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Guide to Research Networking Research Networking Affinity Group Informatics KFC DRAFT: August 26, 2011.

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Presentation on theme: "Guide to Research Networking Research Networking Affinity Group Informatics KFC DRAFT: August 26, 2011."— Presentation transcript:

1 Guide to Research Networking Research Networking Affinity Group Informatics KFC DRAFT: August 26, 2011

2 Vision for Research Networking Integrate institution-level resources, national research networks, publicly available data, and restricted data into the expertise profiles; Enable more rapid discovery and recommendation of researchers, expertise, and resources; Support the development of collaborative science teams to address new or existing research challenges; Provide tools that support research, such as CV generation; and, Facilitate evaluation of research, scholarly activity, and resources, especially over time.

3 What are Research Networking Tools? Research Networking tools (RN tools) provide knowledge management systems for the research enterprise through the creation of expertise profiles for faculty, investigators, scholars, clinicians, and facilities. They are different from search engines such as Google in that they access information in databases and other data not on web pages. They differ from human connector social networking systems such as LinkedIn or Facebook in that they represent authoritative or institutional compendia of data rather than individually asserted information, making them more powerful

4 RN Tools: New Collaborative Opportunities Discovery of collaborators who can fill missing translational roles Formation of disease or issue specific research teams Intelligence gathering on topical or institutional funding trends Support of virtual teams undertaking science Robust analytics and visualization to examine research trends Identification of partners to support recruitment for clinical trials Creation of digital vitae and other documentation for grant applications Outreach to patient and Industry for CER and trials

5 Complex research problems require cross-disciplinary collaborative investigation, with more work done in teams Effective practices and tools can support the efforts to initiate and nurture partnerships and secure collaborative extramural research funding are needed Facilitating collaboration can reduce time spent searching, more quickly find matches, and help make interdisciplinary matches Traditional networks are inadequate for translational research and disadvantage junior investigators Why Research Networking Tools?

6 Low capability (eg., Excel, Dept. systems, Google) Connectivity is relationship based Serendipitous Tendency to return to previous collaborators Institutional memory difficult to sustain Difficult to go beyond your own institution or scholarly domain Information tends to lag practice Current Collaborator-Finding Limitations

7 Research Networking Tools Cyber-enabled/web-based knowledge management system for the research enterprise that: Manage Faculty expertise/profiles Connect enterprise systems, national research networks, publicly available research data, and restricted data about faculty expertise and scholarly/research activity Facilitate new collaborations through discovery of expertise Provide recommender systems for non-intuitive discovery Have analytics to evaluate research, scholarly activity, and resources and changes over time

8 Implementing RN Tools: Financial Considerations Ongoing costs such as personnel, hardware, software, and data can be mitigated by partnerships with research program teams, bioinformaticians, librarians, offices of research administration, and institutional administrators. Funding may be identified by combining initiatives across organization (including Development, PR, Tech Transfer, Deans, Research Administration) into more strategic investments. Costs can be lowered by eliminating redundant systems or implementation teams. Improvements to institutional productivity could more than pay for system costs.

9 Implementing RN Tools: Administrative Considerations Central offices of research, provosts offices, and/or human resources are typically the stewards of information about faculty members, and may be the logical group to manage an RN tool solution. Entities concerned with faculty assessment can leverage RN tools to support research development, improve grant competitiveness, reduce administrative overhead, and better translational science RN tools can reduce redundant data collection from faculty (annual reviews, department updates, promotion & tenure review, CV updates)

10 Implementing RN Tools: Policy Considerations Policies need to be established for the public release of personal information (attributes). Some institutional data (eg., student records) are sensitive or private. Policies need to reflect the needs of multiple constituencies in different situations. Processes need to be established by which individuals or persons in certain roles are allowed to add or modify information, with appropriate audit trails. There need to be ongoing checks and balances for the appropriate use of information, and monitoring of that usage.

11 Implementing RN Tools: Technical Underpinnings Ontology standards describe data in common terms and allow it to be mapped across institutions and applications. Architecture standards structure the data and make it available to software applications. Software applications provide an array of services, including: data auto-ingest from institutional systems, manage profiles and generate reports, flexible and convenient search, collaborator discovery, CV generation, etc. for investigators.

