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The CARMEN Neuroscience Server

Lookup NU author(s): Professor Paul Watson, Terri Jackson, Dr Georgios Pitsilis, Dr Frank Gibson, Dr Phillip Lord

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Abstract

Understanding the brain is one of the major scientific challenges. It requires the capability to synthesize a detailed and applicable understanding of the way in which information is encoded, accessed, analysed, archived and decoded in neuronal networks. Data is difficult and expensive to produce, but is rarely shared and collaboratively exploited. The main reason for this is that a proliferation of techniques produce voluminous data in a variety of heterogenous and proprietary formats; this is then locally described and curated and is often not computationally amenable. The EPSRC CARMEN e-Science Pilot project (www.carmen.org.uk) is addressing these challenges by leveraging e-Science infrastructure and expertise to support the virtual integration of research teams, and multi-modal experimentation. CARMEN will allow data sharing and integration, supported by metadata and an expandable range of services accessible to users for raw, transformed and live experimental data. Achieving this requires progress in a number of areas including: the ability to store, curate and deploy services as well as data; standardised metadata for neuroscience; and, advanced tools for searching and visualising time-series and related data. This paper gives an overview of the CARMEN infrastructure, and illustrates its functionality by describing the application of an early prototype to a specific neuroscience scenario.


Publication metadata

Author(s): Watson P, Jackson T, Pitsilis G, Gibson F, Austin J, Fletcher M, Liang B, Lord P

Publication type: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2007

Pages: 10

Print publication date: 01/08/2007

Source Publication Date: August 2007

Report Number: 1042

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne

URL: http://www.cs.ncl.ac.uk/publications/trs/papers/1042.pdf


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