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.

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Data Management Plan Advising? A New Business Venture for Libraries

  1. 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. 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. 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. 4. SO…WHO’S GOING TO TAKE THIS ON? Researchers? Research Office? Central IT? Sponsored Research? University Library? 4
  5. 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. 6. THREE POINT SERVICE STRATEGY1. Assessment through data interviews2. Planning through DMPs3. Implementation support 6
  7. 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. 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. 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. 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
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  12. 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. 13. THANK YOU!Andrew SallansHead of Strategic Data Initiatives, SciDaC GroupUniversity of Virginia LibraryEmail: als9q@virginia.eduTwitter: asallans 13