“You’re not allowed bioinformatics anymore”(biomickwatson.wordpress.com)
biomickwatson.wordpress.com
“You’re not allowed bioinformatics anymore”
http://biomickwatson.wordpress.com/2014/07/21/youre-not-allowed-bioinformatics-anymore/
10 comments
Strawman scientist?
> “Yes. Now, let’s see. It’s an amazing, visionary proposal, a great collaboration, and congratulations on pulling it together. I just have one question” said the director “This proposal will generate a huge amount of data – how do you plan to deal with it all?”
“Oh that’s easy!” answered Smith. “It’s all on page 6. We’ve requested funds to employ a bioinformatician for the lifetime of the project. They’ll deal with all of the data” he stated, triumphantly.
I dunno...this sounds like a problematic mindset across many, many domains in science. What makes the field of biology less prone to it?
> “Yes. Now, let’s see. It’s an amazing, visionary proposal, a great collaboration, and congratulations on pulling it together. I just have one question” said the director “This proposal will generate a huge amount of data – how do you plan to deal with it all?”
“Oh that’s easy!” answered Smith. “It’s all on page 6. We’ve requested funds to employ a bioinformatician for the lifetime of the project. They’ll deal with all of the data” he stated, triumphantly.
I dunno...this sounds like a problematic mindset across many, many domains in science. What makes the field of biology less prone to it?
The "strawman scientist" has tenure--I'm sure they can take it.
Given the behavior and stories of lots of PIs and advisors, and how they treat grad students these days (especially in biology and medicine), I'm not sure that we need to be sticking up for them.
Given the behavior and stories of lots of PIs and advisors, and how they treat grad students these days (especially in biology and medicine), I'm not sure that we need to be sticking up for them.
As someone who's worked with micro biologists I can assure you it's not a straw man.
As someone who has worked with microbiologists, maybe you could start by spelling microbiologist right.
Maybe you could not be a cunt
It's a thought experiment, not a critique of all scientists. I can definitely see elements of that straw man in some scientists that I have met.
Okay, but what is the point of the thought experiment? If the point is, as the addendum at the bottom indicates, to convince scientists to support a bioinformatics group rather than having their own bioinformatician, the same arguments and reasoning the post shows could've been presented without the whole "you're not allowed bioinformatics" framing device, which is certainly attention-getting but far less likely to be persuasive because you're behaving confrontationally towards the very people you're trying to persuade.
Read the essay that inspired this essay (linked), published by some little British journal, (Nature, I think?). The point is to express a similar frustration.
It isn't just Bioinformatics. Working at the edge of a research group doing technical work that no one else can comprehend can be very difficult and lonely. Don't underestimate the utility of a technical mentor that can understand what it is that you are doing.
The silver lining is that you can have a lot of freedom in what you learn and what you do and that you can become completely indispensable.
The silver lining is that you can have a lot of freedom in what you learn and what you do and that you can become completely indispensable.
In general anybody who's really good at data analysis and is willing to be collaborative need not worry about finding employment in the foreseeable future.
Is anyone else irked whenever someone uses "data" as a plural? It's an uncountable singular for chrissake!
I shall make a point in using it wrongly just to irk pedantic grammar nazi's like yourself.
Do you have evidence? If so, I'd like to see those data.
http://www.oxforddictionaries.com/definition/english/data
Languages change slowly over time. I find myself using plural data when I'm talking science and singular data when I'm talking about hard drives. Hadn't noticed until your post!
Strange isn't it?
Languages change slowly over time. I find myself using plural data when I'm talking science and singular data when I'm talking about hard drives. Hadn't noticed until your post!
Strange isn't it?
I have always used the plural data when talking about scientific data. Years of writing papers pretty much drilled that into my fingers.
Historically, data is the plural of datum.
Honestly, I am only irked when people insist that data must be used as a singular or that it must be used as a plural. You'll find it listed both ways in good dictionaries:
> Both constructions are standard. The plural construction is more common in print, evidently because the house style of several publishers mandates it.
http://www.merriam-webster.com/dictionary/data
> Both constructions are standard. The plural construction is more common in print, evidently because the house style of several publishers mandates it.
http://www.merriam-webster.com/dictionary/data
The commentary is a subset of what's in the wonderful M-W "Concise Dictionary of English Usage." The entry for "data, datum" starts "The word data is a queer fish."
The most complete online version of it that Google knows about is at http://www-old.accademiadellacrusca.it/forum/htdocs/phpBB2/v... .
Some key points and quotes:
- data isn't an ordinary plural. "Ordinary plurals ... can be modified by cardinal numbers; ... no one, it seems, can tell you how many data."
