The document presents a study on the types, quality, and quantity of metadata associated with image resources on the Gateway to Oklahoma History website. Key findings indicate that while there is substantial structured data and metadata, improvements are needed in descriptive filenames and the application of schema.org tags to enhance web findability. The research highlights the importance of active participation by cultural heritage institutions in search engine optimization to ensure their resources are visible online.

1.
Figure 4: An Individual Record from the Gateway to Oklahoma History Website
Emily Kolvitz, University of Oklahoma, School of Library and Information Studies & Bynder LLC
RESEARCH
METHODS
INTRODUCTION
Figure 3: Detailed Information on the types, quality and quantity of metadata from Gateway to
Oklahoma History Image Resources
Image Source: http://gateway.okhistory.org/ark:/67531/metadc317314/
Image Source: http://gateway.okhistory.org/ark:/67531/metadc190366/
Image Source: http://gateway.okhistory.org/ark:/67531/metadc316961/
Image Source: http://gateway.okhistory.org/ark:/67531/metadc316903
Figure 2: Screen capture of Embedded Metadata
Figure 1: Structured Data
Linter Tool Results
CONCLUSION
© Copyright
Emily Kolvitz, Product Consultant at Bynder, LLC
MLIS from The University of Oklahoma
Emily Kolvitz emily@getbynder.com
+1 405 471 2570 https://www.theinformationprofessional.com
https://www.getbynder.com
FURTHER
INFORMATION
ACKNOWLEDGEMENTS
I would like to thank The University of Oklahoma, who offered resources to make this project
possible and also The Oklahoma History Center for allowing me to work on such a wonderful
digitization project for the Gateway to Oklahoma History during my study.
RESULTS
Image Resource Findability on the World Wide Web is still
very much a land-grab. For the Semantic Web to become a
reality online businesses and individuals have to get their
hands dirty and also come face-to-face with the realization that
search engine giants are increasingly becoming the go-to tool
for information resource retrieval. “Increasingly, students use
Web search engines such as Google to locate information
resources rather than seek out library online catalogs or
databases of scholarly journal articles” (Lippincott 2013). This
puts the search engine giant in a unique position to dictate
how the future of search will work on the Web - and therefore,
your organization’s future presence (or lack thereof) on the
Web.
Image search and retrieval is a more difficult area than
text search and retrieval because accessibility to the
image content is largely dependent on the context
presented in and around the image resource. The
widespread adoption of Schema.org and structured data on
the Web did not gain traction until the big four search engines
(Google, Bing, Yahoo, and Yandex ) agreed that a standard
was needed to pave the way forward. “On-page markup helps
search engines understand the information on web pages and
provide richer search results. A shared markup vocabulary
makes easier for webmasters to decide on a markup schema
and get the maximum benefit for their efforts,” (Schema.org
2014).Structured data is only one piece of the findability
algorithm. Metadata near content, embedded within
content, or listed in the alt text of an html document all tell
machines something about the content inside of the
record as well.
There are no guardians of the Web, ensuring structured data
is uniformly applied to all records with equal attention and care
and there is no standard, mandated requirement for records on
the Web to provide context for image resource findability. Most
search engines do not crawl embedded XMP data or the
invisible Web, leaving text near images, file names or text in the
alt-text in html markup as the only context for image resources.
The search algorithms for image retrieval are subject to change
frequently (Kritzinger 2013) and additionally, social media sites
and organizations strip embedded data from images
(Embedded Metadata Manifesto 2014). Embedded metadata
provides context and provenance for image resources.
Even with the dramatic adoption of structured data markup
utilizing schema.org vocabularies, there still remains
metadata opportunities on the Web. Reicks recommends
embedded metadata as a strategy for online findability by
showcasing examples of applications that parse embedded
data into structured data around images on the Web such as
PhotoShelter and LicenseStream (2010).
The one variable in the equation of Web findability that remains
a staple is good quality metadata under the hood of the
Website. In this case study, a methodology is applied to the
Gateway to Oklahoma History’s Website. This study can
be generalized to organizations looking to benchmark their own
findability maturity on the Web from an image-centric viewpoint.
The following research question informed this project:
What are the types and quality of structured data, XMP, and metadata records available for image resources appearing on
the website?
Utilizing the Structured Data Linter Tool and Phil Harvey’s ExifTool, information was gathered to quantify these research questions.
Image records on the Gateway to Oklahoma History’s website were investigated for the types, quality and quantity of embedded
metadata and structured data.
The Gateway to Oklahoma History’s Website has a wealth of structured data and metadata pertaining to
its image resources. Search queries utilizing structured data markup tags and/or embedded metadata yielded
relevant and accurate results during a normal web search, but did not yield relevant and/or accurate image
resources during an image search. Descriptive filenames were not used for image resources, which is an
important part of image retrieval through web search engines.
Adding Schema.org tags to the on-page markup, to accompany the structured data already present is
another area for improvement. An interesting finding from this research was that embedded metadata was
only found on the largest, original version of the image resource, and never on smaller derivative images.
Structured data included in the on-page mark-up included Open Graph Protocol and Dublin Core. IPTC was the
primarily type of embedded metadata present for the image resources.
The results and methodology for this research can help GLAM institutions (Galleries, Libraries,
Archives & Museums) by bringing awareness to the state of structured data and image resource
findability for cultural heritage institutions on the Web. GLAMs must be active in the SEO space,
support machine-readable language in the markup of their sites, and utilize Schema.org
vocabularies and descriptive filenames for relevancy in search engine results.
The Digital Library Federation, which is a program of the Council on Library and Information
Resources, concludes that “Getting found means repository objects must be included in the indexes
of major search engines because most students and faculty now begin their research with Internet
search engines. Digital repositories created by libraries will be largely invisible to users if their
contents are not indexed in these search engines” (Digital Library Foundation 2014).
REFERENCES
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