科学数据汇聚的模式分析及对我国的发展建议

doi:10.11867/j.issn.1001-8166.2020.070

地球科学进展 ›› 2020, Vol. 35 ›› Issue (8): 839 -847. doi: 10.11867/j.issn.1001-8166.2020.070

王卷乐 ^1, ⁴(

),石蕾 ²,王玉洁 ¹,高孟绪 ²,徐波 ²,王超 ²,王明明 ¹,王艳杰 ^1, ³,周业智 ^1, ³

^1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京 100101
^2.国家科技基础条件平台中心，北京 100862
^3.中国矿业大学（北京）地球科学与测绘工程学院，北京 100083
^4.江苏省地理信息资源开发与利用协同创新中心，江苏南京 210023

收稿日期:2020-05-11 修回日期:2020-07-06 出版日期:2020-08-10
通讯作者: 王卷乐 E-mail:wangjl@igsnrr.ac.cn
基金资助:
中国科学院战略性先导科技专项（A类）(XDA19040501);国家科技基础条件平台专项课题“科学数据汇聚的模式与方法研究”(2017DDJ1ZZ15)

Analysis of the Modes of Aggregation of Scientific Data and Proposals for its Development in China

Juanle Wang ^1, ⁴( ),Lei Shi ²,Yujie Wang ¹,Mengxu Gao ²,Bo Xu ²,Chao Wang ²,Mingming Wang ¹,Yanjie Wang ^1, ³,Yezhi Zhou ^1, ³

^1.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^2.National Science and Technology Infrastructure Center, Beijing 100862, China
^3.College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
^4.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2020-05-11 Revised:2020-07-06 Online:2020-08-10 Published:2020-09-15
Contact: Juanle Wang E-mail:wangjl@igsnrr.ac.cn
About author:Wang Juanle (1976-), male, Luoyang City, He’nan Province, Professor. Research areas include scientific data sharing, geographic information systems and remote sensing applications. E-mail: wangjl@igsnrr.ac.cn
Supported by:
the Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences “Big data-driven 'Beautiful China' panoramic evaluation and decision support”(XDA19040501);The Special Foundation of National Science & Technology Infrastructure Center "Research on models and methods of scientific data aggregation"(2017DDJ1ZZ15)

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科学数据的汇聚是抢占科学数据这一战略资源上游和竞争高地的重要手段。把握科学数据汇聚模式的国际态势和科学规律，探索适合我国国情的科学数据汇聚模式和方法是当前急需解决的问题。从国际组织、国际科学计划、政府机构和专业数据中心等方面调研分析国内外科学数据汇聚现状,梳理归纳出科学数据汇聚的5种模式，包括科研项目集中向指定数据中心/仓储汇聚模式、科研项目分散向数据中心/仓储汇聚模式、科学家个人以论文出版方式向数据中心/仓储汇聚模式、科研项目/科学家个人向数据共享目录/网络汇聚模式、大数据计算/处理平台和公民科学开放汇聚模式，并对各模式和相应典型案例进行剖析。在此基础上，提出我国科学数据汇聚在科学数据管理办法落实、数据汇聚中心认证、期刊论文数据汇聚、数据汇聚网络构建、国际资源汇聚和数据汇聚全链条建设等6个方面的发展建议。

关键词: 科学数据汇聚, 模式分析, 数据中心建设, 数据共享

Scientific data are strategic resources, and the aggregation of scientific data is an important method to seize the upstream and competitive highlands of scientific data. Notably, it is challenging to grasp the international situation and the scientific laws concerning the mode of scientific data aggregation; exploring the modes and methods of scientific data aggregation that are suitable for China's national conditions is also difficult. This paper investigated and analyzed the modes of scientific data aggregation both at home and abroad from the viewpoints of international organizations, international scientific programs, government agencies, and professional data centers. Five modes of scientific data aggregation were summarized, including scientific research projects converging to designated data centers/repositories, scientific research projects dispersing to data centers/repositories, individual scientists submitting datasets to data centers/repositories with published papers, scientific research projects/individual scientists sharing directories/networks, big data computing/processing platform, and citizen science models of open and public convergence. This paper analyzed each mode and the corresponding cases. On this basis, the paper put forward six suggestions for the reasonable aggregation of scientific data in China, including the implementation of the “Measurement of Scientific Data Management”, certification of data aggregation centers, scientific data collection and publishing in journals, construction of data aggregation networks, aggregation of international resources, and construction of the whole data aggregation chain.

Key words: Scientific data aggregation, Mode analysis, Data center construction, Data sharing

中图分类号:

G203

图1 科学数据汇聚模式分类

Fig.1 Classification of scientific data aggregation mode

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