Data curation has emerged as a strategic growth area for academic libraries. Many libraries have conducted needs assessments as a precursor towards developing services; however there have been few comparisons of the findings across institutions. This panel brings together four librarians from different institutions to discuss both common and distinct findings from their respective needs assessments. The panelists will speculate on the application of these findings at their specific libraries and in academic libraries generally.

1. COMING TO AN
UNDERSTANDING
A Cross-institutional Examination
of Assessments of Data Curation
Needs
Jake Carlson - Purdue University
Dianne Dietrich - Cornell University
Gail Steinhart - Cornell University
Alison Valk - Georgia Institute of Technology
Stephanie Wright - University of Washington

2. Dianne Dietrich
Planning & Data Management
Plans

3. Planning and Data Management
Plans
May 2010
October
2010
December
2010
January
2011
NSF press
release
indicating intent
to require data
management
plans with
grant
proposals.
NSF releases
specifics for
data
management
plan
requirement.
Cornell survey
distributed to
PIs and Co-PIs
of NSF grants.
NSF
requirement
goes into
effect.

4. Planning and Data Management
Plans
 How prepared are researchers to address data
management plan requirements?
 What is the potential impact of researcher
plans on existing Cornell services?

5. Planning and Data Management
Plans
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Each bar represents a question where respondents were asked to select "Yes", "No", or "I'm not sure"
Percentage of respondents who answered "I'm not sure"
for questions where that was an option
Adapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readiness
to Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).

6. Planning and Data Management
Plans
0% 10% 20% 30% 40% 50%
No data
Up to 1 GB
1 GB - 100 GB
100 GB - 1 TB
1 TB - 100 TB
More than 100 TB
Responses to the question: "Given the NSF expectation to
share data ... how much data would you intend to share?"
Adapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readiness
to Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).

7. Planning and Data Management
Plans
Yes
30%
I'm not sure
61%
No: 9%
I do not plan
to create
metadata
26%
I'm not sure
if I plan to
create metadata
32% I do plan to
create metadata
42%
Have you produced or do you anticipate
producing metadata for this project?
Adapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readiness
to Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).
If you plan on
creating
metadata, does
it conform to
known
standards in
your discipline?

8. Planning and Data Management
Plans
0
10
20
30
40
50
60
70
Own
infrastructure
Campus solution Commercial
solution
Numberofresponses
Backup Strategy
Anticipated Backup Strategy by Size of Data
More than 100 TB
1 TB - 100 TB
100 GB - 1 TB
1 GB - 100 GB
Up to 1 GB
Adapted from Steinhart, et al. (2012) Prepared to Plan? A Snapshot of Research Readiness
to Address Data Management Planning Requirements. Journal of eScience Librarianship 1(2).

9. Stephanie Wright
Management

10. Management: UW
 Backgroun
d
 Services
 Survey &
Interviews

11. Management: Organization
Survey
 Guidance on data
organization (file
structure, file
naming, etc.) ranked
13th out of 14
 Tracking updates to
data (versioning)
ranked 8th
Image Credit: radrice “data cat finds no data”
http://blog.looxii.com/wp-content/uploads/2011/06/new-data-cat.jpg

12. Management: Organization
Interviews
 Whatever makes
sense to organizer
 More
planning, better
organization
 Especially true of
larger, well-funded
projects
“But that really was
sort of something we
addressed after the
fact, after we started
to go, „Huh, I‟m
naming them this
way, you‟re naming
them that way, and I
have no idea what
your naming
conventions mean.‟”

13. Management: Description
Survey
 1/3 didn‟t know of
metadata standard
 16% were able to
identify metadata
standard
 Metadata service
ranked 10th out of 14
Image & Quote Credit: NYU Health Sciences Libraries “Data Sharing and Management
Snafu in 3 Short Acts” http://www.youtube.com/watch?v=N2zK3sAtr-4
“Everything you
need to know about
the data is in the
article.”

