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 transparent data visualisations.
Tools¶
Datawrapper, where you can upload your data to generate tables and charts.
upset graphs are a straightforward way to visualize set intersections in a matrix layout, which can help in analysing multiple datasets at once.
Using Python Plotly you can add annotations and animations as extra visual cues to highlight important features.
Annotating visualisations in Python plotly (blog and video)
Accessibility¶
Tips to improve interpretability and accessibility by Dr Tracey Weissgerber (video)
[Writing Alt Text to communicate the meaning in data visualizations] by Hare, 2022
Colours¶
Resources¶
Books and Articles¶
Creating clear and informative image-based figures for scientific publications by Jambor et al., 2021
A Field Guide to Digital Color by Stone, 2003
The Grammar of Graphics by Wilkinson, 1999
The Visual Display of Quantitative Information by Tufte, 2001
Other text resources and examples¶
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
Outline of grammar of graphics
A Grammar of Graphics - Excellent summary of the grammar of graphics layers or ‘functional pipeline’
Leland Wilkinson - The Grammar of Graphics - Leland himself giving a quick high level summary of the grammar of graphics
EMBL Keynote Lecture 2019 - Data visualization and data science, Hadley Wickham
Practical high level intros to Tufte’s principles
Podcasts¶
Other¶
- Hare, E. (2022). Writing Alt Text to communicate the meaning in data visualizations. Urban Institute. https://www.urban.org/sites/default/files/2022-12/Do%20No%20Harm%20Guide%20Centering%20Accessibility%20in%20Data%20Visualization.pdf
- Wilke, C. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O’Reilly Media. https://clauswilke.com/dataviz/
- Healy, K. (2018). Data Visualization: A practical introduction. Princeton University Press. https://socviz.co/
- Jambor, H., Antonietti, A., Alicea, B., Audisio, T. L., Auer, S., Bhardway, V., Burgess, S. J., Ferling, I., Gazda, M. A., Hoeppner, L. H., Ilangovan, V., Lo, H., Olson, M., Mohamed, S. Y., Sarabipour, S., Varma, A., Walavalkar, K., Wissink, E. M., & Weissgerber, T. L. (2021). Creating clear and informative image-based figures for scientific publications. PLOS Biology. 10.1371/journal.pbio.3001161
- Wickham, H. (2010). A Layered Grammar of Graphics. Journal of Computational and Graphical Statistics. 10.1198/jcgs.2009.07098
- Stone, M. (2003). A Field Guide to Digital Color. AK Peters, Ltd. https://openlibrary.org/works/OL6130168W/A_Field_Guide_to_Digital_Color
- Wilkinson, L. (1999). The Grammar of Graphics. Springer. https://openlibrary.org/works/OL18235300W/The_grammar_of_graphics
- Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press. https://openlibrary.org/works/OL2824012W/The_Visual_Display_of_Quantitative_Information
- Li, C. (2023). Friends Don’t Let Friends Make Bad Graphs. 10.5281/zenodo.7097522