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.
Conclude your science. Don’t summarise it.
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.
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?
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.