[This post continues the PhD student paper-writing series from here]
At this point, we have covered establishing the focus of your scientific paper: you should already have a clear vision of what your paper is about, and the essence of this vision should be encapsulated in its abstract. You should also have the necessary ingredients at hand: the results to be presented in your paper together with ideas for schematic diagrams, organised into film-script categories according to their function and role in the story (Setup, Confrontation, Resolution, Epilogue).
The next step is to expand the storyline laid out by the abstract, and to outline the different sections of your paper. This begins with choosing what sections make up your paper. Depending on your target journal, you may need to follow strict guidelines—the commonly used Introduction–Methods–Results–Discussion structure for instance—or to come up with a structure of your own. Even for short letter-format papers that may or may not have subheadings, it pays off to have a clear idea of what goes where. Usually, this is not a difficult task: all scientific papers begin with an introduction and end with a discussion, even if these span just a few paragraphs, and the results are sandwiched in between. Methods may be explained before the results, or after the discussion as an appendix of sorts (like in Nature and other glossy magazines).
What is more involved is choosing the order of presentation within the section structure. Here, a solid, tried-and-tested approach is to begin with the figures and their order of appearance. If you have followed the approach of this blog, your figures already come with handy labels (Setup, Confrontation, Resolution, Epilogue) and therefore you already have a good overall idea of their order. If your paper has to follow the standard structure, schematic diagrams and result figures will generally be placed in the Results section, so that figures of the Setup category come first and those of the Epilogue come last; schematic diagrams of the Setup category are an exception as they may belong to Methods or even Introduction. In any case, you’ve already done much of the work before you have even begun outlining the main text: the categories of the figures mostly determine their order. What remains to be done is to choose the order within each section in which the results and schematic diagrams are shown: what figure leads to the next?
The order of figures should tell a clear story, so that each builds on the previous ones. You can use a multi-panel figure to tell a self-contained part of the story, a miniature story arc. You can combine, for example, a schematic diagram that explains your experiment (Setup), some basic statistics of your data (Setup), and a result plot or two that contains an unexpected finding (Confrontation). This mini-story multi-panel figure is a technique that I often employ in letter-format papers where the story has to move fast and get to the point quickly; it already brings the story close to its Resolution and the key result.
When you have chosen the figures, write a draft version of the caption for each figure. You may need to revise the captions later; at this stage you may still be unsure of issues like notation and nomenclature, so don’t pay too much attention to details yet. However, try to write the figure captions so that they are self-consistent enough for a hasty reader to understand most of your paper just by glancing through the figures. After all, this is exactly what great many readers do; this is what the editor of your journal does too, before deciding whether your paper is worth a closer look or deserves to be rejected outright. How long and how self-consistent the captions should be depends, again, on your journal; in some of the letter-format top-tier journals, captions tend to be very long, while in the lesser journals where us mere mortals publish, figures are discussed at length in the main text and therefore the captions can be shorter. In any case, please make sure that your caption tells what the reader should learn from looking at the figure. A caption that only tells that here we see Y plotted as a function of X is not enough; it is redundant if you have remembered to label your axes in the figure already. Always tell the reader what the message of the figure is.
Because much of the story will be told by your figures, let us talk about figure quality for a while. Figures are tremendously important; those who only skim through the paper won’t see much else. Figures make the first impression and first impressions matter. Clear, high-quality figures with a professional look tell that a lot of effort has been put into the paper, and the reader is more likely to trust its contents. Amateurish-looking figures with a colour scheme that looks like PowerPoint in the 1990’s leave the reader wondering if the results are of the same dubious quality.
So do make sure that your figures look good. How to do this? First, learn the ropes of whatever program you use to generate your figures, whether it is a Python or R library, or a stand-alone piece of software (like Gnuplot that has been around since the dawn of man; it will probably outlast even cockroaches once mankind is no more). In particular, learn how to change fonts, how to increase or decrease font sizes, and how to use proper LaTeX-type fonts wherever appropriate. Learn how to choose and manipulate colours and colour schemes and symbols and shadings. Learn how to produce figures of chosen dimensions, so that you can later assemble them into multi-panel plots of your choice and combine them with schematic diagrams. Learn to match figure sizes to your target journal’s column width; not having to scale the figures takes some guesswork out of choosing font sizes (see below).
