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Data visualisation includes charts, graphs, and other visual tools to present complex data in a clear and concise manner. Using these visual tools more clearly identifies trends, patterns, and correlations, enhancing you ability to interpret findings and communicate results effectively.


Why is data visualisation important?

  • Clearly presents the results of your findings
  • Highlights the significant areas of the results compared to tabular data
  • Makes your research more accessible to the broader community

Graphs for data visualisation

Different types of information can best be presented using different graphs. Find the right graph for your data in the chart below

Tools for data visualisation

The table below includes a list of freely available tools to create your own visualisation.
De-identify sensitive data before entering it into an online site.

Tool Use Limitations
Datawrapper charts maps and tables small list of available visualisation types
Gephi network graphs lacks advanced analytical capabilities
PSPP histograms, scatterplots, chart charts  not as much functionality as SPSS(available at Clayton, Casey, Dandenong and Moorabbin libraries)
Sci2 graphs, charts and maps overly complex visuals if using large data sets
Tableau Public

charts, graphs, histograms, scatterplots, maps.

all visualisations are saved to a public profile
BioRender icons, charts, graphs free version only allows for 5 figures 
Mind the Graph infographics, graphs  free version has limited functionality

*D3.js and Observable plot are also available for free to use, however we recommend them to those with more coding experience


Examples


Recommended resources:

Visual design skills for researchers webinar recording

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