Good data management practices are becoming increasingly important in the digital age. Because we now have the technology to freely share research data and also because funding agencies want to do more with decreasing research funds, many funding agencies and journals require authors and grantees to share their research data. To provide training in this area, Tobin Magle, the Morgan Library's Data Management Specialist, is putting on a series of data management workshops called "Data and Donuts". Join us to learn about data management topics throughout the research data lifecycle.

1. The Impact of
Data
Management
C. Tobin Magle, PhD
Sept. 29, 2016
9:00-10:00 a.m.
Morgan Library Computer
Classroom 173

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

3. 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

4. *ok not everything, but most things

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

6. See arXiv:1402.4578 for details

7. 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

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

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

11. 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

12. 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

13. In other words…
In other words…

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

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

16. But wait…
Barriers to
data sharing

17. “But it’s mine, I don’t want to share!”
• Usually funded by public money
• See White House statement
• If you work for CSU, the
university actually owns your
data
• You are the steward
• CSU promotes open data

18. “But my data are too small to be useful”

19. “But I work with sensitive/private data”
• CAN share deidentified data
• CAN share summary data
• https://clinicaltrials.gov/
• Controlled access
• See dbGaP @ NCBI re: NIH genomic data
sharing policy
• Release metadata so people know the data
exist and ask for it
• Identifying personal genomes by surname
inference
• https://www.ncbi.nlm.nih.gov/pubmed/23329047

20. “But I’m planning applying for a patent!”
• Ok data sharing isn’t right
for you
• But good data management
practices have benefits
even if you don’t share!
• Can share later

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

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

23. Hypothesis
The research cycle

24. Hypothesis
Experimental
design
The research cycle

25. Hypothesis Data
Experimental
design
The research cycle

26. Hypothesis Data
Experimental
design
Results
The research cycle

27. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

28. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

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

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

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

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

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

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