A direct and quantifiable impact on science to come out of my PhD was the 50-odd times that I brewed coffee for the department morning tea. Scientists turned up and got coffee; I got thanked for helping make that happen.
Despite its impact, brewing coffee is not listed on my CV1. Instead, I have publications. Yet, compared to coffee, the direct impacts of these publications are hard to define.
Continue reading “Introductions in scientific papers can give warped and inflated perspectives”In computer programming1, code smells are “surface indications that usually correspond to deeper problems in the system”. Duplicated code is one example. Copying a code fragment into many different places is generally considered bad form; Don’t Repeat Yourself is a well known principle of software development. However, duplicating code can be beneficial if, say, it makes the code easier to read and maintain.
Although code smells are undesirable, “they are not technically incorrect and do not prevent the program from functioning.”
By this description, I’d argue that smells also exist in scientific papers. Hence, I’m proposing a few of these easy-to-spot (aka sniffable) features that may point to a deeper underlying issue.
Scientific writing is obsessed with other scientific writing1 and itself.
Phrases like ‘this paper‘ and ‘this study‘ are everywhere in scientific writing2—which is not a problem per se. Used well, these phrases concisely differentiate the current study from others. Used poorly, these phrases fill the word count without adding value to the reader.
Never, for example, start a Conclusion with ‘In this paper, we showed . . .’ or ‘The main conclusions of this paper are . . .“. The first few words of a Conclusion (any section, in fact) are precious. Don’t waste them reminding me that I’m reading a paper in which you’ve shown or concluded something. Tell me something profound—something about your science.
“In this paper, we showed . . .” is a signpost (aka metadiscourse). It’s writing about the writing. And it’s a main reason that so much of science writing, like any academic writing, is so boring.
Continue reading “Don’t write about your scientific paper, just write it”Line graphs are the Swiss army knives of data visualisation. They can be almost anything… which is both good and bad.
Many graphs serve one clear purpose. Take the five graphs below:
Even without labels, it’s clear what role each of these graphs serves:
In other words, if I’m presented with one of the graphs above, I have an immediate head start on interpreting it. If, instead, I’m presented with a line graph, I’m forced to read the axes labels and limits first.
Deciphering text is the slow way to intake information. Shape is fastest, then colour, and only then text. This so-called Sequence of Cognition, popularised by Alina Wheeler, is something marketers need to know about.
Continue reading “Line graphs: the best and worst way to visualise data”I typically write 100–200 lines of code each time I develop a scientific figure that is destined for publication. This is a dangerous length because it’s easy to create a functioning mess. With shorter code fragments, it’s feasible to start over from scratch, and with thousands of lines of code, it makes sense to invest time upfront to organise and plan. But in between these extremes lurks the appeal to write a script that feels coherent at the time, but just creates problems for future you.
Let’s say you want to create a moderately complicated figure like this:
A script for this figure could be envisaged as a series of sequential steps:
Comments within code are harmless, right? They don’t affect run-time, so you might as well use them whenever there’s any doubt something is unclear.
I hope you aren’t nodding your head, because a liberal use of comments is the wrong approach. Not all types of code comments are evil, but many are rightfully despised by programmers as (i) band-aid solutions to bad code, (ii) redundant, or even (iii) worse than no comment at all.
The same is true for scientific figures and their captions. In fact, many of the rules discussed in the post Best Practices for Writing Code Comments remain valid when we replace comments and code with captions and figures, respectively.
Continue reading “Captioning a scientific figure is like commenting code”Inspired by 250 things an architect should know but 60% less ambitious
Click on each word (not its number) for a brief elaboration
Four distinct datasets (x vs y) that produce the same summary statistics (mean, variance, correlation coefficient, and line of best fit)
A colour space that defines colours in terms of their Hue (e.g., red or blue), Saturation (vivid to washed out), and Lightness (white to black)
The area within a design (website, poster, figure, etc) that lacks text, images, or other elements
A questionable approach to research: Hypothesising After the Results are Known
Problems in which an answer cannot be estimated outright but is instead derived as the product of more easily estimated quantities (e.g., how many grains of rice are eaten across the world every year?)
