271–280 of 299 posts
https://wiki.debian.org/DebianGenomics https://blends.debian.org/med/tasks/ https://blends.debian.org/science/tasks/
The code is bad because transient Phds and Post-docs are writing it. If there was money in it then the best software developer would already be working on it. Sadly there is none.
Sometimes I really don't understand. Much of the field's code does not even have testing, and it is baffling for me to think how the results are believed to be correct in the first place if there is no rigorous testing.
"From my experience, what works incredibly well is a partnership between biologists and software engineers: the biologists first come up with the first concept of the tool, which is purely focused on ensuring good results. After this first iteration is completed, engineers then come in and rewrite the tool using modern engineering practices with things like speed and reliability in mind." Like others have pointed out…
How many of us have heard from some guy who has said, essentially, "I have an idea for the next , and I've already sketched out a prototype. There's just the small matter of programming and we'll make $LARGE_SUM?
Imagine this happening in the business world. A partnership between SMEs and software engineers. Oh, we do this all the time, that's why software engineers get paid well: we turn ideas into working code. Anyone ever heard of a product manager "banging out a prototype" and then handing if off to the software engineers to rewrite?
The more I re-read the passage from the article, the worse it sounds.
I'm a software engineer who works on genomics. I see a lot of negativity in this thread, which mirrors my experience: in most places, you'll be paid like a researcher with the respect of a lab assistant – unless you have a PhD and a postdoc. That said, it's possible find work that's respected and pays well. Most of that kind of work is happening in the context of startups or freelancing. My favorite example of this i…
As an outside observer to this area, something doesn't add up. It sounds like software tooling is desperately needed to advance the entire field across the board, yet it seems that few startups or founders are attempting to tackle this problem or, if they exist, aren't having much of an impact (perhaps, yet?).
One would imagine that all of this inefficiency, suffering and bottlenecking of incredible therapies to cure diseases and advance human knowledge would be a siren call for capital allocators to unlock value by solving this pain point -- but here we are, in some cases still 20 years and counting.
I can buy the argument that FAANGs have had amazing compensation packages over the past 2 decades, but this still doesn't address the reason why nobody else has bothered or been able to to "disrupt" (yes, air quotes) the industry in this regard and harvest such seemingly low-hanging fruit.
I see a few comments talking about the PI and grant-funding model -- but if the promised value was sufficiently large then I find it hard to believe that this wouldn't have been a competitive candidate alongside other recent buzzword-laden investment trends such blockchain & AI that pulled down so much VC funding over the past decade.
Clearly, I'm missing a piece of the value puzzle as to why founders and startups are few and far between to specifically address the dire straits that biological software engineering (computational biology, bioinformatics, systems biology, etc.) finds itself in.
I am a career SW engineer that has worked on genomics in a startup. The field is genuinely exciting. The endemic disease of the field is the leadership. A leadership made out of Principal Investigators forged in academia, appear simply incapable of producing any item which is not articles (or equivalents thereof).
Do you think that's true of pharmaceuticals/biotechs as well? Or just academia?
They have more cash to play with, but their leadership fails in the same pattern.
I work in this field at a large medical research institution. There is a significant amount of genomics analysis that occurs here on a day-to-day basis. The genomic processing pipeline work all falls directly into my group. There is next to zero demand for tool development internally. I do it on the side of "normal" IT data management because I love high performance computing, algorithms, and multithreaded hackery. B…
I'm a software engineer who works on genomics. I see a lot of negativity in this thread, which mirrors my experience: in most places, you'll be paid like a researcher with the respect of a lab assistant – unless you have a PhD and a postdoc. That said, it's possible find work that's respected and pays well. Most of that kind of work is happening in the context of startups or freelancing. My favorite example of this i…
>> I see a lot of negativity in this thread, which mirrors my experience: in most places, you'll be paid like a researcher with the respect of a lab assistant – unless you have a PhD and a postdoc. As an outside observer to this area, something doesn't add up. It sounds like software tooling is desperately needed to advance the entire field across the board, yet it seems that few startups or founders are attempting t…
Earlier quoted context omitted.
