共查询到20条相似文献，搜索用时 446 毫秒
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[目的/意义] “双一流”建设背景下,服务ESI（Essential Science Indicators,基本科学指标）学科建设是高校图书馆的主要任务之一,有助于充分发挥图书馆的资源与情报服务优势,延伸和深化图书馆学科服务,为ESI学科建设提供强有力支持,助推学校ESI学科的持续快速发展,推动图书馆服务的转型升级。[方法/过程] 在分析图书馆服务ESI学科建设优势的基础上,根据现有文献和服务实践,总结、归纳图书馆服务ESI学科建设的主要内容,提出针对性的服务策略建议。[结果/结论] 图书馆能够为学校ESI学科建设提供编制ESI期刊投稿指南、组织整理和计量分析ESI论文、建设ESI学科文献信息资源、评估ESI学科竞争力、提供决策支持和政策建议等服务,可以通过采取建立ESI学科服务平台、设计ESI学科服务模式、打造ESI学科服务团队、构建ESI学科服务体系、创建ESI学科服务品牌等措施,确立图书馆在服务学校ESI学科建设中的核心主体地位。 相似文献
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为直观了解国内一流大学与世界一流大学的差距,论文基于ESI数据库,从ESI Top1%学科、ESI高被引论文、ESI热点论文、ESI论文数量增长趋势、ESI论文被引用数增长趋势以及单引数增长趋势等方面,对地球科学领域世界排名前五的大学与国内排名前五的大学以及亚洲排名第一的东京大学共计11所国内外一流大学进行科研绩效分析.结果表明,国内一流大学与国外一流大学还存在比较大的差距,但从地球科学ESI论文数和被引用数的增长趋势来看,国内一流大学的进步比较明显.国内大学要加强与世界一流大学的基础研究合作与交流,引导科研人员瞄准学科前沿与研究热点,注重学科结构的优化调整,促使更多学科早日进入世界一流行列. 相似文献
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ESI前1%学科具有较强的国际影响力,是高校争创一流的优选学科,也是高校图书馆资源建设的重点。通过对山东农业大学图书馆ESI前1%学科图书的馆藏占比、入藏时间、更新指数及资源内部二级类目的借阅比、入藏比、文献符合度展开研究,分析ESI学科图书资源结构的合理性及资源保障和利用水平,为优化资源结构、提高图书利用率和有针对性地制定ESI学科图书馆藏发展计划提供决策依据。 相似文献
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[目的/意义]ESI学科排名是国内外学科评价的重要指标之一。以清华大学为例,介绍一套切实可行的数据分析方法,尝试对学术机构入围ESI学科排名世界前1%的时间进行预测。[方法/过程]首先通过ESI模拟检索,将检索结果与ESI末位入围机构进行被引频次比较,找到“入围差距”,确定临近入围ESI的潜力学科,然后运用曲线拟合模型方法,预测入围时间。之后进一步对3种曲线函数的拟合优度进行比较研究,并分析预测误差可能产生的原因。[结果/结论]后续实际验证表明,本文给出的预测时间基本准确。此预测方法对学术机构掌握重点学科发展趋势、衡量与世界一流学科差距具有实际参考价值。 相似文献
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近年来,Essential Science Indicators (ESI)数据库研究前沿成为国内外情报学界的研究热点。本文从ESI研究前沿的一些基本问题出发,条分缕析地说明其功能在于揭示研究热点,不适合直接用于分析比较各国研究水平。在此基础上,本文设计了一套基于ESI研究前沿的研究水平比较方法,尝试解决研究前沿碎片化、基础论文重要性不完备等关键问题,并引入知识元分析方法分析比较各国研究水平。本文以钙钛矿太阳能电池这一研究热点为例,成功地对该方法进行了验证,并结合验证结果对该方法进行了讨论。 相似文献
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引文索引的功能与科学评价——以美国《基本科学指标》引文数据库为例 总被引：1，自引：0，他引：1
首先概述了引文索引的两个基本评价功能,即检索功能和评价功能,并论述了在实际的评价工作中如何发挥它们的功效,然后,阐述了如何利用美国基本科学指标引文数据库(ESI)来实现评价功能,最后提出了利用ESI进行世界大学科研竞争力评价的构想。 相似文献
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以东华大学为例,凭借美国ESI分析工具为研究载体,从其入围世界前1%的学科及其论文数量、论文总被引频次、论文篇均被引用次、高被引论文等方面,同国内进入ESI相关领域的高校进行比对,分析了东华大学近几年的学科发展情况,客观评价其研究状况、学科研究特点和学术影响力。 相似文献
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高Altmetrics指标科技论文学术影响力研究 总被引：9，自引：0，他引：9
引入"公平性测试"方法以消除时间窗口对被引次数的影响。以高Altmetrics指标论文作为样本,选取与样本论文发表在同一期刊同一期上前后两篇论文作为参照。利用Altmetric.com、Web of Science分别获取273篇样本及参照论文的Altmetric分数、底层数据值和被引用次数。通过比较分析后发现:Altmetrics和引文数两种指标反映出读者对文献的不同关注方向,底层数据源中大众媒体对于Altmetric分数的影响最明显,高Altmetrics指标论文同时具有较高的学术影响力。作为一种早期指标,高Altmetrics指标在一定程度上能够被视作文章在未来获得高被引的风向标。 相似文献
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We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics’ ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers’ semantic information).In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts.In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years.Lastly, we also find that personalising PageRank with papers’ citation counts reduces time bias but increases field bias. Similarly, using papers’ associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers’ citation counts and time-rescaled for citation windows smaller than 7 to 10 years. 相似文献
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《Journal of Informetrics》2022,16(1):101235
With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction. 相似文献
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Gui-Yuan Shi Yi-Xiu Kong Guang-Hui Yuan Rui-Jie Wu An Zeng Matúš Medo 《Journal of Informetrics》2019,13(2):717-725