- "To summarize, data has never been the plural of a count noun in English. It is used in two constructions - plural, with plural apparatus, and singular, as a mass noun, with singular apparatus. Both constructions are fully standard at any level of formality. The plural construction is more common. If you are an editor for a publisher whose house style insists on the plural construction only, take care to be consistent ..."
- "There have been more occurrences of datum in popular sources since [about the middle 1960s]. Perhaps the insistence of many editors that data is a plural has accelerated the tendency for datum to be used as a singular of data"
The most complete online version of it that Google knows about is at http://www-old.accademiadellacrusca.it/forum/htdocs/phpBB2/v... .
Some key points and quotes:
- data isn't an ordinary plural. "Ordinary plurals ... can be modified by cardinal numbers; ... no one, it seems, can tell you how many data."
- "To summarize, data has never been the plural of a count noun in English. It is used in two constructions - plural, with plural apparatus, and singular, as a mass noun, with singular apparatus. Both constructions are fully standard at any level of formality. The plural construction is more common. If you are an editor for a publisher whose house style insists on the plural construction only, take care to be consistent ..."
- "There have been more occurrences of datum in popular sources since [about the middle 1960s]. Perhaps the insistence of many editors that data is a plural has accelerated the tendency for datum to be used as a singular of data"
The field is slowly coming around. I've been very harsh when peer-reviewing papers without proper code release and documentation. I know that I'm not alone. Both investigators and funding agencies are starting to understand the message. This is especially true as older folks retire and a new computationally-savvy group of folks ascends into senior positions on editorial boards, faculty review panels, and grant review panels.
Things may look messy in science, and they often are, but I'm optimistic about the future.
Things may look messy in science, and they often are, but I'm optimistic about the future.
I'm hoping so.
I got a MS degree in Bioinformatics and for the past three years the only role it has served is collecting dust in my closet upstairs :)
I was the young bioinformatician in 2006 and when working in a lab it felt very isolating. The PI and Postdocs just had me solve simple computational problems (or even IT problems). It felt very much like I was a cog in their grant writing machine rather than a collaborator that deserved any kind of authorship in a publication.
And looking back there was no one to teach me about good practices of writing software like source control, SOLID, testing, etc. Or even storage of our microarrays.
I eventually went to work for a biotech consultancy but I discovered that biotech software was a gimmick used to hike up the prices on software. Sure it was a niche field but we would charge clients hundreds of thousands of dollars for software that was barely functional.
I think a lot of Research groups were badly burned by this and eventually started trying to do everything in house. I eventually became disillusioned/burned out and left the field entirely.
I've been out of the field for four years now but still feel badly as I felt I've wasted my training. I still have a retainer client as a way back 'in' back into bioinformatics.
It would be nice to find these 'bioinformatics groups' and see how they're successfully collaborating with other labs/research groups.
I got a MS degree in Bioinformatics and for the past three years the only role it has served is collecting dust in my closet upstairs :)
I was the young bioinformatician in 2006 and when working in a lab it felt very isolating. The PI and Postdocs just had me solve simple computational problems (or even IT problems). It felt very much like I was a cog in their grant writing machine rather than a collaborator that deserved any kind of authorship in a publication.
And looking back there was no one to teach me about good practices of writing software like source control, SOLID, testing, etc. Or even storage of our microarrays.
I eventually went to work for a biotech consultancy but I discovered that biotech software was a gimmick used to hike up the prices on software. Sure it was a niche field but we would charge clients hundreds of thousands of dollars for software that was barely functional.
I think a lot of Research groups were badly burned by this and eventually started trying to do everything in house. I eventually became disillusioned/burned out and left the field entirely.
I've been out of the field for four years now but still feel badly as I felt I've wasted my training. I still have a retainer client as a way back 'in' back into bioinformatics.
It would be nice to find these 'bioinformatics groups' and see how they're successfully collaborating with other labs/research groups.
And this is exactly why I'm trying to find a new place to work. I finally realized after a few years that I need some real mentorship to be fully competent in a research environment (even after an MS), and four years of "trial by fire" in a lab which generates data from a ton of tiny experiments (rather than a few larger experiments) with 100 biologists and only two of us bioinformaticians, and a pile of ten years worth of old microarray data that no one has any sample annotation for (but it's invaluable!)-- was less of a constructive learning experience than it seemed like it was going to be. I need to find/create an environment that will allow me to use the motivation I know I used to have for this.
