• Title/Summary/Keyword: Collaborative Curation Environment

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Development of Collaborative Environment for Community-driven Scientific Data Curation (커뮤니티 주도적 과학 데이터 큐레이션 협업 환경의 개발)

  • Choi, Dong-Hoon;Park, Jae-Won;Kim, ByungKyu;Shin, Jin-Sup
    • The Journal of the Korea Contents Association
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    • v.17 no.9
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    • pp.1-11
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    • 2017
  • The importance of data curation is increasingly recognized as the need of data reuse drastically grows. Due to recent data explosion, scientists invest almost 90% of their efforts in the retrieval and collection of data needed to their study. In this paper, we deal with the development and application of a collaborative environment for community-driven data curation which is essential to enhance scientific data reusability and citability. The collaborative scientific data curation environment focuses on the cross-linking between data (or data collections) and their associated literatures to capture and organize inter-relations among research results in a specific domain. Also, plenty of contextual information is provided as metadata in order to support users in understanding data. The cross-linking has been realized by using DOI system to guarantee global accessibility to data and their relationships to literatures. The curation environment has been adopted to build a community-driven curated DB by a globally well-known intrinsically-disorderd protein research group. The curated DB will drastically reduce researchers' efforts to retrieve and collect the data required for scientific discovery.

Curation Service Implementation using Machine Learning Algorithm (기계학습 알고리즘을 이용한 Curation 서비스 구현)

  • Lee, Hyung Ho;Lee, Hak Jae;Kim, Tae Su;Kim, Mi Hyun
    • Smart Media Journal
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    • v.9 no.4
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    • pp.118-125
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    • 2020
  • This paper is conducted for automatically recommending and providing information services desired by users on websites of local governments and public institutions with vast amounts of information, In this system, we defined a method of collecting data based on the SiiRU CMS system that collects and preprocesses data, and a study that provides curation services (contents and menus) to users through a collaborative filtering algorithm based on machine learning. Also, the data used in the paper is conducted based on about 1 million data collected in 2019. The analyzed data can provide important information that cannot be easily accessed by providing a cloud tag service or recommended menu for users to conveniently view, and the environment configuration that can realize this service to local governments and public institutions is also provided.

A Study of Digital Archiving Strategies for Historical Materials (역사자료의 디지털 아카이빙 방안 연구)

  • Chang, Yun-Keum
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.21 no.4
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    • pp.193-203
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    • 2010
  • This study investigates successful overseas cases and projects of digital archiving of historical materials in order to develop continuous digital archiving strategies in this rapidly changing digital information environment. The analysis is based on the information life cycle perspective of collection, preservation, access and use of digital archives as well as the concept of digital curation. Through the analysis, the study proposes nation-wide digital archiving policies, long-term preservation and standardization strategies for continuous services, and collaborative financial support strategies between institutions and countries for successful digital archiving and continuous improvement.