1. Virtual Laboratory Amsterdam L.O. (Bob) Hertzberger Computer Architecture and Parallel Systems Group Department of Computer Science Universiteit van Amsterdam bob@science.uva.nl

2. Virtual Laboratory Amsterdam Outline Technology developments What is Grid What is e-Science e-Science and neuroinformatics Some warnings Challenges

3. Virtual Laboratory Amsterdam Background information ICT developments Processing power doubles every 18 month Memory size doubles every 12 month Network speed doubles every 9 month Something has to be done to harness this development Virtualization of ICT resources  Internet  WEB  Grid

4. Virtual Laboratory Amsterdam Grid Services Harness multi-domain distributed resources Technology push Application Management of comm. & computing Management of comm. & computing Management of comm. & computing

5. Virtual Laboratory Amsterdam Definition of Grid Grid: Sharing resources within virtual organizations in a flexible and uniform way Coordinated problem solving in dynamic multi-institutional (distributed) environment

6. Virtual Laboratory Amsterdam Background information experimental sciences Experiments become increasingly more complex Driven by detector developments  Resolution increases  Automation & robotization increases Results in an increase in amount and complexity of data Demands for multidisciplinary collaboration Something has to be done to harness this development Virtualization of experimental resources enabling sharing

7. Virtual Laboratory Amsterdam Grid Services Harness multi-domain distributed resources Management of comm. & computing Management of comm. & computing Management of comm. & computing Potential Generic part Potential Generic part Potential Generic part Application Specific Part Application Specific Part Application Specific Part Virtual Laboratory Application Oriented Services Application pull

8. Virtual Laboratory Amsterdam The what of e-Science WEB was about exchanging information e-Science is about sharing resources applying Grid: Experimental facilities Data & Information repositories Application services e-Science is about supporting experimental science using Virtual Laboratories e-Science request different design for experimentation because computer is integrated part

9. Virtual Laboratory Amsterdam Experiment Steps & Difficulties designing the experiment performing the experiment analyzing the experiment results Knowledge and Expertise! Experiment Archiving! Information Organization! Logging Information/Data! Approach to Data Analysis and Tools! © April 2002 - E. C. Kaletas, H. Afsarmanesh

10. Virtual Laboratory Amsterdam e-Science Multi-disciplinary activity between: Domain scientist ICT scientist Combining human expertise & knowledge Next generation infrastructure is differentiator

11. Virtual Laboratory Amsterdam Grid Services Harness multi-domain distributed resources Management of comm. & computing VL-E Application Oriented Services Food Informatics Dutch Telescience Medical Diagnosis & Imaging Bio- Diversity Bio- Informatics ASP Data Intensive Science/ LOFAR

12. Virtual Laboratory Amsterdam Grid Middleware Surfnet Network Service (lambda networking) Application specific service Application Potential Generic service & Virtual Lab. services Grid & Network Services Virtual Laboratory VL-E Experimental Environment VL-E Proof of concept Environment Telescience Medical Application Bio ASP Virtual Lab. rapid prototyping (interactive simulation) Additional Grid Services (OGSA services)

13. Virtual Laboratory Amsterdam e-Science and Neuroinformatics e-Science is a new way to do science New problems and new opportunities e-Science is not solving the problems that are inherent for the field it is a supporting science as such comparable with instrumentation in experimental science Sharing and collaboration are essential aspects It is dangerous to put informatics behind a scientific field and than hope the problem becomes a computer science problem bioinformatics, neuroinformatics However, multi disciplinarily is essential

14. Virtual Laboratory Amsterdam Illustration: Challenges handling brain (imaging) data No universally accepted standards for structure and content of data sets Brain imaging is a dynamic phenomena Rapid ongoing methodological developments Neuroimaging methods evolving  Producing more and more complex data Growing domain of phenomena tackled by neuroimaging techniques Rapid changes in knowledge about brain organization

15. Virtual Laboratory Amsterdam Most Critical Problem Imaging of brain functions demand clear specification of behavior conditions data was acquired Tied to motivation and the scientific hypothesis to be tested Lead to highly specific experimental design Number of potentially important behavior parameters large and poorly defined Rich set of subject parameters that can influence brain imaging results Problems in structuring and archiving metadata Can seriously compromise interpretability data

16. Virtual Laboratory Amsterdam More technical Problems Data content What data to archive (raw and and analyzed) Metadata describing method and conditions Organization and structure Flexible data model in database Data import and export Formats that are accepted Utilities for data exchange Data Quality Validity, truthfullness, accuracy with respect original data  www.humanbrainmapping.org

17. Virtual Laboratory Amsterdam Conclusions Data sharing and collaboration becomes essential Like in industrial or administrative automation rationalization is the first process Demands from experimental science at large and neuroinformatics in particular agreement on its procedures and protocols It will be a success in case following is provided Flexibility by the e-Science infrastructure a better understanding of his experiment by the scientist