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Slide 1 - Coalition for Networked Information



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  • Target Audience: Potential VIVO users, local faculty members, administrators, public audiencesPresentation Goals: Provide a general overview of the VIVO project. Describe how VIVO will be implemented at a local level.Provide general information about VIVOs main features, functionalities, and advantages.
  • As research effort become more interdisciplinary, it can be hard to find collaborators outside your own area of expertise. VIVO enables collaboration and understanding across an institution and among institutions – and not just for scientists.
  • Goal of VIVO: Improve all of science by providing the means for sharing and using current, accurate, and precise information regarding scientists’ interest, activities, and accomplishments. Foster team science by providing tools for identifying potential collaborators. Improve collaboration by creating tools using this information for enhancing new and existing teams. Not limited to science – at Cornell, VIVO covers all disciplines across the entire institution
  • Profiles vs. web pages/social sites: richer content & more explicitProfiles are richer in content and much more complete and explicit than typical [web pages or] social networking sites
  • VIVO is useful to many different users and audiences both within and outside of the participating institutions.VIVO can result in increased grant and donor funding. For instance, the Cornell Alumni Affairs and Development Office was contacted by a major company interested in making research funds available to scientists working on issues related to urology; a search in VIVO-Cornell quickly returned several faculty in diverse disciplines working in this area, translating to real dollars.
  • Since VIVO stores profile information drawn from a variety of sources in a single, flexible format, it can be easily “re-skinned” or “re-purposed” to present specialized views into the institution.
  • Authoritative data, diverse formats, filter out private informationTalk about verified dataTalking points: Much of the data in VIVO profiles is ingested from authoritative sources so it is accurate and current, reducing the need for manual input.Private or sensitive information is never imported into VIVO. Only public information will be stored and displayed. Data is housed and maintained at the local institutions. There it can be updated on a regular basis. There are three ways to get data: internal, external, individuals. Internal is authoritative!The rich information in VIVO profiles can be repurposed and shared with other institutional web pages and consumers, reducing cost and increasing efficiencies across the institution.
  • Talking points: Simple formatRead and write standard data and ontologiesLeverage open source and now commercial toolsMore and more data available on the Web (e.g., Bio2RDF)
  • Feel for the VIVO ontology
  • Search results are faceted so information can be located rapidly and with less time spent sorting through information.
  • Emphasis these are links, not just text. Click on the name and it will take you to his profile.
  • This search on homeostasis reveals the departments and interdisciplinary nature of the research. This slide demonstrates that someone in Molecular Genetics might be a good person to work with!Note: refine by facets
  • Each statement is a complete fact unto itself.
  • The “Hendler Hypothesis” – a little semantics goes a long way
  • VitroAll-in-1 ontology editing, data management, and public displayIntegrates data to display on the Web or share as feedsVIVOVisually themed and branded for hosting institutionOntology optimized for discovering people and researchVIVOwebHarvests exposed VIVO data at 1 or more points of shared discoveryCapable of pulling in RDF data from sources other than VIVO
  • Talking points: Much of the data in VIVO profiles is ingested from authoritative sources so it is accurate and current, reducing the need for manual input.The rich information in VIVO profiles can be repurposed and shared with other institutional web pages and consumers, reducing cost and increasing efficiencies across the institution. Private or sensitive information is never imported into VIVO. Only public information will be stored and displayed. Data is housed and maintained at the local institutions. There it can be updated on a regular basis. Search results are faceted so information can be located rapidly and with less time spent sorting through information.