Second, do learn to use a vector graphics software to post-process your figures (and do learn the difference between bitmap images—they are made of pixels—and freely scalable vector images, made of lines and arcs and Bezier curves). At the time of writing, the industry standard (design industry, that is) would be Adobe Illustrator; there are many free alternatives such as Inkscape. With a vector graphics editor, it is easy to assemble multi-panel figures that contain schematic diagrams (drawn with the same editor) and result figures saved in vector formats (PDF, SVG). You can also add text, arrows, indicators, and so on, as well as retouch your result plots, changing line widths, colours of symbols, or their overall appearance. Often, this is much faster than trying to get everything right when producing the plots.
A few words on layout: always align things—nothing spells “I am being careless” more clearly than subplots and schematics that are not neatly lined up (it takes just a few seconds to do this). Use white space properly: leave enough white space so that things can breathe, but don’t leave too much white space so that the figures don’t look barren.
Discussing data visualisation at length is beyond the scope of this blog post, but here are a few remarks. Pay attention to your colour schemes. For plot symbols, there are much nicer and much more informative schemes than the pure-RGB red, green, and blue symbols that some programs use as default; on top, your reader might be colour blind and have a hard time distinguishing between red and green. Always use different symbols AND colours for different curves for maximal clarity. If you want a personalised colour scheme, google for colour scheme generators (you have already learned how to set hexadecimal colour values in your program, right?). For heat maps and similar, pay attention to the neutrality of the colour map you use: make sure that it doesn’t artificially highlight some part of your range of values. In all cases, use colours consistently through your figures. If red and blue are categorical indicators of, say, two different data sets in a graph, do not use a heat map where red and blue indicate high and low values: reserve red and blue for the two data sets, and always use them this way. Likewise, if you use a colour map with a gradient from low to high values, reserve its colours for this purpose alone.
Then, labels and fonts. First, always label your axes. This is self-evident, but I still have to explicitly mention it; even though forgetting to label the axes of a plot should feel roughly like forgetting to get dressed when leaving for work in the morning, it still happens. So, I repeat: label your axes, period. At all stages of your work, even if the plot is just a draft for your eyes. And when labelling, please do make sure that the fonts you use are large enough when the figure is scaled to its intended size; if you have chosen the plot’s dimensions so that no scaling is required, use 10 or 12 pt. Not paying attention to font size is a very common beginner’s problem, and there are even many published paper where a magnifying glass is needed to understand what is going on in the figures. I suspect this has to do with the defaults of the commonly used software packages; default font sizes are almost always tiny. I’ve rarely (if ever) seen plots with annoyingly large fonts, so if in doubt, double your font size.
Figure 1: Do avoid these common problems!
Finally, a few words about “having an eye for design”. While coming up with beautiful and impressive figures seems to come more easily for some, every student can learn to produce good-looking visuals. I’ve many times heard someone say “I cannot draw, and therefore my figures look ugly” but—as with any skill—it just takes time and patience; you do not need to go to art school to learn the essentials. Just like learning how to look at things is the key to learning to draw well, the key to producing great-looking figures is knowing how they should look like, instead of stumbling blindly. This is best learned by imitation. So, next time, take one of your plots that you are not entirely satisfied with, and look up a similar figure in some journal article that you like. Look at the two figures side by side, and try to spot the differences in composition, colours, fonts, line widths, and so on. Then modify your figure and keep on modifying it until you are satisfied with the outcome. Next time, you might not even need a reference figure.
PS I am contemplating expanding these blog posts into a full book or ebook. If this sounds like a good idea, let me know, e.g., by commenting below.