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ViRBO Developers Meeting

ViRBO Developers Meeting. Jan 17-19, 2006. From e GY. The purpose of a Virtual Observatory is to increase efficiency, and enable new science by greatly enhancing access to data, services, and computing resources.

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ViRBO Developers Meeting

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  1. ViRBO Developers Meeting Jan 17-19, 2006

  2. From eGY • The purpose of a Virtual Observatory is to increase efficiency, and enable new science by greatly enhancing access to data, services, and computing resources. • A Virtual Observatory is a suite of software applications on a set of computers that allows users to uniformly find, access, and use resources (data, documents, software, processing capability, image products, and services) from distributed product repositories and service providers. • A Virtual Observatory may have a single subject (for example, the Virtual Solar Observatory) or several grouped under a theme (the US National Virtual Observatory, http://www.us-vo.org/, which is for astronomy). A Virtual Observatory will typically take the form of an internet portal offering users features among the following. • Tools that make it easy to locate and retrieve data from catalogs, archives, and databases worldwide • Tools for data analysis, simulation, and visualization • Tools to compare observations with results obtained from models, simulations, and theory. • Interoperability: services that can be used regardless of the clients computing platform, operating system, and software capabilities • Access to data in near real-time, archived data, and historical data. • Additional information - documentation, user-guides, reports, publications, news, and so on. • VO also refers to Virtual Organization

  3. VOs and data providers • Not a VO: • When you hand off a user to another site • Only one dataset • When you do not deliver, or do not arrange for delivery of the data • When your curation role is not evident • Data Provider: • Acquire data and produce data products (static or dynamic). • Preserve data in useable forms. • Distribute data, and provide easy machine (API) and Internet browser access. • Support a communication mechanism – should support a standards-based messaging system (e.g., ftp, http, SOAP, XML) • Produce, document, and make easily available metadata for product finding and detailed data granule content description. Ideally, maintain a catalogue of detailed data availability information. • Assure the validity and quality of the data. • Document the validation process. • Provide quality information (flags). • Maintain careful versioning including the processing history of a product. • Maintain an awareness of standards (such as community accepted data models), and adhere to them as needed. • Provide software required to read and interpret the data; ideally the routines used by the PI science team should be available to all.

  4. CEDAR

  5. The Earth System Grid DATA storage SECURITY services METADATA services TRANSPORT services LBNL ANALYSIS & VIZ services MONITORING services gridFTP server/client HRM FRAMEWORK services DISK ANL Auth metadata NCAR MySQL GSI CAS server RLS SLAMON daemon TOMCAT AXIS GRAM CAS client GSI NCL openDAPg client LAS server NERSC HPSS gridFTP server/client HRM openDAPg server ORNL NCAR MSS DISK TOMCAT LLNL SLAMON daemon CDAT openDAPg client MySQL Xindice RLS THREDDS catalogs gridFTP server/client HRM gridFTP server/client HRM CAS client MyProxy client MyProxy server GSI ORNL HPSS DISK DISK openDAPg server ISI MySQL MySQL RLS MySQL Xindice RLS MCS OGSA-DAIS CAS client GSI GSI GSI

  6. ACOS at the MLSO Near real-time data from Hawaii from a variety of solar instruments, as a valuable source for space weather, solar variability and basic solar physics

  7. Emerging needs • Interdisciplinary science and engineering (not just between adjacent fields) • Interdisciplinary data assimilation, integration • Web service workflow orchestration (beyond syntax) • Vortals as well as portals (specific to general) • Agency (NASA) and community efforts (eGY, IHY, IPY, IYPE)

  8. What’s ahead? • Virtual Observatories provide both framework and data system elements, users are already confusing VOs and data providers • Many VO’s are noting the need for better glue, scalability, expandability, etc. • Success (to date) in utilizing formal methods for interface specification and development using ontologies • Success in breaking all of the free tools! Commercial tools are under consideration • Challenges exist for reasoning and interface with scientific datatypes, e.g. complex spatial and temporal concepts • For VSTO (and SESDI) - more use-cases, populate the interfaces and test for scalability and interoperation in production settings

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