February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth Learning to Curate Research Data Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library

1. NISO Virtual Conference:
Scientific Data Management
February 18, 2015
Jennifer Doty
Research Data Librarian
Emory University
Atlanta, GA
Learning to Curate
Research Data

2. Overview
Data Curation Working Group
• pilot project
• lessons learned
Data Curation Workshop
• planning
• challenges
• feedback (more lessons learned)
Next Steps…

3. Data Curation Working
Group Pilot Project
Learning to Curate Research Data

4. www.icpsr.umich.edu

5. Collaborative Curation
“We propose that domain specific archives partner
with institution based repositories to provide
expertise, tools, guidelines, and best practices to the
research communities they serve.”
Green, Ann G., and Myron P. Gutmann. (2007) "Building Partnerships Among Social
Science Researchers, Institution-based Repositories, and Domain Specific Data
Archives." OCLC Systems and Services: International Digital Library Perspectives.
23: 35-53. <http://hdl.handle.net/2027.42/41214>

6. Support:

7. Ron Nakao, Stanford
Libbie Stephenson, UCLA
Jon Stiles, UC Berkeley
Jen Doty, Emory
Rob O’Reilly, Emory
Joel Herndon, Duke
Jared Lyle, ICPSR

8. DCWG Pilot Goals
For participants:
• Apply curation theories to practice through actual data
processing.
• Will have a fully curated data collection ready for archiving
at the end of the session.
• Interact with and ask questions of other data specialists
within a working environment.
• Gain first-hand experience using ICPSR’s internal tools and
workflows for curation.
• Understand level of effort to work through collections and
provide assistance to researchers.
• Learn about things not thought about (e.g., costing,
standardized workflows).

9. DCWG Pilot Goals
For ICPSR:
• Engage with outside data curators to learn what others
are doing and thinking.
• Polish internal procedures and tools by opening them
to outside review and critique.
• More data will be curated and archived, benefiting the
ICPSR membership and the entire social science
community.
• Better utilize resources of the Official Representative
(OR) community, including personal relationships and,
especially, their wide-ranging expertise.
• Train a data curation community of support.

11. DCWG Schedule
Week 1 - Introductions & Data Sources
Week 2 – Acquisition
Week 3 - Review
Week 4 – Processing
Week 5 – Metadata
Week 6 – Dissemination
Week 7 - Summary

12. DCWG Topics
Acquisitions
•Gathering information from the data producer
•Legal agreements
•Appraisal
•What to keep, and for how long?
Review
•Quality review - are the data complete, accurate, and well
documented?
•Disclosure review - is there sensitive or private information?
•Create a plan of attack
Processing
•Data cleaning
•Insuring data integrity
•Quality review - is the final package self-contained?
Metadata
•Standards overview
•Variable level metadata
•Study level metadata
Dissemination
•Final packaging and review
•Workflows
•Preservation policies
•Web delivery

13. What's in it for us?
• Well-timed with new
hires in 2012, and
higher-up support for
RDM projects
• Learn from gold
standard holders:
• ICPSR processing
pipeline and tools
• implications of
providing premium level
service for staffing and
resource allocation
Nobel Prize Illustration by Howdy, I’m H. Michael Karshis on Flickr / CC BY 2.0

14. The Data
• Panel Data - all states in the United States, 1972-
2007, annual
• Coded Data - state-level data policies on home
schooling, and relevant court cases
• Publicly-Available Data - a mix of demographic,
economic, and social data from sources such as the
BEA, the Census Bureau, the NCES
• No issues with regard to sensitivity of data or
proprietary restrictions

15. Issues and Considerations
• Data assembled for particular project, not with
long-term archiving and research in mind
• Discrepancies in documentation:
• variable names
• unclear citations
• broken URLs
• variables in data missing from codebook, and vice-versa

16. Issues and Considerations
• Long history with the Principal Investigator for the
project, which meant lots of context about the
project and the data
• Useful in clarifying ambiguities in the data, e.g. “it
makes sense to us” citations
• Even with that context, there was still much work
and back-and-forth involved

17. Issues and Considerations
Absent that prior history, the climb would have been
much more steep…
SteepclimbupbylisaAnguloreidonFlickr/CCBY-NC2.0

18. Lessons Learned
Overall, very impressive
“to see how the sausage is
made”:
• ICPSR processing
pipeline
• SDE infrastructure
• Internal production
and preservation
tools
Sausage machine by Scoobyfoo on Flickr / CC BY-NC-ND 2.0

19. Lessons Learned
Realistically, best equipped
at current levels to provide
consultations and guidance,
but not hands-on data
curation
IBM 1620 in Computer Lab by euthman on Flickr / CC BY-SA

20. Work in Progress
• Intent to archive dataset with ICPSR still holds, but
delayed by:
• necessity for further documentation from investigators
• demands on our time from other projects
• Future plans for archiving datasets created by
campus researchers informed by lessons learned
from participating in pilot project

21. Data Curation Workshop
for Researchers &
Librarians
Learning to Curate Research Data

23. Local Workshop Objectives
• Raise awareness of funder requirements and
journal policies to preserve and share data, and
resources available to help do so
• Educate researchers and librarians in best practices
for documenting and preparing data for long-term
preservation and sharing
• Provide guidance and support to researchers
depositing their data with appropriate domain
repositories (e.g. ICPSR, Dryad)
• Opportunity to reach the researchers where they
reside…

25. Lizzy Rolando, Georgia Tech
Jen Doty, Emory University
Mandy Swygart-Hobaugh, Georgia State University

27. Challenges
• condensing week-long workshop material into one-
day sessions
• identifying topics most relevant to each audience
• time constraints for everyone—January dates
overlapped with start of classes on all 3 campuses

28. Workshop agenda
• Identifying and Finding Data to Archive
• Reviewing Data
• Reviewing Confidential Data
• Cleaning Data
• Describing Data
• Depositing Data
• Disseminating & Publishing Data
• Local data curation resources

29. Participant Feedback
• Mixed responses: generally positive about content
and structure, and all replies useful for revising
material and better marketing
• Appreciated balance of presentations plus exercises
and discussions
• Expressed interest in information related to
planning for data management and curation
• Increased awareness of resources available

30. Next Steps…
For ICPSR:
• revising materials: vary the approach for
researchers and librarians
• Researchers: why best practices matter and how to
apply to projects and data right now
• Librarians: focus on curation topics
• planning for additional offerings in other locations

31. Next Steps…
For institutions:
• identify related training to offer locally
• adopt methods to support our researchers
preparing data for archiving and sharing
• explore additional opportunities to partner with
domain data archives

32. GreenQuestionMarkbymikecoghonFlickr/CCBY

33. Thank You!
Jennifer Doty
Research Data Librarian
Emory University
jennifer.doty@emory.edu

34. References
RDAP14 presentation:
http://www.slideshare.net/asist_org/rdap14-
learning-to-curate-panel-32822019
Overview: http://www.asis.org/Bulletin/Aug-
14/AugSep14_DotyEtAl.html
ACRL webinar: http://connect.ala.org/file-
manager/download/group/85286/Webinar
Slides/20140113_acrl_lyle.pptx