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The CARMEN Neuroinformatics Server

The CARMEN Neuroinformatics Server Paul Watson 1 , Tom Jackson 2 , Georgios Pitsilis 1 , Frank Gibson 1 , Jim Austin 2 , Martyn Fletcher 2 , Bojian Liang 2 , Phillip Lord 1 1 School of Computing Science, Newcastle University 2 Department of Computer Science, University of York.

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The CARMEN Neuroinformatics Server

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  1. The CARMEN Neuroinformatics Server Paul Watson1, Tom Jackson2, Georgios Pitsilis1, Frank Gibson1, Jim Austin2, Martyn Fletcher2, Bojian Liang2, Phillip Lord1 1School of Computing Science, Newcastle University 2Department of Computer Science, University of York

  2. Research Challenge Understanding the brain is the greatest informatics challenge • Enormous implications for science: • medicine • biology • computer science

  3. Collecting the Evidence • 100,000 neuroscientists are • generating vast amounts of data • molecular (genomic/proteomic) • neurophysiological (time-series electrical measures of activity) • anatomical (spatial) • behavioural

  4. Current Problems in Neuroinformatics • Data is: • expensive to collect but rarely shared • proprietary and locally described • The result: • a shortage of analysis techniques that can be applied across neuronal systems • Limited interaction between research centres with complementary expertise

  5. CARMEN CARMEN uses e-science to tackle the problem CARMEN supports the archiving, sharing, discovery, integration and analysis of neuroscience data EPSRC e-Science Pilot Project (2006-10) Builds on previous e-science projects DAME, Gold, myGrid, BROADEN, CISBAN...

  6. CARMEN focuses on Neural Activity • raw voltage signal data is collected using single or multi-electrode array recording neurone 1 neurone 2 neurone 3 cracking the neural code

  7. CARMEN : A Hub & Spoke Structure • Hub: A “CAIRN” repository for the storage and analysis of neuroscience data • Spokes: Neuroscience projects that produce data and analysis • services for the hub, and use it to address key • neuroscience questions WP1 Spike Detection & Sorting WP2 Information TheoreticAnalysis of Derived Signals WP 3 Data-Driven Parameter Determination in Conductance-Based Models Data Storage & Analysis WP5 Measurement and Visualisation of Spike Synchronisation WP6 Multilevel Analysis and Modelling in Networks WP4 Intelligent Database Querying

  8. OMII/ myGrid: Taverna OGSA-DAI, SRB, DAME myGrid & CISBAN DAME: Signal Data Explorer Dynasoar Gold: Role & Task based Security OMII: Grimoire CARMEN Active Information Repository Node

  9. Data Collection from a Multi-Electrode Array Data Visualisation and Exploration Spike Detection Spike Sorting Analysis Visualisation of Analysis Results Currently, this is a semi-manual process CARMEN has automated this…. A Typical CARMEN Scenario

  10. Data Exploration with the Signal Data Explorer

  11. Defining the Process: Workflow

  12. Example Workflow Enactment External Repository Workflow Engine Client INPUT Data Spike Sorting TAVERNA Service SRB FileSystem Available Services Security Query RDBMS Reporting Dynamically Deployed Services in Dynasoar Registry OUTPUT Metadata

  13. Dynamic service deployment A request to s4 cannot be satisfied by an existing deployment of the service R The deployed service remains in place and can be re-used - unlike job scheduling

  14. Routing to an Existing Service Deployment A request for s2 is routed to an existing deployment of the service

  15. Example Graph Output

  16. Example Movie Output

  17. Support for sharing vast amounts of data: How was this data produced? Which workflow produced this data? Is there any data of this type…..? Are there services that process this data? e-Science Challenges: Discovery & Interpretation

  18. Extensible, standardised metadata for neuroscience data formats (e.g. timing, data channels) experimental design (e.g. stimuli or drug treatments) concurrent data (e.g. behaviour, physiological measures) experimental idiosyncrasies (e.g. artifacts) experimental conditions (e.g. animals, temperature) e-Science Challenge: Metadata Design

  19. How to locate patterns in time-series data across multiple levels of abstraction Challenge: Discovery

  20. “Only I am allowed to see this data” “My collaborators can look at this data” “Anyone can see this data” “The funders want the data to be openly available after 1 year” The Gold Project’s Security infrastructure will be used for this Challenge: Controlling Sharing

  21. Reproducible e-Science curating services as well as data repositories of deployable services dynamic service deployment Challenge: Reproducible e-Science

  22. CARMEN CARMEN is delivering an e-Science infrastructure that can be applied across a range of diverse and challenging applications (not only neuroscience) CARMEN enables cooperation and interdisciplinary working in ways currently not possible CARMEN will deliver new results in neuroscience, computer science and medicine Demos on North East Regional e-Science Centre, White Rose and EPSRC stalls

  23. CARMEN Consortium Newcastle: Colin Ingram Paul Watson Stuart Baker Marcus Kaiser Phil Lord Evelyne Sernagor Tom Smulders Miles Whittington York: Jim Austin Tom Jackson Stirling: Leslie Smith Plymouth: Roman Borisyuk Cambridge: Stephen Eglen Warwick: Jianfeng Feng Sheffield: Kevin Gurney Paul Overton Manchester: Stefano Panzeri Leicester: Rodrigio Quian Quiroga Imperial: Simon Schultz St. Andrews: Anne Smith

  24. CARMEN Consortium Commercial Partners - applications in the pharmaceutical sector - interfacing of data acquisition software - application of database infrastructure - commercialisation of analysis tools

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