The Elsevier Grand Challenge: Knowledge Enhancement in the Life Sciences is a contest created to improve the way scientific information is communicated and used. The contest invites members of the scientific community to describe and prototype a tool to improve the interpretation and identification of meaning in (online) journals and text databases relating to the life sciences. Specifically we are looking for new ways to:
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
I should have realized the danger of stepping into the Wikipedia morass, and the comments on today’s earlier post further indicate my folly in doing so. You know, The New York Times gets things wrong, too. As an argument on a sophisticated level, it’s that all texts are constructs reflecting the attitude of the constructor rather than a verifiable external reality; on a less sophisticated level, it’s that all the other kids are smoking pot, too.
I’ve had enough. I’m bringing it down to this challenge.
he Diagnostic Competition is proposed to be the first of a series of international competitions that will be hosted yearly at the International Workshop on Principles of Diagnosis (DX).
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
Our main goal is to provide you with data because you know what you want to do with it. Still, we give some information regarding typical MIR tasks below. We hope to provide snippets of code and benchmarks results to help you getting started. If you want to provide additional information / link to your code / new results / new tasks, please send us an email! We also try to maintain an informal list of publications that use the dataset.