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Research Discovery, Social Networks and
                VIVO
              Chicago, October 8, 2012



                 Michael Conlon, PhD
      Clinical and Translational Science Institute
                  University of Florida
Learned a field, learned the
scientific method, did science
Research Process 1972




Hypothesis    Experiment   Publication
Science and Scientists
Research Process 2012
             Create
                        Augment
             Virtual
Hypothesis                Data
             Organi-
                        Systems
             zation




                        Conduct
               Get
 Consults               Experi-
             Funded
                         ments




  Assemble    Write      Publish   Archive
   Team      proposal    results    data
Six Trends
1. Rise of the small
PubMed new publications per year




                                   2. Increasing volume
                                              of science
3. Increasing complexity of scientific problems




The Problems Got Hard
Data Got Big (IDR Slide)




          4. Data reuse grows
5. Competition rises
 2011 Shanghai ranking of world universities
http://en.wikipedia.org/wiki/Academic_Ranking_of_World_Universities




          Lots of Competitors
6. Internet disintermediation of science continues
How can we know
      what is going on,
        build teams,
solve data-driven problems?
Research Discovery


             Research
             Discovery
… And the connections
   between them
VIVO: Data, Tools and
    Community
Simple tools for presentation,
      traversal, search
Ontology Process
ScienceMap. Examine collections of publications for
individuals, work groups, institutions
Software reads VIVO
                                                                        RDF and displays




                     processOrg<-function(uri){
                       x<-xmlParse(uri)
                       u<-NULL
                       name<-xmlValue(getNodeSet(x,"//rdfs:label")[[1]])
                       subs<-getNodeSet(x,"//j.1:hasSubOrganization")
                       if(length(subs)==0) list(name=name,subs=NULL)
                       else {
                         for(i in 1:length(subs)){
                           sub.uri<-
                     getURI(xmlAttrs(subs[[i]])["resource"])
                           u<-c(u,processOrg(sub.uri))
                         }
                         list(name=name,subs=u)
                       }
                     }




VIVO produces both
  HTML and RDF
Co-Author Network
                   Chris McCarty, Assoc. Prof. & Raffaele Vacca
200                                                                                                                         201
8                                                                                                                           2




                                    •   Data source: Thomson Reuters via UF VIVO
                                    •   Each node represents one author
                                    •   Nodes are sized by Total Publications and linked by a common VIVO publication URI


      Main Component: what changes have occurred between 2008 and 2012?
      • In 2008 the network is split into two groups of approximately the same size – CTSI/HSC versus everything else
      • In 2012 the network consists of one big connected region, with the CTSI acting as a broker between several more
        marginal subgroups
      • In 2012 more of the authors with the highest number of publications are under the CTSI umbrella (note that 2012
        publications data are incomplete)
Co-Funded Network
          Chris McCarty, Assoc. Prof. & Raffaele Vacca
200                                                                                                                             201
8                                                                                                                               2




                 •    Data source: UF Division of Sponsored Research (DSR) database
                 •    Each node represents one Contract PI, Project PI or Co-PI linked by a common PeopleSoft Contract number
                 •    Nodes are sized by Total Awarded in UF fiscal year (July-June)


