Research resources: curating the new eagle-i discovery system

Development of biocuration processes and guidelines for new data types or projects is a challenging task. Each project finds its way toward defining annotation standards and ensuring data consistency with varying degrees of planning and different tools to support and/or report on consistency. Further, this process may be data type specific even within the context of a single project. This article describes our experiences with eagle-i, a 2-year pilot project to develop a federated network of data repositories in which unpublished, unshared or otherwise ‘invisible’ scientific resources could be inventoried and made accessible to the scientific community. During the course of eagle-i development, the main challenges we experienced related to the difficulty of collecting and curating data while the system and the data model were simultaneously built, and a deficiency and diversity of data management strategies in the laboratories from which the source data was obtained. We discuss our approach to biocuration and the importance of improving information management strategies to the research process, specifically with regard to the inventorying and usage of research resources. Finally, we highlight the commonalities and differences between eagle-i and similar efforts with the hope that our lessons learned will assist other biocuration endeavors

eagle-i: An Ontology-Driven Framework For Biomedical Resource Curation And Discovery

The eagle-i Consortium ("http://www.eagle-i.org/home":www.eagle-i.org/home) comprises nine geographically and ethnically diverse universities across America working to build a federated network of research resources. Biomedical research generates many resources that are rarely shared or published, including: reagents, protocols, instruments, expertise, organisms, training opportunities, software, human studies, and biological specimens. The goal of eagle-i is to improve biomedical research by helping researchers more easily find scientific resources that are difficult to discover, reducing time-consuming and expensive duplication of resources. Now in early development, the system will ultimately expand to include research resources at other universities following the end of the two-year pilot phase. An application ontology is being developed to enable representation of core facility and research lab resources in the eagle-i repository, leading to more effective searches and better linkage between data types. The eagle-i ontology will guide users to valid queries via auto-suggestion, ontology browsing, concept-based search, and synonym expansion. The ontology development effort is being guided by active discussions within the ontology community and brings together relevant preexisting ontologies in a logical framework. Components of the data entry and search interfaces are generated directly from the ontology, which allows rapid change in response to user needs and ontology evolution. Each eagle-i institution will populate and manage a local repository using data collection and curation tools. To enhance the quantity and quality of data, the data tools will take advantage of the ontology to support semi-automated annotation of resources. NIH/NCRR ARRA award #U24RR029825