TensionThe ability to see like a data structure afforded us the technology we have today. But it was built for and within a set of societal systems—and stories—that can’t cope with nebulosity. Worse still is the transitional era we’ve entered, in which overwhelming complexity leads more and more people to believe in nothing. That way lies madness. Seeing is a choice, and we need to reclaim that choice. However, we need to see things and do things differently, and build sociotechnical systems that embody this difference.This is best seen through a small example. In our jobs, many of us deal with interpersonal dynamics that sometimes overwhelm the rules. The rules are still there—those that the company operates by and laws that it follows—meaning there are limits to how those interpersonal dynamics can play out. But those rules are rigid and bureaucratic, and most of the time they are irrelevant to what you’re dealing with. People learn to work with and around the rules rather than follow them to the letter. Some of these might be deliberate hacks, ones that are known, and passed down, by an organization’s workers. A work-to-rule strike, or quiet quitting for that matter, is effective at slowing a company to a halt because work is never as routine as schedules, processes, leadership principles, or any other codified rules might allow management to believe.The tension we face is that on an everyday basis, we want things to be simple and certain. But that means ignoring the messiness of reality. And when we delegate that simplicity and certainty to systems—either to institutions or increasingly to software—they feel impersonal and oppressive. People used to say that they felt like large institutions were treating them like a number. For decades, we have literally been numbers in government and corporate data structures. BreakdownAs historian Jill Lepore wrote, we used to be in a world of mystery. Then we began to understand those mysteries and use science to turn them into facts. And then we quantified and operationalized those facts through numbers. We’re currently in a world of data—overwhelming, human-incomprehensible amounts of data—that we use to make predictions even though that data isn’t enough to fully grapple with the complexity of reality.How do we move past this era of breakdown? It’s not by eschewing technology. We need our complex socio-technical systems. We need mental models to make sense of the complexities of our world. But we also need to understand and accept their inherent imperfections. We need to make sure we’re avoiding static and biased patterns—of the sort that a state functionary or a rigid algorithm might produce—while leaving room for the messiness inherent in human interactions. Chapman calls this balance “fluidity,” where society (and really, the tech we use every day) gives us the disparate things we need to be happy while also enabling the complex global society we have today.

Absolute quantitation of microbiota abundance in environmental samples

use of synthetic DNA spikes enables absolute quantification of microbiome members directly in environmental samples [14].

Jesse Stommel and I wrote once that, In the room with our students, we can know if they’re engaged and participating, even as each of them participates in his or her own unique fashion. In an online discussion forum, it’s difficult to observe such nuance, and impossible to quantitatively evaluate it.

Formula for Calculating Concentration

Now it is much clearer that id is really a family of infinitely many functions. It is fair to say that it is an abstract function (as opposed to a concrete one), because its type abstracts over the type variable a. The common and proper mathematical wording is that the type is universally quantified (or often just quantified) over a.

Quantified Types

There is a simple mathematical relationship between the fraction of droplets that are unoccupied (black bar) and the concentration of target molecules.

The concentration of GFP in the sample had been measured using a nanodrop and was ~120μM

How not to perform a differential expression analysis (or science)

Salmon provides fast and bias-aware quantification of transcript expression