Revolutionizing science and engineering research though cyberinfrastructure

1. Revolutionizing science and engineering research though cyberinfrastructure by David G. Messerschmitt Member, NSF Blue Ribbon Panel on Cyberinfrastructure

2. Daniel E. Atkins, Chair University of Michigan Kelvin Droegemeier University of Oklahoma Stuart Feldman IBM Hector Garcia-Molina Stanford University Michael Klein University of Pennsylvania Paul Messina California Institute of Technology David G. Messerschmitt UC-Berkeley Jeremiah P. Ostriker Princeton University Margaret H. Wright New York University NSF Blue Ribbon Advisory Panel on Cyberinfrastructure

3. The vision • “Digital science” will assume a prominent place in science and engineering research, supplementing theoretical and experimental methodologies • Already well along, but • Long term challenges not yet addressed • Needs focused attention and more funding • Major price for not moving now

4. What is digital science? • Vast collection and repositories of data • Modeling, computing, visualization • Distributed instrumentation • Scholarly publishing • Distributed software applications • Collaboration • Transparency to geography and institution • Cyberinfrastructure supporting all the above

5. What is cyberinfrastructure? • Cyberinfrastructure supporting digital science is not just scientific computing and networking, but also • Support for collaboration • Data and information repositories and access • Preserving data and artifacts • Support for distributed applications • Etc.

6. Core technologies incorporated into cyberinfrastructure Layered structure Conduct of science and engineering research Applications of information technology to science and engineering research Cyberinfrastructure supporting applications

7. Cyberinfrastructure supporting applications Applications of information technology Core technologies incorporated into cyberinfrastructure Technology transfer Conduct of science and engineering research Research Development Operations Use

8. Examples of some major technical challenges • Collection, organization, metadata, access to huge and numerous data repositories • Distributed applications across disciplines and organizations and repositories and international boundaries • Preservation over centuries • Data and scholarly collections • Software (for documentation and execution) • Simulation/visualization results

9. Major goals • Enable (rather than inhibit) cross-disciplinary collaboration and shared data • Ease development of parallel and distributed applications • Cyberinfrastructure includes major software • Usability, documentation, support

10. Some tensions Disciplinary needs and innovation Commonality and interoperability Processing, storage, data management Local Shared Support Local Centralized

11. Major organizational challenges • Common standards for metadata, languages, etc. (vs. disciplinary autonomy) • Generic applications (vs. discipline specific) • (Mostly) common infrastructure • Technology transfer to and from industry • Advance IT informed by these technology-driver needs • Integrating domain science and computer science • NSF matrix management across all (!) Directorates • Achieving consensus and coordination in a highly fragmented NSF community • Coordination across agencies and internationally

12. Conclusions • A major opportunity, and also major challenges • Costs of not moving aggressively: fragmentation, inflexibility, duplication of effort, lowered efficacy and effectiveness • About $1B/year added budget in NSF (more in other agencies): Mostly human resources • NSF has opportunity to take the lead, but needs to work with other agencies and international bodies