Seeing Science Clearly

With little formal training or education, many biomedical engineers lack the skills to design and evaluate effective data visualizations. How can biomedical engineers know if a scientific figure is good or not?   When Ahmet Coskun, associate professor in the Wallace H. Coulter Department of Biomedical Engineering, and his team didn’t find the answers they needed in existing literature, they took matters into their own hands.  Coskun explains, “We read all these papers and look at these figures, but how do we really judge the quality of a figure? There was no clear, automated algorithm to do that—so we built one.” 

The team created an algorithm to help researchers evaluate scientific figures called Metrics for Evaluation and Discretization of Biomedical Visuals (MEDVIS), recently published in PLOS One.  

MEDVIS quantitatively evaluates figures on four criteria: white space, number of visualizations, color density, and image complexity. The team selected these criteria based on their importance in data visualization, especially for the type of figures often used in biomedical engineering. The algorithm provides a score and lets researchers know if their visualizations are “good” or if there are areas for improvement.  

In validation studies, MEDVIS results aligned strongly with assessments from trained researchers.  

“Our quantification captures the same things that bother people, like too much white space, cluttered complexity, poor color choices, and it does so consistently,” Coskun said. 

Besides looking at criteria for “good” visualizations, the team also compared 26 commonly used data visualization tools. Criteria included ease of use, customizability, cost, and required background knowledge — for example, how much coding knowledge users need to create visualizations in the platform.  

Their analysis found that the best tool will depend on the user’s audience, technical background, data, and budget. The paper’s authors recommended biomedical engineers consider all these factors when designing data visualizations.