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基金视角下的学科知识流动网络构建与分析 总被引：1，自引：0，他引：1
[目的/意义] 在学科交叉融合的背景和趋势下,科研人员的跨学科研究行为促进了知识在不同学科之间的共享与流动。以基金领域为视角,研究学科知识流动,以期为基金管理工作提出建议。[方法/过程] 提出基于基金代码共现的学科知识流动强度测量方法,并利用社会网络分析方法,对193517个国家自然科学基金项目数据构建学科知识流动网络,探究网络的演变过程以及不同学科层次内知识流动路径。[结果/结论] 通过以上分析,得到如下结论:学科知识流动网络是无标度网络,随着时间的推移,网络的规模和结构都发生显著的变化,网络中重要节点呈非稳态;不同学部之间,存在若干条较为重要的知识流动路径;在某一个学部内,具有较广影响力的一类学科往往更容易构成知识流动链条。 相似文献
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《Journal of Informetrics》2020,14(4):101075
Greater collaboration generally produces higher category normalised citation impact (CNCI) and more influential science. Citation differences between domestic and international collaborative articles are known, but obscured in analyses of countries’ CNCIs, compromising evaluation insights. Here, we address this problem by deconstructing and distinguishing domestic and international collaboration types to explore differences in article citation rates between collaboration type and countries. Using Web of Science article data covering 2009–2018, we find that individual country citation and CNCI profiles vary significantly between collaboration types (e.g., domestic single institution and international bilateral) and credit counting methods (full and fractional). The ‘boosting’ effect of international collaboration is greatest where total research capacity is smallest, which could mislead interpretation of performance for policy and management purposes. By incorporating collaboration type into the CNCI calculation, we define a new metric labelled Collab-CNCI. This can account for collaboration effects without presuming credit (as fractional counting does). We recommend that analysts should: (1) partition all article datasets so that citation counts can be normalised by collaboration type (Collab-CNCI) to enable improved interpretation for research policy and management; and (2) consider filtering out smaller entities from multinational and multi-institutional analyses where their inclusion is likely to obscure interpretation. 相似文献
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《Journal of Informetrics》2023,17(3):101413
Understanding paper citation dynamics and accurately predicting future citation counts of papers is of significant interest, and thus modeling citation dynamics as an information cascade has recently attracted considerable attention. Nevertheless, most of these recent deep learning-based information cascade prediction models are focused on the embedding of each individual node rather than the entire structure of the cascade graph, which limits the robustness of the model. Thus, instead of learning the representation of each node in the cascade, we propose learning the dynamic structural representation of the entire information cascade graph with the degree distribution vectors corresponding to different timestamps as the input of a sequential deep neural network, named CasDENN. Extensive experiments on datasets from academic paper citations (APS) and social media post forwards (Weibo) show a dramatic improvement over state-of-the-art baselines, where the prediction error can be reduced by approximately 8%–10% and the running time is less than 10% of the fast baseline. 相似文献
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《Journal of Informetrics》2020,14(4):101080
It is widely accepted that data is fundamental for research and should therefore be cited as textual scientific publications. However, issues like data citation, handling and counting the credit generated by such citations, remain open research questions.Data credit is a new measure of value built on top of data citation, which enables us to annotate data with a value, representing its importance. Data credit can be considered as a new tool that, together with traditional citations, helps to recognize the value of data and its creators in a world that is ever more depending on data.In this paper we define data credit distribution (DCD) as a process by which credit generated by citations is given to the single elements of a database. We focus on a scenario where a paper cites data from a database obtained by issuing a query. The citation generates credit which is then divided among the database entities responsible for generating the query output. One key aspect of our work is to credit not only the explicitly cited entities, but even those that contribute to their existence, but which are not accounted in the query output.We propose a data credit distribution strategy (CDS) based on data provenance and implement a system that uses the information provided by data citations to distribute the credit in a relational database accordingly.As use case and for evaluation purposes, we adopt the IUPHAR/BPS Guide to Pharmacology (GtoPdb), a curated relational database. We show how credit can be used to highlight areas of the database that are frequently used. Moreover, we also underline how credit rewards data and authors based on their research impact, and not merely on the number of citations. This can lead to designing new bibliometrics for data citations. 相似文献
