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1.
《Journal of Informetrics》2023,17(3):101426
Risk plays a fundamental role in scientific discoveries, and thus it is critical that the level of risk can be systematically quantified. We propose a novel approach to measuring risk entailed in a particular mode of discovery process – knowledge recombination. The recombination of extant knowledge serves as an important route to generate new knowledge, but attempts of recombination often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expressions and computes the probability of failing to combine a pair of knowledge elements. Testing the calculated risk indicator on survey data, we confirm that our indicator is correlated with self-assessed risk. Further, as risk and novelty have been confounded in the literature, we examine and suggest the divergence of the bibliometric novelty and risk indicators. Finally, we demonstrate that our risk indicator is negatively associated with future citation impact, suggesting that risk-taking itself may not necessarily pay off. Our approach can assist decision making of scientists and relevant parties such as policymakers, funding bodies, and R&D managers. 相似文献
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The scientific impact of a publication can be determined not only based on the number of times it is cited but also based on the citation speed with which its content is noted by the scientific community. Here we present the citation speed index as a meaningful complement to the h index: whereas for the calculation of the h index the impact of publications is based on number of citations, for the calculation of the speed index it is the number of months that have elapsed since the first citation, the citation speed with which the results of publications find reception in the scientific community. The speed index is defined as follows: a group of papers has the index s if for s of its Np papers the first citation was at least s months ago, and for the other (Np ? s) papers the first citation was ≤s months ago. 相似文献
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The Citation Cloud of a biomedical article: a free,public, web-based tool enabling citation analysis
Neil R. Smalheiser Jodi Schneider Vetle I. Torvik Dean P. Fragnito Eric E. Tirk 《Journal of the Medical Library Association》2022,110(1):103
Background:An article''s citations are useful for finding related articles that may not be readily found by keyword searches or textual similarity. Citation analysis is also important for analyzing scientific innovation and the structure of the biomedical literature. We wanted to facilitate citation analysis for the broad community by providing a user-friendly interface for accessing and analyzing citation data for biomedical articles.Case Presentation:We seeded the Citation Cloud dataset with over 465 million open access citations culled from six different sources: PubMed Central, Microsoft Academic Graph, ArnetMiner, Semantic Scholar, Open Citations, and the NIH iCite dataset. We implemented a free, public extension to PubMed that allows any user to visualize and analyze the entire citation cloud around any paper of interest A: the set of articles cited by A, those which cite A, those which are co-cited with A, and those which are bibliographically coupled to A.Conclusions:Citation Cloud greatly enables the study of citations by the scientific community, including relatively advanced analyses (co-citations and bibliographic coupling) that cannot be undertaken using other available tools. The tool can be accessed by running any PubMed query on the Anne O''Tate value-added search interface and clicking on the Citations button next to any retrieved article. 相似文献
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The rise of software as a research object is mirrored by increasing interests in quantitative studies of scientific software. However, inconsistent citation practices have led most existing studies of this type to base their analysis of software impact on software name mentions, as identified in full-text publications. Despite its limitations, citation data exists in much greater quantities and covers a broader array of scientific fields than full-text data, and thus can support investigations with much wider scope. This paper aims to analyze the extent to which citation data can be used to reconstruct the impact of software. Specifically, we identify the variety of citable objects related to the lme4 R package and examine how the package’s impact is dispersed across these objects. Our results shed light on a little-discussed challenge of using citation data to measure software impact: even within the category of formal citation, the same software object might be cited in different forms. We consider the implications of this challenge and propose a method to reconstruct the impact of lme4 through its citations nonetheless. 相似文献