  • The VIVO application was originally developed at Cornell University in 2003. UF implemented in 2007 and in sept 2009 Cornell, UF and five partner institutions received the $12 million stimulus grant from the NIH to enable national networking of researchersAll 7 institutions have VIVO installed and data is stored and maintained locally by the institution
  • Because the data is: RDF compliantData is openWe have a core ontology that all VIVOs conform with – making the data interoperable and consistentAnd enables networking on a national scaleWe will continue to build the network
  • Through varying types of participation.Adoption -Institutions (columbia, nw), consortias (CTSAs) and federal agencies can install VIVO locally. If institutions already have another platform – they can also participate by sharing their semantic web-compliant data with the VIVO network.Participation can also include the provision of data. Whereas VIVO enables networking of researchers, Eagle-I enables resource discovery. Each resource may have a “creater” and, thus, has the potential to be linked to a researcher profile. Database aggregators and professional societies are also curators of data. Open access systems (such as PubMed) or subscription databases (such as Scopus/Web of Science), or even Collexis (a similar resource to VIVO) can participate through the provision of dataCollexis has a separate system called biomed experts which is a profile system – and VIVO data can be added to those profiles – and also has a large amount of disambiguated publications extracted from pubmed which can be purchased for a local VIVO system.
  • Additional participation can include application developers such as DERI or Digital Vita, who find ways to extract and reuse VIVO dataLastly, as data is linked open data, search engines, professional societies and other producers of web-compliant data can participate through VIVO data consumption
  • There are a number of challenges VIVO faces in facilitating adoption.What researchers want from VIVO can be very different from what institutions wantAt UF researchers are interested in CVs, biosketches and collaborations. UF administration are more focused on faculty reporting needs – although they are also interested in increasing interdisciplinary collaborations. Finding common ground is important as we build the VIVO network.Additionally, VIVO was developed primarily for a researching community – which has been enthusiastic and willing to participate. Enthusiasm may vary based on where a researcher is at in their career, but VIVO is generally welcomed.As we expand to the biomedical community, we are finding that the clinical community is driven by different business needs. Looking at this graph of adoption trends – Innovators and early adopters are on the front end; and a small amount of laggards on the back in, all of which tend to be excited about new technologies – regardless of marketing. In the middle of this graph, however, we have early and late majority, which includes 68% of potential adopters. Our challenge is to prove to people that it’s going to last and the return they are investing is worth the effort.One of the advantages of VIVO is the linkage with local data sources – reducing the effort of individuals who want to use VIVO.Reaching their needs is crucial to VIVO’s success.
  • VIVO chooses to meet these participation challenges with the help of libraries – neutral campus entities that understand their user communities and their research environment.Libraries are increasingly involved in IT decisions – in fact my first librarian position was in a merged library/IT department.Libraries have subject experts. While I work closely with VIVO at UF, I also provide reference and instructional support to the agricultural sciences. I know what it takes to facilitate adoption within my agricultural science community.Librarians have good knowledge of their user communities – they understand what drives their institution’s research environment.Of course, librarians have a strong service ethic and have a long history of providing academic supportFacilitate the resolution of data integration issues endemic to legacy data/faculty reporting systems.
  • So what do librarians DO?Identification of content types – ontology ;development and interface refinementWe negotiate with the owners of local data sources to explain what data we need and why we need it.We provide local and national support and training through the development of documentation, presentations and help-desk servicesUsing the website we liaise with potential collaborators – giving demos and answering questionsCreating a community of supportAnd delivering usability feedback to the technical team.Marketing – through demonstrations, conferences, workshops and the development PR materials designed both to attract new participants and to assist participants with local adoption
  • Our assumption is that if we provide strong user support and iterative user testing that will faciliatate participation – because will have confidence that it is being supported.


