From W3C Wiki
Semantic Web for Life Sciences: A Business Case
The most important and immediate benefit of SW-LS is Search.
Scientists are overloaded with both data and literature. It's getting almost impossible to simply "know what is known" - there is always another paper to read, evaluate, prioritize and incorporate into an individual's mental model of science. And there's always a ton of raw data behind each paper.
How does a scientist incorporate all of that information into the raw data that she generates in her own work? In a big pharmaceutical company this is non-trivial. She could be generating gigabytes of data from massively parallel gene analyses and be dealing with that headache, much less trying to bring in the rest of the world.
What is needed is better Search. Google can't answer these queries very well, but these are exceptionally basic queries. Taken from Timo Hannay of Nature's work.
- Papers about the hedgehog gene
- Papers that disagree with the paper I'm reading
- The paper where this idea first came from
- The most commonly cited reviews about hedgehog genes
- The names and contact details of authors who have used method W to investigate hedgehog genes
- Molecular biology research groups within 100 miles of Boston that have used method Y
- The work/collaborations of Dr Z
Google isn't enough. Something will come along to fill the void and help scientists answer these questions. We believe that taking the web approach - pre-competitive, open and not vulnerable to vendor lock-in - makes the most sense. Here's the why and the how.
Why SW-LS makes Search easier and more accurate (i.e., the "killer scenario")
How SW-LS makes search easier and more accurate (i.e., the revenge of the acronyms)
Other Business Cases
- Managing complexity. Life sciences organizations are frequently distributed and organizationally complex. Adoption of and adherence to standards simplifies the efforts required to integrate systems and architectures during the inveitable mergers and acquisitions in business and in the reuse of data and knowledge in non-profit driven organizations.