The Virtual Radiation Belt Observatory (ViRBO) and the Future of the VxO Environment

1. The Virtual Radiation Belt Observatory (ViRBO) and the Future of the VxO Environment R.S. Weigel Department of Computational and Data Sciences George Mason University, Fairfax, Virginia

2. Contributors Software * Eric Kihn (NOAA/NGDC, ViRBO Web and API) * Mikhail Zhizhin (RFO, ViRBO Web and API) * Dmitry Mishin (RFO, ViRBO Web and API) * Dmitry Kokovin (RFO, ViRBO Web and API) * Jeremy Faden (Cottage Systems, Autoplot) Data * Dan Baker (CU/LASP, Data and metadata) * Sebastian Boudier (Onera, Data calibration) * Janet Green (NOAA/SEC, Data and metadata) * Shri Kanekal (CU/LASP, Data and metadata) * Reiner Friedel (LANL, Data and calibration) * Paul O'Brien (Aerospace, Data and metadata)

3. The Heliophysics Data Environment

4. Outline • What is a VxO? • Goal of VO effort • Where to start • FAQ • Relevance of VO effort to Scientist • Steps to create VO environment • Needed services • ViRBO

5. What is a VxO • Virtual Observatory where x represents a domain such as thermosphere/ionosphere or radiation belt • For a given community, a VxO provides • A comprehensive list of available data • Community-specific services on data • Connections to other VxOs with relevant services

6. Goal of VO effort • “VOs should make ‘standard’ scientific research much more efficient.”  [Roberts et al., 2005] • Rate of science output must keep up with rate of potential science output given amount of available data • The framework which is used must allow for this • Requires improvements in: search interfaces analysis visualization access communication • … all parts of the chain from instrument or model output to end user must continuously improve.

7. More ambitious goal • Ultimately VOs and associated services should enable us to answer “higher-order” queries such as “Show me the data from cases where a large CME and its effects that was observed by SOHO was also observed in situ in the heliosphere, in the Earth's magnetotail, and in responses in the magnetospheres and ionospheres of any planets.” Roberts et al, 2005 [http://hpde.gsfc.nasa.gov/VO_Framework_7_Jan_05.html]

8. Other goals • … “If the new paradigm is successful, even the PI teams should be tempted to use VOs for looking at their own data.” 

9. History • In (funded) development for about 3 years • A fundamental change in the data approach • Virtual Observatories for Radiation Belt, Thermosphere/Ionosphere, Magnetosphere, Solar, and Heliosphere. • Newer services include an event list manager, waves, and an energetic particles.

10. Where to start • Look at http://vspo.gsfc.nasa.gov/ • Key function: High-level pointers to known data sets. (Also another interface to data from CDAWeb/SPDF, which can be confusing). • If you know of a data set that you can’t find a reference to there, let them know. • For more detailed information and services, VSPO will return pointers to relevant Virtual Observatory.

11. Frequently asked Questions • Do I need to learn more data interfaces? • The boundaries between the VO and the traditional data center (“Big Box/Little Box”). To be discussed throughout

12. Big Box/Small Box implementations • Two implementation extremes • Data Center/Big Box: Store all data in a central location. • VO/Small Box: VO creates domain-specific metadata and uses metadata to enable services. No local data storage. • What is emerging • VO sometimes create services or work on problems with overlap with Big Box. Usually means there is a un-met demand for a service. • Services are generalized and designed to meet community need. • VOs sometimes store data. Again because of an un-met need. VOs recognize existing “Big Boxes” will eventually fill the need.

13. Relevance to Scientist • Many of us have become data managers and solve similar problems do not depend on science specialty. • We build ad-hoc solutions • If you have a problem, ask if it is cross-domain. • If it is, there is probably another person or VO working on a similar problem. There is usually potential for leveraging effort. • Example: Event list • Example: Web browser visualization • Identify and develop cross-cutting services. They eventually become “core” services.

14. The Heliophysics Data Environment

15. Steps to create VO environment • Describe the data in a standard format (SPASE; http://spase-group.org/). Primary VxO activity in past 3 years. If you know of a data product that does not have associated metadata, tell a VO. • Create services • Connect services

16. Steps to create VO environment • Describe the data in a standard format (SPASE; http://spase-group.org/). Primary VxO activity in past 3 years. If you know of a data product that does not have associated metadata, tell a VO. • Create services • Connect services

17. Key Concept • Everything should be viewed as a service

18. Example Services • CCMC • VMR – Will add services to CCMC data • SSCWeb – Orbit calculations • ViRBO - L* calculations (using SSCWeb data)

19. Metadata Creators Standard Query Edit Management Data Creators Ingestion service (with versioning!) Data Base API General Mass store and backup Fast Cache (Content delivery service) Filter service Visualization Pre-computed Static and on demand Interactive and dynamic Needed Services, Tools, and Roles

20. Notes • Each is an intellectual challenge in its own right. • Many entities do some of these things to some extent.

21. Specialized cross-domain needs Content Delivery Service Finished data and metadata products Filter Service Data API Software interface Basic DB Mass Store & Backup Ingest Service & API API and software Logging Service Service Data base

22. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Metadata Management service and API Basic DB Mass Store & Backup Ingest Service & API Includes version control! API Logging Service Service Data base

23. VxOwareNotes: http://vxoware.orgImplementation: http://virbo.org/meta

24. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Metadata Management service and API Basic DB Mass Store & Backup Ingest Service & API API Logging Service Service Data base

25. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Basic DB Mass Store & Backup Ingest Service & API API Logging Service Service Data base

26. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Basic DB Mass Store & Backup Ingest Service & API API Logging Service Service Data base

27. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Basic DB Mass Store & Backup Ingest Service & API API Logging Service Service Data base

28. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Basic DB Mass Store & Backup Ingest Service & API API Logging Service Service Data base

29. Specialized cross-domain needs Content Delivery Service Filter Service Data API Software interface Basic DB Mass Store & Backup Ingest Service & API API Logging Service Service Data base

30. Autoplot + Time Series Server

31. ViRBO • Breaking the VO paradigm in many ways (not just connecting services and creating metadata) • Improved high-level tools and services are fine, but first • Increase data accessibility • Increase amount of available data • We have: • Created many climatological data sets • Simplified access to many existing data sets. • Creating a time series server • See virbo.org for more …

32. ViRBO Current Projects • VxOware • Autoplot • Time Series Server

33. Future and existing services • L and L* data base • L-sort plots • Fly-through AP-8/AE-8 and AP-9/AE-9 • Hosting SVN for AP-9/AE-9 development • Hosting meeting notes and documents • Work with RBSP to develop or connect services • Many more …

34. Onward • Virtual Observatories provide services • Researchers guide Virtual Observatory activities