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UVa Library Scientific Data Consulting Group (SciDaC):  New Partnerships and Services to Support Scientific Data in the Library

UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and Services to Support Scientific Data in the Library



A. Sallans. "UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and Services to Support Scientific Data in the Library." Presented at the 2011 International Association for ...

A. Sallans. "UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and Services to Support Scientific Data in the Library." Presented at the 2011 International Association for Social Science Information Services and Technology.



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    UVa Library Scientific Data Consulting Group (SciDaC):  New Partnerships and Services to Support Scientific Data in the Library UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and Services to Support Scientific Data in the Library Presentation Transcript

    • 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 2
    • 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 3  DHHS - $197 million
    • 4Source: “Mens Lacrosse NCAA CHAMPS! (by Matt Riley) 5/31/2011” photo gallery, http://www.virginiasports.com/
    • 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. 5
    • 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 6
    • 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
    • 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 8
    • PHASE 2 - SCIENTIFIC DATA CONSULTINGGROUP  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 9
    • 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 10
    • “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 11
    • 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) 12
    • SO…WHO’S GOING TO TAKE THIS ON? Researchers? VPR? CIO? OSP? UL? 13
    • 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 14
    • 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 15
    • STAKEHOLDER ANALYSIS (ABBREVIATED)Internal External Researchers  Funding agencies Graduate Students  Broader research Grant Administrators community Deans  “The Public” VP/CIO VPR OSP UL 16
    • 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 17 Promotion of initiative to institution
    • CLEAR PRIORITIES1. Data interviews/assessments2. Response to NSF Data Management Plan (DMP) Mandate3. Leadership on data for the Institutional Repository (IR) 18
    • CONSULTING ACTIVITIES Interviews/assessments Data management planning templates LOTS of documentation Constant and continuous refinement of process Focus on helping researchers improve process 19
    • 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
    • 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
    • PRIORITY 2 – DATA MANAGEMENTPLANNING 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 22
    • 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 23
    • 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 24
    • 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 25
    • COLLABORATIONS Internal  Library / VPR / CIO / OSP  Institutional Repository Team  Kuali Coeus team External  DMP Tool  DataONE  Conference/professional networks 26
    • 27
    • CHALLENGES Involving subject librarians? Gaining institutional buy-in? Meeting demand? 28
    • HOW TO INVOLVE SUBJECT LIBRARIANS?UVa 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 29
    • 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 30
    • 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
    • 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
    • THANK YOU!Andrew SallansHead of Strategic Data Initiatives, SciDaC GroupUniversity of Virginia LibraryEmail: als9q@virginia.eduTwitter: asallanshttp://www.lib.virginia.edu/brown/data 33