Let me share three short stories.
Story 1: Steve Jobs was obsessed with the design of his products. When designing the first Macintosh, Jobs was adamant about the circuit boards being neat and orderly. The circuit boards! The innards of the computer! My guess is that 99% of users never looked inside the computer, and surely several of the 1% who did look inside never noticed the care and skill that went into making the circuit board look nice. Sure, it may have looked like an orderly circuit board, and it may seem like a waste of resources because making the circuit board orderly does not inherently improve the performance of the computer. But it is this concern about excellence and quality being carried throughout all of the product, inside and out, not just the part of the product that most users see, as being essential to what made the Mac the Mac.
Story 2: My nephew loves Legos. At a recent family function, I vividly remember him sitting on the floor methodically assembling his Lego model. His focus was intense. He was in a state of flow. He couldn’t care less about whether anybody was watching him work; he was on a mission to create something awesome. He’d look at the schematic, find the next piece, and put the piece in the right spot. Snap! Repeat! After the last step, looking at what he assembled with his own two hands, he felt like Michelangelo just unveiled the David. He loves building his Legos because the more he does it, the better he gets.
Story 3: Some graduate student is in a lab somewhere right now tinkering with ggplots on her laptop. She tries out different shapes in her scatter plot. Now different colors. Is the font too big? Too small? Should I use theme_minimal() or theme_bw()? What location of the legend makes it easiest for a reader to intuit the essential information from the figure? After hours of tinkering, honing, polishing, she creates a figure that is just right. When she presents that figure, she glances at the audience’s reaction to her masterpiece.
What do these three stories have in common? Craftsmanship.
Today I want to give a nod to the often overlooked academic craftsmanship that I see in my colleagues’ work. You know, the little things that researchers do in the process of creating their research products that give them pride. The little things that make a merely publishable manuscript into scientific poetry, an adequate figure into a piece of art, and an ordinary lecture into the academic version of the Beatles' Sgt. Pepper's Lonely Heart's Club Band.
Let me first stake a flag in the ground before the rabble gets aroused. When I say academic craftsmanship, I do not mean “flair.” Even the craftiest craftsman who ever crafted a craft is incapable of consistently producing significant results with N = 20. Also, when I say academic craftsmanship, I do not mean having a knack for being able to “tell a good story” to an editor and three anonymous reviewers (although that does seem to be skill that some people have developed). Craftsmanship cannot compensate for vague hypotheses or poor inferences. When I say academic craftsmanship, I simply mean the details that take care, patience, and skill that evoke a sense of pride and satisfaction.
Here is one of my favorite examples of academic craftsmanship.
Check out the correlation graph between the original effect size and the replication effect size for the Reproducibility Project: Psychology (http://shinyapps.org/apps/RGraphCompendium/index.php#reproducibility-project-the-correlation-graph ). First off, the overall figure is packed with information—there is the scatterplot, the reference line for a replication effect size of zero and a reference line for a slope of 1 (i.e., original effect size = replication effect size), the density plots on the upper and right borders of the scatterplot, rug marks for individual points, the sizes of the points correspond to replication power, the colors of the points correspond the p-values, etc.—but overall the figure amazingly does not seem cluttered. The essential information is intuitive and easily consumable. There are details such as the color of the points that match the color of the density plots that match the color of the rug ticks. Matching colors seems like the obvious choice, yet somebody had to intentionally make these decisions. You can breathe in the overall pattern of results without much effort. Informative, clean-looking, intuitive. This is a hard combination to execute successfully.
After seeing this figure, most people probably think “big deal, how else would you make this figure?” Believe me, I once spent 90 minutes at an SPSP poster session shaking my head at a horrible figure! It was ugly. It was not intuitive. It was my poster.
Now let’s look under the hood. Open up the R-code that accompanies this figure. Notice how there is annotation throughout the code; not too much, but just enough. Notice the subtleties in the code such as the use of white space between lines to avoid looking cluttered. Notice how major sections of the code are marked like this:
The series of hashes and the use of CAPS is effective in visually marking this major section. Does this level of care make the R-code run better? Not one bit. However, it is extremely helpful to the reader. This clean R-code is akin to the orderly circuit board in the Mac.
This is just one example. But I see craftsmanship all over the place. A clever metaphor, a nicely worded results section, the satisfaction of listening to the cadence of a well-rehearsed lecture, etc. Perhaps I will share more of these examples in the future. For now I only have one request. If this post is discussed on social media, I would like people to share their favorite examples of academic craftsmanship.