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 might promote new insights.
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?
(("3D data visualisation of the Pillars of Creation" by European Southern Observatory is licensed under CC BY 2.0.)
The issue with finding the right tool for data visualizations may be that we are spoiled for choice. There are literally thousands of existing proprietary and open source applications capable of delivering different kinds of data visualizations, and more are being developed every day. Bear 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). It's always a good idea to see what data visualization tools are more commonly used in your research field -- certain kinds of tools may suit certain disciplinary areas better, and communities of practice may form which might help build knowledge of particular software applications.
Take a look at some of the popular tools and training resources listed here, and in the table below, as a starting point, and consult your colleagues for their recommendations. Keep in mind that data visualization tools are 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! (But please be gentle -- researchdata@jcu.edu.au).
Some other story-telling/timeline tools:
Source: Mark Thomas, Duke University Libraries - Google Fusion Tables LibGuide
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:
Storytelling with Data by Don't simply show your data--tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!
Beautiful Evidence by Science and art have in common intense seeing, the wide-eyed observing that generates empirical information. Beautiful Evidence is about how seeing turns into showing, how empirical observations turn into explanations and evidence presentations. The book identifies excellent and effective methods for presenting information, suggests new designs, and provides tools for assessing the credibility of evidence presentations.Here we will see many close readings of serious evidence presentations-ranging through evolutionary trees and rocket science to economics, art history, and sculpture. Insistent application of the principles of analytical thinking helps both insiders and outsiders assess the credibility of evidence.
The Visual Display of Quantitative Information by 
The Design of Everyday Things by The ultimate guide to human-centered design Even the smartest among us can feel inept as we fail to figure out which light switch or oven burner to turn on, or whether to push, pull, or slide a door. The fault, argues this ingenious-even liberating-book, lies not in ourselves, but in product design that ignores the needs of users and the principles of cognitive psychology. The problems range from ambiguous and hidden controls to arbitrary relationships between controls and functions, coupled with a lack of feedback or other assistance and unreasonable demands on memorization. The Design of Everyday Things shows that good, usable design is possible. The rules are simple: make things visible, exploit natural relationships that couple function and control, and make intelligent use of constraints. The goal: guide the user effortlessly to the right action on the right control at the right time. The Design of Everyday Things is a powerful primer on how--and why--some products satisfy customers while others only frustrate them.
It can be instructive to look at examples of bad visualizations - to recognise one's own mistakes and to evaluate visualizations critically. We might consider this an important digital literacy skill, in an age of infographics, 'alternative facts' and fake news!
Okay and here's one good one. This visualization by Jer Thorp was commissioned by the publishers of Popular Science. It illustrates when different significant cultural and technical terms emerged, and shows their frequency and decline, over the magazine's 140 year history:
("Popular Science - Process" by blprnt_van [Jer Thorp] is licensed under CC BY 2.0.)
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