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Authorship and Inappropriate Authorship from an Ethical Publication Perspective (출판 윤리적 관점에서의 저자됨과 부적절한 저자됨)

  • Sung Hun Kim;Jung Im Jung
    • Journal of the Korean Society of Radiology
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    • v.83 no.4
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    • pp.752-758
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    • 2022
  • Authorship is important for research integrity and publication ethics, acting as the basis for credit and academic achievement. Moreover, publication achievements have a significant impact on research grants and academic careers. Inappropriate authorship has been reported for several reasons, including complex interests and competitive environments. One form of this is representative authorship misuse, which includes honorary and ghost authorships. Kin co-authorship, such as parent-children authorship, is another form of inappropriate authorship that has recently emerged as a social problem in Korea. To address these issues, the International Committee of Medical Journal Editors (ICMJE) has established criteria for authorship. Similarly, many journals use the Contributor Roles Taxonomy (CRediT) and Open Researcher and Contributor ID (ORCID) systems to prevent authorship misuse and systematically evaluate author credit and responsibility. Herein, this article reviews authorship and inappropriate authorship, as well as introduces methods to avoid authorship misuse.

A Study on the Management of Name Identifier System for ISNI-based Data Integration (ISNI 기반 데이터 융합을 위한 저자식별체계 운용에 관한 연구)

  • Lee, Seungmin;Kwak, Seung-Jin;Oh, Sanghee;Park, Jin Ho
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.30 no.1
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    • pp.29-51
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    • 2019
  • Most fields of society have constructed and utilized various name identifier systems such and International Standard Name Identifier(ISNI), Open Researcher and Contributor ID(ORCID), and Interested Parties Information System(IPI) in order to uniquely identify individual authors and institutions and to associate them to data related to creative works. Although it might be inevitable to apply name identifier systems in the current data environment with rapid association and integration of data across fields, there are many problems to be addressed when utilizing those systems. In order to overcome these problems and construct better information ecological system by associating and linking data from various fields, this research analyzed advanced cases for data integration based on ISNI. Through the analysis, it suggested managemental refinements for efficiently utilizing ISNI in data integration and association.

A Study on Utilization of ORCID based Author Identifier at National Level (국가 차원의 ORCID 기반 저자 식별자 활용에 관한 연구)

  • Kim, Eun-Jeong;Noh, Kyung-Ran
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.28 no.3
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    • pp.151-174
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    • 2017
  • The diffusion of the internet, the advancement of ICT technology, and digital diffusion have facilitated the streamlining and acceleration of scholarly communication and speeding up research, and the paradigm of scholarly information dissemination is changing. This study introduces the ORCID, a unique author identifier, and examines the ORCID organization's activities, the advantages given to researchers and research institutes, and the membership status. In addition, this paper examines adoptions and utilizations of ORCID in major countries including USA, UK, Italy, and China. Based on this, this paper suggests the necessary considerations for utilizing ORCID in terms of governance, system elements, policy and institutional aspects in an effort to identify authors at national level.

A Study on Improvement for Identification of Original Authors in Online Academic Information Service (온라인 학술정보 서비스 상 원저작자 식별 개선 방안 연구)

  • Jung-Wan Yeom;Song-Hwa Hong;Sang-Hyun Joo;Sam-Hyun Chun
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.3
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    • pp.133-138
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    • 2024
  • In the modern academic research environment, the advancement of digital technology provides researchers with increasingly diverse and rich access to information, but at the same time, the issue of author identification has emerged as a new challenge. The problem of author identification is a major factor that undermines the transparency and accuracy of academic communication, potentially causing confusion in the accurate attribution of research results and the construction of research networks. In response, identifier systems such as the International Standard Name Identifier (ISNI) and Open Researcher and Contributor ID (ORCID) have been introduced, but still face limitations due to low participation by authors and inaccurate entry of information. This study focuses on researching information management methods for identification from the moment author information is first entered into the system, proposing ways to improve the accuracy of author identification and maximize the efficiency of academic information services. Through this, it aims to renew awareness of the issue of author identification within the academic community and present concrete measures that related institutions and researchers can take to solve this problem.

Classification for early diagnosis for breast cancer base on Neural Network (뉴럴네트워크 기반의 유방암 조기 진단을 위한 분류)

  • Yoon, Hee-Jin
    • Journal of the Korea Convergence Society
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    • v.8 no.12
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    • pp.49-53
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    • 2017
  • Breast cancer is the sccond most female cancer patient in the entire female cancer patient, and has emerged as the highest contributor to female cancer deaths. If breast cancer id detected early, the cure rate is 92 percent. However, if early detection fails, breast cancer has a very high rate of metastasis. The transition from cancer to cancer has become more successful as cancer progresses. Early diagnosis of cancer is an important factor in improving quality of life. Examples of breast cancer include Mammograph, ultrasound, and Momotome. Mommography is not only painful for the examiner, but also for easy access to breast cancer exam inations. In this paper, breast cancer diagnosis data mammograph data was used. In addition, the Neural Network were classified for early diagnosis of breast cancer early using NEWFM. After learning of data using NEWFM, the accuracy of the breast cancer data classification was 84.4391%.