Science is full of abstractions. A line plot is an abstraction. A false-colour image is an abstraction. Even scientific notation like 6.0 × 1023 is an abstraction. Abstractions like these are central to science, invoked all the time, and easy to understand. Other abstractions are far from simple and may cause more confusion than clarity.
Abstraction is a slippery slope. Things can quickly get out of hand. Let’s start with a simple abstraction like velocity. The units, metres per second or kilometres per hour, tell us what it is: how fast something moves. I imagine most scientists would still be comfortable if I increase the level of abstraction by calculating the acceleration (i.e., moving from m s−1 to m s−2). But what if I had instead starting talking about a quantity in m2 s−1, not m s−2? Diffusivity and viscosity are such quantities; they measure how quickly something spreads. All I did was change m s−1 to m2 s−1 and I’ve taken a concept a kid can understand to something that may trip up an undergraduate physicist.
Web developers and programmers have a lot riding on the quality of their product. If it behaves in an unusual manner, it’ll frustrate users. If it’s unattractive, it won’t attract users. If it’s no good, it will lose users. Users are the primary concern. Consequently, there are fields dedicated to this concern: User Interface (UI) and User Experience (UX).
Because web developers and programmers have a much wider potential audience than scientists, they have a better handle on the importance and behaviour of the end user. Scientists could learn a thing or two about UI/UX.
Journalism and science have very different time scales. A week-old newspaper is barely worth reading. A week-old scientific paper is still warm from the photocopier. Journalism neglects this discrepancy and pressures science to hurry up. Numerous headlines with the eye-roll-inducing opening “A new study shows” imply that only the newest (or weirdest) science is worthy of attention. Google declares about 2000 times as many results for that phrase compared to “an old study shows”.
To make sense of the differences between science and its representation in the media, it first helps to figure out what does and doesn’t make news, and what does and doesn’t get widely shared or discussed. With that established, we’re better positioned to overcome the challenges of communicating all scientific research, not just the sexy aspects that make good headlines.
This article is going to describe … would be a terrible opening for this article. It’s six words that convey nothing. You already know this is an article, and you already know that it’s going to describe something. We don’t see this, fortunately, because the importance of a strong and compelling opening sentence is well recognised. At the paragraph level, however, it’s easy to forget the importance of the first sentence. In scientific cases, a symptom of poor or lazy writing is opening a paragraph with Figure n shows.
When it comes to visualising your data, the most important question to ask yourself is what’s your point. Wording a paragraph by starting with Figure n shows will not convey the point. It tells me what you did, but not why I should care. Using this phrase would be like putting the Methods section of a scientific paper before the Introduction.
Scientific literature is like social media: its content disproportionately comprises successes and achievements. Just as social media seldom features mundane necessities like trips to the supermarket, scientific papers seldom feature abandoned experiments or fruitless pursuits. In fact, we generally work backwards from the results and conclusions when writing these papers. We start with the answer and present only the relevant methods. To a non-scientist, this may sound dishonest and deceptive, but it’s not. Its for the reader’s benefit: a linear narrative is much easier to follow than the actual story with its many tangents, setbacks, and realisations.
Anyone exposed to the process of scientific research quickly learns that it seldom follows the so-called scientific method, those dispassionate experiments meant to objectively test hypotheses. Science is better described as Ready, Fire, Aim. Yes, in that order. This phrase, borrowed from Neil Gershenfeld, concisely captures how science is an iterative procedure without a specific target. Put in some groundwork to figure out the general direction (Ready), but take what might otherwise be a shot in the dark (Fire), then spend time making sense of what you hit (Aim). You may well fail and hit nothing, but as Gershenfeld elaborates, you can’t hit anything unexpected by aiming first.
If the result confirms the hypothesis, then you’ve made a measurement. If the result is contrary to the hypothesis, then you’ve made a discovery – Enrico Fermi
A computer is a better artist than I am. If I can tell it what to draw, it will produce attractive results. To make a nice schematic, the hardest part is to tell the computer what I want to draw. Fortunately for us so-called left-brain types prevalent throughout the sciences, a familiarity with scientific software can overcome a lack of artistic talent, allow rapid iteration of a design, and even provide creative inspiration.
Invoking my scientific software skills, I am able to produce elegant figures:
My favourite aspect of a Nature paper is the figure captions. Not the paper’s innovative science. Not the paper’s succinct length. The figure captions. Why? Because the journal’s simple act of bolding the first sentence of a figure caption can force authors to clarify the purpose of the figure. This is one of several seemingly minor formatting issues that ultimately improves a paper’s readibility.