  • 1. D. Krafft, V. Davis: Presenters
    Co-Authors: J. Corson-Rikert, M.Conlon, M. Devare, B.Lowe, B.Caruso, K.Börner, Y.Ding, L.McIntosh; M. Conlon; and
    VIVO Collaboration*
  • 2. VIVO Collaboration:
    Cornell University: Dean Krafft (Cornell PI), Manolo Bevia, Jim Blake, Nick Cappadona, Brian Caruso, Jon Corson-Rikert, Elly Cramer, Medha Devare, John Fereira, Brian Lowe, Stella Mitchell, Holly Mistlebauer, AnupSawant, Christopher Westling, Rebecca Younes. University of Florida: Mike Conlon (VIVO and UF PI), Cecilia Botero, Kerry Britt, Erin Brooks, Amy Buhler, Ellie Bushhousen, Chris Case, Valrie Davis, Nita Ferree, Chris Haines, Rae Jesano, Margeaux Johnson, Sara Kreinest, Yang Li, Paula Markes, Sara Russell Gonzalez, Alexander Rockwell, Nancy Schaefer, Michele R. Tennant, George Hack, Chris Barnes, Narayan Raum,  Brenda Stevens, Alicia Turner, Stephen Williams. Indiana University: Katy Borner (IU PI), William Barnett, Shanshan Chen, Ying Ding,  Russell Duhon, Jon Dunn, Micah Linnemeier, Nianli Ma, Robert McDonald, Barbara Ann O'Leary, Mark Price, Yuyin Sun, Alan Walsh, Brian Wheeler, Angela Zoss. Ponce School of Medicine: Richard Noel (Ponce PI), Ricardo Espada, Damaris Torres.  The Scripps Research Institute: Gerald Joyce (Scripps PI), Greg Dunlap, Catherine Dunn, Brant Kelley, Paula King, Angela Murrell, Barbara Noble, Cary Thomas, Michaeleen Trimarchi. Washington University, St. Louis: Rakesh Nagarajan (WUSTL PI), Kristi L. Holmes, Sunita B. Koul, Leslie D. McIntosh. Weill Cornell Medical College: Curtis Cole (Weill PI), Paul Albert, Victor Brodsky, Adam Cheriff, Oscar Cruz, Dan Dickinson, Chris Huang, Itay Klaz, Peter Michelini, Grace Migliorisi, John Ruffing, Jason Specland, Tru Tran, Jesse Turner, Vinay Varughese.
  • 3. Overview
    What is VIVO?
    How does it work?
    How do we implement it?
    What’s ahead?
  • 4. Problem:
  • 5. What is VIVO?
  • 6. VIVO is:
    A semantic web application that enables the discovery of research and scholarship across disciplines in an institution.
    Populated with detailed profiles of faculty and researchers; displaying items such as publications, teaching, service, and professional affiliations.
    A powerful search functionality for locating people and information within or across institutions.
  • 7. A VIVO profile allows researchers to:
    • Illustrate academic, social, and research networks.
    • 8. Showcase special skills or expertise.
    • 9. Establish connections and communities within research areas and geographic expertise.
    • 10. Summarize credentials and professional achievements.
    • 11. Publish the URL or link the profile to other applications.
  • Who can use VIVO?
    …and many more!
  • 12. VIVO as disseminator
  • 13. How does it work?
  • 14. Data, Data, Data
    VIVO harvests much of its data automatically from verified sources
    • Reducing the need for manual input of data.
    • 15. Centralizing information and providing an integrated source of data at an institutional level.
    External data sources
    Internal data sources
    Individuals may also edit and customize their profiles to suit their professional needs.
  • 16. Storing Data in VIVO
    • Information is stored using the Resource Description Framework (RDF) .
    • 17. Data is structured in the form of “triples” as subject-predicate-object.
    • 18. Concepts and their relationships use a shared ontology to facilitate the harvesting of data from multiple sources.
