Thing 10: Visualise Your Data

Image: “Social Network Analysis Visualization”​ via Wikimedia Commons (CC BY-SA 3.0)​

Data visualisation refers to the visual representation of data to draw out trends, patterns and relationships, and present these in an accessible and easily-recognised form.

Getting Started

Visualisation can be effective when communicating complex research. Data visualisations – which may take the form of charts or graphs, diagrams, images, animations and infographics – can be a powerful way of synthesising your research, and representing it in a way that’s understandable even to a non-expert audience.


  • Purpose: will the visualisation actually allow for a meaningful representation of the data, or will it be “pretty” but without adding any value to the interpretation of your research?
  • Audience: what are your audience’s requirements, how will they access the visualisation, and which part of your data is most relevant to them?
  • Data: What kind of data are you trying to visualise? This will inform what kind of visualisation lends itself, and how this is best displayed.
  • Format: e.g. print, online, static, active… the format will depend on the target audience, and the data to be visualised.
  • Tools: what tools are you already familiar with – will they “do the job”? Are you prepared to invest time if you need to upskill yourself to produce a certain type of visualisation? Make sure to consider the purpose of your visualisation first!



In our 2014 post on data visualisation tools, we introduced Tableau Public, Gapminder and Google Public Data Explorer. Not quite what you are looking for? Here are some more options:

Try This

The Data Visualisation Catalogue gives a useful overview of different information visualisation types. Information is Beautiful showcases some fantastic visualisations of the world’s data.

Another initiative, AURIN (led by the University of Melbourne) offers tools as well as open access to data from more than 60 institutions and data providers.

Learn More

Image credits

Image 1: “Social Network Analysis Visualization” by Martin Grandjean [CC BY-SA 3.0], via Wikimedia Commons.

This post was written by Jennifer Warburton (Manager, Research Publications and Programs) and Julia Kuehns (Liaison Librarian (Research), Arts).

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