Jet was a ubiquitous colourmap that slowly fell out of fashion over the last five years. It was the default colourmap for Matlab and Matplotlib (and probably others) until 2015ish. The replacements, Parula and Viridis, respectively, get a lot of love. A quick Google search will present numerous blog posts and articles maligning Jet and promoting the use of these newer colourmaps. It’s an unpopular opinion but, especially now that they are common and overused, I dislike Parula and Viridis as much as I dislike Jet.
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
Matplotlib, Python’s primary scientific plotting library, provides tools to make many elaborate plots, graphs, and diagrams. Many of these can be animated, but the process isn’t always intuitive. The hardest part is learning how to animate a simple line plot (here’s my easy way). Beyond that, the steps to creating most animations tend to be similar.
The examples below demonstrate the particular methods needed to animate common types of plot. Here I focus on the key components needed for updating each frame of the animation. The full code for the examples is here. It includes liberal and arbitrary use of sines and cosines so as to produce looping gifs.
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:
Pure scientific research is economically viable because it has a fat tail. Science is expensive, but sporadic breakthroughs lead to economic benefits that more than cover the bill for the other studies. If you could buy stock in Pure Scientific Research, it would be a worthwhile investment, with estimated returns on investment of 20–60%. The catch? You have to share your returns with everyone else. They aren’t appropriable as an economist would say.
The importance of pure scientific research to the vast majority of modern life cannot be understated. But this importance is hidden. The tech, pharma, and auto companies that we buy products from undertake their own research and development, but it exists upon a base of fundamental science discovered within university walls. This link between pure research and modern day technology would be more obvious if, as Bruce Parker suggests, there were “Science made this possible” signs on every appliance, drug, car, computer, game machine, and other necessities of life. And let’s not forget that the Internet grew out of a technology physicists developed to help communicate with each other.
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.
A summary that merely repeats previous material is prohibited for the journal Nature and would be be edited out. Other journals are less strict, but perhaps they should follow Nature’s lead and recommend instead that the conclusion offer something new to the reader. This is often easier said than done. Scientists default toward endings that are typically cliche, uncompelling, or just tail off. Let’s look to factual but more expressive forms of writing, such as long-form journalism and narrative non-fiction, for examples of better endings that could be applied to scientific papers and talks.
Journalists arguably have a little more freedom than scientists in how they word the ending of a piece. A memorable quote or a clever joke, perfect fodder for a popular article, would be out of place in a scientific article. Yet there are several forms of conclusion that we could borrow from journalists to provide a more engaging ending. I’ll borrow my examples primarily from The Atlantic, but any decent popular publication can help.
The names we typically associate with scientific genius are from several centuries or millennia ago. Think Newton, Einstein, Archimedes, Galileo, or Darwin. Even famed scientists that are modern by comparison (Richard Feynman, Francis Crick, or Linus Pauling) made discoveries many decades ago. Just as any sports fan will tell you it is pointless to compare athletes from different eras, the same is true, if not more so, for scientists. Whereas athletes are largely playing the same game as they were decades ago, science has changed. We aim to always answer new questions, address ever more complex and interdisciplinary issues, and occasionally develop experiments costing billions of dollars. How, then, does scientific genius manifest in the 21st century? Which circumstances are most conducive to developing scientific genius? And what traits does a genius in the modern scientific realm exhibit?
The benefits of replication studies in science seem obvious and intuitive. Yet they are not particularly prevalent nor encouraged. The typical reasoning is that there’s no value for being the second scientist or group to observe a result. Some1 take this to suggest that the current scientific publishing system is flawed and promotes papers with provocative results rather than technically sound methods. Journals like PLOS One that disregard perceived importance are the exception. There are, however, a number of advantages of the status quo.
In a previous post, I listed a range of Matlab’s idiosyncrasies and flaws that seemed so much more blatant once I returned from several years of Python use. This post is a continuation, except this time highlighting ways in which Python makes life simpler rather than Matlab making life more difficult.
If you haven’t tried Python and you’re on the fence about whether it’s worth learning, let the points below convince you.