12 RN Tools: Stages of Adoption and Use Stage 1: Identification. Understand needs for research networking, and institutional champions to create momentum. Stage 2: Initial. Installing a RN system with a one-time extraction of scholarly activity data of institutional data, combined with grants (eg., RePORTER) and publications (eg., PubMed) to elucidate expertise. Stage 3: Managed. Integrates network analyses of team science activities, predictive analytics, and prospective grant opportunity assessment to deliver initial institutional value. Stage 4: Defined. Regular updates by faculty or administrators or pushed from repositories and public databases to create institutional momentum and lower barriers of adoption. Stage 5: Optimized. The exchange of data between the RN tool and institutional and external system(s) is regularized so that faculty members can update information at any point and it is updated centrally to provide more functionality and lower costs.

13 Implementing RN Tools: Some of the 41 Existing Applications (that we know of) VIVO (Florida): SciVal Experts (Michigan): Harvard Catalyst Profiles (Harvard): Loki (Iowa): Community Academic Profiles (Stanford): profiles/ HUBzero (Indiana): LatticeGrid (Northwestern): Digital Vita (Pittsburgh):

14 Contributors William Barnett, Indiana University School of Medicine, Indianapolis, IN Griffin Weber; Harvard Medical School; Boston, Massachusetts, USA Mike Conlon; University of Florida; Gainesville, Florida, USA David Eichmann; University of Iowa; Iowa City, Iowa, USA Warren Kibbe; Northwestern University; Chicago, Illinois, USA Holly Falk-Krzesinski; Northwestern University; Chicago, Illinois, USA Michael Halaas; Stanford University School of Medicine; Menlo Park, California, USA Layne Johnson; University of Minnesota; Minneapolis, Minnesota, USA Eric Meeks; University of California, San Francisco; San Francisco, California, USA Donald Mitchell; Stanford University School of Medicine; Stanford, California, USA Titus Schleyer; University of Pittsburgh; Pittsburgh, Pennsylvania, USA Sarah Stallings; University of Colorado, Denver; Aurora, Colorado, USA Michael Warden; Elsevier; Ann Arbor, Michigan, USA Maninder Kahlon; University of California, San Francisco; San Francisco, California, USA Daniel Berleant; University of Arkansas at Little Rock, USA

15 References 1. K. Börner et al., Science Translational Medicine 2, cm24 (2010). 2. N. S. Contractor, P. R. Monge, Management Communication Quarterly 16, 249 (2002). 3. N. S. Contractor, S. Wasserman, K. Faust, Academy of Management Review 31, 691 (2006). 4. D. Cressman, J. A. Holbrook, B. S. Lewis, B. Wixted, Understanding the Structure of Formal Research Networks (Simon Fraser University, Vancouver, BC, 2011). 5. M. Disis, J. Slattery, Science Translational Medicine 2, 22cm9 (March 10, 2010, 2010). 6. H. Falk-Krzesinski, P. L. Shaw, L. Wimbiscus-Yoon, paper presented at the National VIVO Conference: Enabling National Networking of Scientists, Queens, NY, H. J. Falk-Krzesinski et al., Clinical and Translational Sciences 3, 263 (2010). 8. N. F. Fried, S. Gibbons, in D-Lib Magazine. (2005), vol V. Gewin, Nature 468, 993 (Dec 16, 2010). 10. R. Guimerà, B. Uzzi, J. Spiro, L. A. N. Amaral, Science 308, 697 (2005). 11. Y. Huang, N. Contractor, Y. Yao, paper presented at the The Proceedings of the 9th Annual International Digital Government Research Conference, B. F. Jones, S. Wuchty, B. Uzzi, Science 322, 1259 (Nov 21, 2008). 13. W. Kibbe. (2010). 14. J. S. Pober, C. S. Neuhauser, J. M. Pober, FASEB J 15, 2303 (Nov, 2001). 15. T. Schleyer, ACM Transactions on Computer-Human Interaction Accepted for Publication, (2011). 16. T. Schleyer et al., J Med Internet Res 10, e24 (2008). 17. D. Stewart, in Gartner Blog Network. (Gartner, Stamford, CT, 2011), vol D. L. Stewart, in Connected Knowledge D. L. Stewart, Ed. (Oregon Health & Science University, Portland, OR 2010). 19. D. L. Stewart, in Connected Knowledge D. L. Stewart, Ed. (Oregon Health & Science University, Portland, OR 2010). 20. D. L. Stewart, in Connected Knowledge D. L. Stewart, Ed. (Oregon Health & Science University, Portland, OR 2010). 21. B. Wieder, in The Wired Campus. (The Chronicle of Higher Education, Washington, DC, 2011), vol S. Wuchty, B. F. Jones, B. Uzzi, Science 316, 1036 (2007).

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