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J Biomol Tech. 2010 September; 21(3 Suppl): S64.
PMCID: PMC2918121

Eagle-i: Making Invisible Resources, Visible

M. Haendel,1 M. Wilson,1 C. Torniai,1 E. Segerdell,1 C. Shaffer,1 R. Frost,2 D. Bourges,2 and J. Brownstein2
1Oregon Health & Sciences University, Portland, OR, United States;
2Harvard University, Cambridge, MA, United States;
3Montana State University, Bozeman, MT, United States



The eagle-i Consortium – Dartmouth College, Harvard Medical School, Jackson State University, Morehouse School of Medicine, Montana State University, Oregon Health and Science University (OHSU), the University of Alaska, the University of Hawaii, and the University of Puerto Rico – aims to make invisible resources for scientific research visible by developing a searchable network of resource repositories at research institutions nationwide. Now in early development, it is hoped that the system will scale beyond the consortium at the end of the two-year pilot. Data Model & Ontology: The eagle-i ontology development team at the OHSU Library is generating the data model and ontologies necessary for resource indexing and querying. Our indexing system will enable cores and research labs to represent resources within a defined vocabulary, leading to more effective searches and better linkage between data types. This effort is being guided by active discussions within the ontology community ( bringing together relevant preexisting ontologies in a logical framework. The goal of these discussions is to provide context for interoperability and domain-wide standards for resource types used throughout biomedical research. Research community feedback is welcomed. Architecture Development, led by a team at Harvard, includes four main components: tools for data collection, management and curation; an institutional resource repository; a federated network; and a central search application. Each participating institution will populate and manage their repository locally, using data collection and curation tools. To help improve search performance, data tools will support the semi-automatic annotation of resources. A central search application will use a federated protocol to broadcast queries to all repositories and display aggregated results. The search application will leverage the eagle-i ontologies to help guide users to valid queries via auto-suggestions and taxonomy browsing and improve search result quality via concept-based search and synonym expansion. Website: NIH/NCRR ARRA award #U24RR029825

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