This is our presentation for Educause 2012 entitled New Perspectives for Business Intelligence: Library and Research Technologies and Research Collaboration for New Data Models held on Nov 8, 2012.

1. November 7, 2012

2. New Perspectives for Business
Intelligence: Library and Research
Technologies and Research
Collaboration for New Data Models
William Barnett-Indiana University
Robert H. McDonald (@mcdonald)-Indiana University
Mike Winkler (@winkler4)-University of Pennsylvania
Joe Zucca-University of Pennsylvania
November 8, 2012

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http://slidesha.re/
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4. BI Research Engage Page
Take Notes-Ask Questions?
http://bit.ly/RHg0C8

5. Outline
 General Strategies for Research Business
Intelligence in the Academy
 Data Openness/Transparency for Research
Business Intelligence
 Research Business Intelligence Use Cases
 Research Support/Team Science
 Libraries
 Discussion on comprehensive strategies
and needs for Research Business
Intelligance in the academy
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6. Consortia Science
 teste
Birney, Nature 489
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7. Team Science
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8. BI Data Supporting Consortia Science
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9. IU Faculty Profile Mapping
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10. Typical VIVO Data Ingest/Cleaning Workflow
RIS2N3 Components
LOCAL CLIENT SERVER CONTEXT
RIS FILES
VIVO
RIS2N3
Jena
N3
Jena
MySQL
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https://github.com/dgcliff/RIS2N3

11. Varying Views of Research Intelligence Data
Administrator View Researcher View
Research
Intelligence
Development View Team Science View
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12. Types of Systems in the RI Path
 Faculty Profile Systems
 Faculty Annual Review Systems
 Research Profile Systems
 Research Profile System Comparative
Analytics (Peer to Peer)
 Resource Profile Systems
 Research Management Systems
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13. Open Source vs Vended Systems
 Faculty Profile/Networking Systems
 Open Source
 VIVO
 Digital Vita
 Loki
 Harvard Profiles
 CAP/Stanford Profiles
 Vended
 Symplectics (MacMillan part of Digital Science)
 SciVal Experts (Elsevier)
 Pivot (Proquest)
 Research In View (Thomson Reuters)
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14. Data Openness and Transparency
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15. Research Data, Instruments and Resources
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16. Linked Open Data in the Enterprise
 Not at Enterprise Level
 Graph Databases
 NoSQL Stacks
 Semantic Triple Store
Systems
 Data Policy/
 Governance
 Public Profiles
 Faculty
 Resources
 Instruments
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17. Thank you!
Robert H. McDonald
rhmcdona@indiana.edu
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18. Business Intelligence in
Translational Research:
Research Networking as a
Test Case
William Barnett, Indiana University

