5-14-13 An Introduction to VIVO Presentation Slides
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“Hot Topics: The DuraSpace Community Webinar Series, "Series Five: VIVO: Research Discovery and Networking.” Webinar #1: An Introduction to VIVO, May 14, 2013 Presented by: Dean Krafft, Chief Technology Strategist at Cornell University Library and Chair of the VIVO-DuraSpace Management Committee, Brian Lowe, Semantic Applications Programmer, Cornell and Jon Corson-Rikert, VIVO Development Lead, Cornell
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5-14-13 An Introduction to VIVO Presentation Slides
- 1. May 14, 2013 Hot Topics: DuraSpace Community Webinar Series Hot Topics: The DuraSpace Community Webinar Series Series Five: “VIVO: Research Discovery & Networking ” Curated by Dean Krafft
- 2. May 14, 2013 Hot Topics: DuraSpace Community Webinar Series Webinar 1: Overview of VIVO Presented by: Brian Lowe, Semantic Applications Programmer, Cornell Jon Corson-Rikert, VIVO Development Lead, Cornell Dean Krafft, Chief Technology Strategist at Cornell University Library and Chair of the VIVO-DuraSpace Management Committee
- 3. What is VIVO? • A semantic-web-based researcher and research discovery tool – People plus much more • Institution-wide, publicly-visible information – For external as well as internal audiences • An open, shared platform for connecting scholars, communities, campuses, and countries using Linked Open Data
- 4. How did we get here? 31 authors 6 institutions
- 5. A brief VIVO history 2003-2005 First realization for the life sciences at Cornell, as a relational database 2006-2008 Expansion to all disciplines at Cornell, and conversion to Semantic Web 2009-2012 National Institutes of Health-sponsored VIVO: Enabling the National Networking of Scientists project transforms VIVO to a multi-institutional open source platform 2013-2014 VIVO Incubator Project with DuraSpace for open community development
- 6. Major opportunity, 2009 NIH … “invites applications designed to develop, enhance, or extend infrastructure for connecting people and resources to facilitate national discovery of individuals and of scientific resources by scientists and students to encourage interdisciplinary collaboration and scientific exchange.”
- 8. VIVO Collaboration Cornell University Dean Krafft (Cornell PI) Manolo Bevia Jim Blake Nick Cappadona Brian Caruso Jon Corson-Rikert Elly Cramer Medha Devare Elizabeth Hines Huda Khan Depak Konidena Brian Lowe Joseph McEnerney Holly Mistlebauer Stella Mitchell Anup Sawant Christopher Westling Tim Worrall Rebecca Younes University of Florida Mike Conlon (VIVO and UF PI) Beth Auten Michael Barbieri Chris Barnes Kaitlin Blackburn Cecilia Botero Kerry Britt Erin Brooks Amy Buhler Ellie Bushhousen Linda Butson Chris Case Christine Cogar Valrie Davis Mary Edwards Nita Ferree Rolando Garcia-Milan George Hack Chris Haines Sara Henning Rae Jesano Margeaux Johnson Meghan Latorre Yang Li Jennifer Lyon Paula Markes Hannah Norton James Pence Narayan Raum Nicholas Rejack Alexander Rockwell Sara Russell Gonzalez Nancy Schaefer Dale Scheppler Nicholas Skaggs Matthew Tedder Michele R. Tennant Alicia Turner Stephen Williams Indiana University Katy Borner (IU PI) Kavitha Chandrasekar Bin Chen Shanshan Chen Ryan Cobine Jeni Coffey Suresh Deivasigamani Ying Ding Russell Duhon Jon Dunn Poornima Gopinath Julie Hardesty Brian Keese Namrata Lele Micah Linnemeier Nianli Ma Robert H. McDonald Asik Pradhan Gongaju Mark Price Michael Stamper Yuyin Sun Chintan Tank Alan Walsh Brian Wheeler Feng Wu Angela Zoss Ponce School of Medicine Richard J. Noel, Jr. (Ponce PI) Ricardo Espada Colon Damaris Torres Cruz Michael Vega Negrón This project is funded by the National Institutes of Health, U24 RR029822 "VIVO: Enabling National Networking of Scientists” The Scripps Research Institute Gerald Joyce (Scripps PI) Catherine Dunn Sam Katkov Brant Kelley Paula King Angela Murrell Barbara Noble Cary Thomas Michaeleen Trimarchi Washington University School of Medicine in St. Louis Rakesh Nagarajan (WUSTL PI) Kristi L. Holmes Caerie Houchins George Joseph Sunita B. Koul Leslie D. McIntosh Weill Cornell Medical College Curtis Cole (Weill PI) Paul Albert Victor Brodsky Mark Bronnimann Adam Cheriff Oscar Cruz Dan Dickinson Richard Hu Chris Huang Itay Klaz Kenneth Lee Peter Michelini Grace Migliorisi John Ruffing Jason Specland Tru Tran Vinay Varughese Virgil Wong
- 9. What does VIVO do? • Integrates multiple sources of data – Systems of record – Faculty activity reporting – External sources (e.g., Scopus, PubMed, NIH RePORTER) • Provides a review and editing interface – Single sign-on for self-editing or by proxy • Provides integrated, filterable feeds to other websites
- 10. People
- 11. People and what they do
- 13. Enabling an (inter)national network • Open software • Open data • Local control • Decentralized infrastructure
- 14. What does VIVO model? • People and more – Organizations, grants, programs, projects, publications, events, facilities, and research resources • Relationships among the above – Meaningful – Bidirectional – Navigable context • Links to URIs elsewhere – Concepts, identifiers – People, places, organizations, events
- 15. Typical data sources • HR – people, appointments • Research administration – grants & contracts • Registrar – courses • Faculty reporting system(s) – publications, service, research areas, awards • Events calendar • Internal and external news • External repositories – e.g., Pubmed, Scopus
- 16. Value for institutions • Common data substrate – Public, granular and direct – Discovery via external and internal search engines – Available for reuse at many levels • Distributed curation – E.g., affiliations beyond what HR system tracks – Data coordination across functional silos – Feeding changes back to systems of record – Direct linking across campuses • Data that is visible gets fixed
