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By no means underestimate the significance of a very good determine


    I appear to finish up often explaining to college students and colleagues that it’s a good suggestion to spend a great deal of time to make your scientific figures essentially the most informative and engaging potential.

    However it’s a nice steadiness between overly flashy and downright boring. For sure, empirical accuracy is paramount.

    However why must you care, so long as the required info is transferred to the reader? A very powerful reply to that query is that you’re making an attempt to be a magnet for editors, reviewers, and readers alike in a extremely aggressive sea of knowledge. Positive, if the work is sweet and the paper well-written, you’ll nonetheless garner a readership; nevertheless, if you happen to give your readers a little bit of visible pleasure within the course of, they’re more likely to (a) keep in mind and (b) cite your paper.

    I attempt to ask myself the next when making a determine — with out pointless bells and whistles, would I current this determine in a presentation to a gaggle of colleagues? Would I current it to an viewers of non-experts? Would I need this determine to seem in a information article about my work? After all, all of those venues require differing levels of accuracy, complexity, and aesthetics, however a very good determine ought to ideally serve to coach throughout very completely different audiences concurrently.

    A sub-question value asking right here is whether or not you suppose a colleague would use your determine in certainly one of their shows. Consider the final time you made a presentation and located that excellent determine that brilliantly portrays the purpose you are attempting to get throughout. That’s the type of determine you must attempt to make in your individual analysis papers.

    I due to this fact are inclined to spend fairly a little bit of time crafting my figures, and after years of creating errors and getting a number of issues proper, and retrospectively discovering which figures seem to garner extra consideration than others, I can supply some primary recommendation in regards to the DOs and DON’Ts of determine making. All through the next part I present some examples from my very own papers that I believe display a number of the ideas.

    tables vs. graphs — The very first query you must ask your self is whether or not you possibly can flip that boring and ugly desk right into a graph of some kind. Do you really want that desk? Are you able to not simply translate the cell entries right into a bar/column/xy plot? When you can, you must. When a desk can not simply be translated right into a determine, more often than not it in all probability belongs within the Supplementary Data anyway.

    white house — White house is a type of points that you don’t essentially realise is the rationale you don’t just like the look of a specific graph. In case your axis scales are such that many of the knowledge seem at one excessive, in case your panels have enormous gaps between them (see subsequent entry), or there may be only a large gap someplace within the determine, you have to rethink the configuration of the knowledge. You are able to do numerous issues to take away white house, together with transferring parts nearer collectively, or including icons (see under), altering axis scales (see additionally under). A pleasant, tight (however not too cluttered) determine is far more visually interesting than one the place large white holes distract your consideration.

    panels — In case your figures look cluttered at one excessive, or a bit bare on the different, it’s time to think about multi-panel plots. Such plots will let you put lots of info in a single determine, supplied you don’t attempt to swamp your reader with every thing and the kitchen sink in a single go. Some suggestions for good multi-panel figures embody: avoiding panel titles (see extra under; panel letters or numbers of ample dimension often, however not all the time suffice), standardising panel dimension, avoiding repetition of axis labels and titles amongst panels (see extra under), and standardised axis scales (the place potential).

    titles — Determine or panel titles are often pointless and distracting, however you’ll need to embody a straightforward method to establish what completely different symbols/traces/colors point out through a legend, and naturally, an in depth follow-up clarification within the caption. Easy letters, numbers, or symbols for sub-components typically do the trick and keep away from cluttering the determine with an excessive amount of annotation.

    captions — Talking of captions, the age-old suggestion {that a} determine must be stand-alone actually comes into play when crafting a determine. Can informal observer skimming by your paper perceive the that means based mostly on the determine and caption collectively, or are they required to learn your entire textual content to get it? If the latter, your determine is just not stand-alone and must be fleshed out slightly extra.

    abbreviations — aside from panel indicators, I have a tendency to not use abbreviations/acronyms/initialisms in my graphs for the straightforward cause that it’s not fast obvious what they imply. I detest these varieties in just about all scientific work anyway, so I additionally advise protecting them out of your figures (my Australian state abbreviations proven under however 😉 ).

    keep away from repeating labels — As talked about above, keep away from repeating labels and titles amongst axes which can be the identical in (often) multi-panel plots. If the axis scale is similar throughout, say, the rows of panels, then all you want is the title and labels on the primary panel on the left, with all subsequent panels merely repeating the axis ticks. The identical applies within the x axis for columns of panels. Not solely does this simplify the design, it additionally saves an enormous quantity of white house.

    to log or not log — Usually, a pleasant logarithmic (or different) transformation of an axis can tighten up the show and render a wonky distribution extra visually interesting. It could additionally do away with pointless white house. Nevertheless, remember that any transformation adjustments the graph’s interpretation, in order that try to be very clear what the development signifies.

    axis segments — In reference to transformations, in case you are involved about deceptive interpretation, or a metamorphosis fails to unravel the white-space drawback, a segmented axis can produce a way more interesting determine. Say 90% of your knowledge fall between 1 and 10, however you may have a number of knowledge within the 100s or 1000s. Breaking the axis up so that the majority of it refers back to the 1:10 vary, with slightly devoted to the intense values, can actually assist interpretation.

