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eagle-i making the invisible visible

eagle-i making the invisible visible. Lee M. Nadler, M.D. on behalf of the eagle-I Consortium. NCRR 56 Day ARRA Challenge. Convene a “diverse” group of at least 6 institutions to deliver: An approach to identify research resources

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eagle-i making the invisible visible

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  1. eagle-imaking the invisible visible Lee M. Nadler, M.D. on behalf of the eagle-I Consortium

  2. NCRR 56 Day ARRA Challenge • Convene a “diverse” group of at least 6 institutions to deliver: • An approach to identify research resources • A method to catalogue, enter, and store the information locally • A federated network capable of querying member institutions and prove that it works • A product that can be validated, exported across America and sustained

  3. eagle-i consortium --From Sea to Shining Sea NINE institutions diverse in geography, culture and resources

  4. Deliver a national research resource discovery network • Onsite teams each capable of discovering and inventorying research resources • A data inquiry and inventory management system at each site • Cycles of resource discovery, curation, dissemination, and assessment • A semantic search application that can find available research resources that are often invisible eagle-i must create:

  5. Deliverables • Federated system with 9 sites • Effectiveness – “make the invisible visible” • Scalability Resource types Quantity of resources Number of sites • Functionality (obesity use case)

  6. eagle-i Architecture Search Application Federated Network (SPIN) Institutional Repositories (RDF) Data Entry & Curation Tools Data Build Team Data Curators eagle-i ontology Resource Navigators

  7. Key Architecture Elements • Distributed Network – for local control and incremental expansion • Ontology Driven – for rich search semantics, linking to outside data and flexibility for change/expansion of resource types over time • Open Interfaces – for connectivity with outside data and systems • Data Privacy Controls – to encourage contribution of “sensitive” resources

  8. Inventory Management System • User Interface Query • Research Resources Inventory Product Product All Sites Build Team -- Harvard Resource Navigation • Application Team • Data Tools Team • Inventory Management System Team Data Administration Data Curation Teams (OHSU and Harvard) • Data Models • Ontologies Product Building The Product Data Curators

  9. Data Entry Tools • Data Tools • Search

  10. Data Entry Tools Field names and drop down lists in the data entry tool are populated by the ontology

  11. Finding What You Need External (Gene/OMIM) Users may want to query Users may want to query disease gene A junior researcher studying obesity wants to investigate the genetic basis of insulin resistance in model systems and humans. eagle-i resource

  12. Types insulin resistance into the search box

  13. Results are returned for all resources from all institutions related to insulin resistance.

  14. Interested in reagents thus refines search to reagents only.

  15. The result set was too broad. “Entrez Gene” provides access to genes related to human disease to help narrow search results.

  16. The investigator wants to find and animal model, so the resource is refined from insulin resistance to insulin resistance in the mouse.

  17. IRS-1 looks promising, so the researcher clicks on the link to go to Entrez Gene for more information.

  18. The researcher clicks through to Entrez Gene to confirm that IRS-1 is a gene of interest, and searches eagle-i for resources related to IRS-.1

  19. Plasmids for IRS-1 found and the investigator contacts the researcher to determine their availability.

  20. Much Work Left To Complete During Year 2 • Populating resources from all sites, curation, use cases, sprint test cycles • Improve and expand the system based on user feedback (integration with PubMed, MGI, other repositories) • Implement connections to outside systems via standard interfaces • Begin planning expansion to other institutions

  21. Challenges to Adoption and Sustainability • Develop sustainable models for data collection • Provide value back to the data stewards • Provide value back to the lab • Develop sustainable models for institutional investment • Ensure that local IT systems are low cost and easy to administer • Provide value back to the institution • Address data privacy concerns • Sensitive resources

  22. eagle-i consortium

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