- 17. The Semantic Web • Turn data into a web of simple links • Use ontology to explain how things are linked • Use reasoning to add new links automatically • Be flexible and extensible
- 18. The VIVO ontology • Describe people and organizations in the process of doing research • Stay discipline neutral • Use existing scientific domain terminology to describe content of research
- 19. What is Linked Open Data (LOD)? • Data – Structured information, not just documents with text – A common, simple format • Open – Available, visible, mine-able – Anyone can post, consume, and reuse • Linked – Directly by reference – Indirectly through common references and inference
- 20. Linked Open Data
- 21. Linked data indexed for search Ponce VIVO Ponce VIVO WashU VIVO WashU VIVO IU VIVO IU VIVO Cornel l Ithaca VIVO Cornel l Ithaca VIVO Weill Cornel l VIVO Weill Cornel l VIVO eagle-i research resources eagle-i research resources Harvard Profiles RDF Harvard Profiles RDF Other VIVOs Other VIVOs Digital Vita RDF Digital Vita RDF Iowa Loki RDF Iowa Loki RDF Linked Open DataLinked Open Data vivo search .org UF VIVO UF VIVO Scripps VIVO Scripps VIVO Solr search index Solr search index another Solr index another Solr index
- 26. Implementation challenges • A simple idea – take the basic public information about researchers at Cornell and make it easy to find for academic purposes • Why is this hard?
- 27. Policy issues • Dirty data • Lack even of common definitions of organizations or who’s faculty • Data ownership • Many dimensions of privacy • Short-term “go it alone” vs. common good
- 28. Enter data once, use it many times
- 32. Weill Cornell research reporting • How has the number of publications co- authored with other institutions changed year to year?
- 34. Multi-institutional scenarios for VIVO • Multiple campuses of one university • University and federal lab connections – E.g., Colorado ties with regional federal labs • Consortia – 60 CTSAs • International – 13 Netherlands universities and the National Library – AgriVIVO
- 35. Benefits across institutions • Sharing experience provides clarity and new ideas • Incentives from sharing development, tools, customizations • Potential data-level connectivity – Research is happening increasingly in teams that span institutions – Meeting the needs of short and long-term virtual organizations
- 36. From outputs to outcomes • Outputs like papers and patents can be tracked – Collaborative ontology effort to adequately represent the humanities • Outcomes such as economic impact or societal benefit are much harder to identify • Questions about return on research investment beg for consistent, comparable data – over time – across institutions – across domains
- 39. Partnerships – ORCID • Open Researcher and Contributor ID – Attribution for works of any type • ORCID and VIVO – ORCID is an attribute in a VIVO profile – Tools being tested for submission of researcher registrations from VIVO http://orcid.org
- 40. VIVO/DuraSpace Partnership • DuraSpace is a not-for-profit organization supporting the DSpace and Fedora repositories • Serves as the open source community home for future VIVO development • Provides a legal and financial framework, extensive tools, and proven track record of managing community developed open source projects • Joint two-year initial governance based on founding sponsors, management team, and dedicated development and leadership effort
- 42. Meeting about VIVO • 2nd Australian VIVO Days in February • CU Boulder hosted 50 attendees for the 3rd VIVO Implementation Fest in April • May 20th VIVO event for New York City area institutions • August 2013 will be the 4th Annual VIVO Conference – approximately 200-250 attendees, with workshops, papers, keynotes, invited talks, and posters
- 44. Research Informatics Infrastructure • USDA adopting for intramural research, and also using VIVO to knit together data from their 7 major agencies to fulfill reporting mandates to Office of Science & Technology Policy and Congress • National Center for Atmospheric Research (NCAR) is piloting VIVO to coordinate large, multi-year, multi- institutional, multi-instrument research projects
- 45. Research Informatics Infrastructure – cont. • Accurate, structured VIVO data can feed external profiling and discovery systems (ORCID, Google Scholar, Academic Analytics, etc.) • VIVO extensibility allows it to represent research resources and tie them to research datasets, publications, and researchers, promoting data discovery and reuse
- 48. VIVO for atmospheric and space physics
- 51. CTSAconnect and the ISF • VIVO and eagle-i team members won NIH funding in 2012 for a project to unify their ontologies and extend both in the clinical domain • The unified ontology is known as the Integrated Semantic Framework, or ISF • VIVO 1.6 and eagle-i’s next release will use the ISF • This combined ontology is modular to allow selective data population based on local needs
- 52. Tying biomedical research to clinical delivery
- 54. Challenges • Communicating VIVO’s goals to faculty, administrators, funders, and other institutions • Adapting to constant changes in data sources • Fully exploiting the opportunities provided by VIVO linked open data • Co-existing in a world where not everyone uses VIVO • Positioning VIVO on a sustainable path
- 55. Next Webinar: Case Studies • Tuesday, June 4 • Colorado • Duke • Brown • Weill Cornell Medical College
- 56. 3rd Webinar – Technical Deep Dive • Tuesday, June 11 • Ontology & Linked Data • Open source technologies used • What’s coming in v1.6 • VIVO technical community touch points • Many ways to participate, benefit, and contribute
- 57. May 14, 2013 Hot Topics: DuraSpace Community Webinar Series Questions?
