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The Technical Infrastructure of the NSDL

Explore the architecture, services, and tools of the National Science Digital Library (NSDL). Learn about the NSDL 1.0 structure, lessons learned, the Fedora-based NSDL Data Repository (NDR), NSDL 2.0 services, and collaboration opportunities. Discover the building blocks, infrastructure overview, metadata aggregation, search architecture, lessons learned, and the vision for NSDL 2.0. Gain insights into creating a dynamic, living digital library for STEM education.

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The Technical Infrastructure of the NSDL

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  1. The Technical Infrastructure of the NSDL Dean Krafft, Cornell Universitydean@cs.cornell.edu

  2. NSDL Technical Overview Structure of the talk: • NSDL 1.0 Architecture and Lessons Learned • The Fedora-based NSDL Data Repository (NDR) and NSDL 2.0 • Inspiring Contribution and Collaboration - ExpertVoices • Other NSDL 2.0 Services and Tools • Q&A

  3. What is the NSDL? • An NSF-funded $20 million/year program in Science, Technology, Engineering and Mathematics (STEM) education • A digital library describing over a million carefully selected online STEM resources from over 100 collections (at http://nsdl.org) • A core integration team (Cornell, UCAR, Columbia) working with 9 “pathways” portals and over 200 NSF grantees • A large community of researchers, librarians, content providers, developers, students, and teachers

  4. What are the building blocks? • A distributed set of NSDL collections • A central repository of aggregated information about Science, Technology, Engineering, and Mathematics digital resources • A set of services that build on the repository, initially search and archive • A set of portals that expose NSDL STEM resources to a variety of user communities

  5. Infrastructure overview: NSDL 1.0 Search Service Central Metadata Repository STEM Collections on the Web Collection Registration System Protocol: OAI-PMH HTTP REST SQL Archive Service NSDL.org Portal

  6. NSDL 1.0 Ingest • Create a “union catalog” of Dublin Core metadata records for STEM resources • Harvest those records from collections using OAI-PMH (openarchives.org) • Normalize and augment metadata records (change to qualified DC) • Store records in an Oracle DB and re-serve qualified DC through OAI-PMH

  7. Metadata Aggregation Metadata Repository (Oracle DB) OAI-PMH Metadata Ingest Service NSDL Dublin Core OAI-PMH Server … Harvest Management/ Collection Registration System NSDL DC Collection OAI Servers

  8. NSDL 1.0 Search • Harvest metadata records from MR using OAI-PMH • Crawl URLs specified in the metadata using Nutch • Build a search index using metadata plus full-text of available content pages • Expose search in a web portal at nsdl.org for K-gray access to NSDL resources

  9. SearchArchitecture Metadata Repository Pathway Portal OAI-PMH … Search and Discovery Server OAI Harvester Pathway Portal Lucene index generator REST Query Interface Lucene Query Engine Nutch Harvester NSDL.org Portal Web Content http/ftp

  10. NSDL 1.0 Lessons • Rather than one portal for everyone, support communities with common interests: Pathways now provide discipline and area-specific portals • Metadata is expensive: unlike traditional libraries, e.g. through OCLC, digital collections have very “mixed quality” metadata, with unusual and inconsistent coding • On the good side: Oracle DB and OAI-PMH server scaled successfully to over 1 million catalog records

  11. NSDL 1.0 Lessons continued • OAI-provided collections need 3 types of expertise: domain (resources & pedagogy), metadata (vocabulary & formatting), and technical (XML schema, UTF8, HTTP, OAI-PMH). • In many cases it took several months from first contact to successful OAI harvest, and the average harvest failure rate has stayed at 25%-50%, with only 23% of that transient failures • Incremental harvesting fundamental to efficient processing, but problematic: issues with persisting deleted records and recovering from partial harvests • Result: some automation, but high people cost

  12. NSDL 1.0 Summary • Metadata Repository was quick to implement using known technologies, but • Limited model • Metadata-centric orientation • No content – only metadata • Limited relationships – collection/item • Limits on context, structure, and access • Severe limits on contribution and collaboration • One-way data flow: NSDL → Users

  13. Going beyond the card catalog • Create an NSDL that guides not just resource discovery, but resource selection, use, and contribution • Supports creating “context” for resources • Presents resources in context: in a lesson plan; with ratings; correlated with education standards • Supports creating a permanent archive of resources • Enables community tools for structuring, evaluation, annotation, contribution, collaboration • Goal: Create a dynamic, living library

