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Data Integr Life Sci. 2013 Jul;7970:105-112.

Next Generation Cancer Data Discovery, Access, and Integration Using Prizms and Nanopublications.

Author information

1
Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street Troy, NY 12180, USA; Department of Pathology, Yale School of Medicine, 300 George St., New Haven, CT, 06510, USA, http://krauthammerlab.med.yale.edu.
2
Department of Cognitive Science, Rensselaer Polytechnic Institute, 110 8th Street Troy, NY 12180, USA, http://tw.rpi.edu.
3
Department of Pathology, Yale School of Medicine, 300 George St., New Haven, CT, 06510, USA, http://krauthammerlab.med.yale.edu.
4
Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street Troy, NY 12180, USA; Department of Cognitive Science, Rensselaer Polytechnic Institute, 110 8th Street Troy, NY 12180, USA, http://tw.rpi.edu.

Abstract

To encourage data sharing in the life sciences, supporting tools need to minimize effort and maximize incentives. We have created infrastructure that makes it easy to create portals that supports dataset sharing and simplified publishing of the datasets as high quality linked data. We report here on our infrastructure and its use in the creation of a melanoma dataset portal. This portal is based on the Comprehensive Knowledge Archive Network (CKAN) and Prizms, an infrastructure to acquire, integrate, and publish data using Linked Data principles. In addition, we introduce an extension to CKAN that makes it easy for others to cite datasets from within both publications and subsequently-derived datasets using the emerging nanopublication and World Wide Web Consortium provenance standards.

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