    uncertainty — Do your development traces have any related uncertainty (e.g., commonplace deviations/errors)? Do your bars have measurement error? When you’ve got ANY related knowledge errors, don’t simply present the central tendency. Add all uncertainty within the type of error bars, shaded uncertainty areas, and so forth.

    knowledge distributions — Many journals today require you to show all the info uncertainty in a plot, such that bar graphs with little T error bars are now not acceptable. Nice methods to show the info distribution is thru issues like boxplots, however even higher are violin plots now rising in recognition. If I’ve a distribution, I now often embody a all of the jittered knowledge on prime of the violin plot itself.

    to 3D or not 3D — You’ve seen it on the telly hundreds of occasions earlier than: a bar graph with a mysterious third dimension displaying ‘columns’ as an alternative of bars. Don’t do that. Until you may have a 3rd dimension in your knowledge, don’t make one up. Three-dimensional graphs would possibly look interesting, however they’re often empirically deceptive.

    color — Within the not-too-distant previous, color was typically frowned upon for scientific papers, primarily resulting from the price of reproducing color pictures in print. Nowadays that limitation is much less and fewer relevant, as a result of most publication is now on-line, and color prices not more than greyscale/black-and-white figures. That stated, don’t go loopy with colors. Many people are slightly color blind, and fortuitously, many colourblind-friendly color schemes at the moment are accessible on most graphing functions. The opposite cause too many colors will be distracting is that they don’t conform to any empirical symbolisation. In different phrases, do your completely different colors point out some factor of the info (categorisation, origin, and so forth.)? If not, hold them to a minimal. Simply in case somebody must print nonetheless today, additionally take into consideration whether or not all the knowledge will likely be retained in your color determine ought to somebody want to supply it in greyscale. If that proves difficult, rethink your color scheme.

    borders — Usually I attempt to hold borders so simple as potential. There isn’t any want for a whole field in a bivariate plot, however a map typically has ‘boundary’ results (e.g., the sudden disappearance of a shoreline), which will be solved elegantly with a easy line border. Too many borders makes a determine look cumbersome and blocky. Too few can result in misinterpretation of parts aren’t simply separated upon first look.

    font — Usually journals require any quantity/phrase fonts within the graph to be in keeping with the font of the primary textual content. If that’s the case, you must observe their conference. If not, then a easy, interesting, but non-flashy font must be used for all determine parts (axis titles, legends, axis labels, and so forth., and so forth.). Don’t combine and match fonts on the identical determine.

    are the info steady? — I typically see graphs the place single values (e.g., frequencies, discrete temporal values, and so forth.) are joined by some form of line, implying that you’ve got knowledge between the discrete values. When you don’t, don’t attempt to suggest a steady distribution between the adjoining classes. Select a format that shows the info most precisely. Alongside these similar traces, nice, bloody excessive bars from zero to the worth at hand are inclined to condense all the knowledge into one excessive of the graph. Right here, some extent is far more appropriate.

    pointless capitalisation — I see this rather a lot. Axis labels, axis titles, panel titles, and so forth. with capitalised first phrases. It doesn’t assist that the majority functions mechanically capitalise the primary phrase in a textual content field. Ask your self whether or not it’s a correct noun; if not, don’t capitalise. Most labels should not the primary phrase of sentences, so standardise and hold your capitalisation just for the phrases requiring it.

    icons/pictures — I discussed above that icons can typically that gaping white-space problem. A cleverly positioned icon or simplified picture of the topic at hand can typically accompany a formidable graph and make it pleasing to peruse and reproduce. Once more, use with moderation, and check out to ensure your icons are high-resolution (in any other case, they have an inclination to look beginner).

    shading — Do you may have icons, arrows, and so forth. that appear just a bit too boring? Usually a really delicate shadow can present slightly perspective. However just like the 3D problem, keep away from inferring an empirical dimension. One other highly effective use of shading (drop shadows, glows, and so forth.) is to assist differentiate textual content from background element.

    backgrounds — It’s typically tempting to incorporate a background color and even a picture behind your graph. This is usually a powerfully aesthetic element if finished subtly, however actually distracting if finished with out care.

    use a number of functions — I’ve but to seek out the ‘excellent’ graphing software, so I have a tendency to make use of many on the similar time to supply the best-quality figures. The generic R plotting services are crap, though ggplot makes figures much more aesthetically pleasing (however requires much more coding know-how). Excel is loathesome for figures. I typically use R to supply the abstract info, then import the info right into a devoted graphing software (e.g., GraphPad Prism, and so forth.), which I can then import right into a GIS software if I want to mix issues with maps. Or, I can produce subplots in a single software and combination them in Powerpoint, or some such. The important thing right here is to be versatile, and ensure the ultimate output will be exported at excessive decision (vector or no less than 600 dpi).

    CJA Bradshaw