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?
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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