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Standardizing scholarly output
Melissa Haendel
haendel@ohsu.edu
@ontowonka
VIVO 2014
Austin
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 tr...
The Research Life Cycle: Publish
EXPERIMENT
COLLABORATE
PUBLISHDEPOSIT DATA
FUND
University
publishers
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 ...
VIVO-ISF Content and modularization
eagle-i
Research resources
VIVO
Person profiling
CTSA ShareCenter
Discussions, request...
Inclusion or referencing of domain-
specific vocabularies in VIVO-ISF
Either utilize external services with stable URIs (e...
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 tool...
Collecting and publishing expertise by
connecting clinical and and research
activities and resources
Step 1
Aggregate
Data...
Step 1
Aggregate
Clinical Data
Step 2
Map Data to
ISF
Step 4
Publish Linked
Data
Step 3
Compute
Expertise
Provider ID ICD ...
Step 1
Aggregate
Clinical Data
Step 2
Map Data to
VIVO-ISF
Step 4
Publish Linked
Data
Step 3
Compute
Expertise
Provider ID...
Step 1
Aggregate
Clinical Data
Step 2
Map Data to
ISF
Step 4
Publish Linked
Data
Step 3
Compute
Expertise
Compute Expertise
Step 1
Aggregate
Clinical Data
Step 2
Map Data to
ISF
Step 4
Publish Linked
Data
Step 3
Compute
Expertise
Linked Data
clou...
Integrating public and private research
profile data
The Research Life Cycle: Funding
Most collaboration suggestion tools
...
Clinical and Translational Activity
Reporting tool
The Research Life Cycle: Funding
Funding
proposals
Grants &
awards
Publ...
Clinical and Translational Activity
Reporting tool
The Research Life Cycle: Funding
See Robin Champieux and our poster ent...
Ferrets Ontology
Ferrets
OR
Ontology
=> At inter-institutional
level can see interaction
between previously
unconnected gr...
Integrating data from 40+ institutions
VIVO, SciVal, LOKI, Profiles, etc.
Mapping all the classes and properties to VIVO-I...
Integrating data from different
profiling systems
The Research Life Cycle: Funding
What kinds of questions can we answer?
...
 We can profile people based on the diversity of their
activities and products of research
 VIVO-ISF can be used as a st...
Working with others
We have an opportunity to engage other communities.
Some new activities:
 HCLS W3C dataset working gr...
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Standardizing scholarly output with the VIVO ontology

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Presented as part of a panel discussion on implementing VIVO and use of the ontology.

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  • This shows the use-cases for URIs that don’t fall under the typical OWL class/individual modeling of data. There is a need for an agreed on set of codes, concepts, types, etc. of things in addition to classes and individuals. It is also just another perspective on the domain where there is frequently a need to talk about a whole set (an OWL class) as if it is a single primitive thing (an instance) and SKOS is a formalization of this idea.
  • These codes come from billing data, and are an example of one kind of data that can be aggregated using the ISF.
  • Aggregated encounter data are mapped to the ISF clinical module using Java scripts based on OWL API to generate RDF data
  • Person activities and products of research all can be used to represent expertise and link clinical and basic expertise.
    Use of ISF will enable integration with multiple datasets to discover useful clinical associations and patterns
  • The key point here is that the connections we can now see in inter-institutional collaboration, using publications as evidence, can be leveraged to target the ontological coverage at an individual site, establishing joint interests by investigators/communities based upon methods, materials, instruments, etc. – other ways of connecting peopole

    At interinstituional level we can see interaction between previously unconnected groups via intervening persons/groups at another institution

    Expanded representation
    expands connections
    • currently sites
    • true payoff – concept
    coverage expansion


  • Transcript of "Standardizing scholarly output with the VIVO ontology"