      Main Component: what changes have occurred between 2008 and 2012?
      •   More of Health Science Center comes under the CTSI umbrella
      •   The CTSI has a broader reach in the whole network
      •   Increasingly the CTSI incorporates all researchers in relevant areas (areas not relevant to CTSI
          research fields naturally remain out of its network)
Augmenting data
VIVO Searchlight
Collaboration and Coordination
•   At UF – Libraries, CTSI, AHC IT, Office of Research, Enterprise Systems,
    Registrar, Business Services
•   Federal – OSTP, NIH, NLM, NSF, USDA, EPA, FDA, NASA, FDP, …
•   Partners -- Symplectic, Pivot, Elsevier, Thomson-Reuters, ORCID,
    CiteSeer, CrossRef, OCLC, DuraSpace, CNI, Total-Impact, …
•   Ontology– EuroCRIS, CASRAI, NCBO, Eagle-I, CTSAconnect, …
•   Professional Societies – APA, AAAS, AIRI, AAMC, ABRF, …
•   International – Australia, China, Netherlands, UK, Canada, Brazil, …
•   Semantic Web community – DERI, Tim Berners-Lee, MyExperiment,
    Concept Web Alliance, Open Pharma Space (EU), Linked Data, …
•   Social Network Analysis Community – Northwestern, Davis, UCF, …
•   Universities -- Melbourne, Duke, Penn, Colorado, Eindhoven, Pittsburgh,
    Leicester, Cambridge, Stony Brook, Weill, Indiana, Scripps, Washington U,
    Ponce, Northwestern, Iowa, Harvard, UCSF, Florida, Stanford, MIT, Brown,
    Johns Hopkins, OHSU, Minnesota, and the CTSA consortium
•   Application and service providers – over 100
•   Software downloads (over 30,000) and contact list (over 1,600)
Community
4th Annual VIVO Conference
August 14-15, 2013
St. Louis, Missouri, USA
http://vivoweb.org/conference

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Research Discovery, Social Networks and VIVO

  • 1. Research Discovery, Social Networks and VIVO Chicago, October 8, 2012 Michael Conlon, PhD Clinical and Translational Science Institute University of Florida
  • 2. Learned a field, learned the scientific method, did science
  • 3. Research Process 1972 Hypothesis Experiment Publication
  • 5. Research Process 2012 Create Augment Virtual Hypothesis Data Organi- Systems zation Conduct Get Consults Experi- Funded ments Assemble Write Publish Archive Team proposal results data
  • 7. 1. Rise of the small
  • 8. PubMed new publications per year 2. Increasing volume of science
  • 9. 3. Increasing complexity of scientific problems The Problems Got Hard
  • 10. Data Got Big (IDR Slide) 4. Data reuse grows
  • 11. 5. Competition rises 2011 Shanghai ranking of world universities http://en.wikipedia.org/wiki/Academic_Ranking_of_World_Universities Lots of Competitors
  • 12. 6. Internet disintermediation of science continues
  • 13. How can we know what is going on, build teams, solve data-driven problems?
  • 14. Research Discovery Research Discovery
  • 15. … And the connections between them
  • 16. VIVO: Data, Tools and Community
  • 17. Simple tools for presentation, traversal, search
  • 19.
  • 20. ScienceMap. Examine collections of publications for individuals, work groups, institutions
  • 21. Software reads VIVO RDF and displays processOrg<-function(uri){ x<-xmlParse(uri) u<-NULL name<-xmlValue(getNodeSet(x,"//rdfs:label")[[1]]) subs<-getNodeSet(x,"//j.1:hasSubOrganization") if(length(subs)==0) list(name=name,subs=NULL) else { for(i in 1:length(subs)){ sub.uri<- getURI(xmlAttrs(subs[[i]])["resource"]) u<-c(u,processOrg(sub.uri)) } list(name=name,subs=u) } } VIVO produces both HTML and RDF
  • 22. Co-Author Network Chris McCarty, Assoc. Prof. & Raffaele Vacca 200 201 8 2 • Data source: Thomson Reuters via UF VIVO • Each node represents one author • Nodes are sized by Total Publications and linked by a common VIVO publication URI Main Component: what changes have occurred between 2008 and 2012? • In 2008 the network is split into two groups of approximately the same size – CTSI/HSC versus everything else • In 2012 the network consists of one big connected region, with the CTSI acting as a broker between several more marginal subgroups • In 2012 more of the authors with the highest number of publications are under the CTSI umbrella (note that 2012 publications data are incomplete)
  • 23. Co-Funded Network Chris McCarty, Assoc. Prof. & Raffaele Vacca 200 201 8 2 • Data source: UF Division of Sponsored Research (DSR) database • Each node represents one Contract PI, Project PI or Co-PI linked by a common PeopleSoft Contract number • Nodes are sized by Total Awarded in UF fiscal year (July-June) Main Component: what changes have occurred between 2008 and 2012? • More of Health Science Center comes under the CTSI umbrella • The CTSI has a broader reach in the whole network • Increasingly the CTSI incorporates all researchers in relevant areas (areas not relevant to CTSI research fields naturally remain out of its network)
  • 24.
  • 27. Collaboration and Coordination • At UF – Libraries, CTSI, AHC IT, Office of Research, Enterprise Systems, Registrar, Business Services • Federal – OSTP, NIH, NLM, NSF, USDA, EPA, FDA, NASA, FDP, … • Partners -- Symplectic, Pivot, Elsevier, Thomson-Reuters, ORCID, CiteSeer, CrossRef, OCLC, DuraSpace, CNI, Total-Impact, … • Ontology– EuroCRIS, CASRAI, NCBO, Eagle-I, CTSAconnect, … • Professional Societies – APA, AAAS, AIRI, AAMC, ABRF, … • International – Australia, China, Netherlands, UK, Canada, Brazil, … • Semantic Web community – DERI, Tim Berners-Lee, MyExperiment, Concept Web Alliance, Open Pharma Space (EU), Linked Data, … • Social Network Analysis Community – Northwestern, Davis, UCF, … • Universities -- Melbourne, Duke, Penn, Colorado, Eindhoven, Pittsburgh, Leicester, Cambridge, Stony Brook, Weill, Indiana, Scripps, Washington U, Ponce, Northwestern, Iowa, Harvard, UCSF, Florida, Stanford, MIT, Brown, Johns Hopkins, OHSU, Minnesota, and the CTSA consortium • Application and service providers – over 100 • Software downloads (over 30,000) and contact list (over 1,600)
  • 29. 4th Annual VIVO Conference August 14-15, 2013 St. Louis, Missouri, USA http://vivoweb.org/conference