  14. NSDL 2.0: NSDL Data Repository • Goals: • Architecture of participation: service-based, not a monolithic application/single user experience • Remixable data sources and data transformations • Harnessing (and capturing) collective intelligence • A free market of millions of inter-related resources (create the “long tail”) • Two-way data flow: NSDL ↔ users • Solution: Fedora-based NSDL Data Repository

  15. Fedora: the NDR middleware • A Flexible, Extensible Digital Object Repository Architecture (http://www.fedora.info) • Open source project with $2.2 million in Mellon funding 2002-2007 • Collaboration of Cornell and Univ. of Virginia • Key funded users include: • eSciDoc project (collaboration of the Max Planck Society and FIZ Karlsruhe) • VTLS Corp., Harris Corp., Library of Congress • Australian Research Repositories Online to the World (ARROW) • Royal Library Denmark, National Library, and DTU

  16. The Fedora Vision: A Repository for Rich Information Networks

  17. What is Fedora? • An architecture, toolkit, and implementation: middleware, not a vertical application • DSpace in contrast: a vertical application with a fixed workflow targeted at users • Stores arbitrary internal and external digital objects, disseminations (transformations and combinations), relationships among objects • Entirely SOAP/REST based, disseminations are URLs • XML data store; RDBMS cache; RDF triplestore supports relationship queries

  18. Fedora Key Features • Content aggregation • Digital object model to combine information entities in novel ways • Knowledge integration • Ontology-based relationships among objects • Information reuse • Create secondary, tertiary objects • Information transformation • Combine objects with computational services • Collaboration and contribution • Enable annotation, info sharing, workflow, contextualization • Information management and preservation • XML-based object storage • Service-oriented architecture; web services • Store relationships and service linkages with objects

  19. Fedora Digital Object Model Component View Digital object identifier Persistent ID ( PID ) Relations (RELS-EXT) Reserved Datastreams Key object metadata Dublin Core (DC) Audit Trail (AUDIT) Datastreams Set of content or metadata items (local or external URL redirects) Datastream Datastream Disseminators Web-service methods for distributing views of recombined content Default Disseminator Disseminator

  20. Implementing the NDR with Fedora • Multiple Object Types: • Resources (with local or remote content) • Metadata • Aggregations (collections) • Metadata Providers (branding) • Agents • Relationships with arbitrary graph queries: • Structural (part of) • Equivalence • Annotation

  21. Draft NDR API Characteristics • Uses REST calls for all interactions • Specializes Fedora for NDR objects/relationships • Disseminations allow combining metadata from multiple sources, or related content • Authentication: Requests signed with private key associated with an agent • Authorization: Agent can become a metadata provider or aggregator; can create resources • Documentation being developed at http://ndr.comm.nsdl.org

  22. NDR Architecture

  23. An Information Network Overlay • Think of the NDR as a lens for viewing science content on the net • Content can be: • Local: stored directly in the NDR • Remote: accessed through a URL • Computed: derived from a database or web service • Archived: an older version stored at SDSC • It all has a repository-based URL

  24. Network Overlay View User View API/UI Repository View with Relations & Annotations Resources on the Web

  25. Status of the NDR • Repository in test load • over 875,000 metadata records • over 2 million digital objects • Over 163 million RDF triples (lots) • Scaling challenges • moved to 64-bit architecture with 32GB memory • need to carefully structure RDF queries • can scale current system by factor of five • need to move to more powerful triplestore (Oracle) • Estimating fully operational beta version of new NDR in June

  26. How should we use the NDR? • The NDR provides powerful capabilities for: • Creating context around resources • Enabling the NSDL community to directly contribute resources and context • Representing a web of relationships among science resources and information about those resources • How do we use it? Here’s one specific example …

  27. Issues in STEM Education • Issue: Need to support scientific inquiry • Issue: Students need a better understanding of the processes of scientific research • Issue: Teachers are often under-prepared to teach science and math • Issue: Scientists need tools to make science and math research more available

  28. Addressing the Needs • In Response: NSDL is building an educational tool that… • Models scientific inquiry and exposes the processes of scientific research • Promotes and facilitates conversations between research and education communities • Brings content expertise into the classroom to support under-prepared teachers • Allows scientists, teachers, and media specialists to collaboratively develop instructional context around NSDL resources

