DAVE
KLEINSCHMIDT

2021 was, for me, a year of steady reminders that, eventually, we have to let go of the things we love. I quit my tenure-track academic position in January, and had to let go of my academic career, the foundation of my identity for at least the last decade and a half of my life (and if I’m being honest, probably longer than that). We all collectively had to let go of the brief and delicious summer months where it looked like our Covid vaccines would allow us to get back to living like we had in the before times. I had to let go of the season of racing cyclocross that I had diligently trained all year for, after I managed to crash myself out of the very first race of the season while warming up. I was off the bike for a month with an MCL sprain.

Harder still, we suddenly had to let go of my cat Freya, who’s been a constant companion through the last 10 years, seeing me through my PhD, a breakup, a move to NJ, another move to another part of NJ, the tumultuous first few years of the tenure track, the tumultuous end of my tenure track, and, finally, yet another move. I still am regularly convinced that I hear her yowling murderously out the window, nearly six months after her kidneys suddenly gave out.

And, of course, there was my mom’s cancer.

In my role at Beacon, I spend most of my working day interacting with Julia on a remote machine, and I wanted to document the workflow that I’ve settled on. The very short version is that emacs/TRAMP, emacs-jupyter, and IJulia together provide a pretty smooth—but not perfect—experience.

TL;DR I’m joining Beacon Biosignals as a research scientist and leaving my tenure-track position.

I just submitted a paper where I tried to follow my own advice to cite the code you use. This project was done with R, which makes it really easy to automatically generate citations for packages. With just a bit of glue code you can automatically generate in-text citations, too.

One of my grad school procrastination projects was learning how to brew beer. I started off using a website called Hopville to keep track of the recipes I brewed, until they were acquired in 2013 by Brewtoad. Both sites provided a really convenient way to play around with recipe ideas, learn from others, and keep track of how each step of each brew went which was really helpful as a beginner.

Now, just five years later, Brewtoad is shutting down.¹ With no way to easily grab an archive of the dozens of recipes and brew logs I’ve saved on the site, and no public API.² So, the only remaining option is to go through and download the HTML for each page, one-by-one. I could do that myself but I don’t have time for that think that’s a task more appropriate for a computer. So I wrote a Julia script to scrape a user’s recipes and brew logs.

TL;DR: cite the software you use in your research!

In lab meeting the other day someone asked what the major R packages for analyzing psycholinguistic data are, and I had a hard time thinking of any.¹ That made me think about why software is such a small part of our scholarly output. Part of the reason might simply be that there’s not enough overlap in the specific kinds of analyses we do to justify creating brand new packages, rather than using domain-general tools (like the tidyverse).

But I think there may be a deeper explanation: it’s hard to write good, useful software, and academia does not reward that particular kind of hard work.

Why a blog? Because why not.

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