Identifying the future influential papers among the newly published ones is an important yet challenging issue in bibliometrics. As newly published papers have no or limited citation history, linear extrapolation of their citation counts—which is motivated by the well-known preferential attachment mechanism—is not applicable. We translate the recently introduced notion of discoverers to the citation network setting, and show that there are authors who frequently cite recent papers that become highly-cited in the future; these authors are referred to as discoverers. We develop a method for early identification of highly-cited papers based on the early citations from discoverers. The results show that the identified discoverers have a consistent citing pattern over time, and the early citations from them can be used as a valuable indicator to predict the future citation counts of a paper. The discoverers themselves are potential future outstanding researchers as they receive more citations than average. 相似文献
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[目的/意义] 研究Altmetrics指标的主要特征及其与传统文献计量指标的相关性,以及它们随时间的演化情况;同时,基于Altmetrics指标全面评价学术论文的社会影响力和学术影响力,对于发展和完善Altmetrics计量系统至关重要。[方法/过程] 以2014-2016年Altmetric Top 100论文为样本,对每年的高Altmetrics指标论文的来源期刊、学科分布、获取方式、作者地域及研究机构分布进行统计分析,并讨论这些论文的社会影响力,同时对论文的Altmetric分数与其Web of Science上的被引频次进行相关性分析,研究相关性随时间的动态演化。[结果/结论] 研究结果表明,高Altmetrics指标论文主要来源于一些高影响因子期刊,其学科主要集中于医疗健康与生物科学,论文作者主要来自于欧美发达国家的高水平研究机构,且高Altmetrics指标论文中开放及自由获取的比例逐年增加;Altmetric分数能够定量地反映学术论文在社交和新闻媒体上被公众关注的程度,从而在一定程度上体现出学术论文的社会影响力;高Altmetrics指标论文的Altmetric分数与其被引频次存在一定正相关,表明高Altmetrics指标论文同时具有较高的学术影响力。 相似文献
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《Journal of Informetrics》2020,14(4):101066
Author keywords for scientific literature are terms selected and created by authors. Although most studies have focused on how to apply author keywords to represent their research interests, little is known about the process of how authors select keywords. To fill this research gap, this study presents a pilot study on author keyword selection behavior. Our empirical results show that the average percentages of author keywords appearing in titles, abstracts, and both titles and abstracts are 31%, 52.1%, and 56.7%, respectively. Meanwhile, we find that keywords also appear in references and high-frequency keywords. The proportions of author-selected keywords appearing in the references and high-frequency keywords are 41.6% and 56.1%, respectively. In addition, keywords of papers written by core authors (productive authors) are found to appear less frequently in titles and abstracts in their papers than that of others, and appear more frequently in references and high-frequency keywords. The percentages of keywords appearing in titles and abstracts in scientific papers are negatively correlated with citation counts of papers. In contrast, the percentages of author keywords appearing in high-frequency keywords are positively associated with citation counts of papers. 相似文献
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Gabriel-Alexandru Vîiu 《Journal of Informetrics》2018,12(2):401-415
