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Research Data Management: Models, Challenges, and Actions

Research Data Management: Models, Challenges, and Actions
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Author(s): Yuhong Cui (Beijing Institute of Technology, China)
Copyright: 2017
Pages: 12
Source title: Academic Library Development and Administration in China
Source Author(s)/Editor(s): Lian Ruan (Illinois Fire Service Institute at the University of Illinois at Urbana-Champaign, USA), Qiang Zhu (Peking University, China) and Ying Ye (Nanjing University, China)
DOI: 10.4018/978-1-5225-0550-1.ch011

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Abstract

Research Data is acknowledged as constituting elements of the big data landscape and currency of science. With the rise of the open science movement and data sharing policies, research data management has been an emerging practice among domain-specific science and library and information science, while academic libraries have become deeply involved in the development of principles and best practices for managing data for long-term use. Research data management practices in China have a much shorter history and face more challenges. This paper is to explore the development of research data management in China across different disciplines. A conceptual framework of Research Data Management for Development (RDM4D) is proposed to demonstrate the status of data sharing policies, data repositories and libraries actions within three dimensions of societies, technology and humanity in China. Our goal is to bridge the gap between China and other countries and explore which are the proper actions for Chinese academic libraries.

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