    1. 1. Standardizing scholarly output Melissa Haendel haendel@ohsu.edu @ontowonka VIVO 2014 Austin
    2. 2. The Research Life Cycle EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND
    3. 3. The Research Life Cycle: Funding EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND FundRef NIH Reporter ScienCV Biosketches
    4. 4. The Research Life Cycle: Experiment EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND
    5. 5. The Research Life Cycle: Collaborate EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND Expertise SciTS Mentoring Research trending
    6. 6. The Research Life Cycle: Publish EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND University publishers Blogs
    7. 7. The Research Life Cycle: Deposit Data EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND Data repositories Metadata
    8. 8. The Research Life Cycle EXPERIMENT COLLABORATE PUBLISHDEPOSIT DATA FUND VIVO-ISF
    9. 9. Goal: Create a semantic representation of scholarly activities and products that would enable identification of potential collaborators, relevant resources, and expertise across scientific disciplines net w o r k
    10. 10. VIVO-ISF Content and modularization eagle-i Research resources VIVO Person profiling CTSA ShareCenter Discussions, requests, share documents VIVO-ISF Person Contact Organizations Affiliations Roles Events Services Clinical Expertise Reagents Organisms Credentials
    11. 11. Inclusion or referencing of domain- specific vocabularies in VIVO-ISF Either utilize external services with stable URIs (e.g. UMLS) or import classes/instances
    12. 12. VIVO-ISF for data integration The Research Life Cycle: Funding Three harmonization stories ‘s data
    13. 13. 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
    14. 14. Collecting and publishing expertise by connecting clinical and and research activities and resources Step 1 Aggregate Data Step 2 Map Data to ISF Step 4 Publish Linked Data Step 3 Compute Expertise
    15. 15. Step 1 Aggregate Clinical Data Step 2 Map Data to ISF Step 4 Publish Linked Data Step 3 Compute Expertise Provider ID ICD Code Value Code Count Unique Patient 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 1 Regional enteritis of large intestine (ICD9CM 555.1) 1234568 745.12 10 5 Corrected transposition of great vessels (ICD9CM 745.12) Aggregate data
    16. 16. Step 1 Aggregate Clinical Data Step 2 Map Data to VIVO-ISF Step 4 Publish Linked Data Step 3 Compute Expertise Provider ID ICD Code Value Code Count Unique Patient 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 1 Regional enteritis of large intestine (ICD9CM 555.1) 1234568 745.12 10 5 Corrected transposition of great vessels (ICD9CM 745.12) Aggregated Clinical Data VIVO-ISF RDF triples Java scripts OWL API Map Data to VIVO-ISF
    17. 17. Step 1 Aggregate Clinical Data Step 2 Map Data to ISF Step 4 Publish Linked Data Step 3 Compute Expertise Compute Expertise
    18. 18. Step 1 Aggregate Clinical Data Step 2 Map Data to ISF Step 4 Publish Linked Data Step 3 Compute Expertise Linked Data cloud SPARQL Endpoints OtherAPIs … Triple Stores Several means to access and query data Publish Linked data
    19. 19. 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)
    20. 20. Clinical and Translational Activity Reporting tool The Research Life Cycle: Funding Funding proposals Grants & awards Publications People Institutions IRB protocols
    21. 21. Clinical and Translational Activity Reporting tool The Research Life Cycle: Funding See Robin Champieux and our poster entitled:
    22. 22. Ferrets Ontology Ferrets OR Ontology => At inter-institutional level can see interaction between previously unconnected groups via intervening persons/groups at another institution Integrating research data across institutions David Eichmann http://research.icts.uiowa.edu/polyglot/
    23. 23. Integrating data from 40+ institutions VIVO, 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 classes Profile sites: 31 unique classes Domains: vivoweb.org purl.org www.w3.org xmlns.com www.findanexpert.unimelb.edu.au vivo.libr.tue.nl purl.obolibrary.org griffith.edu.au Etc..... Integrating research data across institutions Mapping predicates http://vivoweb.org/ontology/core#hasSubjectArea 8455029 http://vivoweb.org/ontology/core#authorInAuthorship 1444239 http://orng.info/ontology/orng#hasYouTube 402 Also helps us understand what extensions exist that should be implmeneted centrally
    24. 24. 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
    25. 25.  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:
    26. 26. 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?
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