institutional research data management: arl libraries spec survey results
DESCRIPTION
Institutional Research Data Management: ARL libraries SPEC Survey Results. 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 Libraries Data Management Services - PowerPoint PPT PresentationTRANSCRIPT
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 depositsMin Max Median
IR’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; 11Repository; 10
Systems, 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