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László Csató 《Journal of Informetrics》2019,13(4):100975
Journal ranking is becoming more important in assessing the quality of academic research. Several indices have been suggested for this purpose, typically on the basis of a citation graph between the journals. We follow an axiomatic approach and find an impossibility theorem: any self-consistent ranking method, which satisfies a natural monotonicity property, should depend on the level of aggregation. Our result presents a trade-off between two axiomatic properties and reveals a dilemma of aggregation. 相似文献
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This paper investigates the citation impact of three large geographical areas – the U.S., the European Union (EU), and the rest of the world (RW) – at different aggregation levels. The difficulty is that 42% of the 3.6 million articles in our Thomson Scientific dataset are assigned to several sub-fields among a set of 219 Web of Science categories. We follow a multiplicative approach in which every article is wholly counted as many times as it appears at each aggregation level. We compute the crown indicator and the Mean Normalized Citation Score (MNCS) using for the first time sub-field normalization procedures for the multiplicative case. We also compute a third indicator that does not correct for differences in citation practices across sub-fields. It is found that: (1) No geographical area is systematically favored (or penalized) by any of the two normalized indicators. (2) According to the MNCS, only in six out of 80 disciplines – but in none of 20 fields – is the EU ahead of the U.S. In contrast, the normalized U.S./EU gap is greater than 20% in 44 disciplines, 13 fields, and for all sciences as a whole. The dominance of the EU over the RW is even greater. (3) The U.S. appears to devote relatively more – and the RW less – publication effort to sub-fields with a high mean citation rate, which explains why the U.S./EU and EU/RW gaps for all sciences as a whole increase by 4.5 and 5.6 percentage points in the un-normalized case. The results with a fractional approach are very similar indeed. 相似文献
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Given the growing use of impact metrics in the evaluation of scholars, journals, academic institutions, and even countries, there is a critical need for means to compare scientific impact across disciplinary boundaries. Unfortunately, citation-based metrics are strongly biased by diverse field sizes and publication and citation practices. As a result, we have witnessed an explosion in the number of newly proposed metrics that claim to be “universal.” However, there is currently no way to objectively assess whether a normalized metric can actually compensate for disciplinary bias. We introduce a new method to assess the universality of any scholarly impact metric, and apply it to evaluate a number of established metrics. We also define a very simple new metric hs, which proves to be universal, thus allowing to compare the impact of scholars across scientific disciplines. These results move us closer to a formal methodology in the measure of scholarly impact. 相似文献
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《Journal of Informetrics》2019,13(4):100977
Main path analysis is a popular method for extracting the backbone of scientific evolution from a (paper) citation network. The first and core step of main path analysis, called search path counting, is to weight citation arcs by the number of scientific influence paths from old to new papers. Search path counting shows high potential in scientific impact evaluation due to its semantic similarity to the meaning of scientific impact indicator, i.e. how many papers are influenced to what extent. In addition, the algorithmic idea of search path counting also resembles many known indirect citation impact indicators. Inspired by the above observations, this paper presents the FSPC (Forward Search Path Count) framework as an alternative scientific impact indicator based on indirect citations. Two critical assumptions are made to ensure the effectiveness of FSPC. First, knowledge decay is introduced to weight scientific influence paths in decreasing order of length. Second, path capping is introduced to mimic human literature search and citing behavior. By experiments on two well-studied datasets against two carefully created gold standard sets of papers, we have demonstrated that FSPC is able to achieve surprisingly good performance in not only recognizing high-impact papers but also identifying undercited papers. 相似文献