  29. ExpertVoices

  30. What is Expert Voices? • A system using blogging technology to: • Support STEM conversations among scientists, teachers and students • Tie NSDL resources to real-world science news • Create context for resources to enhance discovery, selection and use • Enable NSDL community members to become NSDL contributors: of resources, questions, reviews, annotations, and metadata • Expert Voices ≠ LiveJournal • Contributors are carefully selected, contributions are about science, the process of science, and education

  31. Expert Voices As An Educational Tool • Topic-based discussion (e.g. tsunamis) with pointers to related resources • Research outreach (Criterion 2) – explaining and documenting NSF-funded research • Experts can add resources with topical context to the NSDL • Resources can be reviewed and annotated • Question/answer and discussion forum: scientist ↔ teacher ↔ student ↔ librarian

  32. Broadening Participation: An Expert Voices Learning Scenario • “Hurricane Season Blog” run by a National Weather Service hurricane expert, an Earth Science teacher, and a school media specialist familiar with NSDL resources • Expert creates an entry for Hurricane Gertrude • “On track to hit Ft. Lauderdale in 72 hours” • “Currently undergoing eyewall replacement cycle” • “Expecting 15 foot storm surge” • Media specialist adds links to NSDL resources: Hurricane Hunters site, latest satellite photos, and USGS flooding and flood plain site (storm surge context) • Teacher makes connections to relevant standards and appropriate pedagogy for use by other teachers • Students experience engaging real-time, real-world applications of science lessons

  33. Broadening Participation: An Expert Voices Outreach Scenario • NSF grantee: Bioluminescence researcher wants to make research K-12 accessible • Creates an Expert Voices conversation • Enables his students and researchers to document process and results – how science really works • Writes about publications and educational resources (e.g. www.photobiology.info) • Adds these to the NSDL, creating audience-level metadata • Entries serve as annotations that create K-12 context for the college-level research

  34. Expert Voices Implementation • Open source multi-user blogging system • Published entries become NSDL resources • Owner controls publication of entries and visibility of comments • Entries can contain linked references to NSDL resources, references to URLs that should become resources, and new resource metadata • Integrated with NSDL community sign-on

  35. Expert VoicesImplementation • Initial blog system is multi-user WordPress • WordPress plug-ins provide NDR integration and Shibboleth authentication • Publication of blog entry creates: • Content, as a new resource with simple metadata • New NDR resources • New metadata for any referenced resources in content • Graph of relationships between entry and all referenced resources • Blog available as independent RSS feed

  36. NDR Entry for Expert Voices Existing Collection Topic- based Blog New Audience MD New Metadata Member of Metadata Provider Member of Blog Entry Metadata Provider Metadata for Metadata for Annotates Referenced New Resource 1 Referenced Existing Resource 2 Inferred relationship between resources

  37. But Expert Voices is just the beginning…

  38. NDR Application: OnRamp • NDR-integrated multi-user, multi-project content management system • Supports NSDL single sign-on and group management • Decentralized workflow for the creation and distribution of both simple and complex content • Disseminates content in multiple publication and online forms • Delivery estimated 3Q06

  39. NDR Application: Instructional Architect • Created by Mimi Recker and colleagues at Utah State University • Teacher develops a lesson plan, incorporating NSDL and other resources • Assigns subject, grade level, ed standard • Distributes to class or public • Available now, with NDR integration in design

  40. NDR Application: Integrated Wiki • Community of approved contributors (e.g. teachers, librarians, scientists) are granted edit access on OpenNSDL wiki • New resources and metadata are created as wiki pages and reflected into the NDR • Non-wiki-based NDR resources and metadata are displayed as read-only wiki pages, subject to comment and linking • User and project pages organize NDR resources

  41. NDR Application: Content Assignment Tool • Developed by Anne Diekema, Elizabeth Liddy, et al. at the Syracuse University Center for Natural Language Processing • Uses text analysis and machine learning to suggest Educational Standards alignment for resources • Content expert assigns standard, and system learns from the assignment • Standalone tool available now; standards associated with resources in the NDR by 3Q06

  42. Content Assignment Tool

  43. Other applications in development • Automated grade-level assignment based on vocabulary analysis (San Diego Supercomputer Center) • iVia-based Expert-Guided crawl: Tool for Pathways and others to turn websites into resource collections (UC Riverside) • Automated subject assignment (UC Riverside) • MyNSDL: Bookmark and tag STEM resources within and outside the NSDL (Cornell)

  44. STEM Collections NSDL 2.0 Ecosystem Search Service Archive Service Fedora- based NDR Protocol: OAI-PMH HTTP REST NDR API …

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