standardizing scholarly output with the vivo ontology
DESCRIPTION
Presented as part of a panel discussion on implementing VIVO and use of the ontology.TRANSCRIPT
The Research Life Cycle
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
The Research Life Cycle: Funding
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
FundRef
NIH Reporter
ScienCV
Biosketches
The Research Life Cycle: Experiment
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
The Research Life Cycle: Collaborate
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
Expertise
SciTS
Mentoring
Research trending
The Research Life Cycle: Publish
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
Universitypublishers
Blogs
The Research Life Cycle: Deposit Data
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
Data repositories
Metadata
The Research Life Cycle
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
VIVO-ISF
Goal:
Create a semantic representation of scholarly activities and products that would enable identification of potential collaborators, relevant resources, and expertise across scientific disciplines
n et w o r k
VIVO-ISF Content and modularization
eagle-iResearch resources
VIVOPerson profiling
CTSA ShareCenterDiscussions, requests,
share documents
VIVO-ISF
PersonContact
OrganizationsAffiliations
RolesEventsServices
Clinical Expertise
ReagentsOrganisms
Credentials
Inclusion or referencing of domain-specific vocabularies in VIVO-ISF
Either utilize external services with stable URIs (e.g. UMLS) or import classes/instances
VIVO-ISF for data integration
The Research Life Cycle: Funding
Three harmonization stories
‘s data
Integrating clinical and basic research expertise data
The Research Life Cycle: Funding
Most collaboration suggestion tools are based on publication and sometimes awarded grant data.
But this often misses clinician collaborators who don’t publish or write grants much
Collecting and publishing expertise by connecting clinical and and research
activities and resources
Step 1Aggregate
Data
Step 2Map Data to
ISF
Step 4Publish Linked
Data
Step 3Compute Expertise
Step 1Aggregate
Clinical Data
Step 2Map Data to
ISF
Step 4Publish Linked
Data
Step 3Compute Expertise
Provider ID ICD Code Value Code CountUnique Patient
Count Code Label
1234567 552.00 1 1Unilateral or unspecified femoral hernia with obstruction (ICD9CM
552.00)
1234567 553.02 8 6Bilateral femoral hernia without
mention of obstruction or gangrene (ICD9CM 553.02)
1234567 555.1 4 1Regional enteritis of large intestine
(ICD9CM 555.1)
1234568 745.12 10 5Corrected transposition of great
vessels (ICD9CM 745.12)
Aggregate data
Step 1Aggregate
Clinical Data
Step 2Map Data to
VIVO-ISF
Step 4Publish Linked
Data
Step 3Compute Expertise
Provider ID ICD Code ValueCode Count
UniquePatient Count Code Label
1234567 552.00 1 1
Unilateral or unspecified femoral
hernia with obstruction (ICD9CM 552.00)
1234567 553.02 8 6
Bilateral femoral hernia without mention of
obstruction or gangrene (ICD9CM 553.02)
1234567 555.1 4 1Regional enteritis of
large intestine (ICD9CM 555.1)
1234568 745.12 10 5Corrected transposition
of great vessels (ICD9CM 745.12)
AggregatedClinical Data
VIVO-ISF
RDFtriples
Java scriptsOWL API
Map Data to VIVO-ISF
Step 1Aggregate
Clinical Data
Step 2Map Data to
ISF
Step 4Publish Linked
Data
Step 3Compute Expertise
Compute Expertise
Step 1Aggregate
Clinical Data
Step 2Map Data to
ISF
Step 4Publish Linked
Data
Step 3Compute Expertise
Linked Data cloud
SPA
RQ
LEn
dp
oin
tsO
the
r A
PIs
…
Triple StoresSeveral means to access and
query data
Publish Linked data
Integrating public and private research profile data
The Research Life Cycle: Funding
Most collaboration suggestion tools are based on publication and sometimes awarded grant data.
But this is old news for Research Administration who wants to plan for what is happening at their institution NOW.
=> Clinical and Translational Activity Reporting tool (CTAR)
Clinical and Translational Activity Reporting tool
The Research Life Cycle: Funding
Funding proposals
Grants & awards
Publications People InstitutionsIRBprotocols
Clinical and Translational Activity Reporting tool
The Research Life Cycle: Funding
See Robin Champieux and our poster entitled:
Ferrets Ontology
FerretsOROntology
=> At inter-institutional level can see interaction between previously unconnected groups via intervening persons/groups at another institution
Integrating research data across institutions
David Eichmannhttp://research.icts.uiowa.edu/polyglot/
Integrating data from 40+ institutionsVIVO, SciVal, LOKI, Profiles, etc.
Mapping all the classes and properties to VIVO-ISF and making the integrated data set available
Classes from:VIVO sites: 480 unique classesProfile sites: 31 unique classes
Domains:vivoweb.orgpurl.orgwww.w3.org xmlns.comwww.findanexpert.unimelb.edu.auvivo.libr.tue.nlpurl.obolibrary.orggriffith.edu.au
Etc.....
Integrating research data across institutions
Mapping predicateshttp://vivoweb.org/ontology/core#hasSubjectArea
8455029http://vivoweb.org/ontology/core#authorInAuthorship
1444239http://orng.info/ontology/orng#hasYouTube
402
Also helps us understand what extensions exist that should be implmeneted centrally
Integrating data from different profiling systems
The Research Life Cycle: Funding
What kinds of questions can we answer?
Who in the southeast has expertise in sleep and does work on mice?
How much collaboration goes on intra versus inter-institutionally based upon all scholarly activities and products?
How can we identify external advisors for an interdisciplinary training program?
What gaps exist in research funding topics across institutions that an institutions may have expertise in?
@ontowonka #vivoisf – tweet me your ideas
We can profile people based on the diversity of their activities and products of research
VIVO-ISF can be used as a standard to integrate research profiling and scholarly contributions across different domains, sources, and systems
Applications such as VIVO, eagle-i, LOKI, Profiles, SciVal/Pure, Symplectic, and ScienCV can exchange data using VIVO-ISF
Realizing these goals is the result of wide community participation and feedback (THANK YOU!)
And… the moral(s) of the stories are:
Working with others
We have an opportunity to engage other communities. Some new activities:
HCLS W3C dataset working group working to describe roles and relationships between people and data (e.g. producer, curator, maintainer, analysis, etc.)
CASRAI-XI contributor roles WG defining roles for people on publications
Converis and CASRAI effort to evaluate how to best use VIVO-ISF to aid CV creation and provide content back to the institutions (and beyond).
ScienCV data model alignment to support data integration
Integration of research data with biological data in the Monarch Initiative and the Neuroscience Information Framework
What are some other opportunities for VIVO-ISF to aid data integration?