国内外科学数据管理办法研究进展
收稿日期: 2019-07-15
网络出版日期: 2019-11-28
基金资助
中国科学院战略性先导科技专项（A类）——地球大数据科学工程(XDA19020304);中国工程院战略咨询研究项目——智慧农业发展战略研究(2019-ZD-5);国家地球系统科学数据中心（www.geodata.cn）(2005DKA32300);中国工程科技知识中心——地理资源与生态知识服务系统（http://geo.ckcest.cn）(CKCEST-2019-1-4)
Advances in the Study of Domestic and Foreign Scientific Data Management Methods
Received date: 2019-07-15
Online published: 2019-11-28
科学数据是信息化时代重要的战略资源，高效管理和广泛流通是提升科学数据资源价值的关键途径。信息技术的快速发展和科技计划的大量投入促使科学数据资源数量日益激增，对信息时代的科学数据管理提出了更大的挑战。随着工业社会向信息社会的转变，国内外对科学数据管理的重视程度也在不断提高，各类数据管理机构和政府支撑部门通过建设数据集群、完善保障措施、优化发展理念、加大资助力度等方式，不断推动科学数据管理和共享走向成熟。本文通过综合调研国内外科学数据管理的理念、政策、实践及成果，分析总结了国际上科学数据管理的先进经验，对标我国同类研究中存在的问题与挑战，提出了未来一段时间内中国科学数据管理发展的方向和建议：(1)建议持续规范各类科学数据资源管理，完善保障机制，提升标准化程度；(2)建议加强对数据资源的深层次挖掘，实现从数据到信息、知识、智慧和决策的升华；(3)建议加强数据科学技术人才培养，从政府层面落实数据科学家计划，为科学数据管理提供人才支撑；(4)建议拓宽国际合作渠道，加强合作的力度和广度，提升现有国家科学数据中心的国际影响力，为我国数据科学发展建设提供战略指导，为信息化时代的综合国力提升凝聚核心竞争力。
柏永青,杨雅萍,孙九林 . 国内外科学数据管理办法研究进展[J]. 农业大数据学报, 2019 , 1(3) : 5 -20 . DOI: 10.19788/j.issn.2096-6369.190301
Scientific data are important strategic resources in the information age. Efficient management and wide circulation can critically enhance the value of scientific data resources. Rapid information technology developments and large investments in science and technology projects have led to an explosion in the number of scientific data resources, which poses a greater challenge to scientific data management. The transformation from industrial society to information society increases the importance of scientific data management, domestically and internationally. Many data management institutions and government departments promote robust scientific data management and sharing through the construction of data clusters, improvement of security measures, optimization of development concepts, and increased funding. This paper analyzes and summarizes the advanced experience of international scientific data management, based on a comprehensive survey of the concepts, policies, practices and achievements of scientific data management at domestic and foreign institutions. It proposes future directions and suggestions for the development of scientific data management in China. Recommendations for the future include the following: (1) Continuously standardize and improve the management of various scientific data resources to ensure a mechanism to improve the standardization level. (2) Strengthen deep mining of data resources to realize the transformation from data to information, knowledge, wisdom and decision-making. (3) Strengthen data science and technology talents training, implement data scientist programs from the government level, and provide talent support for scientific data management. (4) Broaden international cooperation channels, strengthen cooperation, promote the international influence of existing national science data centers, provide strategic guidance for the development and construction of data science in China, and build core competitiveness to enhance the comprehensive national strength in the information age.
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