated by partners on plain and easy language

What is plain language? | plainlanguage.gov

Plain-Language-Checklist.pdf (gmsavt.org)

How to Write Using Plain Language – Green Mountain Self-Advocates (gmsavt.org)

Accessible Data Visualizations


For decades the State of the States in Intellectual and Developmental Disabilities has been working with our project advisory board and experts with lived experience to customize our data and briefs to meet the needs of our community. In 2018, we began validating our understanding of accessible data visualizations. We partnered with the VisuaLab at the University of Colorado, Boulder, Dr. Danielle Albers Szafir and Keke Wu to apply data visualization research methods to investigate accessibility for people with cognitive disabilities. Our research and subsequent guidelines on accessibility of data visualizations for people with IDD won a best paper award at the CHI 2021 Virtual Conference

Project team who created accessible data visualizations. From left to right: Keke Wu, Emma Petersen, Tahmina Ahmad, David Burlinson, Shea Tanis, and Danielle Albers Szafir.

CHI Paper.pdf 

We continue our work on accessible data visualization for people with IDD. For more information or to participate in one of our studies contact the project team.

Accessible Visualization Design Guidelines. 
1. Avoid pie charts
2. Use familiar metaphors
3. Manage Visual Complexity 
4. Use Discrete Encoding for Axis-Aligned Representation

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