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.
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.
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.
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:
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.
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:
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.
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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