University of Minnesota’s Lisa Johnston talks about five ways your library can support researchers when sharing their data. From the October 22, 2015 webinar, How to assist researchers in sharing their research data: http://libraryconnect.elsevier.com/library-connect-webinars?commid=175949

1. Dat a Sharing
Five ways that YOUR Library can support
researchers when sharing their data
Library Connect Webinar:
How to assist researchers in sharing their research data
Lisa Johnston
University of Minnesota - Twin Cities
October 22, 2015

2. 5 ways that your Library might help researchers
share their data:
1. Keep doing what you do….be an information resource
2. Educate on data management skills/best practices
3. Develop policy + institutional guidelines for data
4. Create a data sharing service: Lots of options!
5. Curate and archive institutional data for reuse

3. 1. Keep doing what you do….be an information
resource

4. Library websit e pulls campus resources t oget her
http://lib.umn.edu/datamanagement

5. Bring dat a service providers t oget her in an informal way
Past RDM
Discussion Topics:
● Data Storage Options on
Campus
● Metadata Standards
● Spatial Data
● Best Practices for De-
identifying Research Data
● Data Repositories (Local,
National)
● Data Services at the
Supercomputing institute
● Practical Examplesfor
Managing data (Sciences)
https://sites.google.com/a/umn.edu/rdm-cop/home

6. Keep up-t o-dat e informat ion for administ rat ion
http://lib.umn.edu/datamanagement/funding

7. 2. Educate on data management skills/best
practices
Image: http://www.spellboundblog.com/wp-content/uploads/2008/09/floppy_photo.jpg

8. Offer workshop on “How t o writ e a Dat a Management Plan
(DMP)”
● For researchers
● Discussion Based
● RCRCECredit
● Departments
request custom
sessions
● Co-teach with
Liaison
“Training Researcherson Data Management: A Scalable, Cross-Disciplinary
Approach," (2012) Available in the Journal of eScience Librarianship (Vol. 1: Iss. 2)
https://www.lib.umn.edu/datamanagement/workshops

9. Provide DMP Templat es and offer in-person consult at ions
● One-on-One consults
● DMP Template
● Boilerplate text
● DMPOnline Tool (CDL)
● Examples
https://www.lib.umn.edu/datamanagement/DMP

10. Graduat e programs do not always include dat a informat ion
lit eracy (DIL) skills/ compet encies.
http://datainfolit.org

11. U of MN Dat a Management Online Course
● Hybrid online
and in person
workshops
● Structured
around DMP
● Hands on
activities
● Direct
application to
their data
http://z.umn.edu/datamgmt15
Johnston & Jeffryes(2014). “Steal ThisIdea.” ACRL NewsOct 2014.
Slides, handouts, activitiesfor 5-session Data Management Course
http://z.umn.edu/teachdatamgmt

12. Scaffold DIL skills for undergrads using Personal
Informat ion management t ools/ st ories
https://www.lib.umn.edu/pim/archiving

13. 3. Develop policy + institutional guidelines
for data
Image: http://www.businesscomputingworld.co.uk/wp-content/uploads/2012/01/Computer-Connections.jpg

14. Institution-wide data policies define roles and
responsibilities for long-term data management
issues
Also read:
Erway, R. (2013). Starting the Conversation: University-
wide Research Data Management Policy. EDUCAUSE
Review Online.
Policy: http://policy.umn.edu/research/researchdata

15. Ensures accessibility and preservation of research data through curation,
metadata, repositories, and other access and retrieval mechanisms to meet
federal, state, sponsor, and University requirements.
Trains and supports researchers in the creation and implementation of data
management plans.
Research Data Management Responsibilities
University of Minnesota Libraries

16. 4. Create a data sharing service: Lots of
options!
Image Http://cdn.slashgear.com/wp-content/uploads/2012/10/google-datacenter-tech-13.jpg

17. Typical Dat a Sharing

18. Dat a Sharing Techniques
Ways to Share your Data Pros? Cons?
Post online to a personal or project website
Publish data in a journal as a “supplement” to
your main research article.
Make your data “Available on request” via email
or dropbox to those who ask.
Deposit in a disciplinary repository (e.g. Dryad,
FlyBase, etc.)
Deposit in a general/commercial repository
(e.g. FigShare, Mendeley, etc.)
Deposit in an institutional repository, such as
the Data Repository for the University of
Minnesota (DRUM)
Lisa Johnston, University of Minnesota Libraries (ljohnsto@umn.edu)

19. DRUM
ht t p:/ / z.umn.edu/ drum
Available to U of M
researchers and
provides:
○ Open access
○ Curation services
○ Permanent
identifiers (DOI)
○ Flexible Licenses
○ File download
analytics
○ Preservation

21. Data Repository of the University of Minnesota
(DRUM)
● Utilize existing repository technologies for
cost savings/efficiencies (DSpace, open
source software)
Custom upload form and metadata schema for
research data
Apply Creative Commons licenses
Curation workflow allows for review of data
before openly available

22. 5. Curate institutional research data for
sharing and long-term preservation/reuse
Image: http://www.fujitsu.com/img/INTSTG/products/bpm/business-process-management-582x240.jpg

23. What is data curation?
Data curation steps may include appraisal, ingest, arrangement and
description, metadata creation, format transformation, dissemination and
access, archiving, and preservation of digital research data.
Twin Cities Housing GIS Data, UMN

24. What was the process to curate the data?
Stage 0: Stage 1: Stage 2: Stage 3: Stage 4: Stage 5: Stage N:
Receive
Data
Appraise /
Inventory
Organize Treatment
Actions /
Processing
Description/
Metadata
Access Reuse Data
Workflow Stages drafted by the “Digital Curation Sandbox” participants
borrowed from DCC Curation Lifecycle.
http://hdl.handle.net/11299/162338

25. What happens aft er submission t o DRUM?
After submission, U of Minn researcher receives a
confirmation email
Within two business days, we will review their data and
contact them about proposed modifications to the
submission
Missing files
Changes/additions to data documentation
Reshaping directory structure
Converting proprietary software to more
archival-friendly formats

26. Document Ext ensive Informat ion for Sharing
Methodological Information
Data collection
Processing
Analysis steps
Data-Specific Information
File abbreviations
Name glossary
http://z.umn.edu/readme

27. Data Sharing = Services in Support of
Research Data Lifecycle
Grant
Prep
Storage
Data
Collection
Analysis
Preservation
Project
Begins
Published
Results
Project
Close
Sharing
Data Management Plan
(DMP) Consultation
Metadata Consultation
Data Management Best
Practices/Training
Data
Management
Data Curation &
Repository Services

28. Thanks and questions!
Visit our website
http://lib.umn.edu/datamanagement
Lisa Johnston, DMCI Lead University Libraries, ljohnsto@umn.edu