Data visualization uses statistical graphs, plots, information graphics and other tools to create visual representations of data. The goal is to summarise and communicate data clearly, precisely and efficiently so that it becomes insightful.
There are many types of visualizations and thousands of tools available and they range greatly in complexity (e.g. from bar graphs to heat maps, networks, 3D models etc) and specificity. Regardless, and to paraphrase Martin Schweitzer from the Australian Research Data Commons, the key question to ask is "Is the visualization illuminating, useful, and does it have integrity?
Keep in mind that many tools are open-source or free but they may lack longevity. See this blog post by Andy Tattersall for some good questions to ask before adopting a new research technology.
Take a look at some of the popular tools and training resources listed here as a starting point and consult your colleagues for their recommendations. Keep in mind that data visualization is often used for exploratory data analysis and not just for displaying results. Some of these tools are designed to do both.
If you know about a great data visualization tool let us know and we'll include it here - or tell us if we've listed a dud!
Source: Mark Thomas, Duke University Libraries - Google Fusion Tables LibGuide
It can be instructive to look at examples of bad visualizations - to recognise ones own mistakes and to evaluate visualizations critically. This is an important digital literacy skill in an age of infographics, alternate facts and fake news!
Martin Schweitzer from ANDS discusses the principles for designing good data visualizations and goes through examples of good and bad ones in this excellent webinar: Data visualisation - Design and principles (56 min.)
Martin refers to some classic texts on visualization design in his presentation. These and many others are available to JCU staff and students:
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