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VIVO Development: Where Are We and What’s Next?

VIVO Development: Where Are We and What’s Next?. 2011 VIVO Conference. Overview of the hour. Brief intros by 5 team leads Highlights of their teams’ accomplishments Expected future development activities Open discussion Current and future development directions

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VIVO Development: Where Are We and What’s Next?

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  1. VIVO Development:Where Are We andWhat’s Next? 2011 VIVO Conference

  2. Overview of the hour • Brief intros by 5 team leads • Highlights of their teams’ accomplishments • Expected future development activities • Open discussion • Current and future development directions • What VIVO lacks in infrastructure or features • Ideas about related platforms and tools • Apps and other ways to leverage VIVO • Making developers more responsive to implementation and/or outreach needs • Making VIVO development work as open source community

  3. Our speakers (in order of appearance)

  4. Enabling efforts for development • User testing • Feedback from implementation sites, including several outside the grant • Ideas and advice from the researcher networking and Semantic Web communities • Especially the Technical Advisory Board • Collaborations with eagle-i and mini-grants • Individual contributions • Growing involvement beyond academia • Strong support from NIH staff

  5. Application Development Team •  Jim Blake •  Brian Caruso •  Deepak Konidena •  Rebecca Younes

  6. Accomplishments • Conversion from JSPs to FreeMarker templates • Solrand other search improvements • Image upload and storage • Authentication and authorization improvements • Serving linked data and LDIB

  7. Goals • Migration of editing to FreeMarker • Generalization of editing • Linked data syncing and discovery • Extension capabilities

  8. Semantic Development Team • Huda Khan • Brian Lowe • Stella Mitchell • AnupSawant

  9. Accomplishments • SDB (“SPARQL Database”) for main triple store • RDF datasets – manage data in named graphs • Built-in, custom reasoning for VIVO • Rich export • Use of graph structure for search relevance ranking

  10. Future • Even better triple stores? • More complex reasoning while minimizing memory needs • BIBO and FOAF as as intended, Dublin Core • Improved provenance support – better features around named graphs • Making connections and reasoning across VIVOs and the Semantic Web

  11. User Interface Team • Elly Cramer • ManoloBevia • Tim Worrall • Miles Worthington • Nick Cappadona

  12. Accomplishments Make the user’s experience as simple, productive,efficient and enjoyable as possible • Re-architected theming • Migration to FreeMarker • Wilma • Search • Iterative design cycle with testing • Collaboration • Fun

  13. Future • Create triples with external resources as objects • Editing • Roles and permissions • Data consumption • Search • Installation and build/deploy • Annihilation of JSPs

  14. Visualization Team • Chin Hua Kong • Micah Linnemeier • Chintan Tank

  15. Accomplishments • Leverage VIVO core to build visualizations on top of it • Develop visualization architecture • Develop visualizations on 3 levels • Individual/personal level • Sparkline • Ego-centric e.g. co-author, co-pi • Map of Science • Organization level • Map of Science • Temporal Graph • National level • National Researcher Networking • Implement caching strategy for fetching data for memory & time intensive queries • Facilitate downloads of processed data behind each visualization

  16. Future • Empower user to utilize VIVO data to do visualizations, e.g., bi-modal network, degree of separation between researchers • Export visualizations as image or pdfreports • New visualizations • Improved caching strategy

  17. Interfacing & packaging team • Chris Barnes • Narayan Raum • Stephen Williams • Nicholas Skaggs • Christopher Haines • James Pence • Michael Barbieri

  18. Accomplishments • A virtual machine image for each VIVO release and an Amazon EC2 Image for 1.3 • A data harvesting library with example scripts to download, translate, score, and import into VIVO HR, grant, image and publication data • Examples of using VIVO’s linked open data and sparql endpoints to use VIVO data in external applications • Integration of the VIVO Harvester with the VIVO GUI

  19. Future • Allow users to load data into the VIVO application outside the administrative interface • Hook the Harvester library into existing data manipulation GUIs such as Taverna or Google Refine • Implement new deploy functions that allow build.xml to end at creating a Web application ARchive (WAR) file

  20. Questions and Discussion

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