1. KAREN What you can do with an advanced research and education network!

2. 2 Introductions  John and Sam We do not know your science We want to facilitate discussion This is an opportunity to report back to REANNZ on issues and barriers  Who are you?

3. 3 Today’s Plan  Introduction  Collaboration – now and in the future  Lunch  Tools  Capability Development  Wrap up

4. 4 Introduction  Motivation A paradigm shift  Research Networks  E-Research What is it?  International trends Examples

5. 5 The New Research Paradigm Credit: GEANT2

6. 6 Case Study: Serious Disease Genes Revealed  Wellcome Trust Case Control Consortium  50 research groups  200 scientists  DNA from 17,000 patients  15,000 polymorphic markers  Learned more in 12 months than last 15 years

7. 7 Case Study: Functional MRI (fMRI) Data Center  Online repository of neuroimaging data  A typical study comprises 3 groups 20 subjects/group 5 runs/subject 300 volumes/run  90,000 volumes, 60 GB raw data  1.2 million files processed  100s of such studies in total Credit Ian Foster, University of Chicago

8. 8 www.fmridc.org

9. 9 Global R&E Network Pathways DISCLAIMER - This network map was a best estimate of expected connectivity for 2005, several changes in connectivity and planned connectivity have happened since it was created Credit: John Silvester, USC, Chair CENIC

10. 10 Kiwi Advanced Research and Education Network Credit: KAREN. http:// www.karen.net.nz

11. 11 KAREN  Went live Dec 2006  10Gb/s NZ Backbone  $40million, Government Funding  $5million Capability Build Programme  Linking all 8 Universities and all 9 Crown Research Institutes, + National Library  ~622Mb/s link to US  ~133Mb/s link to Australia Credit: KAREN. http://www.karen.net.nz

12. 12 Advanced Research and Education Networks (ARENs ) Credit: GEANT2

13. 13 What is e-Research?  Collaboration  Access to and management of data and knowledge  Advanced computing methods  Shared resources  New research techniques

14. 14 Characterising e-Research CharacteristicTraditional ResearchE-Research ParticipantsIndividual researcher or small local research team Diversely skilled, distributed research team DataLocally generated, stored and accessible Generated, stored and accessible from distributed locations Computation and Instrumentation Batch compute jobs or jobs run on researcher’s own computers or research instruments Large-scale, or on demand computation or access to shared instruments NetworkingNot reliant on networksReliant on research networks and middleware Dissemination of Research Via print publications or conference presentations Via web sites and specialized web portals Credit: Bill Appelbe and David Bannon, Victorian Partnership for Advanced Computing. eResearch: Paradigm Shift or Propaganda? http://www.jrpit.acs.org.au/jrpit/JRPITVolumes/JRPIT39/JRPIT39.2.83.pdfhttp://www.jrpit.acs.org.au/jrpit/JRPITVolumes/JRPIT39/JRPIT39.2.83.pdf

15. 15 Discussion  Where does your research fit into this characterisation of traditional research and e-research?  How does this compare with the research that you were doing 5 years ago?

16. 16 Current Environment - Set of Tools experiment data storage analysis email web sites video conference scientist instrument HPC Credit: BeSTGrid. http://www.bestgrid.orghttp://www.bestgrid.org

17. 17 Future Environment Research Collaboratories experiment data storage HPC analysis messaging web portals video conference scientist Grid Middleware scientist instrument Credit: BeSTGrid. http://www.bestgrid.orghttp://www.bestgrid.org

18. 18 The Researcher’s View  Why do I care? New collaborative opportunities New funding opportunities NZ competitiveness  What’s in it for me? Key resource is often somewhere else More data, more tools Collaborating with the best  How do I get involved? Move from silo to GRID Credit: BeSTGrid. http://www.bestgrid.orghttp://www.bestgrid.org