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20. How Traditional Research Works…
Get Funding
Write Proposal Do Research
Publish Results
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21. How Traditional Medical Research Works…
Basic Pre-clinical Clinical
Pharma
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22. How CTSAs want Translational Research to Work
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23. Why Research Networking?
Translational research is a team sport
1. Investigators don’t know of potential collaborators in their
institutions to improve research
2. Investigators don’t know of complementary investigators or
opportunities to make their projects more competitive.
3. Investigators don’t know of partners to cross translational
boundaries.
4. Investigators don’t know of non-research partners
(industry, public sector, public) needed for trials
recruitment, implementation, or commercialization
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24. What is Research Networking?
1. An approach that strives to help overcome barriers by
connecting people to undertake translational research
2. Institutional repositories to manage rich faculty profiles of
grants, publications, classes, etc. and expose them publicly.
3. An information model based on individuals and cohorts.
4. A national federated architecture of Linked Open Data that
can connect these repositories.
5. Applications that consume these profile data to accomplish
translational goals
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25. NIH Investments in Research Networking
 VIVO – a project to develop an ontology and
architectural standards to create, manage, and
share rich faculty profile information.
 Eagle-I – a project to develop an ontology and
architectural standards to create, manage, and
share rich resource profile information
 CTSAConnect – a project to create an
integrated ontology to connect
faculty, resource, and other data
It’s all about Linked Open Data…
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26. What are Big Challenge Use Cases in
Translational Research?
1. Finding Funding
2. Recruiting Volunteers for Clinical Trials
3. Creating Translational Teams/Processes
4. Education and Training
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27. How Does NIH measure Translational Success?
 Logic Model from each CTSA, documented as
XML files, exported to NIH annually.
 Logic Model is:
 Activities – things that happen
 Outcomes – science that results from the things
that happen
 Impacts – what good comes of the science that
comes from the things that happen
This is what we’ll use today…
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28. How can Research Networking Help
Find Funding?
By matching investigators with funding
opportunities
 Activities – Community of Science and SciVal Funding
commercial applications potentially provide better
funding matches
 Outcomes – unknown if they are any better than
traditional means
 Impacts – unclear if there is any differentiation
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29. How can Research Networking Help
Recruiting Volunteers?
By matching researchers with community
groups and volunteers
 Activities – A few initial attempts to start
developing VIVO-like profiles of community
groups
 Outcomes - None
 Impacts - None
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30. How can Research Networking Help
Creating Translational Teams?
With Applications that are used to discover complimentary and
next step collaborators
 Activities – Many faculty profile systems developed and
implemented and one national pilot, direct2experts.org, has
been launched
 Outcomes – some CTSAs show increased activity among
groups that have not collaborated before
 Impacts – some new teams and multi-team systems have
begun to form. Unclear of link to profile systems.
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31. Direct2Experts
• 44 Institutions (at
present).
• Returns summary
numbers by
institution.
• Finding individuals
is a manual
institution-by-
institution basis.
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32. CTSAConnect (ctsaconnect.org)
A semantic framework that will facilitate the production and
consumption of Linked Open Data about
investigators, physicians, biomedical research
resources, services, and clinical activities. Use cases:
 Team Formation
 Cross-Institutional Collaboration
 Evaluation and Reporting
But… Where are the applications?
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33. How can Research Networking Help
Education and Training?
Mentor matching and MD – PhD awareness
 Activities - mentoring has been happening
and translational education programs have
sprung up without these systems
 Outcomes – no use of Research Networking
 Impacts – none.
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34. Other BI Roles for Research Networking?
 Investigators
 Automated CV generation, particularly for
center grants
 Research topic trend analysis
 Administrators
 Competitive landscape review
 Productivity assessments for tenure, etc.
 Research
 Network Science and Science of Team Science
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35. Rich profiles could help find
collaborators, subjects, resources, mentors, a
nd funding and pursue
other, undetermined, great things.
There are business models that can help
sustain an institutional strategy
Challenges: applications that deliver
value, policy, data quality, and momentum.
It is early yet, and this has so Tweet us: #EDU12 #busintel #E12_SESS113
far been a
technology looking for a problem to solve.

36. Thank you!
Bill Barnett
barnettw@iu.edu
Thanks to Dave Eichmann for reviewing an early version of this presentation!
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37. Mike Winkler | Joe Zucca
University of Pennsylvania Libraries

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41. ’
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50. Srvice Genre
Library
Cognzt Staff
Parameters
Orgn’l Unit
User &
Program Budget cntr
Parameters
College | Dept Bibliographic
Parameters
Rank
Course
Title
Host College Date | Time URI
Host Dept Location Format
Environmental
Instructor IP Address
Parameters Cost| Supplr
Grant Spnsr URL
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53. ’
Analytics
Repository
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58. Questions?
 Online – Tweet them to us:
 #EDU12 #busintel
 Or write them into our engage page
 http://bit.ly/RHg0C8 (open googledoc)
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60. MetriDoc Tiered Architecture
Abstracts 4 key functions, exposes interfaces for interoperability
1. Extract 2. Transform 3. Load 4. Query
Target Source,
Resolution Results
e.g. Relais,
Sources Document
Illiad, ILS
e.g. IdM, WorldCat
Ingest Log Query Srvc
Resolve User
Parse Codes & IDs Interface
Normalize Data
Format Repo
Refined Refined
output output
Local
Query Data
Document Stores
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