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DAWG: Distributed Data Archive Discovery and Integration for Education

DAWG aims to facilitate discovery, integration, and dissemination of educational content that utilizes datasets from distributed data archives. It provides tools for parsing, processing, and visualizing datasets, and emphasizes community participation by engaging builders of educational materials and applications.

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DAWG: Distributed Data Archive Discovery and Integration for Education

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  1. DAWG Goals (1 of 4) • Facilitate discovery across distributed data archives • Provide tools to help instructors and learners, parse, process, and visualize datasets • Facilitate the integration of seemingly disparate datasets, and • Facilitate the development and dissemination of educational content that utilizes datasets and datastreams

  2. Principles for Data Access in DLESE (2 of 4) • rely on partnerships for • provision of data sets • provision of technologies & tools • NSDL participation/compatibility • follow patterns that reflect the state of the art • OpenGIS Web-Mapping Testbed • Global Change Master Directory • Digital Earth (Reference Model, etc.) • Distributed Oceanographic Data System (DODS) • Alexandria (Gazeteer, etc.) • NPACI Access Grid • Unidata, VisAD... • enable community participation by emphasize service, beyond end-users of data sets, to • builders of educational materials • builders of applications • providers of data

  3. Principles for Data Access in DLESE (3 of 4) • gain early success • pick a few common data-set types & compatible tools • offer a few representative data sets • augment discovery with a gazeteer • ensure end-to-end effectiveness • place/feature-specific data discovery • tool-specific data discovery • data-specific tool discovery • application-based viewing/analysis (thick client) • browser-based data viewing/analysis (thin client) • but don’t preempt long-term success • employ XML-based metadata harvesting • extensibility for usage metadata... • data sets as nested collections... • ?

  4. Principles for Data Access in DLESE (4 of 4) • aim for long-term success • address key challenges • persistent identifiers • personalized data collections • middleware matching tools to data types • scalability limits on human-generated metadata • follow emerging standards/patterns • support embedding DLESE Data Discovery/Access in applications • create linkages/embedding between data and educational resources • conceptualize/support chains of data derivations/transformations • link strategically to research needs and developments in research • improved data access (remote, real-time, etc.) • facilitated data use (i.e., enhanced usage metadata) • analysis & visualization • integration & synthesis • improved data discovery • NSB report on Environmental Science & Engineering...

  5. Data Access Components of the DLESE Effort • Data Discovery • Create/operate a discovery/characterization environment for data sets & data services • Help providers to expose suitable metadata... • Support creation of personalized data collections • Data Use • Create/operate a tool discovery/characterization environment • Help providers expose data for remote access • Help builders of materials/applications access data • Foster creation of useful data-transformation services • Educational Linkage • interact with other DLESE components • link full spectrum of educational resources, services, and support with data discovery

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