Scientific figure design tips and tricks

Reilly Pidgeon • Posted: March 19, 2021

One of the lessons taught in scientific communications classes is to be kind to the reader. Essentially, you should strive to make their life as easy as possible when reading your text. This lesson should also be applied to scientific figures.

Since I’m in the middle of preparing figures myself, I thought I would share some tips and tricks to improve the quality of your figures and your quality of life when making them.

Selecting Software Based on Your Data

Data come in many formats and file types, and specific tools exist for each of these formats (raster or vector).

Raster images (think pictures) are pixel-based, which means that they are composed of tiny squares (pixels) bunched together. From far, these images often look good, but up-close, the same image can become blurry (pixelated). Raster images are best suited for microscopy panels, molecular structures, and scans of gels. The image formats are typically .png, .jpeg, .tiff.

The best way to process raster images after acquisition/rendering is using raster-based programs like FIJI (free), Adobe Photoshop (subscription), or Affinity Photo (perpetual license). You can then import these images into vector-based software to create your figure layout (discussed below).

When you look at the figures in most high-impact journals, you’ll notice that the quality of graphs remains the same regardless of the size of the page (i.e. how much you zoom in or out). These graphs are vector-based and are the gold-standard for publication-quality figures.

A raster-based graph (left) versus a vector-based graph (right).

Vector images are defined by points on a Cartesian plane. Mathematical equations describe each element (points, lines, curves, and shapes) of the image. Look at the fonts we use daily – each letter is just a shape made up of points that are joined by curves. Below is a comparison of raster-based and vector-based images (zoom in to see how they differ).

Vector-based graphs can be produced in programs like GraphPad, RStudio, and even Excel. All you need to do is export/save your graphs in vector image formats (.svg, .pdf, .eps, .emf). These images can then be imported into vector-based programs like Inkscape (free), Adobe Illustrator (subscription), or Affinity Designer (perpetual license). The advantage of working with graphs in a vector format is that they can (usually) easily be edited and tailored to fit your figure layout. If you don’t like your colour scheme anymore, you can easily change it without re-exporting your graphs.

Design Principles

We can often feel that something isn’t quite right with a visual. Some of my biggest pet-peeves are when elements aren’t aligned, when fonts are inconsistent, and when the elements of a layout are crammed together. To avoid making these mistakes, it’s helpful to keep in mind some graphic design principles:

  • Alignment: Graphic elements should be connected in an ordered fashion in a figure layout (e.g. align axes/labels and ensure that related elements are equally spaced).
  • Balance: Data can be represented in multiple ways. Each type of graph or image has a visual “weight”. These different visual “weights” should be balanced in the figure layout.
  • Colour: The efficient use of colour can greatly enhance the legibility of figures; however, careful attention should be paid to colour combinations (see Crameri, F., Shephard, G.E. & Heron, P.J. Nat Commun 11, 5444 (2020).
  • Contrast: Elements in your figures should stand out from each other.
  • Proximity: Related elements (e.g. graphs and legends/labels) should be grouped together.
  • Repetition: Consistency in figure design is key. A common pattern (e.g. colours, symbols, graph sizes, labels, emphasis, etc.) helps a figure remain organized. Repetition also satisfies the reader’s expectations and prevents their eyes from jumping around the page.
  • Space: Unoccupied space is just as important as the space occupied by visual elements. Let your figures breathe!

Above are some mock data I generated to illustrate how design principles apply to scientific figures. The top figure layout (blatantly) disregards each design principle listed above: it is messy, crowded, and lacks legibility. The layout just below has the same data in the same space on the page but is much clearer. By using a common template, colour scheme, and font family, a mediocre layout can be revitalized to improve the clarity of your data.

Optimizing Your Workflow

Whether you are preparing figures for publication or for a big presentation, you can benefit from optimizing your figure design workflow. Below are a few tips and tricks:

  • Search for figure design guidelines (journal-specific) and follow them from the start – it takes more time to go back and change everything to fit the style
  • Draw a rough layout of the elements you wish to present on paper – this will make your life easier down the line
  • Set the dimensions and layout of your figure by using rulers and placeholders (I often put a rectangle with specific dimensions as a placeholder to see how I want to organize my space) – this also tells me which dimensions I should use for my graphs
  • Get familiar with the software you’ll be using – there are many great tutorials out there!
  • Learn shortcuts for your vector-based program of choice – you can learn through tutorials, by searching “software name” + shortcuts, or by assigning your own shortcuts in the preferences menu
  • Use the align and distribute tools – these will save you a lot of “finicking” time
  • Settle on a naming convention for your files – you’ll accumulate many versions of your figures and you’ll need to be able to find them easily

Making high-quality figures can be long and tedious; however, by understanding the different types of data, graphic design principles, and how to adapt your workflow to journal guidelines, you can improve the quality of your figures and your quality of life when making them.

Further reading/guides:

About Reilly

Reilly is a PhD student in pharmacology and therapeutics in the Castagner Lab at McGill University. He has been playing around in Photoshop since his pre-teen years. He has designed an album cover for a podcast and promotional posters for hackathons and conferences around Montreal. Reilly is also one of two communications officers in the GlycoNet GTA-EC 2020-2021.

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