undefined | Better HN

0 pointsmechanical_fish14y ago0 comments

I now see a terrible flaw in my essay of last night: I took the cheap and easy rhetorical route of emphasizing the costs of the fanciest equipment. Whereas the thing that kills you in science budgeting is actually the mundane stuff that you need in vast quantities.

Perhaps I should have talked about something boring, like absolutely clean, absolutely sterile containers. There is nothing sexy about containers and pipettes, and they are individually cheap. But they add up. The most reliable technique is to just buy quantities of disposable ones. You can try to save money by using recyclable containers instead, by washing dishes a lot, but washing dishes by hand is expensive even if you don't do it very patiently and carefully. And you need to be really careful, because if you move your cell culture from dish to dish to dish ten times, and even one of those dishes is contaminated, guess what? Your experiment might now be contaminated. And for every Alexander Fleming (a lucky guy whose contaminated experiment turned out to be a Nobel-Prize-winning medical breakthrough) there are a thousand experiments that have to be discarded because the data isn't reliable.

But even that argument is misleading, because the most expensive ingredient in science is not even a material good. It's time. Science is about patience and consistency. Doing an experiment once is not science. Doing it one thousand times and getting absolutely consistent results -- that is science. The work of being a scientist is about carefully building and debugging a reliable sequence of steps ("grow, filter, sort, lyse, plate, stain, image"): A sequence that can be repeated over and over to obtain thousands of data points that are extremely self-consistent.

The reason why professional scientists use such expensive equipment is that the equipment is actually cheap compared to the cost of spending two years taking data that turn out to be full of errors because your tools weren't reliable. Too much random error and you won't see your data amid the noise; too much systematic error and you might eventually have to throw out 100% of your work and start over. Trust me: If you want to experience soul-crushing misery [1], work sixty hours a week taking data for two years, then set the data on fire because it's unsalvageable. I have seen this happen many times. It has happened to me. It happens all the time in science, but you can't afford to have it happen too often.

So, yeah, you can take pictures of earth from space for $200, but can you take the same picture one thousand times, under consistent lighting and from consistent altitude and position? Yeah, you can build an electron microscope in your basement, but will it keep working every day for five years while you take your data? How much maintenance will it require? How much time will you waste waiting for it to pump down every time you change samples, or tweaking the knobs for hours every time to get a usable picture? Will it spread thin layers of carbon on your electronics, or go through a six-month phase where it can't focus well enough to see your samples?

---

[1] Fortunately, the misery is temporary. If it isn't, you're just not cut out to be an experimental scientist. Science requires reserves of forward-looking optimism! So what if yesterday was hell? It was a learning experience that will make tomorrow better! ;)

0 comments

ippisl14y ago

Thanks for a very detailed and interesting description of the scientific process.

From the way you describe it, it seems that doing science in your area, is not the science that would attract small hacker groups , because it's long and boring.

hackers tend to contribute to the interesting parts of open source , i.e. languages like python and ruby, and organizations tend to do the big, boring heavy lifting, things like android , linux , etc.

By the way, what about technologies like combinatorial testing, automation , and miniturization ? what would be their affect on the way biology is done ?

Goladus14y ago

I wouldn't say it's boring. Not everyone has the patience science requires but for those that do, there's plenty of incentive to do disciplined data collection.

It's most likely just that the resources required to do it are still out of reach, unlike the python and ruby communities where your biggest expenses are a computer with a text editor and access to an internet connection.

Even some computing resources are out of reach for individuals, even if just barely. Amazon hosts public datasets, which is nice, but but the 500,000 instance-hours of HPC compute time needed to do analysis is not going to be in the budget of a hacker-in-the-basement (ie someone with no funding, whether it be from grants, revenue, or investment).

j / k navigate · click thread line to collapse