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《Journal of Informetrics》2007,1(2):161-169
The Web of Science is no longer the only database which offers citation indexing of the social sciences. Scopus, CSA Illumina and Google Scholar are new entrants in this market. The holdings and citation records of these four databases were assessed against two sets of data one drawn from the 2001 Research Assessment Exercise and the other from the International bibliography of the Social Sciences. Initially, CSA Illumina's coverage at journal title level appeared to be the most comprehensive. But when recall and average citation count was tested at article level and rankings extrapolated by submission frequency to individual journal titles, Scopus was ranked first. When issues of functionality, the quality of record processing and depth of coverage are taken into account, Scopus and Web of Science have a significant advantage over the other two databases. From this analysis, Scopus offers the best coverage from amongst these databases and could be used as an alternative to the Web of Science as a tool to evaluate the research impact in the social sciences. 相似文献
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[目的/意义]比较Innography、WIPS和Patentics3个专利检索系统的引文分析功能,旨在帮助用户识别满足自己需要的专利引文分析工具,同时提出进一步完善专利检索系统引文分析功能的建议。[方法/过程]通过比较3个专利检索系统引文收录范围、引文检索方式、前引后引查找功能、引文类型区分功能、非专利文献查找功能、可视化呈现功能和引文检索准确度,发现3个专利检索系统都具备基础的引文分析功能,但各有不足,难以满足现实需求。[结果/结论]Innography应加强数据清洗,WIPS需扩大引文收录范围,Patentics则要增强系统稳定性,三者在检索数据准确性方面都有待提高。 相似文献
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The process of assessing individual authors should rely upon a proper aggregation of reliable and valid papers’ quality metrics. Citations are merely one possible way to measure appreciation of publications. In this study we propose some new, SJR- and SNIP-based indicators, which not only take into account the broadly conceived popularity of a paper (manifested by the number of citations), but also other factors like its potential, or the quality of papers that cite a given publication. We explore the relation and correlation between different metrics and study how they affect the values of a real-valued generalized h-index calculated for 11 prominent scientometricians. We note that the h-index is a very unstable impact function, highly sensitive for applying input elements’ scaling. Our analysis is not only of theoretical significance: data scaling is often performed to normalize citations across disciplines. Uncontrolled application of this operation may lead to unfair and biased (toward some groups) decisions. This puts the validity of authors assessment and ranking using the h-index into question. Obviously, a good impact function to be used in practice should not be as much sensitive to changing input data as the analyzed one. 相似文献
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基于合作模式的引文数据库发展策略 总被引：4，自引：0，他引：4
引文数据库是利用引文分析思想编制的数据库,具有一定的科研评价作用。国外引文数据库历史长,质量较高,而国内引文数据库历史短,尚存在不少问题,如数据库产品质量不高,生产周期过长,不支持引文链接,引文产品影响小等。为改善上述局面,国内引文数据库可采取合作发展策略,合作控制引文规范,提供引文链接,合作建库、合作营销等。 相似文献
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Yunrong Li Filippo Radicchi Claudio Castellano Javier Ruiz-Castillo 《Journal of Informetrics》2013,7(3):746-755
Wide differences in publication and citation practices make impossible the direct comparison of raw citation counts across scientific disciplines. Recent research has studied new and traditional normalization procedures aimed at suppressing as much as possible these disproportions in citation numbers among scientific domains. Using the recently introduced IDCP (Inequality due to Differences in Citation Practices) method, this paper rigorously tests the performance of six cited-side normalization procedures based on the Thomson Reuters classification system consisting of 172 sub-fields. We use six yearly datasets from 1980 to 2004, with widely varying citation windows from the publication year to May 2011. The main findings are the following three. Firstly, as observed in previous research, within each year the shapes of