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苏文.基于文献计量的生态系统观测研究网络长期观测数据应用研究.生态学报,2019,39(13):5005~5013 本文二维码信息
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基于文献计量的生态系统观测研究网络长期观测数据应用研究
Using bibliometrics to analyze the application of long-term observation data of ecosystem research networks
投稿时间:2018-05-22  修订日期:2019-03-08
DOI: 10.5846/stxb201805221121
关键词生态系统观测研究网络  长期观测数据  文献计量  数据应用
Key Wordsecosystem research network  long-term observation data  bibliometric analysis  data application
基金项目国家重点研发计划(2017YFC0503803)
作者单位E-mail
苏文 中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室, 北京 100101 suw@igsnrr.ac.cn 
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摘要:
基于CNKI 数据库,采用文献计量和知识图谱的方法,通过对应用生态系统观测研究网络长期定位观测数据的文献进行分析,探讨长期观测数据的应用领域、具体用途、用户特点及不同生态站数据的应用状况与研究主题,以期为提高生态系统观测研究网络长期观测数据的共享服务能力、充分发挥长期观测数据的价值提供参考。分析结果表明:生态系统观测研究网络长期观测数据受到越来越多学者的关注,其应用学科领域以林业、农业基础科学为主,同时不断扩展到其他学科中,呈多元化态势;数据主要在生态系统服务研究、模型模拟、人工林研究、水污染研究、生物多样性研究、小麦玉米研究、土壤水分研究等方面发挥作用;数据的主要用户群体为高等院校和科研院所,不同机构应用长期观测数据开展的研究各有侧重;各生态站的长期观测数据能够为揭示其所代表生态区和生态系统类型的生态系统结构与功能、能量流动与养分循环的变化规律,分析主要生态环境问题的现状、动态变化及驱动机制等方面提供重要支撑。最后,对生态系统观测研究网络长期观测数据应用的相关方面提出几点建议:(1)健全数据引用机制,制定相应的科学数据引用和著录标准;(2)发挥生态网络长期观测数据优势,开展专题数据产品的生产,充分开发生态网络长期观测数据的潜在价值;(3)加大和稳定生态站的经费投入,提高生态站的观测能力和水平,同时还要完善、优化生态站布局。
Abstract:
The ecosystem research network is a data-intensive science platform for field observation and research, whose major goal is long-term monitoring and networked study of ecosystems. Since 1998, field stations have continuously measured and recorded more than 300 monitoring variations in hydrological, pedological, atmospheric, and biological elements of major terrestrial and aquatic ecosystems in China, such as cropland, forest, grassland, desert, marshes, lakes, and bays according to standard monitoring protocols, and large amounts of long-term observation data have been collected. These data are crucial resources for research on ecology and other relevant disciplines; thus, a national treasure. Many efforts have been made to share the data. To ensure the value of long-term observation data and improve data sharing services, a bibliometric analysis has been applied to obtain an overview of the situation and characteristics using the CNKI database. A total of 160 documents published between 2001 and 2018 related to the subject have been retrieved and analyzed according to five main aspects: publication years, subject areas, author keywords, institutions, and field stations. The results indicated that the long-term observation data of the ecosystem research network has attracted more and more attention from researchers. The major research areas of data application are forestry and basic agricultural science, and continue to expand into other disciplines. The data is mainly used in ecosystem service research, model simulation, plantation research, water pollution research, biodiversity research, wheat and corn research, and soil moisture research to support the spatial pattern analysis and environmental drive mechanisms of ecosystem processes, and the discovery and verification of commonality laws of different ecosystems. Two main user groups of long-term observation data can be distinguished: universities and research institutes. The main user institutions include institutes affiliated to the Chinese Academy of Sciences, forestry and agricultural universities, comprehensive universities, normal universities, and institutes affiliated to the China Academy of Forestry Sciences; different institutions vary in data usage combined with their geographical location, research direction, and dominant disciplines. The long-term observation data of each field station could provide important support for revealing the rules of ecosystem structure and functions, energy flow and materials cycles, and analyzing the current situation, dynamic change, and driving mechanisms of the main ecological and environmental problems of the ecological zone and ecosystem that the field station represents. Some suggestions have been offered for the application of long-term observation data of the ecosystem research network such as to (i) standardize scientific data citation to improve the data citation mechanism of the ecosystem research network, (ii) develop thematic data products by analyzing and mining the data to provide the full advantage of long-term observation data, and (iii) increase the investment in field stations to improve the ability and level of observation, and optimize the layout of field stations.
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