1. www.eagle-i.org eagle-i making 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 InstitutionNCRR Programs Harvard UniversityCTSA, BIRN, NPRC Oregon Health & Science University CTSA, NPRC Dartmouth CollegeCOBRE, INBRE Jackson State University RCMI, RTRN Montana State University COBRE, INBRE Morehouse School of Medicine RCMI, RCRII, RTRN, CCRE University of Alaska Fairbanks COBRE, INBRE University of Hawaii Manoa RCMI, RCRII, RTRN, CCRE, COBRE, INBRE, University of Puerto Rico RCMI, INBRE, RTRN, CCHD, NPRC

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

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. Building The Product Application Team Data Tools Team Inventory Management System Team Data Administration Resource Navigation All Sites Build Team -- Harvard Data Curation Teams (OHSU and Harvard) Product Data Models Ontologies Inventory Management System User Interface Query Research Resources Inventory 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) disease Users may want to query eagle-i resource gene Users may want to query A junior researcher studying obesity wants to investigate the genetic basis of insulin resistance in model systems and humans.

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. Oregon Health and Science University (OR) David W. Robinson, PhD University of Alaska Fairbanks (AK) Bert Boyer, PhD University of Hawaii Manoa (HI) Richard Yanagihara, MD University of Puerto Rico (PR) Emma Fernandez- Repollet Dartmouth College (NH) Jason H. Moore, PhD Harvard University (MA) Lee Nadler, MD; Douglas MacFadden MCS Jackson State University (MS) James L. Perkins, PhD Morehouse School of Medicine (GA) Gary H. Gibbons, MD Montana State University (MT) Sara L. Young, MEd eagle-i consortium