The UVA Library Scientific Data Consulting Group (SciDaC) provides new partnerships and services to support scientific data management in research. SciDaC was formed in 2010 to focus on data consulting after restructuring from the Research Computing Lab. SciDaC conducts data interviews and assessments, assists with NSF Data Management Plan requirements, and works to integrate research data into the institutional repository. Future work includes expanding disciplinary support, integrating into the research proposal process, and advising on data policy.

1. UVA LIBRARY SCIENTIFIC DATA
CONSULTING GROUP (SCIDAC):
NEW PARTNERSHIPS AND SERVICES TO
SUPPORT SCIENTIFIC DATA IN THE LIBRARY
Andrew Sallans
Head of Strategic Data Initiatives
Sherry Lake
Senior Scientific Data Consultant
IASSIST 2011
1 June 2011

2. OUTLINE
 Phase 1 – Research Computing Lab
 Phase 2 – Scientific Data Consulting Group
1. Data assessment interviews
2. Data management planning
3. Integration of processes with IR
 Partnerships
 Internal
 External
 Challenges
 Future opportunities
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3. BACKGROUND ON THE UNIVERSITY OF VIRGINIA
 “Mr. Jefferson’s University”
 Size
 About 14,000 undergraduate
students
 About 6,000 graduate students
 About 2,000 faculty
 Annual research dollars –
FY10 $375 million
 DE (Ed) - $10 million
 DOE- $10 million
 DOD- $15 million
 NSF - $29 million
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 DHHS - $197 million

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Source: “Men's Lacrosse NCAA CHAMPS! (by Matt Riley) 5/31/2011” photo gallery, http://www.virginiasports.com/

5. PHASE 1: RESEARCH COMPUTING LAB
 Began planning in 2005.
 Central IT: seeking greater
visibility.
 Library: seeking new ways
to support scientific research.
 Collocation provided mutual
benefits.
 Staff combined in 2006,
moved to Library locations
(Research Computing Lab &
Scholars’ Lab), setup new
service points and services.
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6. RESEARCH COMPUTING LAB RESPONSE
 Aiming to provide support across the entire
scientific research data lifecycle
 Staff with expertise in:
 Data
 Quantitative data, statistics
 Modeling, visualization
 Scientific publishing
 Emphasis on consulting, not drop-off services
 Partnership with traditional librarians to help
ease transition to new support models
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7. SAMPLE RCL CONSULTATIONS
 STS Undergrad Environmental Justice (2008)
 Development of technology solutions for empowering the
citizen scientist
 Web 2.0 tools, data collection/management
 Data analysis
 Economics Graduate Student (2008/2009)
 Airline flight price modeling
 Screen scraping, data collection/management
 Data analysis
 Mountain Lake Beetle Project (2009)
 Mobile data acquisition/collection solution
 Database development/management, programming
 Data analysis
 Archiving of dissertation data (2009)
 EVSC student, ModelMaker 4.0 data
 Biology student, IDL, Matlab, R code 7

8. TAKE-AWAYS
 This is the future
 Heavily growing space, lots of opportunity
 Requires big investment and commitment, the
biggest being training and priority alignment
 Libraries and institutions need to make decisions
on what to do and what not to do
 It’s a culture change for both libraries,
institutions, and researchers
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9. PHASE 2 - SCIENTIFIC DATA CONSULTING
GROUP
 December 2009/January 2010: rethinking the
model
 Budgetary pressures
 Changes in organizational priorities
 Emerging demands in research community
 Spring 2010: decision to focus on data
 May 2010: close of RCL, start of SciDaC
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10. 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
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11. “SCIENTISTS SEEKING NSF FUNDING WILL SOON BE
REQUIRED 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 11

12. THE CHALLENGE FOR INSTITUTIONS
Data is expensive
 Time, instrumentation, inability to reproduce
Increasing regulation
 Granting agencies and journals require
submission
Inadequate training
 No formal data management curriculum
Preservation is not a priority
 For most researchers, preservation takes time
away from the work that is rewarded
(publication, teaching) 12