14. Management: Description
Interviews
 Documentation is
biggest challenge in
data management
 Recognize role of
metatadata
 Time consuming, no
immediate benefit
 Data planning vs. data
forensics
“If I was gonna make
(the data) available
to other people, I
would feel some
responsibility in
documenting it a
little bit better.”
(Social Sciences)

15. Management: Summary
Services needed:
 Training on best
practices or general
strategies
 Tools that integrate
description and
organization of data
into the workflow
“I kind of feel like we’re
just making our way
through the wilderness.
And if there were
somebody who could
kind of hold our hands
and say, „Look, data
management is important
and here are some
strategies for going about
it…‟ That would be
great.”

16. Jake Carlson
Sharing

17. Sharing: Purdue
Background on Purdue‟s
work:
Primarily Interview
Driven
• Data Curation Profiles
• Data Management
Plans
• Data Information
Literacy

18. Sharing
 Willingness to Share
Generally, faculty are open
to sharing their data with
others.
There is an “underground
economy” of data sharing.
Factors in deciding whether
or not to share:
What will this person do
with my data?
How much time & effort will
it take me?Image Credit: andrew_mc_d “Share” http://www.flickr.com/photos/andrew_mc_d/452728652/

19. Sharing

20. Sharing
 Control
Issues in sharing data publicly:
Timing over when to release data.
Use - If anyone can get the data, anyone
can use it for whatever they want to
Misinterpretation - there‟s no guarantee
that someone won‟t misconstrue the data

21. Sharing
 Attribution
Generally expressed as need for others
to cite the data set (though not always)
“So for in my personal opinion, data citations
won‟t help me too much. Paper citations count
for everything. It counts for impact of the paper, it
counts for tenure, it counts for the profile of my
work.”
- Professor of Biochemistry

22. Sharing
 Documentation and
Description
"If you ask someone if you can
see their raw data, you might as
well be asking if you can look at
their underwear. It's really
problematic."
- Agronomy Professor

23. Sharing
 Services for Data Sharing at Purdue
Consultation & Collaboration with Data Producers
 Support "local" sharing
 Workflows
 Documentation
 Description
 Support "external" sharing
 Workflows
 Documentation
 Description

24. Alison Valk
Preservation

25. Background
“Develop campus
partnerships to
collect, manage, share, an
d preserve Georgia Tech
digital research data.”
“Improve and develop new
resources & services to
assist researchers with
data stewardship”
Preservation

26. IRB-approved research to determine
gaps in data curation services
provided to researchers.
Data assessment survey
Series of campus wide interviews
NSF DMP content analysis
Preservation

27. By combining information gathered
via the survey and the interviews, we
developed a clearer picture of the
research data curation needs on
campus.Out of 77 who completed
survey-
o 44 agreed to be interviewed
o 26 interviews completed
Preservation

28. Interview Team
Chris Doty
Susan Parham
Elizabeth Rolando
Alison Valk
10 Interview questions
“How important is it for you to
archive / preserve your data?”
“How important is it for you or
others to have access to your data
over the long-term?”
Preservation
Transcrib
e
interviews
Web application for
Qualitative & Mixed Methods research
Visualize major discussion points
or code correlations
Code

29. Correlation between
cost of working with
data –
to how strongly
participants feel data
should be
preserved…
Preservation

30. Storage prices no longer
cost prohibitive
Preservation

31. Lack of metadata or
curation =
unusable data
Data is often “lost”
when project participants
such as grad students leave
institution
Computing
professor:
“I don’t want to
micromanage my
research assistants”
Preservation

32. Some researchers
are using
Cloud based
tools, such as
DropBox etc. for
archiving –
Little concern for
security risks
associated.
Preservation

33. Next Steps:
Select Case studies-
o Researchers have volunteered to allow us
to archive their research data.
Increased Outreach- New Services
o Customized DMPtool
o Departmental Data Management Workshops
o More robust web presence
o Proof-of-concept Library hosted
Research Data Repository
Preservation

34. Questions?
 Jake Carlson
 @jrcarlso
 jakecarlson@purdue.edu
 Dianne Dietrich
 @nemka
 dd388@cornell.edu
 Gail Steinhart
 @gailst
 gss1@cornell.edu
 Alison Valk
 @valkcano
 alison.valk@library.gatech.edu
 Stephanie Wright
 @shefw
 swright@uw.edu