Scholars@Cornell: Visualizing the Scholarship data

1. Scholars@Cornell: Visualizing the Scholarship Data
Muhammad Javed and Sandy Payette
Tech. Lead
August 04, 2017 (VIVO’17)
Project Lead

2. No Scientific Presentation

3. of the
“Dare to _____”
Cornell VIVO

4. Where I came from

5. VIVO in 2004/05

6. VIVO in 2016/17

7. Data in Cornell VIVO

8. SomewhereDups
Cornell VIVO

9. EverywhereLists
Cornell VIVO

10. Dare to
Question!

11. Q: Lists - are they useful ?
Q: How do we keep the data clean and complete ?
Dare to Question
Q: Is the content in VIVO easily searchable ?
Q: Can we extract any implicit knowledge from the
given data?
and more…

12. Data
Size/Type Quality
Update
- Publication data > 60K
- Grants data > 2K
- People > 3.5K
- Clean
- Complete
- Things vs Strings
- All of the above
- Batch
- Daily
- Real time
- Stream
VIVO Data: The Value Proposition
Analysis
- What questions we can answer?

14. • What are the hot research areas?
• Who is collabora0ng with Whom ?
Internal/Global
• Top Journals where faculty publish?
• in last one year
• in last five years
• for College of Engineering
• overall
We believed that,
we can extract implicit knowledge graphs from the given clean data!
• What are research outputs of an organiza0on?
(faculty repor0ng)
• Who are the domain experts in what area?
• What the top funding agencies for a College?
• Are they missing some?
• Who Co-authored, how oJen?

15. Data to Knowledge Germination
Global Collaboration (GC) Map Person-2-Subject Area Network MapKeyword Cloud CoAuthorship Wheels
Fingerprints/Domain expertise
of a Faculty
CrossUnit/InterDept
CoAuthorships b/w Faculty
Global
Collaborations
Research Interests
of a Faculty
Article Journal
Keywords
Abstract/
Subject Area
Classification
Author
Affiliation
Position
Organization
MeSH

16. Scholars@Cornell
scholars.cornell.eduvivo.cornell.edu
Going away soon Live Now

17. Analyses & My Hats
UI / UX Analysis
Analyses
Requirement
Analysis
Gap
Analysis
Data
Analysis
Analyses
VIVO OntologyOntology/Data Model Analysis
Gap
Analysis
Data Curation &
Work arounds
Find the Missing Piece

18. Where I am at…
Scholars@Cornell

19. FRESH START
VIVO Framework
Feed Machine
&
Symplectic Elements
Data Distribution API
User Interface
Scholars@Cornell
Visualizations
data generation

20. • Data Quality is a high priority.
•Pages in Scholars@Cornell are non-editable.
•A limited editing can be done in Symplectic Elements.
•No manually entered data, neither by faculty nor by any
curator.
•Present only what we can assert or infer from research
data.
•Used D3 Visualization to present the inferred
knowledge.
Some other notes:

21. DEMO

22. Find a Domain Expert across institution

23. Global Collaborations of the Academic Units

24. Research Interests of a department

25. Research Keywordsof a faculty

26. Internal Collaborationsof the Academic Units

27. Where I am
going…

28. Near Future Plans
• Current focus was on Journal Article category. Next step
is to analyze and model other types of publications.
• Data Modeling between a published article and the
Working paper/Preprint.
• Viz. Data Download (in .svg, .json etc.).
• Embedding Visualizations in different academic unit web
pages.
• Exploring more Data -> Knowledge Case Scenarios..

29. More aggregate views

30. &
@ScholarsCornell