19. 19 Example e-Research Projects  BioCoRE  SCOOP  SEEK/EcoGrid

20. 20

21. 21 BioCoRE  Seamlessly access local and remote technology  Co-author papers  Access high performance computing  Share molecular visualisations  Chat room  Lab book  Notifications, etc.  http://www.ks.uiuc.edu/Research/biocore/ http://www.ks.uiuc.edu/Research/biocore/

22. 22 The Control Panel

23. 23 Projects

24. 24 Project Summary  Review State of recent job submissions Who is logged in What tasks members are working on Recent discussion topics Recent files added to BioFS

25. 25 Project Status  See Current work Future work  Modify Schedule of upcoming tasks  Display Current task

26. 26 Publishing VMD Sessions

27. 27 Configuring NAMD Simulations

28. 28 Job Management  A Grid Portal Submit web form Monitor progress  BioCoRE Obtains resources Moves files Executes jobs Places results

29. 29 Message Board

30. 30 Lab Book

31. 31 Website Library

32. 32 BioCoRE File System

33. 33 SURA Coastal Ocean Observing and Predicting Programme

34. 34 SCOOP  Promote effective and rapid fusion of observed oceanic data with numerical models  Facilitate the rapid dissemination of information to operational, scientific, and public or private users  http://scoop.sura.org/ http://scoop.sura.org/

35. 35 SCOOP Goals  Create an open access, distributed lab for oceanography by: Supporting data standards development and implementation Demonstrating benefits/added value of diverse communities moving to common standards for info exchange Creating an environmental prediction system –a research tool that can also support relevant agency decision-making to improve society

36. 36 The results of the analysis are visualized and disseminated in a form that can be readily incorporated into decision-support tools used by emergency response personnel. For verification, all relevant and available observations are aggregated and compared with predictions, which provides a real-time measure of accuracy and quality for the predictions. Real-Time Ensemble Prediction Results from each of the predictions in the ensemble are then aggregated for analysis. Results include maps that show the probability of inundation with street level detail. Each forecast wind field is used as input for numerical predictions of storm surge and wave fields. Because each individual element in this ensemble of surge and wave predictions involves a numerical calculation that could take many hours on a large supercomputer cluster, they are farmed out to the available computational resources within the distributed network. Hurricane warnings issued by the NOAA National Hurricane Center (NHC) are used to create an ensemble of forecast wind fields. Each of these wind fields represents a plausible set of forecast winds over the entire region of interest for several days into the future.

37. 37 Distributed Facility for Coastal Prediction wind forecasts water level model wave watch model OpenIOOS data

38. 38 Science Environment for Ecological Knowledge  Aims to extend ecological and biodiversity research capabilities by fundamentally improving how researchers: gain global access to ecological data and information find and use distributed computational services exercise powerful new methods for capturing, reproducing & analysing data  http://seek.ecoinformatics.org/ http://seek.ecoinformatics.org/

39. 39 SEEK’s Integrated Systems  EcoGrid Next generation internet architecture enables data storage, sharing, access and analysis  Semantic Mediation System Advanced reasoning system determines if data and analytical components can be automatically used in a selected workflow  Analysis and Modeling System Ecologists design, modify and incorporate analyses to compose new workflows and models in a visual, automated environment

40. 40 EcoGrid  Seamless access to and manipulation of data and metadata stored at different nodes  Authentication via single sign-on  Web services for executing analytical pipelines  Registry of data and compute nodes  Rapid ingest of new data sources as well as decades of legacy data  Extensible relevant metadata based on the Ecological Metadata Language  Data replication provides fault tolerance, disaster recovery and load balancing

41. 41 Kepler Workflow Tool  Example of the 'R' system in a Kepler workflow

42. 42 Things to take away  The research lifecycle is changing – an evolution rather than a sea-change  Bigger and more complex problems require new methodologies and relationships  Policy and funding are increasingly dictating collaboration  Advanced networks are essential  It’s more about data than technology  Many social and organisational factors

43. 43 A Final Message Credit: GEANT2