Speaker of Workshop 2
Will talk about: Enabling knowledge generation and reproducible research by embedding provenance models in metadata stores
Satrajit Ghosh is a research scientist in the McGovern Institute for Brain Research at MIT, a faculty member for the Speech and Hearing Biosciences and Technology program at Harvard Medical School and a member of the INCF taskforce on Standards for DataSharing. His work focuses on enhancing interoperability across brain imaging software and on data mining with the intent of optimizing solutions for translating brain imaging research to clinical applications. He is a lead architect of the Nipype (nipy.org/nipype) project and an ardent proponent of implementing W3C provenance standards and standard vocabularies across brain imaging.
Reproducible research requires that information pertaining to all aspects of a research activity are captured and represented richly. However, most scientific domains, including neuroscience, only capture pieces of information that are deemed relevant. In this talk, we provide an overview of the components necessary to create this information-rich landscape and describe a prototype platform for knowledge exploration. In particular, we focus on a technology agnostic data provenance model as the core representation and Semantic Web technologies that leverage such a representation. While the data and analysis methods are related to brain imaging, the same principles and architecture are applicable to any scientific domain.