EPIC: A Proposed Model for Approaching Metadata Improvement

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Abstract: This paper outlines iterative steps involved in metadata improvement within a digital library: Evaluate, Prioritize, Identify, and Correct (EPIC). The process involves evaluating metadata values system-wide to identify errors; prioritizing errors according to local criteria; identifying records containing a particular error; and correcting individual records to eliminate the error. Based on the experiences at the University of North Texas (UNT) Libraries, we propose that these cyclical steps can serve as a model for organizations that are planning and conducting metadata quality assessment.

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6 p.

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Tarver, Hannah & Phillips, Mark Edward 2020.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by the UNT Libraries Digital Projects Unit to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 102 times, with 5 in the last month. More information about this article can be viewed below.

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Abstract: This paper outlines iterative steps involved in metadata improvement within a digital library: Evaluate, Prioritize, Identify, and Correct (EPIC). The process involves evaluating metadata values system-wide to identify errors; prioritizing errors according to local criteria; identifying records containing a particular error; and correcting individual records to eliminate the error. Based on the experiences at the University of North Texas (UNT) Libraries, we propose that these cyclical steps can serve as a model for organizations that are planning and conducting metadata quality assessment.

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6 p.

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The final authenticated version is available online at https://doi.org/10.1007/978-3-030-71903-6_22

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  • International Conference on Metadata and Semantics Research, December 2-4, 2020, Athens, Greece (virtual)

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  • Publication Title: Communications in Computer and Information Science
  • Volume: 1355
  • Page Start: 228
  • Page End: 233
  • Preferred Citation: Tarver H., Phillips M.E. (2021) EPIC: A Proposed Model for Approaching Metadata Improvement. In: Garoufallou E., Ovalle-Perandones MA. (eds) Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science, vol 1355. Springer, Cham. https://doi.org/10.1007/978-3-030-71903-6_22

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UNT Scholarly Works

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EPIC: A Proposed Model for Approaching Metadata Improvement (Presentation)

EPIC: A Proposed Model for Approaching Metadata Improvement

Presentation for the 14th International Conference on Metadata and Semantics Research proposing a model that delineates the components of metadata improvement as an iterative cycle, based on the work and experiences at UNT.

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EPIC: A Proposed Model for Approaching Metadata Improvement [Presentation], ark:/67531/metadc1747970

EPIC: An Iterative Model for Metadata Improvement (Article)

EPIC: An Iterative Model for Metadata Improvement

Abstract: This paper provides a case study of iterative metadata correction and enhancement at the University of North Texas (UNT), within a model that we have developed to describe this process: Evaluate, Prioritize, Identify, Correct (EPIC). These steps are illustrated within the paper to show how they function at UNT and why it may serve as a useful tool for other organizations. We suggest that the EPIC model works for ongoing assessment, but is particularly useful for large remediation and enhancement projects to plan timelines and to allocate the people and resources needed to determine what issues should be addressed (evaluate), to rate their level of severity, importance, or difficulty (prioritize), to define subsets or records that are affected (identify), and to make changes based on prioritization (correct).

EPIC: An Iterative Model for Metadata Improvement - ark:/67531/metadc1916232

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  • 2020

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  • Dec. 17, 2020, 10:56 a.m.

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  • Dec. 20, 2021, 6:03 p.m.

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Tarver, Hannah & Phillips, Mark Edward. EPIC: A Proposed Model for Approaching Metadata Improvement, article, 2020; Switzerland. (https://digital.library.unt.edu/ark:/67531/metadc1747971/: accessed March 24, 2023), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Digital Projects Unit.

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