About this seminar

The field of plant biology is undergoing a revolution due to the enormous amount of data that is being produced, either by high-throughput sequencing or non-destructive plant phenotyping platforms. Visualizing big-data is not an easy task but it helps with data interpretation, generation of new hypotheses and fairly presenting your results in a scientific publication. In this seminar, Magda introduces a basic concepts of data visualization, introduce the grammar of graphics and address when different display options (bar / box / dot plots) are appropriate. She also discusses some guidelines regarding effective figure design and how to strike a balance between story telling and correct data representation, and how to extend the appeal of your data to wider community by integrating it into an interactive apps.

Participants will be able to:

  • Understand the basic concepts of grammar of graphics using ggplot in R
  • Identify the optimal data visualization methods for addressing specific questions
  • Know the difference between exploratory & illustratory data visualization
  • Perform basic forms of data visualization
  • Design effective figures

Link to video

Link to the slides

Link to DataViz notebook html and RMD

About the speaker

I have Polish roots, but my intellectual germination started in the Netherlands, at the University of Amsterdam, where I did my PhD under the supervision of Prof. Christa Testerink. During my PhD project I studied Root System Architecture of Arabidopsis HapMap population. Currently I am a PostDoc at KAUST, in Saudi Arabia, where I focus on (1) salt-induced changes in root-to-shoot ratio in Arabidopsis, (2) study the expression patterns in plants with enhanced sodium accumulation in their roots and (3) develop tools for data analysis / visualization using R/Shiny. I am passionate about capturing plant architecture using simple models, understanding plant physiology and salinity tolerance. I love coding in R, BIG data analysis and sharing whatever I know with whomever cares to listen.

Effective figure checklist

  • The figure is self-contained: understandable without additional information
  • Every element is labelled or explained in the caption, including x and y axes and units
  • x and y axis: scales show appropriate variation of the data, or are comparable
  • Readability and contrast are appropriate
  • Every use of color has a reason
  • The figure works in grayscale (except for very complex figures)
  • If there are groupings, they help understand the message without manipulating
  • There are no channel inconsistencies within the figure
  • It is as simple as possible: i.e. no decorations, every piece that could be eliminated without losing information has been eliminated
  • Has been validated by other people

Useful resources

Short papers:

• Rolandi et al 2011. A Brief Guide to Designing Effective Figures for the Scientific Paper. Advanced Materials 23

• Rougier et al 2014. Ten Simple Rules for Better Figures. Plos Computational Biology 10:9

Design for scientists/ data:

• Carter. 2013. Designing science presentations.  Not just for figures, very clear

• Munzner. 2014. Visualization, analysis and design. From a computer-graphics perspective

• Tufte. 2001. The visual display of quantitative information. From a theory-of-design perspective

• Meirelles. 2013. Design for information.  Advanced information visualizations (maps, time-space, flows)

Graphic design more generally:

• Krause. 2004. Design basics index. Very concise and to the point

Samara. 2014. Design elements: a graphic design manual. Reference book

• Nature Points of View column. A collection of posts on all aspects of data visualization.




Useful resources

Short papers:

• Rolandi et al 2011. A Brief Guide to Designing Effective Figures for the Scientific Paper. Advanced Materials 23

• Rougier et al 2014. Ten Simple Rules for Better Figures. Plos Computational Biology 10:9

Design for scientists/ data:

• Carter. 2013. Designing science presentations.  Not just for figures, very clear

• Munzner. 2014. Visualization, analysis and design. From a computer-graphics perspective

• Tufte. 2001. The visual display of quantitative information. From a theory-of-design perspective

• Meirelles. 2013. Design for information.  Advanced information visualizations (maps, time-space, flows)

Graphic design more generally:

• Krause. 2004. Design basics index. Very concise and to the point

Samara. 2014. Design elements: a graphic design manual. Reference book

• Nature Points of View column. A collection of posts on all aspects of data visualization.