Let's recap what you have read in this module.

By acquainting yourself with your audience, as well as with the principles and practices of visualisation, you can get a head start in the visualisation process. If you additionally take human perception and cognitive processes into account, you may also find it easier to know when you might want to diverge from established principles and practices.

In biology and medicine in particular, a visual image might be anything from an exploratory diagnostic image to an explanatory image used to educate the public about a disease. Exploratory visualisations help researchers or others with discipline-specific knowledge understand the data better, or in a new way. Explanatory visualisations explain research data to an audience that lacks discipline-specific knowledge.

Visualisation, whether it has an exploratory or explanatory purpose, is a creative and pedagogical process. Aesthetic or technical decisions have to be made, but the basic principles can be summarised in Mackinlay's two design criteria, expressiveness and effectiveness. Your visualisation should communicate your data, no more and no less. It should furthermore be formulated in such a way that it is understandable to your intended audience, whether that audience is you, your research group, clinicians or the general public.

The resources and source material recommended in this course all convey an aspect of the visualisation process. They are provided here as food for thought, discussion and reflection. Each suggested method or approach has strengths as well as weaknesses. No complete and comprehensive model for the visualisation of biological data exists, even if Munzner's system comes fairly close. For that reason, we have chosen to focus on a broad selection of perspectives. As the field develops, new methods and tools will be created.

Recommended reading

The following articles summarise both the theoretical foundations for the visualisation of data, as well as their practical application:

Steven L. Franconeri, Lace M. Padilla, Jessica Hillman et al. "The Science of Visual Data Communication: What Works," Psychological Science in the Public Interest, (2021). (Log in to the KI Library for access).

Note: Franconeri et al.'s article is an overview of which guidelines and principles were regarded as the theoretical foundation for communicating visualised data at time of publication. The authors discuss graphic coding of data in regards to human perception. It's a very useful summary of the field.

Jonathan Schwabish. "The Practice of Visual Data Communication: What Works." Psychological Science in the Public Interest, (2021). (Log in to the KI Library for access).

Note: This article reads as a practice-oriented commentary on Franconeri et al., which is more theoretical. Schwabish describes a number of practical principles that can help practitioners to apply theory to practice and get the creative visualisation process started. He emphasises that visualisation is not just for experts, nor does it require expensive or advanced software or equipment.