The JPG format is optimised for photos, whereas PNGs are for graphs and diagrams
That and which, although similar, have opposing implications about whether a clause is restrictive or not
Basing a decision on only numbers or other objective measures without reference to any qualitative factors
A tool for tracking and recording all changes to software and other digital files as they evolve
The human tendency to better remember what happened at the start and the end and forget what happened in the middle
Online repositories for datasets, code, and other research output
A typical person living in a western country will have an annual footprint of 5–20 tonnes CO2
Well known scientists get cited more often than lesser known ones leading to a positive feedback loop
A metaphor for an answer that might gloss over details, be vague, or rely on many approximations
Scales that increase geometrically (e.g., 1, 2, 4, 8, 16, …) rather than linearly (2, 4, 6, 8, …)
It can sound odd, but data were collected is correct and data was collected is not
A sentence structure to avoid because the initial words only make sense as the sentence nears it end
A statistical tendency for outliers in an initial experiment to deviate less in a subsequent experiment
Software for all manner of image manipulations and conversions that can be run from the command line
The default command line interface
A widely used approach for smoothing time-series data
The value 1.618…; an aesthetically pleasing aspect ratio for a rectangle among many other claims to fame
Flattening the earth to a two-dimensional image can be achieved in numerous ways, each with its own pros and cons
Unique digital identifiers that can point to publications, datasets, software, and more
An early name in data visualisation and author of several books on the topic
A line at the beginning or end of a paragraph that is separated from the rest by a page break
One of the most expensive scientific experiments took ~3 billion Swiss Francs to build (or ~5 billion US dollars back in 2001)
Electron microscopes can resolve objects as small as 0.1 nanometers
Tweaking a fundamentally flawed theory in a last-ditch effort to make it explain observations
Among many reasons, basic scientific research (i) lowers the barrier for firms that want to develop new products and (ii) develops skilled scientists and engineers who can capitalise on research undertaken elsewhere
Shockley speculated that a small number of scientists can be exponentially more productive in total because the creation of a scientific paper is the combination of many individual tasks, and productivity in each of these tasks multiplies together to give overall productivity.
Adjusting the spacing between individual letters in text to improve aesthetics
Depending on scientific field, the last author either did the least work, is the group leader, obtained funding for the project, or has a surname near the end of the alphabet
An open source project that simplifies and promotes interactive use of many programming languages
The uncertainty of a derived quantity (e.g., kinetic energy derived from speed and mass) can be calculated from the uncertainty of the input quantities following simple—though sometime tedious—arithmetic
The standard way to access a remote server via the command line
Although similar, they should not be confused; a hyphen (-) is a short dash used to combine words, whereas a minus sign is longer (−)
A line of text should have 60–70 characters (counting spaces) for a single-column layout and 40–50 for multiple columns (see page 32 of Detail in Typography)
A predecessor to PDF that was developed in the late 1980s and is almost obsolete
For line drawings, the edge is known as the stroke and the interior is known as the fill
The order doesn’t matter, but knowing the individual letters is worthwhile
The smoothing of text to improve its appearance (especially relevant at coarse resolution)
A three-panel image or collection of images (and an easy way to create an attractive title slide)
Better than passive voice in most cases
An algorithm that makes much of modern technology possible
Statements and methods purportedly grounded in science but obviously flawed
A unique digital identifier for a researcher that is linked with their scholarly works
Your phone likely has billions of them
A computer’s short-term memory in a sense (distinct from the long-term memory that is the hard drive)
About $50 000
One of the building blocks of any programming language that, typically, (i) takes one or more inputs, (ii) does whatever to those inputs, and (iii) returns an output
The incorrect assumption that a claim is true because it is coming from an authority figure
Studies in which the methodology and hypothesis are published before data are obtained
Misrepresenting a claim or changing its context so as to make it easier to argue against
NASA, for example, currently has about 30 active earth-observing satellites producing about 30 TB of data each day
The simplest way in most programming languages to make a computer do something again and again
Most people are overconfident in their understanding of a complex phenomenon or procedure until they try to explain it step by step
Calculating a line of best fit is one of those things everyone should do manually at least once to understand the procedure that can otherwise be a black box
An approach to statistics in which probabilities are continually updated as new information is obtained
Two distinct ways of thinking: system 1 is fast and driven by intuition and emotion, whereas system 2 is slower and more deliberate
Use-inspired basic research, or the view that basic and applied research aren’t mutually exclusive
In any dispute the intensity of feeling is inversely proportional to the value of the issues at stake
The tendency to underestimate the time needed to complete a task (e.g., writing a scientific paper) even with prior experience in the same or similar tasks
The system used by computers that allows a small number of bits (a zero or one) to represent a wide range of numbers (e.g., 64 bits can be used to closely approximate any number, positive or negative, up to 1.8×10308)
A format coined by scientist-turned-filmmaker Randy Olson that aims to drill down to the essence of an idea: Nothing in ___ makes sense except in light of ___ (e.g., nothing in biology makes sense except in light of evolution)
A visual metaphor for the design process that works equally well for the process of doing science
Design laws, grounded in psychology, for how humans perceive combinations of objects or elements
An inefficient problem solving technique where you rely on your previous approaches that worked in the past despite there being better methods
