Linked Data, an opportunity to mitigate complexity in pharmaceutical research and development<br />Bosse Andersson and Kerstin Forsberg AstraZeneca R&D Clinical Information Strategy<br />email@example.com on Twitter, LinkedIn, Slideshare, Blogspot, citulike<br />
Complexity<br />Pathophysiology?<br />Targets?<br />Phenotypes?<br />Biomarkers?<br />Costs?<br />QoL?<br />Outcomes?<br />Association and interpretationof all data needed <br />has become a too complex task for individuals, or even teams to handle. <br />Health care, pharma, academiaShared datasets <br />Different decisions and different types of applications.<br />
Opportunities<br />Improve the research utility of shared datasets <br />organized for associations, <br />prepared for not yet defined use,<br />ready for automation where computers can function alongside us to mitigate the complexity<br />
It’s like sex and teenagers …<br />Everybody is talking about Linked Data and Semantic Web, …<br />… many have tried parts of it, but …<br />few (if non) have done the real thing, and applied a sustainable and long term approach to use Linked Data principles and Semantic Web technologies to improve the research utility of their data.<br />
Linked Data Management Apragmatic and iterative process<br />Three enablers<br />Linked Data principles, including establishing global namespaces of URI:s<br />Semantic web standards, also for provenance data and dataset descriptions <br />Open ontologies, with the required level of precision and consistency<br />Computers to work alongside scientists to automatically<br />Identify entities and assign URI:s<br />Structure data and capture provenance data<br />Mine for causal relationships and inconsistency<br />
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