Why take this course?
A good figure carries your evidence at a glance. A poorly plotted figure hides it or misleads. Learn a repeatable standard that works on screen, in black-and-white print and during peer review.
Plotting Clear Research Figures
FREE ONLINE LEARNING MODULE
Plot figures your reader can trust.
Learn a practical method for turning research data into figures that are clear, accurate and ready for reports, theses and publication.
What you’ll be able to do
- Recognise issues that reduce figure clarity and make graphs difficult to read or interpret
- Understand the fundamental attributes of a clear and effective research figure
- Assess whether a figure communicates its evidence well or needs improvement
- Apply the framework to present information through graphs more clearly
Course contents
- Why clear figures matter
- Common faults and their consequences
- The five-decision plotting method
- Before-and-after worked example
- Pitfalls and final checklist
- Assessment, result and course feedback
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COURSE 2
Plotting Clear Research Figures
Build figures that communicate clearly before a reader reaches your discussion.
WELCOME
What you will learn
Welcome to the module.
This course will help you produce figures that communicate on their own, remain legible in black-and-white print and meet the expectations of theses, journals and peer reviewers.
- Explain why figure quality affects communication and publication.
- Recognise recurring plotting faults and explain why they matter.
- Apply five plotting decisions in a reliable order.
- Critique an unfamiliar figure and propose actionable corrections.
LESSON 1
Why clear figures matter
Welcome to Lesson 1.
A figure is often the first part of a paper that a reader examines and the last part they remember. Explore the cards to understand what clear plotting protects.
CommunicationClick to reveal
A clear figure lets the reader grasp the result in seconds. Unclear labels and indistinguishable lines force the reader to decode the plot instead of understanding it.
Peer reviewClick to reveal
Reviewers often return papers whose figures cannot be read. Basic plotting faults can suggest carelessness and invite closer scrutiny of the wider study.
Print and accessibilityClick to reveal
Colour-only distinctions collapse in greyscale and exclude some readers. Lines and markers should remain distinct without colour.
Lasting recordClick to reveal
Figures in theses and papers become permanent records. Consistent plotting habits prevent costly redrawing and replacement later.
LESSON 2
Common faults and why they happen
Welcome to Lesson 2.
Most unclear figures repeat the same mistakes. Learn to recognise them before a reviewer does.
Poor axis labels
Vague labels such as “time” or “value” leave the quantity unclear.
Vague labels such as “time” or “value” leave the quantity unclear.
Missing units
A physical quantity without a unit cannot be interpreted reliably.
A physical quantity without a unit cannot be interpreted reliably.
Undefined symbols
Codes such as S1 and S2 force readers to search for meaning.
Codes such as S1 and S2 force readers to search for meaning.
Colour-only lines
Several solid coloured lines merge when printed in greyscale.
Several solid coloured lines merge when printed in greyscale.
Hidden scales
A logarithmic axis drawn like a linear one misrepresents the data pattern.
A logarithmic axis drawn like a linear one misrepresents the data pattern.
Grid clutter
Heavy major and minor grids compete with the evidence.
Heavy major and minor grids compete with the evidence.
Legend over data
A poorly placed legend hides the lines it is meant to explain.
A poorly placed legend hides the lines it is meant to explain.
Vague caption
A caption such as “Results” does not let the figure stand alone.
A caption such as “Results” does not let the figure stand alone.
LESSON 3
The five-decision plotting method
Welcome to Lesson 3.
Build each figure by making these five decisions in order. This sequence prevents most plotting faults before they appear.
Lines and markers
Design for black-and-white. Use clearly different line styles and marker shapes, with filled and open markers where useful. Photocopy your figure mentally: every series should remain identifiable.
Axes, labels and units
Name every plotted quantity and give its unit in parentheses, such as Applied stress (MPa). The label must match the data.
Scales and gridlines
State logarithmic scales and use tick spacing that reveals them. Keep grids light. Minor grids usually belong only where a log scale makes them useful.
Legends and captions
Use meaningful series names, position the legend away from the data and write a caption that states the subject and essential conditions.
Journal and thesis standards
Check figure size, resolution, font, line weight and labelling requirements. For a thesis, apply one consistent standard to every figure.
WORKED EXAMPLE
The same data, two very different figures
Study the poor version first. Then move to the improved version and compare each plotting decision. The data are identical; only the presentation changes.
1. Bad example
Start here. Notice how the plotting choices make the evidence difficult to interpret.
- Colour-only solid lines
- Vague labels and missing units
- Undefined legend entries
- Hidden logarithmic scale
- Heavy grid and vague title
2. Improved example
Now compare the corrected figure. Each series remains identifiable and the graph can stand on its own.
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