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Solve for Efficiency With Amazon Mechanical Turk

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This is a quick post about another great use of Amazon Mechanical Turk. Over on Friend Feed Jean-Claude Bradley posted a blog post called Mechanical Turk Does Solubility on Google Spreadsheet, which talks about using Mechanical Turk to process solubility data for the Open Notebook Science Challenge.

What I believe is revolutionary here is that rather than rely on government funding to help process results, scientists are able to use Crowdsourcing to process results in real time and at a very fine unit of granularity. When you combine those characteristics with the Open Notebook Science Challenge, the result is unprecedented transparency.

The blog post referenced above inspired another, titled Generalizability coefficient for Mechanical Turk annotations, that describes using Amazon Mechanical Turk and Crowdsourcing to generate annotations of a thesis. Wow, eliminating (or more likely reducing) one of the biggest pains associated with a thesis. Doctoral candidates everywhere should take note.

Its about more than Grad Student pain relief though: by Crowdsourcing mechanical tasks such as annotation, students are free to focus on data collection and analysis. That makes the entire process more efficient.

These innovative uses of Amazon Mechanical Turk combined with the new Public Data Sets, are an exciting time in both Cloud Computing and in science.


Modified 3/10/2021 – In an effort to ensure a great experience, expired links in this post have been updated or removed from the original post.
Jeff Barr

Jeff Barr

Jeff Barr is Chief Evangelist for AWS. He started this blog in 2004 and has been writing posts just about non-stop ever since.