    Dept. of Genetics
    College of Medicine
    is member of
    Jane Smith
    Genetics Institute
    has affiliations with
    Journal article
    author of
    Book chapter
  • 19. has author
    taught by
    crop management
    research area
    research area for
    headed by
    CSS 4830
    head of
    faculty appointment in
    faculty members
    features person
    Earth and Atmospheric Sciences
    featured in
    Cornell’s supercomputers crunch weather data to help farmers manage chemicals
    Detailed relationships for a researcher
    has author
    Andrew McDonald
    Mining the record: Historical evidence for…
    academic staff
    author of
    research area
    author of
    research area for
    academic staff
    Susan Riha
  • 20. Query and explore
    • By individual
    • 21. Everything about an event, a grant, a person
    • 22. By type
    • 23. Everything about a class of events, grants, or persons
    • 24. By relationship
    • 25. Grants with PIs from different colleges or campuses
    • 26. By combinations and facets
    • 27. Explore any publication, grant, or talk with a relationship to a concept or geographic location
    • 28. Explore orthogonally (navigate a concept or geographic hierarchy)
  • By individual: a grant
  • 29. Browse by Type: seminar series
  • 30. Results by Topic: homeostasis
  • 31. Advantages of an ontology approach
    • Provides the key to meaning
    • 32. Defines a set of classes and properties in a unique namespace
    • 33. Embedded as RDF so data becomes self-describing
    • 34. Definitions available via the namespace URI
    • 35. Helps align RDF from multiple sources
    • 36. VIVO core ontology maps to common shared ontologies organized by domain
    • 37. Local extensions roll up into VIVO core
  • Information flow with shared ontologies
    Log boom on Williston Lake, British Columbia
  • 38. Information flow without shared ontologies
    Log jam, looking up the dalles, Taylor's Falls, St. Croix River, MN. Photo by F.E. Loomis
  • 39. Linked Data principles
    • Tim Berners-Lee:
    • 40. Use URIs as names for things
    • 41. Use HTTP URIs so that people can look up those names
    • 42. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL)
    • 43. Include links to other URIs so that people can discover more things
    • 44.
    • 45.
  • VIVO enables authoritative data about researchers to join the Linked Data cloud
  • 46. Challenges in our approach
    BUT - “I believe that a WEB of Semantic terminology (anchored in URIs qua RDF/OWL) will create a powerful network effect, where the wealth of knowledge (despite being open and uncurated) creates a wide range of  new possibilities, without the need for expressive logics and powerful reasoners (that cannot scale to web sizes).” – Jim Hendler
    Keynote at 2003 Int’l Semantic Web Conference
  • 52. Major VIVO Components
    • A general-purpose, open source Web-based application leveraging semantic standards: ontology editor, data manager, display manager
    • 53. Customizations of this application for VIVO
    • 54. Ontology
    • 55. Display theming
    • 56. Installation
    • 57. Additional new software to enable a distributed network of RDF by harvesting from VIVO instances or other sources
  • VIVO’s three functional layers
    Search and browse interface
    end users
    Display, search and navigation setup
    Curator editing
    Ontology Editing
    ontology editing
    & data flow
    Data ingest
    Data export
  • 58. Local data flow
    local systems of record
    data ingest ontologies
    shared as RDF
    RDF harvest
    SPARQL endpoint
    national sources
    Grants DB
    Administrative Staff
  • 59. From local to national
    share as RDF
    text indexing
    filtered RDF
    local sources
    • Cornell University
    • 60. University of Florida
    • 61. Indiana University
    • 62. Ponce School of Medicine
    • 63. The Scripps Research Institute
    • 64. Washington University, St. Louis
    • 65. Weill Cornell Medical College
    share as RDF
    nat’l sources
  • 66. National networking
    Scripps VIVO
    WashU VIVO
    Ponce VIVO
    Triple Store
    Prof. Assn.
    Triple Store
    Cornell VIVO
    Triple Store
    Triple Store
    Linked Open Data
  • 67. How do we implement it?
  • 68. Start with the project participants
    Institutions currently working to enable National Networking with VIVO
    Work to enable National Networking is supported via stimulus funds from the National Center for Research Resources (NCRR) of the National Institutes of Health (NIH).
  • 69. Enable a National Network
    TheNational VIVO Network enables the discovery of research across institutions.
    Data is imported, stored, and maintained by the individual institutions.
    VIVOallows researchers to browse and …enables networking on a national scale.
