Mark2Cure citizen scientists inspected >500 abstracts and submitted >10K annotations demonstrating that citizen science can be a scalable solution to the big data problem of biomedical literature.

A goal for identifying and annotating concepts in biomedical literature is to build a network of Biothings

(On a side note, if you’re interest in the network of biothings, there’s a hackathon coming up…)

We already have great resources and tools for genes, proteins, pathways, etc. Unfortunately, the same cannot be said of biomedical literature because….

Hence the need to find a scalable solution. The Su lab first tested to see if non-experts could perform this task using a microtasking platform

A crowd of non-experts did better than a machine algorithm at this task…

…and could collectively give better results than a single expert

If you’re interested in the results from the AMT experiments, you can read about them here

So far so good, but there’s an issue with this approach as well…

Given the current state of funding in science, a more sustainable solution is needed.

And volunteers stepped in to demonstrate that citizen scientists can and will handle this task.

Just under 600 docs were annotated in Mark2Cure’s public beta experiment, but remember the problem is that there’s an average of TWO articles published every MINUTE!

Andrew finished strong by sharing the inspiring words of the volunteers who contributed to Mark2cure

Of course, if you missed his talk, Andrew was more than happy to share his slide deck

Finally, if you can read, you can help!

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