MAINTAINING QUALITY METADATA: TOWARD EFFECTIVE DIGITAL RESOURCE LIFECYCLE MANAGEMENT Daniel Gelaw Alemneh University of North Texas

ICKM 2008 University of North Texas (UNT) Libraries Digital Initiatives Collaborative Initiatives CyberCemetery GPO NARA – Affiliated Archive Texas Register Archive Secretary of State’s Office Texas Laws and Resolutions Archive Secretary of State’s Office The Portal to Texas History 45 Libraries & Museums Web-at-Risk Project California Digital Library New York University National Digital Newspaper Program (NDNP) Between 1836 and 1922.

ICKM 2008 University of North Texas (UNT) Libraries Digital Initiatives Library Digital Collections: Congressional Research Service Archive 10,000+ CRS Reports World War Poster Collection 500 WWI and WWII Posters Advisory Commission on Intergovernmental Relations 408 reports = 47,874 pages Federal Communications Commission (FCC) Record 136 issues = 43,115 pages (6 of 21 volumes completed) Electronic Theses and Dissertations (ETDs) more in queue Jean-Baptiste Lully (Music) Collection 27 scores = 10,000 pages Other digitization projects

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Metadata Environment Metadata-based digital resource management activities UNT Libraries metadata locally qualified Dublin Core based descriptive metadata. Detailed technical and preservation metadata elements Web based metadata creation and editing Interoperability Metadata Crosswalks Mods Marc oai_dc PREMIS

ICKM 2008 Metadata Quality The two aspects of digital library data quality: The quality of the data in the objects themselves The quality of the metadata associated with the objects Poor metadata quality: Ambiguities Poor recall Poor precision Inconsistency of search results

ICKM 2008 Metadata Quality … Most Common errors: Incorrect Data: Letter transposition Letter omission Letter insertion Letter substitution or misstrokes Missing Data Elements and values not present at all (null) Insufficient or incomplete data Ambiguous Data Confusing or inconsistent data e.g. multiple spellings, multiple possible meanings, mixed cases, initials, etc.

ICKM 2008 Factors Influencing Metadata Quality Local Requirements: Objects Heterogeneity What type of objects will the repository contain? Granularity How will they be described? Functionality What functionality is required? How will it be interfaced?

ICKM 2008 Factors Influencing Metadata Quality … Collaborative Requirements: Diversity of Users How best diverse information-seeking behaviors can be met? Interoperability Will metadata be meaningful within aggregations of various kinds? What is required for interoperability? (Structure, semantics, & syntax) Digital rights issues Will access restrictions be imposed? Are requirements formal or informal? Are there other access and associated digital rights issues?

ICKM 2008 Factors Influencing Metadata Quality… Training Issues Necessary expertise to create and manage rigorous metadata Metadata quality can be determined to a great extent by: knowledge of the source, and knowledge of the methodology used to create the statement Cost Rigorous metadata is resource intensive and too costly

ICKM 2008 UNT Metadata Quality Assurance Mechanisms & Tools The two main stages of metadata qualities assurances: Pre-injust 1. Metadata Creation tools (Templates) Post-injust 2. Metadata Analysis tools (Web-based tools)

ICKM 2008 Quality Assurance Mechanisms and Tools: Templates 1. Metadata Creation Tools (Templates) Validates Mandatory elements Metadata Template Creator Template Reader Controlled vocabularies (UNTLBS)

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UNT Metadata Quality Assurance Mechanisms & Tools… 2. Metadata Analysis Tools NULL Values List/Browse All Values (by each qualifiers and elements) List Authorities Values Graphical reports and other fun stuff Clickable Maps by Institution and Collection Word Clouds by elements Records added overtime and other graphical reports

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Summary Determine level of quality required Partners may have much in common, but they have diverse and sometimes conflicting metadata requirements. Determine nature of gap and how to close it effectiveness, efficiency, practicability, scalability Machine verses human error handling How much of the process can be automated? Human review of results is still essential (e.g. highlighted items) Compromise One size does not fit all! Prioritize Resources very unlikely to be available to meet all requirements Test the workflow Test, retest, and evaluate the quality cycle continuously

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Questions? Thank You!