Scientific crowdsourcing

has taken on a variety of flavours: On the one hand, there are  competitions for developers and data scientists (for example, see the scientific computing podcasts on inSCIght – Episde 12 “Hacking Education : crowd sourcing for the win!

To me, the Mother of all competitions in the life-sciences is CASP – The Critical Assessment of protein Structure Prediction *experiment*.
It’s not a competition, no no, mind you, an experiment. That doesn’t prevent over hundred research & development teams going bonkers over it every two years – after all, it’s a kind of Formula1 world-championship in science, just without the glamour and the girls, mostly. And it has been pretty effective in pushing the boundaries and revealing which methods really DO work.

Following a similar procedure but with a much more general scope (not only protein structures) is a platform for data prediction competitions that allows organizations to post their data and have it scrutinized by the world’s best data scientists. One of the most prominent (and best funded) competitions is curently the Heritage Health Price . Beyound that, Kaggle in Class allows instructors to host data prediction competitions for their students. Competitions are a great way to engage students, giving them the opportunity to put into practice what they learn “in class”.

A site that was new to me is –  where the NLM is challenging people in their “Show Off Your Apps: Innovative Uses of NLM Information” competition to create innovative software applications that use the Library’s vast collection of biomedical data. Reward: $0 – just for the fun of it.

Another competition hosted there is from Elsevier and offers substantially more money: (hey, if we wanted to get rich we had studied more of this business-admin stuff and not science, right? )  The “Apps for Science” competition ends in 3 months where Elsevier is offering $35,000 to software developers to create apps and help more than 15 million researchers, medical professionals, librarians and students accelerate science.

One of the best ideas ever was to use the power of all the idle machines on the web for something more useful than showing some bouncing lines on the screen while sinking the maledives. Unfortunately, SETI@home has run into serious funding problems recently. But the principle is alive and kicking in folding@home with a topic and goals set a bit closer to our heart. I’m happy to see they make good use of the power of new gpGPU architectures and create a virtual supercomputer that is in the same league as the big guys.

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  1. On the Future of Data Science « cistronic

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