  • 70. Participation: potential adopters and data providers
    • Individual institutions – Columbia University, …
    • 71. Federal agencies – NIH (NIH RePORTER), NSF, …
    • 72. Consortia of schools – SURA, CTSA…
    • 73. eagle-i (“enabling resource discovery” U24 award)
    • 74. Publishers/vendors – PubMed, Elsevier, Collexis, ISI…
    • 75. Professional societies – AAAS, …
  • Participation: application developers and consumers
    • Semantic Web community – DERI, others…
    • 76. Search Providers – Google, Bing, Yahoo, …
    • 77. Professional Societies – AAAS, …
    • 78. Producers, consumers of semantic web-compliant data
  • Facilitating adoption: Challenges
    • Scientist vs. administrator
    • 79. Individual vs. institutional
    • 80. Research vs. clinical community
    • 81. Researchers: Early vs. established; enthusiastic, willing to participate
    • 82. Clinicians: Less enthusiastic; “business” accumulated in other ways
    • 83. Innovators, early adopters
    • 84. Excited; willing to experiment
    • 85. Early, late majority
    • 86. Hesitant to adopt new technologies
    • 87. VIVO takes work
  • Facilitating adoption & participation
    • Support and dissemination through libraries
    • 88. Neutral, trusted institutional entities
    • 89. IT, information management and dissemination expertise
    • 90. Subject experts; experience in translational/outreach roles
    • 91. Knowledge of institutional, research, instruction landscape
    • 92. Service ethic; tradition of academic support
    • 93. Recognition of challenges posed by user communities…
    • 94. Facilitate resolving data integration problems endemic to legacy systems
  • Librarians as facilitators: Our model
    • Oversight of initial content development
    • 95. Content types, ontology, interface refinement…
    • 96. Negotiation with campus data stewards for publicly visible data
    • 97. Support and training: local and national level
    • 98. Documentation, presentation/demo templates
    • 99. Help-desk support, videos, tutorials, web site FAQs, etc.
    • 100. Communication/liaising
    • 101. Maintain web site (
    • 102. Engage with potential collaborators, participants
    • 103. Create community of support via user forums
    • 104. Usability: Feedback, new use cases from users to technical team
    • 105. Marketing
    • 106. Demonstrations/exhibits, conferences, workshops, website
    • 107. PR materials
  • What’s ahead?
  • 108. Near-term developments
    • Release 1 (as of Friday April 16th)
    • 109. Deployment at 7 sites on production hardware
    • 110. Mapping local data to R1 ontology
    • 111. Batch data ingest (extract-transform-load)
    • 112. Upcoming Releases
    • 113. Feedback on ontology; develop local ontologies
    • 114. Improved usability
    • 115. Open software (BSD license) and ontology for download
    • 116. Next 6-12 months
    • 117. Expand data ingest framework
    • 118. Publications | people | grants | courses
    • 119. Visualization in-page and at site level
    • 120. National network application design
    • 121. Improvements to VIVO to support modularity & customizability
    • 122. User support material development
    • 123. Expand functionality to meet developing use cases
  • Driving future participation
    • “User scenario”-based ontology and feature development
    • 124. Usability; robust user support
    • 125. Compelling proof of concept within consortium
    • 126. Administrators, scientists, clinicians, students, staff…
    • 127. Addressing interest from other institutions, partners
    • 128.
    • 129. Conferences and workshops: National VIVO Conference (8/12/10; NYC)
    • 130. Individual demos; meetings
    • 131. Exhibits, publicity
  • Network Analysis & Visualization
    • At the individual investigator level
    • 132. In-page, small graphs highlighting publication history, collaborations or co-authorship networks
    • 133. At the department or institutional level
    • 134. Trends in research grants or publications
    • 135. Collaboration networks across a larger group
    • 136. Topical alignment with base maps of science
    • 137. At the network level
    • 138. Patterns and clusters by geography, topic, funding agency, institution, discipline
  • Future versions of VIVO will:
    GenerateCVs and biosketches for faculty reporting or grant proposals.
    Incorporate external data sources for publications and affiliations.
    Display visualizations of complex research networks and relationships.
    Linkdata to external applications and web pages.
    Realizethe full potential of the semantic web.
  • 139. Call to action
    Thank you! Questions?