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Research Data Management Toolkit: Data Visualization

This guide provides information about research data management and the Tropical Data Hub (TDH) Research Data repository

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 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?

Finding The Right Tool

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.

Some Data Visualization Tools

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!

NEWFrom Data to Viz leads you to the most appropriate graph for your data. It links to the code (R, Python, D3.js) to build it and lists common caveats you should avoid.

  • 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

Need a break?

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!

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:

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