Hansen Metadata for Institutional Repositories

Metadata for Institutional
Repositories
Carolyn Hansen | @meta_caro
NISO Training Thursday
February 23, 2017

Hansen | Metadata for IRs
About Me
• Metadata Librarian at University
of Cincinnati (UC) Libraries
• Chair, Project Hydra Descriptive
Metadata Working Group
• Embedded Metadata Subject
Expert for UC Development
Team for self-submission IR
scholar@UC
• Research: linked data, digital
humanities, public history, GIS &
data visualization

Agenda:
• Deﬁning high quality metadata
• The trouble with IRs and
metadata
• Case Study: scholar@UC
• Best practices for getting high
quality metadata
• Resources and guidelines
Image: Silvia Sala. “Ma perché scrivo? È
l'unico mio conforto.” https://ﬂic.kr/p/cok3GL

Deﬁning high quality metadata
• According to Europeana’s “Report & Recommendations from the Task
Force on Metadata Quality,” high quality metadata is…
• Resulting from a series of trusted processes
• Findable
• Readable
• Standardised
• Meaningful to audiences
• Clear on re-use
• Visible

Barriers to getting high quality
metadata
• In even the best situations…
• lack of foresight for online discovery
• treating metadata as an afterthought
• lack of funding and resources
• describing digitized items with little information
— Europeana’s “Report & Recommendations from the Task
Force on Metadata Quality”

The Trouble with IRs and
Metadata
• IRs are not controlled environments like traditional
cataloging applications, may be vendor created or open-
source
• IRs may or not have mediated submissions or quality
control measures
• Metadata created by different streams according to
different standards (ex. ETDs from graduate school v. self-
submission by faculty)
• Librarians/developers/vendors make assumptions about
how users will interpret submission forms

Why IRs need good metadata
• discovery (in the application and on the Web)
depends on high quality, consistent metadata,
particularly of facetable ﬁelds and subject/keyword
terms
• faculty are more likely to use an IR if their material
is discoverable on the Web (Ex. Google Scholar)
• IR metadata may be mixed with existing library
metadata through an integrated discovery layer;
interoperability can be a signiﬁcant problem

Diverse user expectations also makes
getting good metadata difﬁcult
• “Why aren’t there ways to express geospatial
data?”
• “I just want a DOI for my journal article”
• “Can I just use this for dark storage?”
• “Why are there so many required ﬁelds? I don’t
have time to enter all this information.”

Case Study:
• self-submission repository, open-source development based on
Project Hydra Stack (https://scholar.uc.edu/)
• metadata profiles pre-populate submission fields based on work
type the user chooses at the beginning of submission
• only remediation of metadata is quarterly “clean” of facetable
fields; LCSH not used but NAF is in remediation when possible
• in 2015-2016, cohort of early adopters populated the application
with approximately 300 records
• despite “help” content embedded in the application showing how
metadata should be entered, many metadata problems occurred

Example problems
• “Title” ﬁeld: Some faculty users unsure whether to enter the
title of their work or their academic title
• Unintelligible titles and incomplete metadata.
Example => Title of a dataset: “p.txt.”
• Multiple instances of submitters’ name as “creator”
• Entering subjects in facetable ﬁelds using multiple variant
spellings, capitalizations, etc.
• Technical problems like trailing/leading whitespace
impacting facets

Personal names entered
as subject in direct order,
no controlled vocabulary Similar concepts with variant
spelling
acronyms
Could use LCSH terms in
remediation
Entered as subject, not entered in
separate geographic subject ﬁeld

With IRs and metadata,
you get what you ask for, or less
Whether designing GUIs for open-source IRs,
or customizing vendor created interfaces,
remember…

Example: Datasets &
Complex Files

What is the role of the
metadata specialist?
cheerleader?
police?
cleaner?
educator?
all of the above?

Our imperfect solution
• for scholar@UC, we decreased the number of required fields;
basically only the fields needed for a DOI
• we expanded help information and examples embedded within
submission form
• additional descriptive fields are available but are hidden from main
view to prevent input burnout
• sustainability of manual metadata remediation is an unsolved issue
as the amount of content in the IR grows
• datasets and complex files present continual challenges; we
encourage users to add “Read Me” files and other explanatory
documents, but not many users are doing so

Best Practices for getting
high quality metadata
• Develop a baseline metadata profile depending on your institution’s internal needs
as well as harvesting for other platforms; this profile should define required and
optional submission fields
• Determine if baseline metadata profiles differ depending on the material type (ex.
documents v. datasets)
• Be very conscious of the UX experience of user submission forms; do focus group
testing of forms if possible; consider customization of vendor created interfaces if
necessary
• Determine an assessment schedule early, so that metadata specialists can identify
trends of what is/isn’t working regarding metadata submission
• Determine which fields are candidates for remediation or enrichment
• Enrichment projects and remediation should involve users AND metadata
specialists

Resources
• Chapman, Joyce Celeste. “User Feedback and Cost/Value Analysis of Metadata Creation.” http://www2.archivists.org/sites/
all/files/saa_description_presentation_2010_chapman.pdf
• Digital Library Federation (DLF) Assessment Interest Group (AIG) Metadata Working Group. http://
dlfmetadataassessment.github.io/
• “Draft Principles for Evaluating Metadata Standards.” ALCTS/LITA Metadata Standards Committee. http://metaware.buzz/
2015/10/27/draft-principles-for-evaluating-metadata-standards/
• Europeana Pro Data Quality Committee. http://pro.europeana.eu/page/data-quality-committee
• “Final Report.” Task Force on Enrichment and Evaluation. http://pro.europeana.eu/files/Europeana_Professional/Publications/
Metadata%20Quality%20Report.pdf
• Harlow, Christina. “Metadata Quality Analysis.” https://github.com/cmh2166/ShareFest15MetadataQA
• Harper, Corey. Can Metadata be Quantified? (slides presented at 2015 DPLAFest) 2015-04-18. https://schd.ws/hosted_files/
dplafest2015/c1/CanMetadataBeQuantifiedSlides.pdf
• Hydra Metadata Interest Group. https://wiki.duraspace.org/display/hydra/Hydra+Metadata+Interest+Group
• Neatrour, Anna and Myntti, Jeremy. Automating Controlled Vocabulary Reconciliation. (slides presented at DLF Forum 2015)
http://www.slideshare.net/aneatrour/automating-controlled-vocabulary-reconciliation
• “Report and Recommendations from the Task Force on Metadata Quality.” http://pro.europeana.eu/files/
Europeana_Professional/Publications/Metadata%20Quality%20Report.pdf

Connect
Email: carolyn.hansen@uc.edu
Twitter: @meta_caro
Web: www.carolynhansen.org

Thank you!
Questions?

Hansen Metadata for Institutional Repositories

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