Editor's Notes

  1. Learned a field, learned the scientific methods, did science, wrote papers
  2. Science and scientists. Biochemistry at Stanford. They look happy.
  3. The rise of molecular medicine
  4. The rise of the molecular. Personalized medicine. Full base pair sequencing. Mars Curiosity lander. Nanotechnoloy. Metabolomics.
  5. Rapid increase in volume of scientific output. Brazil, Russia, India, China.
  6. More difficult problems. We work to cure cancer. Risk factors, genetics, metabolics, surgical procedures, radiology, chemotherapy, life style changes.
  7. Data got big. Terabytes, Petabytes, Exabytes.
  8. Competition got stiffer
  9. Internet speed and disintermediation. Expectations changing, openness, commonality, reuse, altmetrics (“downloads and citations”)
  10. Research Discovery. What is going on? Where? By Whom?
  11. Sounds, common data models for the things of science and the connections between them. Many kinds of connections between each type of object. Objects have significant complexity. What do we mean by “project” – a human subject study? A clinical trial?
  12. So that’s what we are doing. Open software, community and data model for research discovery. Model people, data, projects, papers, etc. Work across boundaries. Sponsor supported.
  13. So here’s the simple view – a faculty profile. Assembled by machines. Can be finished off by the faculty member. All links are to other objects in the semantic web. Positions, visualizations, organizations, people, web sites. Navigation via search, facets, link traversal. Note the RDF link for techno guys.
  14. A fragment of the VIVO ontology. Open ontology process. Working group. Plug-in ontologies (BIBO, SKOS, FOAF) support local extension. Terminology extensions.
  15. Tools for discovering research. Here, sample of University of Florida publications plotted on the UCSD science.
  16. The open architecture of VIVO and linked data supports development of applications outside VIVO that consume VIVO data. VIVO data is accessible via HTML (for humans) and RDF (for machines). Simple software can process the RDF resulting in powerful cross-site applications. The figure depicts the organization of the University of Florida.
  17. Mention that 2012 data come from only half of the year. As a consequence: (1) Less nodes in 2012 network (less authors in 2012 data); (2) Node sizes smaller on average in 2012 (less publications in 2012 data)
  18. Vivosearch (beta) indexes vivo sites and provides faceted search across the collection with linking to individual objects
  19. VIVO searchlight. For any page on the web, find people whose work is similar to a page you are reading.
  20. We are going to need a community of people who share a common interest in research discovery.