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当期目录

    15 September 2019, Volume 5 Issue 5
    TOPIC:ACADEMIC BIG DATA
    Applications of academic big data in the process of science and technology management
    Ying LIANG, Wei ZHANG, Zhidong YU, Hongzhou SHI
    2019, 5(5):  3-15.  doi:10.11959/j.issn.2096-0271.2019037
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    Academic big data is gradually recognized as an important data foundation for improving the level of science and technology management.Status quo and characteristics of science and technology management informationization were investigated at home and abroad,the development and applications of academic big data were summarized,and the problems were analyzed in applications of academic big data in the process of science and technology management.The needs of science and technology management application were combined,a technology management application framework based on academic big data was designed,scholarly image construction based on knowledge graph and similar author recommendation technology based on network representation learning were applied to assist in improving the overall layout of scientific research and resources utilizing multi-heterogeneous academic big data collection.Effective technical support for improving the efficiency of science and technology management was provided.

    Active scientific research management model and optimization decision mechanism based on big data
    Ruili LUO, Yuanzhuo WANG
    2019, 5(5):  16-24.  doi:10.11959/j.issn.2096-0271.2019038
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    During the scientific research management,a large number of data could be accessed from researchers,projects,output and other multi-dimensional.The big data of scientific research management implicitly contains tremendous highlyinterconnected knowledge.The effective obtaining of rich knowledge from big data,depends on the building of the largescale knowledge graph.An expert graph was built up using open knowledge network,and the associated knowledge and potential information were analyzed.Based on these analysis results,how to change the traditional passive management model into active scientific research management mode was discussed,and then a new management model and decisionmaking mechanism based on big data was established.

    Turing index:cross-domain and cross-generation metric of unraveling scholars’ impact in academic big data
    Yuhang YAO, Junjie OU, Yang LI, Luoyi FU, Xinbing WANG, Guihai CHEN
    2019, 5(5):  25-37.  doi:10.11959/j.issn.2096-0271.2019039
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    Gauging the impact of scholars,despite numerous quantitative indicators proposed,remains hard thanks to nowadays gigantic and inflating academic networks accompanied by huge disparity among domains and generations.Data collected from 14 223 183 scholars,126 438 664 papers,533 556 856 references across 310 domains with a time spanning of year 1865 to 2016,suggest universe power-law distributed yearly increased citations of scholars across domains and generations.Turing index then eliminates the inflation via normalizing different scaling parameters in those power law distributions to assess the absolute domain impact of each scholar.Comparison of Turing index among renowned scholars like Nobel,Fields Medal and Turing laureates confirms their equal significance to their dedicated domains despite the vast domain discrepancies in generations and citations.Turing index provides a new way of thinking for cross-domain and cross-generation impact evaluation,and can provide reference for scholars’ impact assessment and national science foundations worldwide.

    Quantifying patterns in the behavior of scientists in Science of Science study
    Tao JIA, Feng XIA
    2019, 5(5):  38-47.  doi:10.11959/j.issn.2096-0271.2019040
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    The complexity of science,the data availability and the need for better understanding of science promote the research in an emerging field of “Science of Science”.To understand the regularities in science,we need to first understand the behavior patterns of scientists.A few related works were reviewed and introduced,including research interest evolution,internal microscopic driven force in scientific collaboration,the difference of research tuandui and research team,and the multidimensional measure of a scholar’s performance to prevent academic gaming.The results obtained will provide theoretical tools and empirical supports for practical issues related with research policy and research evaluation.

    Research on open-access repositories and data acquisition specifications
    Meng WAN, Yongfeng ZHANG, Zhenhua LI, Dongyun HUO, Yiyang ZHAO, Lian WANG
    2019, 5(5):  48-57.  doi:10.11959/j.issn.2096-0271.2019041
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    Under the background of building academic big data and promoting academic sharing,the current development status of open-access repositories was investigated and domestic and foreign research were summarized on data scale,regional distribution and system software.Taking the establishment of building academic institutional repositories as an example,the data collection requirements were analyzed and the commonly used data acquisition specifications were summarized from the aspects of data attributes,metadata standards and semantic deduplication.Finally,combined with the problems and challenges faced by China in developing open-access repositories,reasonable suggestions were put forwar.

    STUDY
    A bus running length prediction method based on Gradient Boosting
    Yongxuan LAI, Xu YANG, Qi CAO, Huibin CAO, Tian WANG, Fan YANG
    2019, 5(5):  58-78.  doi:10.11959/j.issn.2096-0271.2019042
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    At present,China’s public transport companies rely on experienced staff to estimate the return time of vehicles and then conduct vehicle dispatch.This method often results in large errors and wrang decisions due to the lack of auxiliary prediction methods.Based on the actual needs of bus companies,a prediction method R-GBDT based on dynamic feature selection was proposed.The R-GBDT utilizes feature selection components and model parameter adjustment components to provide predictive components with feature combinations and parameters that conform to the line characteristics,then the fusion component combines the results of other components to form a framework for predicting the final time interval.The experimental results from real bus to off-site data show that compared with other algorithms,the method can greatly improve the accuracy of bus transit time prediction.

    APPLICATION
    Application of academic big data in the connection of enterprises and experts
    Yongfeng ZHANG, Dongyun HUO, Zhenhua LI, Qiang ZHI, Yanxi LI
    2019, 5(5):  79-88.  doi:10.11959/j.issn.2096-0271.2019043
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    Under the background of the innovation-driven strategy,the difficulties faced by enterprises and experts in universities in the process of connecting were analyzed.Based on academic big data technology,an expert data system for industrial fields was built and a full-chain transformation system for scientific and technological achievements was established.By collecting and verifying enterprise needs online,using scholars’ portrait technology to accurately match and push,enterprise experts offline was contacted,project management,risk assessment and technology insurance services were continued to provide.At present,the system has collected tens of thousands of enterprise needs,completed a number of projects with connection of enterprises and experts,improved the accuracy and efficiency of connecting,and achieved mutual benefit and win-win between enterprises and experts.

    Construction methods of geographic information spacetime big data center in Shandong Province
    Xianyin LIU
    2019, 5(5):  89-99.  doi:10.11959/j.issn.2096-0271.2019044
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    New requirements have been put forward on geographic information data management,application and service in the big data era.The existing problems on geographic information data control and application in China were analyzed,and the construction thoughts were stated on geographic information spacetime big data center taking Shandong province as an example.Deep analysis on infrastructure construction,geographic information resources database establishment,data integrated technology and application service system development was made.The study could promote management and application service level of geographic information resources with comparatively broad application prospects.

    FORUM
    Demand and model of agricultural big data construction in China and the way to promote the whole industry chain
    Lei CUI
    2019, 5(5):  100-108.  doi:10.11959/j.issn.2096-0271.2019045
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    The construction of agricultural big data is the key link to build a modern intelligent agriculture.Through case analysis and empirical study,the dynamic logic of agricultural big data construction in China was analysed through four aspects as follows:technology,market,capital and politics,and the historical inevitability of the large agricultural data construction in China and the characteristics and practice of the three typical models were analyzed.On this basis,that the single variety of agricultural products should be chosen as the breakthrough point for the construction of the whole industrial chain in China’s agricultural big data was point out.In the initial stage,every provinces in China should adopt the “one province,one chain” and “one industry,one chain” single product model for demonstration construction.Therefore,a specific path was put forward as follows “one main line,three links,five main bodies and seven strategies”.

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