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Data Management Plan Advising? A New Business Venture for Libraries


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A. Sallans. "Data Management Plan Advising? A New Business Venture for Libraries." Presented at the 2011 Special Libraries Association Conference.

A. Sallans. "Data Management Plan Advising? A New Business Venture for Libraries." Presented at the 2011 Special Libraries Association Conference.

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  • 1. DATA MANAGEMENT PLAN ADVISING?A NEW BUSINESS VENTURE FOR LIBRARIES Andrew Sallans Head of Strategic Data Initiatives Special Libraries Association 15 June 2011
  • 2. “SCIENTISTS SEEKING NSF FUNDING WILL SOON BEREQUIRED TO SUBMIT DATA MANAGEMENT PLANS”Press Release 10-077, May 5, 2010 Policy prior to January 18, 2011: o “To advance science by encouraging data sharing among researchers” o Data obtained with federal funds be accessible to the general public o Grantees must develop and submit specific plans to share materials collected with NSF support, except where this is inappropriate or impossible Policy after January 18, 2011: o All new NSF proposals will be required to include a data management plan in the form of a 2 pg supplementary document (peer reviewed) o New policy is meant to be a 1st step toward a more comprehensive approach to data management o Exact requirements vague, scientific communities will specify 2
  • 3. THE CHALLENGE FOR INSTITUTIONSData is expensive Time, instrumentation, inability to reproduceIncreasing regulation Granting agencies and journals require submissionInadequate training No formal data management curriculumPreservation of data is not a priority For most researchers, preservation takes time away from the work that is rewarded (publication, teaching) 3
  • 4. SO…WHO’S GOING TO TAKE THIS ON? Researchers? Research Office? Central IT? Sponsored Research? University Library? 4
  • 5. WHY THE LIBRARY? A FEW POINTS… Neutral: works across the entire institution Strong in relationship building: has experience fostering discussion and relationships, and cultivates an existing support network Intellectual Property expertise: has dealt with copyright, can translate to data Service-oriented: uniquely positioned as an intellectual service unit within the institution 5
  • 6. THREE POINT SERVICE STRATEGY1. Assessment through data interviews2. Planning through DMPs3. Implementation support 6
  • 7. POINT 1 – DATA ASSESSMENT INTERVIEWS Growing awareness of consulting service Broad assessment Baseline of research data management practices Protocol involves:  60 minute interview discussion (researcher / SciDaC consultants / subject librarian)  Development of a report  SciDaC consultants give researchers improvement recommendations and plan  SciDaC consultants work with researchers to implement recommended solutions Based on Data Asset Framework, Data Curation Profile, and other similar assessment tools 7
  • 8. POINT 2 – DATA MANAGEMENT PLANNING Funding agency requirements - highest priority of responding to and addressing support needs (ie. NSF, others) Risk management – identifying opportunities to improve data management practices as a means of institutional risk management Coordination of effort across institution – Library as leader, coordinates between VPR, CIO, OSP, schools/colleges, etc. Boilerplate versus customized – a balance of generic, institutional DMPs versus boutique and 8 focused only on the project
  • 9. POINT 3 –IMPLEMENTATION SUPPORT Institutional repository “Libra” (  Built upon Hydra architecture  Three components: open access publications, data, and electronic theses/dissertations  Working on figuring out storage and cost models to support management of big and small data from across institution’s research community Consulting with researchers on how to implement the data management plans for their projects Serving as a bridge between the many silos of the institution, with competency in the many areas of research data management 9
  • 10. AN INSIDE VIEW OF DATA MANAGEMENT PLANS Consulted on 14 data management plan (DMP) proposals (since 1/18) DMPs included the following areas:  Biology (3)  Chemical Engineering (2)  Civil Engineering (1)  Computer Science (1)  Education (2)  Electrical Engineering (3)  Environmental Science (2) Gained feedback and insight of reviewing practices on first submitted DMP Development of templates that associate NSF directorate requirements with available resources and support services to streamline plan development and allow researchers to make informed decisions on a tight schedule (currently 7 templates) The bigger picture: a multi-institution, international collaboration to develop web-based DMP authoring tool that: 1. Streamlines DMP development 2. Associates researchers with support resources 10
  • 11. 11
  • 12. CHALLENGES AHEAD… Time: how to best manage staff time  NSF research support alone is going to be very time consuming (UVA had about 140 proposals over the past year, 44 in November alone) Funding: work with leaders to find sources  Make the case  Explore the options  Test the feasibility Strategy: decide how to invest  How might units be reorganized?  How do we expand to other disciplines?  How could staff resources and expertise be refocused?  What additional partnerships would add value? 12
  • 13. THANK YOU!Andrew SallansHead of Strategic Data Initiatives, SciDaC GroupUniversity of Virginia LibraryEmail: als9q@virginia.eduTwitter: asallans 13