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Park, Jung-ran; Tosaka, Yuji – Journal of Education for Library and Information Science, 2015
This study aims at providing in-depth perspectives on the ways in which cataloging and metadata professionals have coped with RDA training and implementation through an e-mail interview method. Results show that the performance-based, "learn-as-you-go," peer learning method is found by practitioners to be most effective in acquiring and…
Descriptors: Academic Libraries, Cataloging, Training, Interviews
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Park, Jung-ran; Yang, Chris; Tosaka, Yuji; Ping, Qing; Mimouni, Houda El – Journal of Electronic Resources Librarianship, 2016
This study is a part of the larger project that develops a sustainable digital repository of professional development resources on emerging data standards and technologies for data organization and management in libraries. Toward that end, the project team developed an automated workflow to crawl for, monitor, and classify relevant web objects…
Descriptors: Professional Development, Pilot Projects, Educational Resources, Electronic Libraries
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Tosaka, Yugi; Park, Jung-ran – Journal of Education for Library and Information Science, 2014
This study aims at gaining a better understanding of the current state and needs of RDA training among cataloging and metadata practitioners. Using nationwide survey data focusing on the academic library sector, this study finds that while training activities since RDA's release in 2010 show a positive correlation with catalogers' levels of RDA…
Descriptors: Cataloging, Academic Libraries, Continuing Education, Training
Peer reviewed Peer reviewed
Park, Jung-ran – Journal of Internet Cataloging, 2002
Describes the characteristics of natural languages that hinder the optimal organization and accessing of networked resources, focusing on the semantic mapping processes utilizing thesauri or metadata. Considers future information retrieval system design, especially networked retrieval and natural language, and gives an example of problems in…
Descriptors: Information Retrieval, Korean, Metadata, Natural Language Processing