Creating realistic fake data

Have you ever noticed the similarities between stock images that convey an increase? More often than not, it’s an arrow initially heading up at about a 30° angle, followed by a downturn, before continuing back up. There’s occasionally a second down-and-up for good measure. It’s sufficiently cliche that Yale Economics should feel a little embarrassed to have incorporated it into their logo.

Typical results from an image search for “increase” alongside Yale Economics’ logo

In the spirit of an English teacher inferring a lot from a little, I wonder if the downturns are intended to imbue some kind of story arc to the progress implied by the ascending arrow? As in, you have to get knocked down before you can get back up. Or maybe the downturns are there to instil a sense of realism?

Absurd as these rhetorical questions sound, they hint at a surprisingly profound issue about fake data and what makes it look real.

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The Goldilocks level of abstraction in science

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.

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What can the fields of UI/UX teach scientists?

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.

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True scientific news doesn’t make the news

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.

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Don’t start paragraphs with Figure n shows …

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.

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Ready, fire, aim: the myth of the scientific method

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

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Scientific software skills make up for minimal artistic talent

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:

From Evolution of the velocity structure in the diurnal warm layer

Now, compare that with my initial sketch…


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The formatting of Nature, Cell and Science papers improves their readability

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.

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Scientific rationale: convenient little white lies?

Physics is like sex: sure, it may give some practical results, but that’s not why we do it quipped Richard Feynman. The oceanographer Curtis Ebbesmeyer1 provides a similar, albeit less memorable quote, when describing his early work on water slabs (aka snarks), which had relevance to both military and pollution issues: such practical matters did not interest me. I found snarks fascinating, even beautiful in their own right. The introductions to many scientific papers, however, are framed in terms of practical results. Hence the rhetorical question implied in the title: are the rationale we as scientists publish convenient little white lies, simply a way to validate undertaking the science that we find personally interesting and intrinsically satisfying?

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Python spoilt me; returning to Matlab is hard

Using Python daily for more than three years as part of my scientific workflow and then abruptly returning to regular Matlab use has made me realise how much better Matlab could be and how evident its idiosyncrasies are. Conversely, while I was aware and noticed that Python makes things simple, it is Matlab’s comparative flaws that really made me come to appreciate just how much has been achieved in the past decade by the community in making Python an indispensable scientific tool.

An aim of this post is to recognize Python’s impressive convenience and versatility. Unfortunately, however, this post more naturally develops by taking the pessimistic approach of highlighting Matlab’s flaws. What follows are several minor, and a few major, annoyances that I’ve noticed on returning to Matlab.

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