Creating professional figures: How to use colour effectively
Since most journals are now publishing online, the use of colour in scientific figures is almost ubiquitous. This is for a good reason – aside from making an article more attractive and appealing to read, colours, when used correctly, can significantly enhance and simplify the information within a figure. However, poor use of colour can be confusing, obscure data, and can potentially distort the entire meaning of your figure. Colour can be either your greatest ally or your worst enemy. The final quality of your figures really can determine whether your paper is accepted or rejected.
Here’s the problem! With so many colours to choose from, selecting colour combinations can be intimidating, and in most cases when picking colours, there is no right or wrong answer (it’s not black and white). Sometimes a beautiful colour combination is just a matter of taste. That said, there are some basic rules and guidelines that should be considered when selecting colour schemes for your data.
Here are some key tips that will help improve your data quality and succeed in publishing higher.
How to use colour effectively
Using colour is not always the best way to make data clearer
First, carefully consider whether the colour is needed at all. Colour in scientific figures is a tool to aid the understanding of the figure better and faster. The primary objective is not to make it attractive, although this can be a welcome side-effect.
If you need to highlight an important element of a figure, colour can be used to enhance this element while keeping other elements black or grey. However, if there is no such need, keep it simple and use greyscale only.
Even when there are multiple elements (e.g., many groups displayed in a line graph), colour might not necessarily be the best option for clearly grouping the elements. A simple solution would be to use different symbols to mark data points of different groups. If you prefer to use only lines and no symbols, different line types can be applied.
Having said that, the addition of colour can enhance such a figure if the correct colour combinations are used:
When using colour, create the right balance
Using colours effectively is not just about using all the colours that match, you must create a good balance. The more colours used, the more complicated it is to balance them. In other words: less is more. Choose just 2-3 colours per figure and vary their shades and saturation to increase the overall number of colour combinations. Then be consistent with the colour palette: use the same colours to highlight the same elements, varying shades where necessary. But how do we know what colours complement each other?
What colours should you use?
Use colour theory – a colour wheel (typically containing 12 hues) helps us to visualize relationships between colours and is designed to match complementary colours in some well-described combinations.
A. Monochromatic: variations of shades, tints, and tones of a single colour.
B. Analogous: colours next to each other on the colour wheel. These palettes are good for displaying similarities in datasets.
C. Complementary: colours opposite on the colour wheel. Complementary colours are most suitable for displaying differences between data sets.
D. Triadic: three colours equally spaced on the wheel. Triad schemes, like complementary colours, are best for showing differences in data but offer more variety in the colours to select from.
Other things to consider
• Use warm colours (colours close to red on the colour wheel), for elements that you wish to be pushed to the foreground.
• Cold colours will be pushed to the background, so use these colours for less important items that play a secondary role.
• Light and dark shades of the same colour can be used to create contrasts.
• Using spot colours: highlight elements which convey the main message of the figure in colour using greyscale for the rest of the image.
A few things to avoid
• Avoid colours that don’t reproduce well, such as dark backgrounds and yellow elements (or other light colours).
• Avoid colours that are difficult to distinguish – i.e., similar looking colours/bright or neon colours.
• Consider readers with colour blindness. Change your data to ensure that they are accurately viewed by someone with colour blindness. For example, use purple and green instead of red and green.