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.<p>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.<p>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 end of the post:<p>"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?"<p>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?
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".<p>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...
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.<p>Things may look messy in science, and they often are, but I'm optimistic about the future.
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.<p>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.
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.<p>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.
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.
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.