Presentation

Report
Institutional Research Data Management:
ARL libraries SPEC Survey Results
David Fearon
Data Management Services
Johns Hopkins University
Sheridan Libraries
Andrew Sallans
Center 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 Libraries
Data Management Services
University of Virginia Library
Data 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
Locating data sources 4
GIS
8
Statistical software 5
Data visualization
59
50
33
9 12
Data analysis 5 19
Programming 310
Service offered 1–3 yrs
Service offered 3+ yrs
• Research proposal stage
services:
• data management plans
• Dissemination & preservation
stage services:
• data repositories and
archiving
Survey themes & interests
• Research data management
– JHU: archiving services
• Resource requirements for
sustaining services
– UVA: staffing and training
– Technical & administrative
needs & challenges
Key finding: RDM Service Offering
 73 academic libraries responded
• (59% of 125 ARL members)
100%
Offer research support services (broadly defined) (73)
Offer data management services (54)
Planning to
23%
offer DMS (17)
68%
84%
100%
Start of RDM Services
20
18
NSF DMP requirement
16
16
(Jan 2011)
Number of Responses (N)
14
12
11
10
8
8
7
6
4
4
2
1
1
1
2006
2007
2008
0
<2006
2009
2010
Year Initiated RDM Services (1996 - 2013)
2011
2012
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
Key finding: RDM Service Offering
Online DMP resources
47
DMP consulting
48
DMP training
33
Other Data Mangement training
23
Research metadata support
42
Data citation support
38
Data sharing & access support
22
Data archiving by library
40
0
10
20
30
40
Data
management
planning
Data
management
support
Data sharing
& archiving
50
60
Data management planning
Online DMP
resources
DMP Tool
23
12
Links to resources
24
29
Customized guidance
87%
N = 47
75%
N = 41
Data management planning
60
89%
N = 48
50
40
30
61%
N = 33
20
10
0
DMP training
DMP consulting
Libraries tracking DMP support N=25
Key Finding: Modest DMP service demand
10
9
9
8
Total DMP Support Contacts in last 2 years
(of 25 libraries tracking their consulting)
7
6
5
5
4
4
3
3
3
2
1
1
0
0-5
6 - 10
11 - 20
21 - 40
41 - 60
Total DMP Sessions (0 - 96)
61-100
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
Assistance locating data
repositories
Direct assistance w/
depositing data
Library hosts a research
data archive
96%
48%
74%
0
20
40
60
Data Archiving Services
Data-specific
repository
13% (5)
Digital
Repositories
13% (5)
Institutional
Repository (IR)
w/datasets
75% (30)
Data Archiving Infrastructure
Primary platform choice
Inst. Repository w/ Data
Data-specific Repository
(top 5)
Dspace
Fedora
Dataverse
Chronopolis
BePress Digital Commons
HubZero (customized)
Hydra
Drupal
DataConservancy
Custom repository
Funding Data Archiving
Internal budgets
Grants
24%
14% Charge researcher
84%
Archive Usage
No. of Researchers w/ deposits
Min Max Median
IR’s w/data
Data Archives
1
2
400
100
10
11
Total size of archived deposits
IR’s w/data
Data Archives
Min
9 GB
3 GB
Max
19 TB
2 TB
Median
10.5 GB
516 GB
Deposit Sources & Support
Sources of deposited data
Publications
30
Dissertations/Theses
30
2
Research Projects
29
5
Prior Projects
Other
22
5
3
IR'S w/data
5 1
Data Archives
Method of depositing data
Library deposits for
researcher
Researchers self-deposit
30
23
5
3
Staffing of RDM Services
Organizational models of RDMS
Key skills and training for positions
Staffing: Organization Structure
for RDM Services
Other structure
6%
Single library
department
11%
Staff from 2 or
more library
departments
51%
Single library
position
15%
Staff from library &
other units in inst.
17%
Number & Type of Positions
Number of Institutes
Institutes' Number of Positions
Providing RDMS
23
9
8
4
1
2
7
2
3
4
5
6
Total Positions within Institute
• Most are permanent
positions (90%), but RDM
roles are less than 50% for
the majority of positions.
Position's % of Time Spent
on RDMS
% of Positions
• Single positions & groups of
6 are common
61.3
20.8
14.6
3.3
0-25
26-50
51-75
% of Time
76-100
Staffing Roles & Job Titles
Data Management, 9
Systems, 9
Repository, 10
Curation, 11
Research Data,
11
GIS or
Geospatial, 12
Subject
Librarian or
Liaison, 50
Digital , 38
Data Services ,
13
Metadata, 17
Data Librarian, 18
Frequency of
Word/Phrases in
Titles (n=231)
Key findings: Skills and Training
Ranked as Important Skills
1. Subject domain expertise
2. Digital/data curation expertise
3. IT experience
75%
60%
59%
Background for current positions (n=228)
MLS/ MLIS
Data curation emphasis
75%
6%
Masters in another domain specialty
PhD in another domain specialty
27%
13%
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: Challenges
Theme
% w/ theme
Collaboration campus-wide
Funding
Faculty Engagement
Technology Infrastructure
Limited Staffing
18
17
15
13
12
37%
35%
31%
27%
24%
Marketing Services
Staff Training
Scoping services
Institutional commitment
12
11
9
7
24%
22%
18%
14%
5
4
3
3
2
10%
8%
6%
6%
4%
Faculty education on need
Evaluating demand
Other
Scaling service expansion
Funding Agency ambiguity
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
• Planned services w/in 2yrs:
• Plans for staffing:
Online DMP resources
63%
Adding 1 or more positions
44%
Research data archiving
54%
Adding RDM role to existing staff 44%
RDM topic training
46%
No staff changes planned
34%
• Plans for RDM funding:
Expecting a funding increase 66%
Decrease
2%
Staying the same
33%
Source: Not yet determined
52%
Regular library budget
36%
External grant funding
26%
Special project budget
16%
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
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

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