Institutional Research Data Management: ARL libraries SPEC Survey Results

1. Institutional Research Data Management: ARL libraries SPEC Survey Results CNI Fall 2013 Membership Meeting Dec 9, 2013. Washington DC

2. ARL SPEC Survey: Research Data Management Services ARL SPEC Kit 334 (July 2013) Johns Hopkins Sheridan Libraries Data Management Services University of Virginia Library Data Management Consultant Group Available for download at ARL.org

3. Survey origins • Built upon the ARL E-Science Working Group survey: • “E-Science and Data Support Services: A Study of ARL Member Institutions" (Soehner, Steeves, & Ward, 2010)

4. Research Data Management Services: expanding research lifecycle support • Research proposal stage services: • data management plans • Dissemination & preservation stage services: • data repositories and archiving

5. Survey themes & interests • Research data management • JHU: archiving services • Resource requirements for sustaining services • UVA: staffing and training • Technical & administrative needs & challenges

6. Key finding: RDM Service Offering • 73 academic libraries responded • (59% of 125 ARL members) Offer research support services (broadly defined) (73) 100% 100% 84% Offer data management services (54) 68% Planning to offer DMS (17) 23%

7. Start of RDM Services NSF DMP requirement (Jan 2011)

8. Key Finding: Motivators Question: What are some key variables in the institutional environment driving these new services? Common reasons: • Responding to grant funder requirements • Library-led initiatives toward supporting research Less common reasons: • Administration/researchers calling for data management support by library • Responding to formal institutional data policies

9. Key finding: RDM Service Offering Data management planning Data management support Data sharing & archiving

10. Data management planning 87% N = 47

11. Data management planning 89% N = 48 61% N = 33

12. Key Finding: Modest DMP service demand

13. Data Archiving Services • Funders are promoting data sharing through repositories • For libraries, may require more staffing/resources beyond reference services. • Archiving: online access to data, facilitated by preservation

14. Data Archiving Services 96% 48% 74%

15. Data Archiving Services

16. Data Archiving Infrastructure Primary platform choice

17. Funding Data Archiving Internal budgets 84% Grants 24% 14% Charge researcher

18. Archive Usage No. of Researchers w/ deposits Total size of archived deposits

19. Deposit Sources & Support Sources of deposited data Method of depositing data

20. Staffingof RDM Services • Organizational models of RDMS • Key skills and training for positions

21. Staffing: Organization Structure for RDM Services

22. Number & Type of Positions • Most are permanent positions (90%), but RDM roles are less than 50% for the majority of positions. • Single positions & groups of 6 are common

23. Staffing Roles & Job Titles Frequency of Word/Phrases in Titles (n=231) Data Management, 9 Data Librarian, 18

24. Key findings: Skills and Training Ranked as Important Skills Background for current positions (n=228)

25. Key Finding: Assessing service effectiveness • Most self-assessment of RDM service effectiveness is informal, ad-hoc • Survey inconclusive on which services and models are most effective, top outreach strategies, etc. • Is faculty/researcher demand sustaining these programs once started? (too early to say) • Challenges for implementing and sustaining services

26. Key Finding: Challenges

27. Limitations: Distribution • Distribution through ARL SPEC Kit network may not have reached all data services staff • Distribution method may have missed representation of non-library services

28. Limitations: Estimations • Poor estimation of actual time invested in RDM services • Poor estimation of actual volume of data being archived or planned

29. Limitations: Terminology • Some terms do not yet seem to have precise common meaning • Variation in interpretation may mean some of the data needs further exploration

30. Limitations: Broader Analysis • Much data, little time • We especially hoped to merge our data with other available organizational data for broader comparison *** Future research project opportunity!***

31. Lesson 1: Collaboration Seems Key • Libraries need to collaborate across the institution to support RDM • Developing these collaborations is seen as one of the biggest challenges

32. Lesson 2: Real Costs Exist • Necessary skills may requiring hiring new staff with different skills or retraining • New skills may cost more • Archiving infrastructure, storage, and curation will incur real cost

33. Lesson 3: Build More Engagement • Poor engagement may lead to a lack of awareness, low perceived value, and resistance to sharing • Trickle down effect from empty mandates --- ie. DMP requirements that aren’t reviewed seriously

34. Lesson 4: Grow Services • Despite the challenges, many respondents see RDM services as an appropriate service for libraries • What comes will involve a balance of institutional and funder policy, technical skills of staff, and financial capabilities

35. Lesson 4: Grow Services • Planned services w/in 2yrs: • Plans for staffing: • Plans for RDM funding:

36. Lesson 5: There Is No Single Path • We interpret the data to suggest merit in many models in different settings • Cross institutional collaboration and offering of services seems to be one of the viable models

37. Credits Our full team: • David Fearon, Johns Hopkins University • Betsy Gunia, Johns Hopkins University • Sherry Lake, University of Virginia • Barbara Pralle, Johns Hopkins University • Andrew Sallans, Center for Open Science With thanks to Lee Ann George, ARL’s SPEC Kit editor And ARL’s E-Science Working Group