If you have a software background and can get some basic domain knowledge, there's lots of open source projects that could use your contribution. Doing fundamental reseach is a taller order. But lots of software, tools, pipelines etc need maintainers, optimizations...
Which projects? That seems like a good place to start.
I'm a software engineer who works on genomics. I see a lot of negativity in this thread, which mirrors my experience: in most places, you'll be paid like a researcher with the respect of a lab assistant – unless you have a PhD and a postdoc. That said, it's possible find work that's respected and pays well. Most of that kind of work is happening in the context of startups or freelancing. My favorite example of this i…
>> I see a lot of negativity in this thread, which mirrors my experience: in most places, you'll be paid like a researcher with the respect of a lab assistant – unless you have a PhD and a postdoc. As an outside observer to this area, something doesn't add up. It sounds like software tooling is desperately needed to advance the entire field across the board, yet it seems that few startups or founders are attempting t…
Why so few tooling companies - is there actually a market for good software in science? For there to be such a market most scientists would have to care about the correctness of their results, and care enough to spend grant money on improvements. They all claim to care, but observation of actual working practices points to the opposite too much of the time (of course there are some good apples!).
In 2020 I got interested in research about COVID, so over the next couple of years I read a lot of papers and source code coming out of the health world. I also talked to some scientists and a coder who worked alongside scientists. He'd worked on malaria research, before deciding to change field because it was so corrupt. He also told me about an attempt to recruit a coder who'd worked on climate models who turned out to be quitting science entirely, for the same reason. The same anti-patterns would crop up repeatedly:
- Programs would turn out to contain serious bugs that totally altered their output when fixed, but it would be ignored because nobody wants to retract papers. Instead scientists would lie or BS about the nature of the errors e.g. claiming huge result changes were actually small and irrelevant.
- Validation would be often non-existent or based on circular reasoning. As a consequence there are either no tests or the tests are meaningless.
- Code is often write-once, run-once. Journals happily accept papers that propose an entirely ad-hoc and situation specific hypothesis that doesn't generalize at all, so very similar code is constantly being written then thrown away by hundreds of different isolated and competing groups.
These issues will sooner or later cause honest programmers to doubt their role. What's the point in fixing bugs if the system doesn't care about incorrect results? How do you know your refactoring was correct if there are no unit tests and nobody can even tell you how to write them? How do you get people to use tools with better error checking if the only thing users care about is convenience of development? How do you create widely adopted abstractions beyond trivial data wrangling if the scientists are effectively being paid by LOC written?
The validation issue is especially neuralgic. Scientists will check if a program they wrote works by simply eyeballing the output and deciding that it looks right. How do they know it looks right? Based on their expertise; you wouldn't understand, it's far too complicated for a non-scientist. Where does that expertise come from? By reading papers with graphs in them. Where do those graphs come from? More unvalidated programs. Missing in a disturbing number of cases - real world data, or acceptance that real data takes precedence over predicted data. Example from [1]: "we believe in checking models against each other, as it's the best way to understand which models work best in what circumstances". Another [2]: "There is agreement in the literature that comparing the results of different models provides important evidence of validity and increases model credibility".
There are a bunch of people in this thread saying things like, oh, I'd love to help humanity but don't want to take the pay cut. To anyone thinking of going into science I'd strongly suggest you start by taking a few days to download papers from the lab you're thinking of joining and carefully checking them for mistakes, logical inconsistencies, absurd assumptions or assertions etc. Check the citations, ensure they actually support the claim being made. That sort of thing. If they have code on github go read it. Otherwise you might end up taking a huge pay cut only to discover that the lab or even whole field you've joined has simply become a self-reinforcing exercise in grant application, in which the software exists mostly for show.
It sounds like academia is simply too toxic, entitled, full of itself and hierarchical to provide an environment with good software practices can thrive.