So for anyone from a CS-oriented background, or who is thinking of doing a degree program in bioinformatics that isn't oriented around research- try to help out in various labs, and find a good mentor. See what environments work best for you, and what sort of problems you want to apply yourself to. The field is developing far faster than most college programs can move, but by getting out there and seeing what skills/knowledge will actually be useful, you can work on filling in the gaps sooner.
So for anyone from a CS-oriented background, or who is thinking of doing a degree program in bioinformatics that isn't oriented around research- try to help out in various labs, and find a good mentor. See what environments work best for you, and what sort of problems you want to apply yourself to. The field is developing far faster than most college programs can move, but by getting out there and seeing what skills/knowledge will actually be useful, you can work on filling in the gaps sooner.
Most of the tasks mentioned would be better served by a software engineer, rather than a bioinformatician.
The author is right to criticize the current state of data sharing and analysis. However, the solution is not to have experimentalists collect their data and then pass it on to someone else. That would be good for the people doing data analysis (provided they know enough to understand the experimental procedures, which is not always the case), but remove many of the incentives to be an experimentalist. Science is much less fun if you don't actually get to make new discoveries.
The problem is that in many fields there is a weird dichotomy between people who know how to get data and people who know what to do with it. This is not a sustainable situation. Proper experimental design requires knowledge of how the data will be analyzed.
My proposed solution is to require that the leaders of research groups have expert knowledge of both experimental procedures and data analysis, because that is the expertise required to pick an appropriate hypothesis and supervise the corresponding scientific project from start to finish. Because students 1) work in a lab with diverse knowledge and 2) desire to become professors themselves, they are likely to acquire these skills as well. Aspiring professors who have substantially greater aptitude for either data collection or data analysis should form a joint lab with a researcher with the complementary skill set so that their students can learn both fields.
The problem is that in many fields there is a weird dichotomy between people who know how to get data and people who know what to do with it. This is not a sustainable situation. Proper experimental design requires knowledge of how the data will be analyzed.
My proposed solution is to require that the leaders of research groups have expert knowledge of both experimental procedures and data analysis, because that is the expertise required to pick an appropriate hypothesis and supervise the corresponding scientific project from start to finish. Because students 1) work in a lab with diverse knowledge and 2) desire to become professors themselves, they are likely to acquire these skills as well. Aspiring professors who have substantially greater aptitude for either data collection or data analysis should form a joint lab with a researcher with the complementary skill set so that their students can learn both fields.
I had a conversation along these very lines today. The hard question is how to fund such a thing. A lot of funding seems to be for the individual researcher to start a group, or for large consortia to work on a project, with precious little middle ground. My ideal lab would in fact be medical biologist, mass spectrometrist and bioinformatician working together. I'm not sure how to do that when each individual has a good, but not insane track record. This is to say nothing of the difficulties in putting such a team together.
There's a lot of data that can be collected without much knowledge of experiment design, consider e.g. all the sequencing projects.
The problem is that if the data ends up in inconsistently formatted spreadsheets or poorly conceived custom formats, the effort required to extract the data for analysis later (especially across multiple projects) can be prohibitive.
The problem is that if the data ends up in inconsistently formatted spreadsheets or poorly conceived custom formats, the effort required to extract the data for analysis later (especially across multiple projects) can be prohibitive.
The worst I have seen was a systems biologist flipping between Excel cells with the arrow keys and staring at the Excel input line to find out if sequences are different. My jaw dropped.
There is the story of course of the years of research that were destroyed because some particular gene names looked suspiciously like dates to Excel.
Yes, I was working on a bioinformatics data warehouse a while back. Yes, we did have to write an import filter to extract data from Excel files.
Yes, I was working on a bioinformatics data warehouse a while back. Yes, we did have to write an import filter to extract data from Excel files.
Lost me at "we've requested funds to employ a bioinformatician for the lifetime of the project". More realistic is "I'll have a grad student deal with the data".
One of the local institutes just dissolved their bioinformatics group because they couldn't convince enough research groups to hand over grant money. They'd be part of the grant proposal in order to secure the grant, but then the money would end up being spent elsewhere...
One of the local institutes just dissolved their bioinformatics group because they couldn't convince enough research groups to hand over grant money. They'd be part of the grant proposal in order to secure the grant, but then the money would end up being spent elsewhere...
What are best practices in data management/reproducibility? The research that I'm involved in has typically used make & git at best, and more typically, hacked together Matlab scripts.
"If you are leading a project that creates huge amounts of data, instead of employing a bioinformatician in your own group, why not collaborate with an existing bioinformatics group and fund a post there?"
If that's your goal, perhaps using a less derisive and incendiary tone towards the straw man scientist in the post would've been good?