sub-field citation distributions are strikingly similar. This paves the way for several normalization procedures to perform reasonably well in reducing the effect on citation inequality of differences in citation practices. Secondly, independently of the year of publication and the length of the citation window, the effect of such differences represents about 13% of total citation inequality. Thirdly, a recently introduced two-parameter normalization scheme outperforms the other normalization procedures over the entire period, reducing citation disproportions to a level very close to the minimum achievable given the data and the classification system. However, the traditional procedure of using sub-field mean citations as normalization factors yields also good results. 相似文献
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We propose two new indices that are able to measure a scientific researcher's overall influence and the level of his/her works’ association with the mainstream research subjects within a scientific field. These two new measures – the total influence index and the mainstream index – differ from traditional performance measures such as the simple citation count and the h-index in that they take into account the indirect influence of an author's work. Indirect influence describes a scientific publication's impact upon subsequent works that do not reference it directly. The two measures capture indirect influence information from the knowledge emanating paths embedded in the citation network of a target scientific field. We take the Hirsch index, data envelopment analysis, and lithium iron phosphate battery technology field to examine the characteristics of these two measures. The results show that the total influence index favors earlier researchers and successfully highlights those researchers who have made crucial contributions to the target scientific field. The mainstream index, in addition to underlining total influence, also spotlights active researchers who enter into a scientific field in a later development stage. In summary, these two new measures are valuable complements to traditional scientific performance measures. 相似文献
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随着电子引文信息的爆炸式增长,为了帮助科研工作者从海量文献数据中发现研究热点、了解领域发展趋势,科研机构和商业集团开发了一系列基于Web的文献检索工具.近年来,伴随着数据挖掘技术和信息可视化技术在知识发现领域中的迅速发展,融合上述两种技术的文献分析工具已经被研发出来并获得了很好的用户反馈.文章首先阐述了传统的文献检索工具的功能及其存在的局限性,调研了融合可视分析技术的文献分析工具并总结其功能和特点以及为文献分析带来的新颖视角,介绍北京邮电大学通信软件工程中心研发的可视分析组件--VisLib及其实用场景,最后展望了科技文献分析工具的进一步发展方向. 相似文献
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This study investigates the use, citation and diffusion of three bibliometric mapping software tools (CiteSpace, HistCite and VOSviewer) in scientific papers. We first conduct a content analysis of a sample of 481 English core journal papers—i.e., papers from journals deemed central to their respective disciplines—in which at least one of these tools is mentioned. This allows us to understand the predominant mention and citation practices surrounding these tools. We then employ several diffusion indicators to gain insight into the diffusion patterns of the three software tools. Overall, we find that researchers mention and cite the tools in diverse ways, many of which fall short of a traditional formal citation. Our results further indicate a clear upward trend in the use of all three tools, though VOSviewer is more frequently used than CiteSpace or HistCite. We also find that these three software tools have seen the fastest and most widespread adoption in library and information science research, where the tools originated. They have since been gradually adopted in other areas of study, initially at a lower diffusion speed but afterward at a rapidly growing rate. 相似文献
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基于F1000与WoS的同行评议与文献计量相关性研究 总被引：1，自引：1，他引：0
为比较同行评议与文献计量方法在科学评价中的有效性及相关性,选取F1000以及Web of Science数据库,采用SPSS16.0软件,将近2000篇论文的F1000因子与Web of Science数据库中指标进行相关性比较。结果显示,F1000因子与统计区间内的被引频次呈显著正相关,同时一些F1000因子很高的论文并没有高频被引,反之亦然。结论指出:从统计学的视角,文献计量指标与同行评议结果具有正向相关性,但是无论是同行评议还是文献计量,单独作为科学评价标准都会有失偏颇,以引文分析为代表的定量指标与同行评议方法的结合将是未来科学评价的主流。 相似文献
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科技期刊参考文献数量与部分引证指标的定量关系初探 总被引：1，自引：0，他引：1
针对目前相关研究缺乏定量分析的现状,探讨中国科技期刊参考文献数量与总被引频次、影响因子的定量关系.根据《中国科技期刊引证报告》2000-2013年的相关统计数据,拟合出相应的关系曲线及公式,分析篇均引文数与平均总被引频次、篇均引文数与平均影响因子的关系和变化趋势.结果表明:篇均引文数与平均总被引频次呈现较好的线性关系;篇均引文数与平均影响因子呈现比较理想的三次多项式关系;拟合曲线与统计数据吻合良好.根据这2个拟合公式进行预测,当篇均引文数达到20时,中国科技核心期刊的平均总被引频次有望超过1 700,平均影响因子有望超过1.0.根据预测结果,如果篇均引文数能在现有基础上提高26％,则平均总被引频次有可能提高44％,平均影响因子有可能提高90％. 相似文献