利用Google Scholar自动统计引文年度被引频次的方法     

和晋飞  吕锋昌  房俊民 《图书情报工作》2014,58(7):88

首先阐述在数字科研时代,在学术交流渠道多元化、网络化的环境下,谷歌学术搜索(Google Scholar)可作为计算机科学领域引文分析的数据源的原因。然后,对目前自动化地采集Google Scholar的引文数据的现状进行概述;进而以统计图灵奖获得者所发表论文的年度引文频次为例,着重阐述如何利用Google Scholar引擎的引文搜索功能设计相关程序,实现对论文各年度被引频次的自动统计;最后,将该方法与Web of Science进行比较,并对实现过程中遇到的问题进行总结。  相似文献   

20世纪以来日本学者的欧阳修诗歌研究     

邱美琼 《赣南师范学院学报》2010,31(1):65-68

20世纪以来日本学者的欧阳修诗歌研究,其成绩主要体现在四大方面,即:欧阳修诗歌材料的整理、选译,欧阳修的生平、思想研究,欧阳修诗歌的艺术特色研究及欧阳修诗论研究。日本学者的欧阳修诗歌研究对我国古典诗学研究具有重要的参照与借鉴意义,也为我们审视自身的研究观念和研究手段提供了反省之鉴。  相似文献   

17～18世纪西方学者的中国文化观     

薛其林 《湘潭师范学院学报(社会科学版)》2000,(5)

:17～ 18世纪的欧洲正值新旧文化交替的时代 ,学者们把研究中国文化同抗拒宗教愚昧、怀疑皇权神圣统一起来 ,从而形成了一股“中国文化热” ,产生了具有代表性的三种中国文化观 :以培尔、莱布尼茨、伏尔泰为代表的由比较而认同赞赏中国文化 ;以马勒伯朗士、维柯、休谟、孟德斯鸠为代表的由比较而批评中国文化 ;以狄德罗、魁奈为代表的“百科全书派”和“重农学派”则整合上述两说而对中国文化作出了理性的批判与评估。这种由赞赏到指斥最后臻于理性评估的正、反、合过程表明西方对中国文化的认识在不断深化  相似文献   

忽必烈信用儒术刍议     

舒顺林 《内蒙古师范大学学报(哲学社会科学版)》1998,(5)

历史上活跃在我国北方的兄弟民族,尽管他们有着各自不同的发展经历与历史特点,但有一个共同的基本趋向,即一旦兴起之后,总是由北向南,不断汲取以儒家文化为主导的中华文化传统,表现出一种规律性的向心运动。忽必烈以前的北方各民族包括北狄、内迁的匈奴人、拓跋、鲜卑、羯、氐、羌、沙陀、女真、契丹等族都是如此,忽必烈则是一位承先启后的集大成者  相似文献   

The role of ingroup assortative sociality in the COVID-19 pandemic: A multilevel analysis of google trends data in the United States     

《Int J Intercult Relat》2021

This study tested how family ties and religiosity, two extended elements of ingroup assortative sociality, would predict group-level COVID-19 severity in the U.S. and how COVID-19 threat would predict ingroup assortative sociality at a weekly level. Multilevel models which analyzed the state-level archival (e.g., religious participation) and Google trends data (e.g., marriage for family ties; prayer for religiosity) on ingroup assortative sociality showed that religious search volume (from 2004 to 2019) significantly and negatively predicted COVID-19 severity (i.e., shorter time delay of first documented cases, shorter overall doubling times, higher reproductive ratio and higher case fatality ratio) across states (Study 1a) and counties (Study 1b) while search volume for family ties only significantly and negatively predicted county-level COVID-19 severity. Multilevel analyses also found that weekly COVID-19 severity weakly predicted weekly search volume of marriage and religion (Study 2a), but when COVID-19 threat was in the collective consciousness in a given week (i.e., Google search volume for coronavirus within 52 weeks), collective levels of ingroup assortative sociality increased from the previous week (Study 2b). Evidence across studies suggested that religiosity, compared with family ties, could serve a more important role for the U.S. people during the deadly pandemic.  相似文献   

基于GEPlugin的Google Earth二次开发     

尹训怡 《人天科学研究》2010,(9):112-114

针对Google Earth的广阔应用前景,从插件GEPlugin的特性谈起,介绍了一种基于GEPlugin的二次开发方式,并给予了实例验证。  相似文献   

百度、Google、Sogou三种图像搜索引擎功能的比较   总被引：1，自引：0，他引：1  

高瑞 《中国科技信息》2010,(18)

本文介绍了图像搜索的几种途径,然后针对百度、Google、Sogou这三种常用的图像搜索引擎进行了用户界面设计、高级检索功能的比较,并通过实际测试分析了它们的检索数量、检索用时、查准率。  相似文献   

Weg3D及Web三维可视化新发展——以WebGL和03D为例     

韩义 《科技广场》2010,(5):81-86

随着计算机和互联网的发展,人们的生活工作环境将越来越向互联网靠拢,当然三维可视化也不例外.在WebOS有一天最终取代单机版操作系统的时候,三维可视化必须为其在互联网的发展中找到突破口.本文简述全球走向网络化的大趋势,同时以WebGL和Google 03D为例介绍三维图形走向网络的最前沿技术动态及简单开发,并同时给出笔者对Web3D发展的一些思考和观点.  相似文献   

跨学科研究对美国顶尖教育学院青年学者科研产出的影响          下载免费PDF全文

姚翔 《复旦教育论坛》2023,21(2):103-111

本研究以美国排名前十教育学院2009年至2019年取得博士学位的青年教师为例，分析跨学科研究的学科知识整合度对其在WoS索引期刊发表论文的数量和影响力的作用关系。多元线性回归模型分析结果显示，跨学科研究的学科知识整合度对学者在WoS索引期刊发表论文的数量以及高被引数量具有显著的正面促进作用，即高跨学科知识整合度带来高产量和高影响力。本研究认为，提升以扩展学科跨度和广度为要点的跨学科研究整合能力是提高青年学者科研产出与效果的重要策略。同时，也要从组织层面进行优化，打破学科壁垒，基于以问题为导向的社会需求逻辑建构跨学科研究组织；改革人才培养模式，优化课程设计体系，保障跨学科人才供给。  相似文献   

Unifying telescope and microscope: A multi-lens framework with open data for modeling emerging events     

《Information processing & management》2022,59(2):102811

Open data is becoming ubiquitous as governments, companies, and even individuals have the option to offer more or less unrestricted access to their non-sensitive data. The benefits of open data, such as accessibility and transparency, have motivated and enabled a large number of research studies and applications in both academia and industry. However, each open data only offers a single perspective, and its potential inherent limitations (e.g., demographic biases) may lead to poor decisions and misjudgments. This paper discusses how to create and use multiple digital lenses empowered by open data, including census data (macro lens), search logs (meso lens), and social data (micro lens), to investigate general real-world events. To reveal the unique angles and perspectives brought by each open lens, we summarize and compare the underpinning open data from eleven dimensions, such as utility, data volume, dynamic variability, and demographic fairness. Then, we propose an easy-to-use and generalized open data driven framework, which automatically retrieves multi-source data, extracts features, and trains machine learning models for the event specified by answering what, when, and where questions. With low labor efforts, the framework’s generalization and automation capabilities guarantee an instant investigation of general events and phenomena, such as disasters, sports events, and political activities. We also conduct two case studies, i.e., the COVID-19 pandemic and Great American Eclipse (see Appendix), to demonstrate its feasibility and effectiveness at different time granularities.  相似文献