13. SO…WHO’S GOING TO TAKE THIS ON?
 Researchers?
 VPR?
 CIO?
 OSP?
 UL?
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14. 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
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15. GETTING STARTED…
 Take what we learned in the RCL experience and
apply it to the focused demands around data
Steps:
 Conduct a stakeholder analysis
 Make a short term plan (12 months)
 Develop clear priorities
 Refine and standardize consulting methods
 Communicate heavily
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16. STAKEHOLDER ANALYSIS (ABBREVIATED)
Internal External
 Researchers  Funding agencies
 Graduate Students  Broader research
 Grant Administrators community
 Deans  “The Public”
 VP/CIO
 VPR
 OSP
 UL
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17. 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 groups
Resource requests:
 Staffing commitment
 Travel/partnership support
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 Promotion of initiative to institution

18. CLEAR PRIORITIES
1. Data interviews/assessments
2. Response to NSF Data Management Plan
(DMP) Mandate
3. Leadership on data for the Institutional
Repository (IR)
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19. CONSULTING ACTIVITIES
 Interviews/assessments
 Data management planning templates
 LOTS of documentation
 Constant and continuous refinement of process
 Focus on helping researchers improve process
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20. 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 20
say the same thing many times in different ways

21. PRIORITY 1 – DATA ASSESSMENT INTERVIEWS
 Initially a means of growing awareness of
consulting service and doing assessment, now a
means of establishing a baseline for research
data management practices with any new “client”
 Protocol involves:
 60 minute interview discussion (researcher / SciDaC
consultants / subject librarian)
 Development of a report
 SciDaC consultants give researchers
recommendations to improve data management
 SciDaC consultants work with researchers to
implement recommended solutions
 Approach has proven to be very effective thus far 21

22. PRIORITY 2 – DATA MANAGEMENT
PLANNING
 Highest priority of responding to and addressing
support needs for funding agency requirements
(ie. NSF, others)
 Getting a handle on data management as a
means of institutional risk management
 Coordination of effort across institution
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23. NSF DATA MANAGEMENT PLAN MANDATE
 Official mandate became active Jan. 18, 2011
 New NSF Directorates/Divisions continue to
release and specify guidelines (examples below)
 Education and Human Resources (EHR)
 Engineering (ENG)
 Geological Sciences (GEO)
 Mathematical and Physical Sciences (MPS)
 Social, Behavioral, and Economic Sciences (SBE)
 Researchers continue to be mostly unaware of the
mandate and how to prepare a DMP
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24. UVA SCIDAC NSF DMP RESPONSE
UVa Library’s Original Request
 Develop boilerplate for researchers to use in proposals
SciDaC 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 plan
Principles
 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
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25. PRIORITY 3 – INTEGRATION WITH IR
 Institutional repository “Libra” (http://libra.virginia.edu)
 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
 Will provide preservation assurance for data in form of
“blobs” or packages (bit preservation, no format migration)
 Currently in process of developing user
interface/ingestion prototype that addresses needs of
small data for release in late July 2011
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26. COLLABORATIONS
 Internal
 Library / VPR / CIO / OSP
 Institutional Repository Team
 Kuali Coeus team
 External
 DMP Tool
 DataONE
 Conference/professional networks
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28. CHALLENGES
 Involving subject librarians?
 Gaining institutional buy-in?
 Meeting demand?
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29. HOW TO INVOLVE SUBJECT LIBRARIANS?
UVa Library Staff Model
 Scientific Data Consultants
 Subject Librarians
Current Training Model
 Brown Bag Data Curation
Discussions
 Data Interviews
Goals and Objectives
 Build Data Literacy
 Create Collaborative Opportunities
 Establish the Library for Data
Preservation
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30. HOW TO GAIN INSTITUTIONAL BUY-IN?
 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
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31. HOW TO MEET DEMAND?
 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 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 do we expand to other disciplines?
 How could staff resources and expertise be refocused?
 What additional partnerships would add value? 31

32. FUTURE DIRECTIONS
 Addressing data management needs of other
disciplines across the institution
 Integration into formal research proposal
process
 Broader data management education
 Increased funded research project
consulting
 Technology consulting
 Expansion of virtual organization partners
and creation of research advisory board
 Guiding of policy revision to address new
interests in data management and preservation 32

33. THANK YOU!
Andrew Sallans
Head of Strategic Data Initiatives, SciDaC Group
University of Virginia Library
Email: als9q@virginia.edu
Twitter: asallans
http://www.lib.virginia.edu/brown/data
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