Institutional Research Data Management: ARL libraries SPEC Survey Results

David FearonData Management

ServicesJohns Hopkins

University Sheridan Libraries

Andrew SallansCenter for Open Science

Formerly at the University of Virginia

Library

CNI Fall 2013 Membership Meeting Dec 9, 2013. Washington DC

ARL SPEC Survey: Research Data Management Services

ARL SPEC Kit 334 (July 2013)

Johns Hopkins Sheridan LibrariesData Management Services

University of Virginia LibraryData Management Consultant Group

Available for download at ARL.org

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)

Research Data Management Services: expanding research lifecycle support

• Research proposal stage services:• data management plans

• Dissemination & preservation stage services:• data repositories and

archivingProgramming

Data analysis

Data visualization

Statistical software

GIS

Locating data sources

3

5

9

5

8

4

10

19

12

33

50

59

Service offered 1–3 yrsService offered 3+ yrs

Survey themes & interests

• Research data management– JHU: archiving services

• Resource requirements for sustaining services– UVA: staffing and training– Technical & administrative

needs & challenges

Offer data management services (54)

100%

68%

Planning to offer DMS (17)

23%

Key finding: RDM Service Offering

Offer research support services (broadly defined) (73)

84%

100%

73 academic libraries responded • (59% of 125 ARL members)

Start of RDM Services

<2006 2006 2007 2008 2009 2010 2011 20120

2

4

6

8

10

12

14

16

18

20

7

1 1 1

4

11

16

8

Year Initiated RDM Services (1996 - 2013)

Num

ber o

f Res

pons

es (N

)

NSF DMP requirement

(Jan 2011)

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 researchLess common reasons:• Administration/researchers calling for data management

support by library• Responding to formal institutional data policies

Data archiving by library

Data sharing & access support

Data citation support

Research metadata support

Other Data Mangement training

DMP training

DMP consulting

Online DMP resources

0 10 20 30 40 50 60

40

22

38

42

23

33

48

47 Data management planning

Data management support

Data sharing & archiving

Key finding: RDM Service Offering

Data management planning

DMP Tool

Online DMP resources

12

23

29

24

Links to resources Customized guidance

75%N = 41

87%N = 47

Data management planning

DMP training DMP consulting0

10

20

30

40

50

60

89%N = 48

61%N = 33

Key Finding: Modest DMP service demand

0 - 5 6 - 10 11 - 20 21 - 40 41 - 60 61-1000

1

2

3

4

5

6

7

8

9

109

3

5

3

4

1

Total DMP Support Contacts in last 2 years(of 25 libraries tracking their consulting)

Total DMP Sessions (0 - 96)

Libr

arie

s tra

ckin

g DM

P su

ppor

t N

=25

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

Data Archiving Services

Library hosts a research data archive

Direct assistance w/ depositing data

Assistance locating data repositories

0 10 20 30 40 50 60

74%

96%

48%

Data Archiving Services

Institutional Repository (IR)

w/datasets75% (30)

Digital Reposi-tories

13% (5)

Data-specific repository13% (5)

Data Archiving Infrastructure

Inst. Repository w/ Data(top 5)

DspaceFedoraBePress Digital Commons

HydraDrupal

Primary platform choice

Data-specific Repository

DataverseChronopolisHubZero (customized)

DataConservancyCustom repository

Internal budgets

Grants

14%

84%

24%

Charge researcher

Funding Data Archiving

Archive UsageNo. of Researchers w/ deposits

Min Max Median

IR’s w/data 1 400 10Data Archives 2 100 11

Total size of archived deposits

Min Max MedianIR’s w/data 9 GB 19 TB 10.5 GBData Archives 3 GB 2 TB 516 GB

Deposit Sources & Support

Other

Prior Projects

Research Projects

Dissertations/Theses

Publications

5

22

29

30

30

1

3

5

2

5

IR'S w/dataData Archives

Sources of deposited data

Researchers self-deposit

Library deposits for researcher

23

30

3

5

Method of depositing data

Staffing of RDM Services

Organizational models of RDMS

Key skills and training for positions

Staffing: Organization Structure for RDM Services

Other structure6%

Single library de-partment

11%

Single library position

15%

Staff from library & other units in inst.

17%

Staff from 2 or more library departments

51%

Number & Type of Positions• Single positions & groups of

6 are common

1 2 3 4 5 6

84

2

97

23

Institutes' Number of Positions Providing RDMS

Total Positions within Institute

Num

ber o

f Ins

titut

es

• Most are permanent positions (90%), but RDM roles are less than 50% for the majority of positions.

0-25 26-50 51-75 76-100

61.3

20.8

3.314.6

Position's % of Time Spent on RDMS

% of Time %

of P

ositi

ons

Subject Librarian

or Liaison; 50

Digital ; 38

Metadata; 17

Data Services ; 13

GIS or Geospatial; 12

Research Data; 11

Curation; 11

Repository; 10Systems, 9

Staffing Roles & Job TitlesData Management, 9

Frequency of Word/Phrases in Titles (n=231)

Data Librarian, 18

Key findings: Skills and TrainingRanked as Important Skills

1. Subject domain expertise 75%2. Digital/data curation expertise 60%3. IT experience 59%

MLS/ MLIS 75%Data curation emphasis 6%Masters in another domain specialty 27%PhD in another domain specialty 13%

Background for current positions (n=228)

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

Key Finding: ChallengesTheme % w/ themeCollaboration campus-wide 18 37%Funding 17 35%Faculty Engagement 15 31%Technology Infrastructure 13 27%Limited Staffing 12 24%

Marketing Services 12 24%Staff Training 11 22%Scoping services 9 18%Institutional commitment 7 14%

Faculty education on need 5 10%Evaluating demand 4 8%Other 3 6%Scaling service expansion 3 6%Funding Agency ambiguity 2 4%

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

Limitations: Estimations

• Poor estimation of actual time invested in RDM services

• Poor estimation of actual volume of data being archived or planned

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

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!***

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

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

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

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

Lesson 4: Grow Services• Plans for staffing:

Source: Not yet determined 52%Regular library budget 36%External grant funding 26%Special project budget 16%

• Plans for RDM funding:Expecting a funding increase 66%Decrease 2%Staying the same 33%

• Planned services w/in 2yrs:Online DMP resources 63%Research data archiving 54%RDM topic training 46%

Adding 1 or more positions 44%Adding RDM role to existing staff 44%

No staff changes planned 34%

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

CreditsOur 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 ScienceWith thanks to Lee Ann George, ARL’s SPEC Kit editor

And ARL’s E-Science Working Group