This document discusses the role of libraries in research data management. It notes that libraries are about organizing and describing information, so they are well-suited to help with research data. Libraries provide data management expertise and services like workshops, consultations, and data archiving. Managing research data is important throughout the research cycle, from study design to analysis to sharing results. Library services can help researchers comply with funder requirements and best practices for reproducible, reusable, and open research.

2. Libraries = books

3. Libraries = digital information

4. Libraries = data?
Libraries are
about organizing
and describing
things.
Why not data?

5. Libraries = expertise

6. Libraries = access

7. Librarians have the skills to help
researches plan, organize, describe
and share research data
Adelaide State Library of South Australia

8. Questions
• Why open data?
• What is the role of libraries?
• How can CSU libraries help?

9. data management != data sharing
• but the same principles apply to both

10. Why should I care about data management?
Rinehart, AK. “Getting emotional about data” College & Research Libraries News September 2015 vol. 76 no. 8 437-440

11. What is data
management?
The policies, practices and procedures needed to
manage the storage, access and preservation of data
produced from a research project

12. *ok not everything, but most things

13. More researchers
https://www.nsf.gov/statistics/2016/nsf16300/digest/nsf16300.pdf

14. See arXiv:1402.4578 for details

15. Working Email
Data are extant
(If status known)
Status of data
(if response)
Response
(if email
working)
doi:10.1016/j.cub.2013.11.014

16. We are losing vast amounts of data
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17. Research funding is tight
http://www.bu.edu/research/articles/funding-for-scientific-research/

18. Funders want to do more with less
http://figshare.com/blog/2015_The_year_of_open_data_mandates/143

19. White House’s 2013 OSTP
“The Obama Administration is committed to the proposition
that citizens deserve easy access to the results of
research their tax dollars have paid for. That’s why, in a
policy memorandum released today, OSTP Director John
Holdren has directed Federal agencies with more than
$100M in R&D expenditures to develop plans to make the
results of federally funded research freely available to
the public—generally within one year of publication.”
http://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research

20. NSF post-award requirements
“Investigators are expected to share with other
researchers, at no more than incremental cost and within
a reasonable time, the primary data, samples, physical
collections and other supporting materials created or
gathered in the course of work under NSF grants.
Grantees are expected to encourage and facilitate
such sharing.”
http://www.nsf.gov/pubs/policydocs/pappguide/nsf11001/aag_6.jsp#VID4

21. It’s good for science
• Improves research reproducibility
• Improves efficiency
• Spurs innovation

22. It’s good for you
• You are the future data user
• Your data get used (and cited)
• Exposure to collaborators
• More competitive grants

23. Where does data
management fit into
research?
Throughout the whole research cycle

24. Hypothesis
The research cycle

25. Hypothesis
Experimental
design
The research cycle

26. Hypothesis Data
Experimental
design
The research cycle

27. Hypothesis Data
Experimental
design
Results
The research cycle

28. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

29. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

30. Hypothesis Data
Experimental
design
ResultsArticle
Data
Management
Plans
The research cycle

31. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Analysis
The research cycle

32. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
The research cycle

33. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
The research cycle

34. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
Reuse
The research cycle

35. How are libraries getting involved?
•We’re NOT the data police
•We want to help!
•We provide services

36. Data Management Services
https://lib.colostate.edu/services/data-management

37. Workshops

38. One on one meetings
• How do I write a DMP?
• How do I organize my data?
• How do I clean and format my data?
• How do I automate my analyses?
• How do I get my data ready to share?

39. Data archiving service
• CSU Digital Repository
• 78 Datasets
• Satisfy requirements for
manuscripts and grants
• At no cost <1 TB
• $150/TB for 5 years
• $300/TB for >5 years

40. Large Projects
• Shortgrass Steppe – LTER (32)
http://hdl.handle.net/10217/100254
• Yellowstone Willows LTREB (21)
http://hdl.handle.net/10217/173646
• RAPID: Characterizing the Response of
a Burned Landscape to an Unusual and
Extreme Rain Event (4)
http://hdl.handle.net/10217/100371

41. Departments
and Schools
Atmospheric science
23%
Bioagricultural
sciences and
pest
management
9%
Biology
9%
Clinical Sciences
18%
Ecology
9%
Ecosystem
science and
sustainability
4%
Psychology
4%
Soil and crop
science
14%
Statistics
5%
The Energy institute
5%

42. Take home points
• Data management requires new skills
• Library staff have those skills
• CSU Libraries can help
• Education
• One on one meetings
• Digital Repository

43. Thanks!
Data Management Services
http://lib.colostate.edu/services/data-management
Need help?
Email library_data@colostate.edu
Contact Tobin
Tobin.magle@colostate.edu
Twitter: @tobinmagle
http://orcid.org/0000-0003-3185-7034