Notes de l'éditeur
- historical overview – motivation here at Cornell before we thought of the larger context access across disciplines via a structure emphasizing connections over hierarchies predicated on information emerging from what people are doing – outputs, yes, but other activities including grants, teaching, and talks
- motivation for the NIH grant and bigger vision of the VIVO network, from the opening scholarly needs and desires realization by NIH of benefits to science when communities have organized community resources like ontologies, databases, and repositories Also aware that NIH can’t fund the full cost -looking for tools that are locally sustainable
- motivation for the NIH grant and bigger vision of the VIVO network, from the opening scholarly needs and desires realization by NIH of benefits to science when communities have organized community resources like ontologies, databases, and repositories Also aware that NIH can’t fund the full cost -looking for tools that are locally sustainable
- As you can see, The VIVO project itself is a rather large, geographically dispersed team. 7 institutions Project areas: development, implementation, ontology, and outreach
- Abawi slide (small) + predicated on information emerging from what people are doing – outputs, yes, but other activities including grants, teaching, and talks
- predicated on information emerging from what people are doing – outputs, yes, but other activities including grants, teaching, and talks
- Reasoning example: sameAs An ontology is a representation of entities and relations … … for a part of reality … … expressed in human and computer interpretable form
- An ontology is a representation of entities and relations … … for a part of reality … … expressed in human and computer interpretable form
- The Mike Conlon slide
- Results link back to home instititution
- See all or see nothing across colleges Information in aggregate has been known to reveal unintended identifiable data
- Our philosophy is you can save time and improve currency and accuracy if you only have to input information one time. This is a feature that is very appealing to faculty. [Cornell intends to participate in the Economic Development Portal being developed by NY????? Having data in VIVO means we can pipe the data to the Portal, faculty don’t have to fill out yet form.] The College of Arts and Sciences is using VIVO to feed core data into departmental websites.
- Another example of the reuse of data is the geographic display of data. CALS faculty report the impact of their work, and include the geographic focus. Using that data we can generate displays of where particular work is being done. The map in the upper right is New York State, but we can instantly render similar maps for the United States and the world.
- Transform static data into a network Leverage relationships – topic, place, and shared activities We’re not just the nodes, we’re the connections
- IICA
- Dean will be talking about this
- But it’s a many-layered problem mention Bill Trochim Fifteen Most Promising Clinical Research Processes and Outcomes Metrics from Evaluation KFC Annual Meeting Time from IRB submission to approval Studies meeting accrual goals Time from notice of grant award to study opening (e.g., investigator initiated studies) Number of technology transfer products Volume of investigators who used services Volume of types of services used Satisfaction/needs assessment Time to publication Influence of research publication (e.g., observed/expected citations) Researcher collaboration (e.g., team science; collaboration index) ROI of pilot and KL2 scholars Time from publication to a research synthesis Career development Career trajectory (e.g., K-R transition) Institutional collaboration (public-private; cross-institutional; community)
- ISF, euroCRIS, CASRAI, Lattes – pushing toward data compatibility across the world's major initiatives
- ISF, euroCRIS, CASRAI, Lattes – pushing toward data compatibility across the world's major initiatives
- University of Colorado Boulder Laboratory for Atmospheric and Space Physics additional information about research projects, equipment, and facilities will be stored behind a firewall Linked to main Boulder VIVO via sameAs
- Focus on “enter once, use many times” Cornell needs data, including expertise, partnerships, and geographic focus for: Cornell sesquicentennial campaign NY state economic development SUNY Knowledge Network Carnegie classification as an institution of community engagement Competitive landscape analysis Highlight Cornell’s uniqueness while feeding into national initiatives Build on our existing partnerships, through the Library, campus-wide teams, and specific units as necessary Strong collaboration already with Weill – build on VIVO’s momentum to make Cornell a leader in this domain