Data as a Library Acquisition

Collection Development Policies for Data @ MSU LibrariesHailey Mooney

Piloting Infrastructure for Data Collections @ MSU LibrariesAaron Collie

RDM CAFÉ Nov. 23, 2015

http://is.gd/collecting_data

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/policy/

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

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

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

“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.

Selection Responsibility

• Subject specialist/Liaison Librarians

– Relevance, collection fit

• Digital/Format specialist Librarians

– Technical and metadata requirements

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• Files may be reformatted for access– Processing of outdated file formats may incur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.

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

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

Criteria: Access

• Data are intended for public open access

• No confidential and sensitive information

• Immediate access preferred

– Embargoes may be considered

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

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

Step 1: Data as an Asset

• A source of information

• Made accessible

• For use

Step 1: Data as an Asset

Does it go here? What about here?

Getting closer? Is this… even..?

Step 1: Data as an Asset

Here it is!

• It’s “in” the library

• On our servers

• For you to use

Step 2: Data as an Object

But librarians love books!

Love, operationalized:

• Acquiring

• Processing

• Cataloging

• Curating

• Circulating

• Conserving

• Referencing

• Consulting

• … AKA… org chart

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/

Step 2: Data as an Object

Step 2: Data as an Object

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

Step 4: Data as a Collection of Objects

Knowledge from the Margins

• 1 event

• 60 papers (conference proceedings)

• Video

• Photos

• Artwork

Step 5: Data as a Repository of Collections

• A place for:

– Collections Data

– Humanities/Textual Data

– ETD Supporting Data

– Faculty Research Data