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Research Data Management Toolkit

This guide provides information about research data management and the Research Data JCU platform

Data Visualization: Introduction

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 modelling of the 'Pillars of Creation', Eagle Nebulae (("3D data visualisation of the Pillars of Creation" by European Southern Observatory is licensed under CC BY 2.0.)

Finding The Right Tool

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 --

Some Data Visualization Tools

  • Gallery of interactive web-based data visualizations using D3.js

  • More complex textual analysis may require the use of programming languages such as Python.

Some other story-telling/timeline tools:

  • ESRI Story Maps allows you to combine maps with narrative text, images and multimedia content.
  • Google Earth Engine has a timelapse editor (1984-2016) to record and share tours of interest with zoom, pan and images within the specified timespan.

      Source: Mark Thomas, Duke University Libraries - Google Fusion Tables LibGuide

Design And Principles

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:

Need a break?

      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:

word frequency heat map("Popular Science - Process" by blprnt_van [Jer Thorp] is licensed under CC BY 2.0.)

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