Research Data Management: Data Visualisation

Data visualisation

Visualising data allows us to:

  • analyse and understand data better
  • move past two dimensional flatland lists
  • encourage the exploration of data complexity in engaging, interesting and compelling ways
  • illustrate the stories behind the data.

Including data visualisations in your collections has the potential to increase the reuse, discovery and connectivity of your research data.

(Source: Australian National Data Service website (


Beyond including visualisations in your data, you might also want to consider enhancing your publications with data visualisations. Elsevier (Scopus, Science Direct) describe it as 'enriching':

When publishing your research online, you are no longer confined to static visuals; you can explore a set of in-article interactive visualization tools that will help you share insights with your readers.

(Source: Elsevier website (


You can also choose to publish (and receive recognition) for your individual visualisations on a platform such as Figshare. Click on this link to see examples of data visualisations published and shared by researchers internationally.

TED Talks on data visualisation

Online training in data visualisation

Data visualisation refers to the graphical display of information for for either data analysis or communication. The following is an online interactive course (provided by LinkedIn Learning), which encourages you to take your knowledge beyond how data can be graphically presented and think more strategically about the best way to appeal to your chosen audience. That is, using data visualisation tools to 'tell a story'.

Visualisation top tips

  1. Select the right chart for the data type. This chart selector can help.
  2. Keep your diagrams simple – avoid unnecessary clutter and distraction.
  3. Maximise the data to ink ratio – only use extra colours, grid lines and labels if the graphics would lose the content when they are removed from the image.
  4. Use graphs to display information about data relationships – for example comparison, trend and proportion. Use tables when precise values are required.
  5. Pie charts can be difficult to understand because our visual perception is not designed to accurately assign quantitative values to two-dimensional areas. Use bar charts instead for a much clearer message.
  6. Help the reader by using explanatory  titles which communicate the main focus of the visualisation.
  7. Label the axes and normally start them at zero, unless all data is clustered at high values.
  8. Use colour, size and position to improve understanding.
  9. Choose colours which can be seen by majority of the population. It’s probably best to avoid using both green and red in the same diagram as they both appear brown to people with colour blindness.
  10. Provide the source of information and dates to support credibility.

(Source: Digital Curation Centre website 'Visualisation top tips' CC BY-NC-ND 2.0 UK)