Also known as the Law of Triviality, bike-shedding is giving undue emphasis on minor matters such as the design of bike sheds to be included within the development of a nuclear power plant
Subsets of a dataset, all of which have a negative statistical trend, can still produce a positive trend in the overall dataset
Work expands to fill the time available for its completion
When an expert in a given field trespasses into another and makes claims where they lack expertise
The strength or effect size of a scientific result tends to decline over successive replications
Misjudging the probability of an event due to more intuitive individuating information (e.g., thinking it’s more likely than not that someone who is 6-foot-8 plays basketball professionally, except that the chances are a fraction of 1%)
The desire to assist a specific individual facing a certain hardship but not a large, unknown group of people facing the same hardship
A somewhat controversial physician/scientist perhaps best known for his claim that most published research findings are false
Deriving incorrect conclusions by overly focusing on clusters of data points that may have arisen by chance
A type of selection bias in which the dataset contains only people who made it past some hurdle
One of the simplest ways to visualise data in which, in place of graphs, a few select metrics are displayed as numbers in large text
The tendency (and salesperson’s boon) for people to focus on relative changes from an initial value rather than the absolute amount
Scientists aim to generate knew knowledge and engineers aim to apply knowledge to solve real-world problems
A measure of the flexibility a scientist has in developing, analysing, and publishing an experiment
As a rule of thumb, the aspect ratio of a line graph should be one in which the changes to be emphasised have a slope of ~45°
The conjecture that basic research informs applied research, which promotes development and production, which ultimately lead to economic growth
An infamous study—that passed peer-review—that purportedly shows that people can essentially see briefly into the future
A teaching philosophy that starts with the big picture rather than tedious fundamentals
One of the original preprint servers (now 30 years old)
A guideline that encourages a design (say, an interface or piece of software) to be built to behave in a way that most users expect it to
Words with a German heritage tend to be simpler and less pretentious than those from Latin
A type of citation measure that counts mentions in blogs, tweets, and other social media rather than standard citations in scientific papers
A (now expensive) AI service that summarises the different ways a paper is cited (supported, contrasted, or mentioned) rather than merely counting the number of citations
A problem solving technique that looks to solve the underlying issue rather than the immediate (and possibly superficial) problem
Something that is complicated may involve a tedious number of straightforward steps, whereas something that is complex may have multiple nonlinear interactions and emergent behaviour
People from Western, Educated, Industrialized, Rich, and Democratic societies who are over-represented in scientific studies involving human subjects
A vector graphics editor that is more than sufficient for a scientist’s needs
A computer scientist notable for, among many things, the creation of the TeX typesetting language and his decision to forgo email as of Jan 1, 1990
Four standard ways to project a three-dimensional object into two dimensions
Entropy of a closed system cannot decrease or, more simply, heat flows from hot to cold
The current global average is about 4 mm/yr, but this varies regionally depending on the vertical movement of land
The comma placed before “and” or “or” in a list of three or more items
I used my laptop to scan the text of 360 scientific papers for use of the word exciting (and excited and other variants). I got 195 matches. That’d suggest that scientists imbue their writing with their own excitement for science. Except that 191 of those matches are physics jargon (as in wind excites ocean waves) rather than the everyday meaning. Remove those and we’re left with ~1% of papers indicating any excitement.
That’s a weird thing to look into is what you’re thinking, so two bits of context. First, there’s lots to be learned about scientific writing by looking at word usage statistics; see my two previous posts. Second, I came across one of these rare uses of exciting with its everyday meaning, and it stood out! Which is messed up. It’s a common word, yet it struck me as out of place in a scientific paper. Not because I think it should be, but because it is.
For comparison, I looked at the words interesting and interestingly. The result: 237 matches, all of which correspond to their everyday usage. (Including interest and interested in my search more than doubles the number of matches.)
Continue reading “Science is interesting, but not exciting… according to our papers”As scientists, we record our findings in perpetuity in PDFs— literally simulations of pieces of paper. It’s time to be more dynamic and invoke a proliferation of media types. We don’t need to get rid of the notion of a paper or stop using a PDF as the version of record. But we do need to complement them with something less static. What follows is an approach I recently took using video.
The final sentence of my latest paper (preprint) steers the reader to a video that stands in place of a Conclusion section. And I’m guessing this video is a much more compelling Conclusion than any possible combination of words.
Here’s the gist of the final paragraph (paraphrased to avoid jargon):
Our simulation was made possible by tuning against measurements from a new instrument. This observation-informed simulation depicts instabilities as they evolve throughout the day. It is best appreciated as an animation (doi.org/10.5281/zenodo.4306935).
The link goes to a copy of the video below:
Too many scientific figures are ugly. I see three possible reasons:
Take the following published scientific figure (suitably disguised):
Let’s list the problems: (1) Space is poorly used and data are cramped. (2) Text is bold for no reason. (3) Multiple fonts are used. (4) Tick marks are barely visible. (5) Some labels don’t fit in their respective boxes. (6) Axis values are unnecessarily repeated. (7) Dashed and dash-dotted lines are ugly. (8) Mathematical symbols are not italicised.
Continue reading “Ugly scientific figures: Are scientists lazy, indifferent, or oblivious?”
Brushing Up Science 