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is now
http://www.researchprofiles.collexis.com/umichigan/
http://www.experts.scival.com/umichigan
SciVal Experts is a customizable directory of research expertise,
automatically populated with publication data.
jho
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Find an ExpertSearch using a concept
you are interested inSTART concept
SciVal Experts identifies
University of Michigan
experts
Add related concepts
to refine your search
(optional)
additional concept Add
See the experts found
Review publication lists for your experts
It uses „fingerprint’ technology to provide researchers, research
administrators, and senior leadership with the ability to understand
and utilize the expertise across their institution.
Knowledge BaseText Fingerprint
Compendex
Fingerprinting is accomplished by:
Creating lists of publications for each author to accurately represent
them• Built automatically using disambiguation algorithm
• Manually checked by a quality assurance team to ensure accuracy
Automatic Process
Algorithm using identifying
elements determines
clusters:
• Name
• Location
• Co-Authors
• Key Concepts
• Journals
• more…
Manual Process
Human quality assurance
team builds profiles,
reviews results quarterlyJohn T. Smith
JT Smith
John Smith
J. Smith
J. Smith
Fingerprinting is accomplished by:
Creating lists of publications for each author to accurately represent
them• Built automatically using disambiguation algorithm
• Manually checked by a quality assurance team to ensure accuracy
Automatic Process
Algorithm using identifying
elements determines
clusters:
• Name
• Location
• Co-Authors
• Key Concepts
• Journals
• more…
Manual Process
Human quality assurance
team builds profiles,
reviews results quarterlyJohn T. Smith
JT Smith
John Smith
J. Smith
J. Smith
C.V.-by-C.V., Publication-
by-Publication validation of
all profiled faculty
Result: Extemely high
quality data in terms of
comprehensiveness,
accuracy, and ease of use
Improved user experience,
adoption, and enables
administrative use
SciVal Experts (formerly Collexis Research Profiles)Extracting concepts from those publications to create a Fingerprint
showing their expertise at both the researcher and department or
center level
jho
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Fingerprints can be aggregated at various levelsAggregating individual fingerprints across organizational units allows for the
organization to know what individuals know and what groups know
together…
Department
Institute
Research Group
• New publications and grants?• Emerging trends?• Who is working together? Who
isn’t?• With which other organizations
(internal and external) are we collaborating?
• What are we good at?
Individual Fingerprints
Aggregated Fingerprints
This information is combined to create an application that lets you
-Search and explore expertise across the University of Michigan
-Analyze Department level information about publications, expertise and
more, and
-View related concepts or publications from similar experts
jho
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Understand which experts are (or are not) working together across the
organization and view external collaborations
Participation across SciVal Experts implementations…
… and in customized multi-institution implementations and other national
networks
Implementation2007: Increased interest in an internal expertise and research
activity system and a non-financial view of faculty activity
“Why can’t you just make a system I can search that says
what everyone is working on? It seems so simple.”
– Biochemistry researcher
Initial approach was an internal C.V. system, but this had a high
data entry burden and faced severe impediments to adoption
Learned about commercial options, including NIH-Collexis
reviewer-finding tools
ImplementationSelected Collexis Research Profiles due to:
-Significantly lower cost of implementation (cost
approximates 1 developer FTE)
-Lower risk, fixed costs leave no potential for a runaway IT
project
-No manual data collection
-Automatic, ongoing data collection requires minimal
internal FTE commitments
-Control of the author-publication (‘who wrote what’) list
-Ability to download underlying data, permissive use
Value Propositions
Implementation guided by layered value propositions:
Macro: Supporting strategic decision making and analysis
Meso: Supporting faculty data infrastructure
Micro: Supporting collaboration and connection
The combination of all three was an easy
institutional support decision.
Implemented 2008-2009; Launched 2009
Site Traffic
~70% search
~20% direct
~10% link
Global user base
Value to Faculty
Faculty
Makes it easier
for faculty to
find each other
Increases faculty
‘discoverability’
to external users:-External collaborators
-Students
-Sponsors
-Media
Value to Departments / Centers
Faculty
Makes it easier to track
publications, working
relationships, and
research trends
Departments / Centers
Value to Schools / Colleges [System]
Faculty
Makes it easier to find experts,
engage with external contacts, and
observe trends
Departments / Centers
School / College
Value to Schools / Colleges [Data]
Faculty
Data feeds allow for customized
analysis and reporting(e.g. publication output, collaborations
between center members, etc.)
Departments / Centers
School / College
Faculty
Value Across Schools / Colleges
Cross-School / College
‘Discoverability’
Faculty
Departments / Centers
School / College
Departments / Centers
School / College
concept
„Micro‟ Use Cases
Supporting Collaboration
System: Finding similar experts for new faculty onboarding
Ad Hoc: Supporting a conference
Finding similar experts for new faculty onboarding
How can we help to match new faculty to the people that they should connect with at Michigan?
“I get most of my information on who to connect with from the people who are near my office.”
– Pediatric neurosurgery researcher
“Most of my connections stem from the people I met on my evaluation/selection committee during hiring after my fellowship.”
