This presentation was provided by Melissa Levine of the University of Michigan during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016

1. Data Curation – Cultivating Past Research Data for Future Consumption
NISO Virtual Conference
August 31, 2016
Ethics and Legal Considerations
Melissa Levine
Lead Copyright Officer
University of Michigan Library

2. The Availability – Usability Gap
<a href="http://www.photoeverywhere.co.uk"rel="nofollow">Travel Photography from
PhotoEverywhere</a>
This work is licensed under a Creative Commons Attribution2.5 License

3. Where we are.

4. Context
Open access funding mandates
Government – NIH 2008, NSF 2010, OSTP 2013
Foundations - Wellcome Trust, Australian Research
Council, World Bank, Gates Foundation
Simple things complex:
making sure authors have the rights they need to deposit
Can only grant what you have to give Grant terms,
contracts, employment / contractor roles
Now: proactive planning in required data plans

5. Issues
Technology outpaces law.
Different countries differ: law, culture in transnational activity
of data.
Different disciplines differ: science, humanities, evolutions –
departments
Moved beyond hiding data in short time.
mandates change culture, expectations, impulse to
squeeze last bit of publication before sharing; sharing
expected and incentivized (anecdotal, varies with
disciplines and funding mandates…)

6. Now.
Assuming preference or mandate for
open is new.
Thinking proactively about the legal,
policy, ethics implications to keep data
as unencumbered as possible
OR appropriately secure (eg ICPSR
model)

7. What we’re doing.

8. Resources – works in progress
Collaborative, evolving, global
DataONE – Primer on Data Management:
What you always wanted to
knowhttps://www.dataone.org/sites/all/doc
uments/DataONE_BP_Primer_020212.pdf
Databib

9. Research Data Alliance RDA
RDA/CODATA Legal Interoperability IG
https://rd-alliance.org/group/rdacodata-legal-interoperability-ig/wiki/legal-principles-data.html
Principles and Implementation Guidelines,
22 August 2016
https://rd-alliance.org/group/rdacodata-legal-interoperability-ig/wiki/draft-implementation-
principles.html
Article
Michael W. Carroll, Sharing Data and Intellectual Property Law: A Primer, August 27, 2015,
PLOS | Biology, http://dx.doi.org/10.1371/journal.pbio.1002235

10. Making research better by enabling
people
to find, share, use, and cite data

11. Using DOIs
1. Take a dataset 2. Describe it
Title
Authors
Year
Description
And others…
3. Assign a DOI
10.1234/exampledata
4. Reuse and reference!
Unique Persistent
5. Enjoy the benefits
Findability
Reusability
Track
citations
Measure
impact
Source: https://www.datacite.org/outreach.html

12. Using DOIs
1. Take a dataset 2. Describe it
Title
Authors
Year
Description
And others…
3. Assign a DOI
10.1234/exampledata
4. Reuse and reference!
Unique Persistent
5. Enjoy the benefits
Findability
Reusability
Track
citations
Measure
impact
Source: https://www.datacite.org/outreach.html

13. What are data?
Numbers
Measurements
Images
Film clips
Sound recordings
Music clips

14. What are data?
Numbers
Measurements
Images
Film clips
Sound recordings
Music clips
Data about
these things:
metadata

15. What are data?
Numbers
Measurements
Images
Film clips
Sound recordings
Music clips
Are these units
subject to some
legal or ethical
concern?
Data about
these units.
Metadata as
data.

16. Rights and responsibilities
Possible actions:
Sharing
Copying
Securing
Reuse
Cite

17. Rights and responsibilities
Possible actions:
Sharing
Copying
Securing
Reuse
Cite
Legal areas:
Copyright
Privacy
Employment
Contract

18. Rights and responsibilities
Possible actions:
Sharing
Copying
Securing
Reuse
Cite
Legal areas:
Copyright
Privacy
Employment
Contract
How
complicated
can this be?

19. Issue spotting: N/ of Data ‘Units’
If: numbers, facts, measurements
Then: no copyright in the data ‘units’ But: are data from
‘protected’ source or one for which access was provided with
conditions? What were the conditions? Need to conform to
those or attempt to negotiate.
If: the data is comprised of components of creative expression
(examples, photos, artwork, copyrightable elements)
Then: copyright may exist in individual units of data and further
exploration will be required to determine whether the data are
eligible copyright - and if so they may not be appropriate for
DBD. Caveat: it may be possible to obtain permissions for the
data units if desired, if resources allow

20. Issue spotting:
N/ of Data as Compilation
Are you an employee?
If yes: work product may be “work for hire” and subject to institutional policy or
employment law. Affects whether and how researcher retains copyright in
his/her scholarly works. What they have to grant.
If no: needs further inquiry. If the creator of the data set is not an employee (e.g.
visiting scholars may or may not be employees for this purpose; grad students
are not employees...), a license or similar documentation will need to be
prepared/signed/retained for deposit in data repository.
What is your affiliation with the university or research institution sponsoring or
hosting the research?
Might there be any ownership that could be asserted by people who worked on your

21. Issue spotting:
N/ of Data as Compilation
Any need for notifications to and/or releases from grad students, contractors? Were they
employees? Is there a policy or assumption on work of grad students? Should there
be something in writing at the start of work with students?
Are there any contracts or agreements associated with your research?
If yes, what are they?
Is your work grant-funded? What were the conditions of the grant in terms of ownership,
use, and reuse of data?
If there are conditions, are they negotiable?
Is your grant-funded work subject to an open access data and/or publishing requirement?
[know in advance which agencies have such policies and the basic conditions]
If yes, which one? (Terrific!)
Does deposit in a particular data repository satisfy that requirement?

22. Issue spotting: N/ of Metadata
CC0 applied to all metadata in data
repository as condition of deposit?
Who produced the metadata?
Keep in mind variations of what we mean
by metadata: non-creative, factual,
descriptive information about the data. [not
field notes, for example...]

23. Issue spotting: Privacy
Is the research product subject to an IRB? What are the parameters?
Is there any information that could be connected back to an individual?
Is it personal information (medical, legal/criminal record)?
Is the person a minor?
If yes to any of the above, an open repository is probably not appropriate.
In any of the above cases, was there formal consent?
If yes, explore further to see if the consent is sufficient to use an open
repository.
HIPPA, FERPA, national security laws related to data sharing

24. Issue spotting: Minors
Is there information about children?
Was it collected with guardian/parental
consent?
Is the child identifiable?
If yes: open repository is probably not the
right repository.

25. Rights and responsibilities
Possible actions:
Sharing
Copying
Securing
Reuse
Cite
Remember these?

26. Data access and intellectual
property
A few more possible access and ownership concerns:
What steps will be taken to protect privacy, security,
confidentiality, intellectual property or other rights?
Does your data have any access concerns? Describe the
process someone would take to access your data.
Who controls it (e.g., PI, student, lab, University, funder) ?
Any special privacy or security requirements (e.g., personal
data, high-security data) ?
Any embargo periods to uphold?

27. https://deepblue.lib.umich.edu/data

28. Examples of other data
services
Social Science Data Services: Harvard-
MIT Data Center
University of Wisconsin-Madison
University of Virginia

29. Where we are going.

30. Data Curation – Cultivating Past Research Data for Future Consumption
NISO Virtual Conference
August 31, 2016
Ethics and Legal Considerations
Melissa Levine
Lead Copyright Officer
University of Michigan Library
mslevine@umich.edu
Thank you.