Data Visualisation

Data visualisation allows you to:

• Extract valuable information and patterns from data

• Communicate this in a clear and comprehensible visual representation

• Enhance the communication of research findings, making it accessible to a broader audience

• Identify data quality issues, as outliers and data errors may become more visible in visualisations

Therefore, data visualisation is an aspect of research data management!

Below follow some resources that may help you in creating clearer, more accessible and more transparant data visualisations.

Tools

• upset graphs

• Python plotly

  • annotating visualisations in python plotly (blog and video)

  • Animations using Python plotly

Accessibility

• Tips to improve interpretability and accessibility by Dr Tracey Weissgerber (video)

• Writing Meaningful Alt-Texts for Data Visualisations in R

Colours

• A detailed guide to colors in data vis style guides (blog)

Resources

Books and Articles

• Fundamentals of Data Visualisation by [Wil19]

• Data Visualisation, A practical introduction by [Hea18]

• Creating clear and informative image-based figures for scientific publications by [JAA+21]

Other text resources and examples

• Data Visualisation Course

• Free visual and communication resources

• A collection of notebooks demonstrating plotting with matplotlib

Videos

• 5 minute videos on Data visualisation - Introduction and motivation and Figure design, design process, fundamentals

• Create Effective Data Visualizations (second part focuses primarily on Tableau)

• Data visualisation for scientific papers videos by ReproducibiliTeach