eagle-i making the invisible visible
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Transcript of eagle-i making the invisible visible
www.eagle-i.org
eagle-imaking 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
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
eagle-i consortium --From Sea to Shining SeaNINE institutions diverse in geography, culture and resources
Institution NCRR Programs
Harvard University CTSA, BIRN, NPRC
Oregon Health & Science University
CTSA, NPRC
Dartmouth College COBRE, 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
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:
Deliverables
• Federated system with 9 sites• Effectiveness – “make the invisible visible”• Scalability
Resource types
Quantity of resources
Number of sites
• Functionality (obesity use case)
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
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
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 Product
Data Curators
Data Entry Tools
Data ToolsSearch
Data Entry Tools
Field names and drop down lists in the data entry tool are populated by
the ontology
Field names and drop down lists in the data entry tool are populated by
the ontology
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.
Types insulin resistance into the search box
Results are returned for all resources from all institutions related to insulin resistance.
Interested in reagents thus refines search to reagents only.
The result set was too broad. “Entrez Gene” provides access to genes related to human disease to help narrow search results.
The investigator wants to find and animal model, so the resource is refined from insulin resistance to insulin resistance in the mouse.
IRS-1 looks promising, so the researcher clicks on the link to go to Entrez Gene for more information.
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
Plasmids for IRS-1 found and the investigator contacts the researcher to determine their availability.
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
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
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