초록

본 연구는 서울특별시 A구 산하 9개 도서관 이용자들의 대출 기록을 모두 조사하여 주제별 장서이용 행태를 분석하고 주제 분야 간 어떠한 연관성을 지니고 있는가를 알아보는데 그 목적이 있다. 이를 위하여 2006년 6월부터 2014년 6월까지 발생한 2,723,115건의 대출 기록을 수집하여 분석하였다. 한국십진분류(KDC)에 따라 총류(000)부터 역사(900)까지 10개 대분류와 총류(000)부터 전기(990)까지 100개 중분류로 나누어 주제별 대출빈도를 분석하였으며, 도서관 이용자들의 대출기록 기록을 중분류에 따른 상관계수에 근거한 군집분석을 통하여 주제별 연관성을 분석하였다.

Abstract

The goal of this study is to analyze use patterns of library collections using circulation statistics of 9 public libraries user’s of the Seoul borough “A”. For this study, the 2,723,115 circulation-related data of 9 public libraries located in borough “A” which were occurred between June 2006 and June 2014 were collected and used. According to the Korea Decimal Classification (KDC), All circulation records is divided into 10 categories from general (000) to history (900) and 100 divisions from general (000) to biography (990), is analyzed the frequency by category and is analyzed by cluster analysis based on thematic relevance.

초록

한국의 dCollection이 학위논문 수집기로 활용되고 있는데 반해, 일본의 레포지토리는 다양한 학술 컨텐츠를 수집, 보존, 확산하고 오픈 엑세스를 실현하기 위한 개별 기관의 자발적인 운영 도구로 발전되고 있다. 본 연구는 일본의 기관 레포지토리 통계 DB인 IRDB를 통해 레포지토리의 특성을 통계적으로 분석하고 구축된 컨텐츠량, 종별 구축 비율, 그리고 종간 상관성을 살펴보았다. 또한 등록된 컨텐츠 특성을 변수로 K-means 군집 분석을 수행함으로써, 일본에 형성된 기관 레포지토리가 어떻게 유형화될 수 있는지 분석하였다. 분석 결과, 일본의 기관 레포지토리는 교내학술논문, 학위논문, 기술보고서, 의학자료, 학술잡지논문 등 다양한 컨텐츠를 수용하고 있을 뿐 아니라, 컨텐츠의 특징에 따라 5개의 차별화된 군집으로 유형화됨으로써 다양한 모습으로 발전되어 가고 있었다.

Abstract

While dCollections of Korea have been initiated by a government for metadata harvesting, institutional repositories of Japan have been managed as instituion’s independent tool for not only collectiong, archiving and distributing their intellecture assets, but also realizing open access. This study analyzes IRDB of Japanese statistically for understanding features of institutional repositories and by clustering the repository on the basis of types of contents, the differences have been brightened. According to analysis result, Japanese repository contains diverse types of contents, such as journal articles, scholarly papers, text books and technical reports. etc. and clustered by five distinguished group with different contents type.

초록

Abstract

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.