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Oksana L. Zavalina

Researcher at University of North Texas

Publications -  60
Citations -  339

Oksana L. Zavalina is an academic researcher from University of North Texas. The author has contributed to research in topics: Metadata & Digital library. The author has an hindex of 9, co-authored 53 publications receiving 306 citations. Previous affiliations of Oksana L. Zavalina include University of Illinois at Urbana–Champaign.

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Beyond size and search: building contextual mass in digital aggregations for scholarly use

TL;DR: The IMLS Digital Collections and Content project developed an aggregation strategy for building Opening History, one of the largest digital cultural heritage aggregations in the country, based on the principle of contextual mass and conspectus-style evaluation of collection-level metadata to identify strong subject areas within the aggregation.
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Evaluating Descriptive Richness in Collection-Level Metadata

TL;DR: It is found that the free-text Description field often provides more accurate and complete representation of subjects and object types than the specified fields; it consistently represents properties such as uniqueness, importance, comprehensiveness, provenance, and creator of items in digital collection.
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Collection definition in federated digital resource development

TL;DR: How the concept of collection is being continuously defined through the processes of digital resource development and federation and how rapidly changing conceptualizations are likely to impact adoption, tailoring, and development of digital collections and their use are discussed.
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Understanding the Information Needs of Large-Scale Digital Library Users

TL;DR: A comparative study of user search logs in two large-scale, domain-specific digital libraries functioning in the United States: the National Science Digital Library and Opening History demonstrates varying levels of use of advanced search options and substantial differences in the search query lengths, search query frequencies, and distribution of search categories in queries.
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Subject Access: Conceptual Models, Functional Requirements, and Empirical Data

TL;DR: Results of a study that used FRBR model as an analytical framework in examination of user search queries in a large-scale digital library provide empirical data to inform the development of RDA sections that cover subject access, particularly subject entities and relationships.