论文标题

Eddy协方差:科学计量评论(1981-2018)

Eddy Covariance: A Scientometric Review (1981-2018)

论文作者

Huang, Tian-Yuan, Liu, Yi-Fei, Wang, Yuan-Chen, Guo, Hai-Qing, Ma, Jun, Zhao, Bin

论文摘要

涡流协方差(EC)测量系统的历史可以追溯到100年前,但是直到最近几十年,EC才获得受欢迎程度并被广泛用于全球变化生态研究,并随着各种期刊的论文中发表的相关工作的爆炸爆炸。从1981年到2018年,调查了与EC相关的8297个文献,我们从科学计量学的角度对EC的科学发展进行了全面和批判性的审查。首先,该论文概述了一般文献统计数据,包括出版物号,国家贡献,生产性机构,活跃的作者,期刊分销,高度引用的文章和基金支持,以提供有关EC研究的信息图。其次,基于关键字分析的网络可视化和建模揭示了研究趋势,从那里我们可以从那里发现EC的知识结构,并在不同时期内检测研究重点和热点过渡。第三,探索了EC研究社区的合作。 FluxNet是将EC研究人员统一的最大全球网络,我们在这里使用合作和引文的书目计量指标量化并评估了其性能。已经对EC的历史发展进行了具体讨论,包括技术成熟和应用促进。考虑到当前的协作障碍,该评论通过分析阻碍数据共享的原因结束,并为未来的数据密集型协作提供了新的模型。

The history of eddy covariance (EC) measuring system could be dated back to 100 years ago, but it was not until the recent decades that EC gains popularity and being widely used in global change ecological studies, with explosion of related work published in papers from various journals. Investigating 8297 literature related with EC from 1981 to 2018, we make a comprehensive and critical review of scientific development of EC from a scientometric perspective. First, the paper outlines general bibliometric statistics, including publication number, country contribution, productive institutions, active authors, journal distribution, highly cited articles and fund support, to provide an informative picture of EC studies. Second, research trends are revealed by network visualization and modeling based on keyword analysis, from where we could discover the knowledge structure of EC and detect the research focus and hotspots transitions at different periods. Third, collaboration in EC research community has been explored. FLUXNET is the largest global network uniting EC researchers, here we have quantified and evaluated its performance by using bibliometric indicators of cooperation and citation. Specific discussions have been given to the historical development of EC, including technical maturation and application promotion. Considering the current barrier for collaboration, the review closes by analyzing the reasons hindering data sharing and makes a prospect of new models for data-intensive collaboration in the future.

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