Practical Applications of e-Science

  • 680 views
Uploaded on

A. Sallans. "Practical Applications of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011

A. Sallans. "Practical Applications of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
680
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
13
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. PRACTICAL APPLICATIONSOF E-SCIENCEAndrew SallansHead of Strategic Data InitiativesUniversity of Virginia LibraryE-Science BootcampClaude Moore Health Sciences Library, University of Virginia4 March 2011
  • 2. ROUND 2:SCIENTIFIC DATA CONSULTING GROUP December/January 2010: rethinking the model  Budgetary pressures  Changes in organizational priorities  Emerging demand in research community Spring 2010: decision to focus on data May 2010: close of RCL, start of SciDaC 2
  • 3. WHAT’S HOT IN 2010? Open data: growing governmental interest in making publicly-funded research more transparent and more available (NIH, NSF) Broader critical review: greater interest evaluating original research data (Nature) Technological advances: sharing of research results easier and faster (Repositories, Web 2.0) Reuse/preservation of research data: increased consideration of the cost and value of research data and need to ensure its longevity 3
  • 4. “SCIENTISTS SEEKING NSF FUNDING WILL SOON BEREQUIRED TO SUBMIT DATA MANAGEMENT PLANS”Press Release 10-077, May 5, 2010 Current Policy: 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 On or around October 2010: 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 4
  • 5. THE CHALLENGE FOR INSTITUTIONSData is expensive Time, instrumentation, inability to reproduceIncreasing regulation Granting agencies and journals require submissionInadequate training No formal data management curriculumPreservation is not a priority For most researchers, preservation takes time away from the work that is rewarded (publication, teaching) 5
  • 6. SO…WHO’S GOING TO TAKE THIS ON? Researchers? VPR? CIO? OSP? UL? 6
  • 7. WHY THE LIBRARY? Neutral: works across the entire institution Strong in relationship building: has experience fostering discussion and relationships, and cultivates an existing support network Intellectual Property experts: has dealt with copyright, can translate to data Service-oriented: uniquely positioned as an intellectual service unit within the institution 7
  • 8. GETTING STARTED… Take what we learned in the RCL experience and apply it to the focused demands around dataSteps: Conduct a stakeholder analysis Make a short term plan (12 months) Develop clear priorities Refine and standardize consulting methods Communicate heavily 8
  • 9. STAKEHOLDER ANALYSIS (ABBREVIATED)Internal External Researchers  Funding agencies Graduate Students  Broader research Grant Administrators community Deans  “The Public” VP/CIO VPR OSP UL 9
  • 10. SHORT TERM PLAN Survey OSP to match grant holders with regulations Educate/engage subject librarians Build political awareness/support Build partnerships with local/national/international groupsResource requests: Staffing commitment Travel/partnership support 10 Promotion of initiative to institution
  • 11. CLEAR PRIORITIES1. Data interviews/assessments2. Response to NSF Data Management Plan (DMP) Mandate3. Leadership on data for the Institutional Repository (IR) 11
  • 12. CONSULTING METHODS Interviews/assessments DMP templates LOTS of documentation Constant and continuous refinement of process Adherence to core principle of helping the researcher improve process (not approaching it theoretically) 12
  • 13. COMMUNICATE HEAVILY Internal  Inform staff of processes, priorities, and progress  Keep stakeholders engaged  Reach the consumers from many angles External  Discuss and share experiences with colleagues at other institutions  Create partnerships to share, build upon resources and experiences, collaborate on tools  Networking (Twitter, LinkedIn, listserves, conference calls, conference presentations) Bottom line: this is a big culture shift, and you do have to 13 say the same thing many times in different ways
  • 14. HOW TO MAKE THIS WORK… Librarians as partners o Consult with and advise researchers o Provide leadership to the institution o Work with existing data organizations In order to succeed, librarians need to: • Build and develop specific expertise • Facilitate communication 14
  • 15. TIME OUT: NSF DMP UPDATE Now effective January 18, 2011 Some earlier proposals also require DMPs (even some in early December) Broad guidelines, but directorates may have specific guidelines for their community Uploaded as 2-page supplemental document in FastLane (with specific format requirements) Formally peer-reviewed, and will require status updates in all progress reports 15
  • 16. UVA SCIDAC NSF DMP RESPONSEUVa Library’s Original Request Develop boilerplate for researchers to use in proposalsSciDaC Group’s Response No boilerplate, successful proposals need customized plans Our approach involves:  Knowledge across many communities (“translational” opportunities)  Leadership on policy/infrastructure development  Development of a template that simplifies writing the planPrinciples Must be easy for researcher Must be supportable by available UVA resources/infrastructure Must be able to be followed-through on if grant is awarded 16
  • 17. ONGOING ISSUES Training: how do you train librarians to meet these new needs? Buy-in: how do you get effective buy-in from people around the institution? Scalability: how do you scale this to support all of the researchers who need support? 17
  • 18. TRAINING LIBRARIANSUVa Library Staff Model Scientific Data Consultants Subject LibrariansCurrent Training Model Brown Bag Data Curation Discussions Data InterviewsGoals and Objectives Build Data Literacy Create Collaborative Opportunities Establish the Library for Data Preservation 18
  • 19. BUY-IN BY THE INSTITUTION Regulations are helpful Partnerships between key stakeholders:  University libraries (UL)  Central IT (CIO)  Research Office (VP for Research)  Sponsored Programs/Research Strategic investment: take ownership, allocate resources, and demonstrate capability 19
  • 20. SCALING UP TO MEET DEMAND 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 money  Redirection/reallocation of grant overhead dollars  Write-in of library staff on grants Strategy: decide how to invest  How might units be reorganized?  How could staff resources and expertise be refocused? 20  What external partnerships would add value?
  • 21. WRAP-UP Libraries are well-positioned to play a vital role in research data support Open Data initiatives are a call to action 21
  • 22. QUESTIONS? Please feel free to contact me with questions at als9q@virginia.edu or 434-243-2180. 22