Characteristic scores and scales (CSS) – a well-established scientometric tool for the study of citation counts – have been used to document a striking phenomenon that characterizes citation distributions at high levels of aggregation: irrespective of scientific field and citation window empirical studies find a persistent pattern whereby about 70% of scientific papers belong to the class of poorly cited papers, about 21% belong to the class of fairly cited papers, 6% to that of remarkably cited papers and 3% to the class of outstandingly cited papers. This article aims to advance the understanding of this remarkable result by examining it in the context of the lognormal distribution, a popular model used to describe citation counts across scientific fields. The article shows that the application of the CSS method to lognormal distributions provides a very good fit to the 70–21–6–3% empirical pattern provided these distributions are characterized by a standard deviation parameter in the range of about 0.8–1.3. The CSS pattern is essentially explainable as an epiphenomenon of the lognormal functional form and, more generally, as a consequence of the skewness of science which is manifest in heavy-tailed citation distributions. 相似文献
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《Journal of Informetrics》2020,14(2):101019
The normalized citation indicator may not be sufficiently reliable when a short citation time window is used, because the citation counts for recently published papers are not as reliable as those for papers published many years ago. In a limited time period, recent publications usually have insufficient time to accumulate citations and the citation counts of these publications are not sufficiently reliable to be used in the citation impact indicators. However, normalization methods themselves cannot solve this problem. To solve this problem, we introduce a weighting factor to the commonly used normalization indicator Category Normalized Citation Impact (CNCI) at the paper level. The weighting factor, which is calculated as the correlation coefficient between citation counts of papers in the given short citation window and those in the fixed long citation window, reflects the degree of reliability of the CNCI value of one paper. To verify the effect of the proposed weighted CNCI indicator, we compared the CNCI score and CNCI ranking of 500 universities before and after introducing the weighting factor. The results showed that although there was a strong positive correlation before and after the introduction of the weighting factor, some universities’ performance and rankings changed dramatically. 相似文献
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[目的/意义] 文章的被引频次一直是量化评价一篇论文学术影响力的重要指标。但在不同学科不同年份发表的论文会因该领域研究论文数、引用滞后等因素呈现较大的差异。因此在对比两篇论文时，难以简单依据被引频次的绝对值来评判论文影响力大小。为此，本文设计了一个新的可计算数学模型，使得每篇论文可以有一个标准化的指标，以便对不同学科不同年份发表的论文的学术影响力进行直接比较。[方法/过程] 通过分析2006、2017两年中国科技类学术期刊各学科论文的被引频次分布规律，采用同学科论文被引频次的分布形态最接近对数正态分布的先设条件，提出一种被引频次标准化指数——Paper Citation Standardized Index （简称PCSI，中文"论文引证标准化指数"）。最后以中国科协优秀科技期刊论文评选结果为例，将它们与论文所属学科全部论文进行实证对比研究。[结果/结论] 结果证明，PCSI对不同年份、不同学科论文的被引频次进行了标准化，反映了被引频次的线性差距，是一种较为理想的单篇论文学术影响力比较评价工具。 相似文献
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[目的/意义] 文章的被引频次一直是量化评价一篇论文学术影响力的重要指标。但在不同学科不同年份发表的论文会因该领域研究论文数、引用滞后等因素呈现较大的差异。因此在对比两篇论文时，难以简单依据被引频次的绝对值来评判论文影响力大小。为此，本文设计了一个新的可计算数学模型，使得每篇论文可以有一个标准化的指标，以便对不同学科不同年份发表的论文的学术影响力进行直接比较。[方法/过程] 通过分析2006、2017两年中国科技类学术期刊各学科论文的被引频次分布规律，采用同学科论文被引频次的分布形态最接近对数正态分布的先设条件，提出一种被引频次标准化指数——Paper Citation Standardized Index （简称PCSI，中文"论文引证标准化指数"）。最后以中国科协优秀科技期刊论文评选结果为例，将它们与论文所属学科全部论文进行实证对比研究。[结果/结论] 结果证明，PCSI对不同年份、不同学科论文的被引频次进行了标准化，反映了被引频次的线性差距，是一种较为理想的单篇论文学术影响力比较评价工具。 相似文献