Presented to the MSU RDM Cafe Nov 24, 2015

1. Data as a Library Acquisition
Collection Development Policies for Data @ MSU Libraries
Hailey Mooney
Piloting Infrastructure for Data Collections @ MSU Libraries
Aaron Collie
RDM CAFÉ Nov. 23, 2015
http://is.gd/collecting_data

2. Collection Development Policies
• Guide the purchase or acceptance of materials
into the Library by specialist Librarians
• Multiple collection development policies
– Subject areas
– Formats
• http://www.lib.msu.edu/about/collections/po
licy/

3. Data in Collection Development
Policies
• Data specifically addressed:
– Digital Research Data
• Produced by MSU researchers, distinct from data that is
purchased, produced, owned, or curated by third parties.
– Data Services (Numeric Data)
• Numeric information includes both data sets and statistical tables
– Digital Text
• Text that is amenable to computational analysis
– Maps/Geography
• Digital data sets for use with Geographic Information Systems
• As of 2015, subject area policies undergoing update to
include data

4. Digital Research Data Collection
Development Policy
• New (drafted July 2014)
• Developed under auspices of MSUL Research
Data Management Guidance team
• Provides scope and criteria for collections
• http://libguides.lib.msu.edu/c.php?g=139267

5. Purpose
• House unique digital data materials produced
by MSU researchers across disciplinary areas
• Provides a service to MSU researchers in need
of data sharing mechanisms
• Caveat: does not unnecessarily replicate data
available elsewhere or replicate the data
curation services available by disciplinary data
repositories

6. “Data”
• Digital data is defined as the primary source
materials used in the process of conducting
research, in electronic form. Digital data takes
a variety of specific formats including numeric,
textual, geospatial, audiovisual/multimedia,
and more.

7. Selection Responsibility
• Subject specialist/Liaison Librarians
– Relevance, collection fit
• Digital/Format specialist Librarians
– Technical and metadata requirements

8. Criteria: Format
• Larger, complex, and heterogeneous data file
collections are more resource-intensive and will
require careful consideration of available
resources
• Data must be complete and ready for distribution
in its final or most useful form
• Preserved in the fidelity received
• Files may be reformatted for access
– Processing of outdated file formats may incur
additional costs which impact selection feasibility.

9. Criteria: Authorship and Intellectual
Property
• authored by at least one MSU researcher
• author must hold the copyright
• Depositor Agreement
– Affirm ownership
– Warrant no identifiable/sensitive data
– Grants MSUL non-exclusive rights to distribute, reproduce,
and retain
– Location, retention, cataloging, preservation, and
disposition of the deposited work by the MSUL will be
conducted in its sole discretion
• Availability of author to assist MSUL with processing as
needed

10. Criteria: Documentation and Data
Quality
• meet general quality standards established by
disciplinary norms, including provision of
adequate documentation and metadata
• accompanied by documentation necessary for
interpretation and re-use
– completed “readme” file may be requested of data
authors
• include a bibliography of related publications
• MSUL does not provide editorial or peer review
of the data

11. Criteria: Access
• Data are intended for public open access
• No confidential and sensitive information
• Immediate access preferred
– Embargoes may be considered

12. Collection Management Issues:
Preservation and Cost to Libraries
• Part of the Libraries’ active and ongoing
collection management activities
• Initial commitment to preservation for digital
data is for a period of 10 years, after which
active collection management and review
policies will be applied

13. Piloting Infrastructure for Data Collections
Step 1: Data as an Asset
Step 2: Data as an Object
Step 3: Data in a Collection
Step 4: Data in a Collection of Objects
Step 5: Data in a Repository of Collections

14. Step 1: Data as an Asset
• A source of information
• Made accessible
• For use

15. Step 1: Data as an Asset
Does it go here? What about here?
Getting closer? Is this… even..?

16. Step 1: Data as an Asset
Here it is!
• It’s “in” the library
• On our servers
• For you to use

17. Step 2: Data as an Object
But librarians love books!
Love, operationalized:
• Acquiring
• Processing
• Cataloging
• Curating
• Circulating
• Conserving
• Referencing
• Consulting
• … AKA… org chart

18. Step 2: Data as an Object
We’re pretty big into
systems.
So.. Now, where does that data
go again…?https://blog.library.gsu.edu/2012/02/02/interli
brary-loan-is-fast-furious/

19. Step 2: Data as an Object

20. Step 2: Data as an Object

21. Step 3: Data as a Collection
etd.lib.msu.edu
• 3000+ dissertations from 2010-present
• 500 – 600 per year
On the way:
• Data (supplemental files)
• Non-PDF dissertations

22. Step 4: Data as a Collection of Objects
Knowledge from the Margins
• 1 event
• 60 papers (conference
proceedings)
• Video
• Photos
• Artwork

23. Step 5: Data as a Repository of
Collections
• A place for:
– Collections Data
– Humanities/Textual Data
– ETD Supporting Data
– Faculty Research Data