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