– Orthopaedic surgery researcher
Finding similar experts for new faculty onboarding
jho
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Organizational and scientific context of a grant or publication
Finding similar experts for new faculty onboarding
Gartner‟s Hype Cycle
Source: http://en.wikipedia.org/wiki/File:Gartner_Hype_Cycle.svg jho
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Short Circuit the Hype Cycle
Source: http://en.wikipedia.org/wiki/File:Gartner_Hype_Cycle.svg jho
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About NormsThe world is a
blunt normalizing
instrument
Achieving different results
requires continuously
introducing positive variation
How do we use the data to support „Health Services Research Day‟?
-Match research interests, project needs, opinions
-Shuffle existing working relationships, rank, etc.
Senior Faculty Junior Faculty
„Pitch‟ Group Mentoring Mixed
Social interaction models for different events throughout the day using a
combination of SciVal Experts concepts and attendee survey data
Supporting a Conference
Equals
„Meso‟ Use Cases
Supporting Leadership (Department Chairs,
Deans)
System: „Research Network‟ visualization and expertise
discovery
Ad Hoc: Strategic analysis
„Research Network‟ visualization and expertise
discovery
“I can connect people in my department based on what
I know about their expertise, but I’d love to a system
to help make sure I’m capturing everything.”
– Pediatric cancer chair
“It would be great to know that I could route someone to
a system that helps expose expertise in case I can’t
respond to their request as soon as possible.”
Ensuring that we deliver on the promise of strategic
recruitment efforts by monitoring collaboration
Jalife J
Cardiology
Department
May 2010
„Research Network‟ visualization and expertise discovery
jho
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Cardiology
Department
April 2011
„Research Network‟ visualization and expertise discovery
jho
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Strategic analysis
What are we good at?
How can we understand our expertise broadly?
What new centers or institutes should we create?
[Social] Network Analysis
So, like Facebook? Sort of.
But networks are everywhere.
And they aren‟t necessarily “social.”
Building Blocks
Nodes [Vertices] – People, Things, Ideas
Links [Edges] – Relationships
or
Recap
Degree (undirected): Number of
connections
In- / Out-Degree (directed): “Popular” /
“Gregarious”
Betweenness: “Bridges” / “Commonalities”
Closeness: “Rumor starting point”
Eigenvector Centrality: “Importance”
Connect:
People to Other People
Things/Ideas to Other Things/Ideas
People to Things/Ideas
Connect:
People to Other People
Things/Ideas to Other Things/Ideas
People to Things/Ideas to Other People
Data You May Already Have
Faculty/Staff and Appointing Departments
Faculty/Staff and Groups
Principal Investigators and Sponsored
Projects
Sponsored Projects and Participants
Authors and Publications
Citations
Specific Examples
Things/Ideas and Other Things/Ideas
Concepts and Other Concepts in
Publications and Sponsored Project
Proposal / Award Data
Specific Examples
People and Things/Ideas
People and Sponsored Projects
Authors and Publication Concepts
Specific Examples
People and Other People
Co-Participation on Sponsored Projects,
Co-Authorship
Strategic Analysis: What are our top disease areas?
Depression Center
Cardiovascular Center
Cancer Center
Pain Center
Brehm Center (Diabetes)
„Macro‟ Use Cases
Funding, Funding, Funding, F-u-n-d-i-n-g
System: RFA targeting
Ad Hoc: Supporting program project applications
RFA Targeting
New funding
announcement,
RFA,
opportunity,
initiative (or
other text)
Mass e-mail,
likely to be
ignored
Targeted, personalized e-mails
Led to design of an automated matching
process using SciVal Funding (currently
being beta tested):
RFA Targeting
Supporting program project applications
Data-gathering challenge at the limits of
„human scale‟• # true/false values = (# investigators2 - # investigators) (# types
of working relationships)
• 100 investigators, 1 type of relationship = 9,900 values
Researcher
1
Researcher
2
Researcher
3
Researcher
4
Researcher
1---
Researcher
2
Co-author: Y
Co-grant: N---
Researcher
3
Co-author: N
Co-grant: N
Co-author: Y
Co-grant: Y---
Researcher
4
Co-author: N
Co-grant: Y
Co-author: N
Co-grant: Y
Co-author: Y
Co-grant: Y---
P30 collaboration network
demonstrates that PI (center)
bridges multiple clusters of
researchers
Supporting program project applications
National Research Networks
Participation in SciVal
Experts community
and national network
search at no additional
cost or effort
SciVal Experts at the Univeristy of Michigan
Future of SciVal Experts at the University of Michigan
University Research Corridor (URC)
-Michigan State University
-Wayne State University
-University of Michigan
Currently in development discussions with business engagement and research groups from all three institutions
Plan is to combine implementations of SciVal Experts across the three institutions, but customized into a business development portal
Organized by strategic initiatives that URC is pursuing, not by organizational structure, such as clean water, stem cell research and more
Scopus-based version beyond the Medical School
Can we do this for the whole University?
Elsevier
Thesauri
16,500 peer-reviewed journals
(including more than 1,200 Open
Access journals)
Extensive conference coverage
(3.6 million conference papers)
600 trade publications
350 book series
Model of publication-driven expertise isn‟t a perfect match for
all departments
Resources
SciVal Experts for the University of Michigan:
http://www.experts.scival.com/umichigan
Elsevier SciVal Experts Info Site:
http://www.info.scival.com/experts
NodeXL
http://nodexl.codeplex.com-Microsoft Research and University Collaborators
-Installs as an Excel 2007+ Template
-Free, easy, and powerful with top-notch visualization