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基于内容的引文情感分析克服了传统基于引用频次的引用同一化问题,是引文内容分析领域一个重要的研究热点。然而引文情感分析依赖于带标注的数据集,目前大规模高质量的引文情感语料资源匮乏,严重制约了该领域的研究。因此,本文在分析引文情感表达方式的基础上提出了一套适用于引文情感表示的标注体系,并详细阐述了语料库建设的技术和方法。采用人机结合的标注策略,借助完善的引文标注系统,构建了规模较大的中文文献的引文情感语料库。统计结果显示,在中文信息处理和科技管理领域情感褒义和贬义总的引用的占比分别为22%和6%,引文情感标注kappa值达到0.852,表明该语料库能够客观地反映作者的情感倾向性,可为论文评价、引文网络分析和情感分析等相关领域的研究提供数据支撑。 相似文献
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社会化引文网络和科学范式的重建 总被引：1，自引：0，他引：1
提出“社会引文网络”概念,对网络环境下的语义链接进行定义。结合引文数据库发展历程和引文技术的应用,指出社会引文网络科学范式必须通过科学家行为和心理、科技管理和评价以及信息组织和检索来重建。最后,从技术、应用和服务模式归纳出引文数据库的发展趋势。 相似文献
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[目的/意义]学科交叉融合使得学科间知识交流日益频繁,从个体引文网络和整体引文网络入手,对我国人文社会科学领域跨学科知识流动进行量化分析,对“新文科”背景下该领域学科的守正与创新具有重要意义。[方法/过程]以2016-2020年23个学科450本期刊的论文引用关系为数据源,基于个体引文网络,从23个学科自身出发,根据学科互引关系确定模糊规则,利用Matlab进行模糊推理,确定学科知识固化程度;基于整体引文网络,运用“累积”的思想,计算学科知识累积流动率和累积影响力,根据知识流动情况划分学科类型。[结果/结论]研究结果表明,从个体引文网络视角分析,语言学、体育学、法学综合知识固化程度较高,统计学综合知识固化程度最低;从整体引文网络视角分析,将该领域23个学科根据知识流动划分为3种类型,经济学和管理学的累积影响力最大。研究发现“累积”思想对学科的评价效力优于直接引文分析,能够挖掘“隐藏”的学科知识流动潜在信息,为我国人文社科领域的学科建设和发展提供一定的启示。 相似文献
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In citation network analysis, complex behavior is reduced to a simple edge, namely, node A cites node B. The implicit assumption is that A is giving credit to, or acknowledging, B. It is also the case that the contributions of all citations are treated equally, even though some citations appear multiply in a text and others appear only once. In this study, we apply text-mining algorithms to a relatively large dataset (866 information science articles containing 32,496 bibliographic references) to demonstrate the differential contributions made by references. We (1) look at the placement of citations across the different sections of a journal article, and (2) identify highly cited works using two different counting methods (CountOne and CountX). We find that (1) the most highly cited works appear in the Introduction and Literature Review sections of citing papers, and (2) the citation rankings produced by CountOne and CountX differ. That is to say, counting the number of times a bibliographic reference is cited in a paper rather than treating all references the same no matter how many times they are invoked in the citing article reveals the differential contributions made by the cited works to the citing paper. 相似文献
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随着交叉科学研究在促进社会发展重大综合性问题解决方面的优势逐渐凸显,越来越多的国家对交叉科学研究给予高度的重视与支持,如何对交叉科学的研究成果进行有效的鉴定与评估也成为科技管理部门亟待解决的重要问题。本文在传统引文指标的基础上,引入PLoS官方平台的使用数据(html浏览、xml下载及pdf下载)作为补充,综合评价交叉科学研究成果的影响力情况。以2009-2013年发表在开源期刊PLoS Computational Biology的研究论文为例,研究结果表明:(1)学科交叉水平与论文影响力之间存在一定的正向关系,学科交叉水平高的论文,对应的使用数据与引用数据要明显高于学科交叉水平较低的论文;(2)论文的使用数据与引用数据相互促进,在引用数据达到峰值时,对应的使用数据也会随之出现一定的回升;(3)学科交叉程度对使用数据与引用数据之间的相关关系也有较为显著的影响。本文从使用数据和引用数据两个维度探索交叉科学研究成果的影响力,为当前交叉科学研究成果影响力的评价提供了新的借鉴与参考。 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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《Journal of Informetrics》2020,14(3):101039
Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., ‘citations in the first two years’, ‘first-cited age’, ‘paper length’, ‘month of publication’, and ‘self-citations of journals’, and the other features contribute only slightly to the prediction. 相似文献
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E. Z. Suleimenov 《Scientific and Technical Information Processing》2010,37(1):33-36
The purpose of the Kazakh publication citation indicator that has been developed in Kazakhstan since 2005 is to carry out
scientometric analysis of scientific publications to determine their citation rate. At present, the bibliographic database
(BDB) on citation includes information on the publication activities and citation index of approximately 30000 Kazakh scientists
and specialists. They had over 18000 scientific papers published in over 500 domestic and foreign journals. The total quantity
of references to papers by Kazakh scientists was more than 28000. The Kazakh analogue of the science citation index determination
system is an efficient tool for analytical work with the BDB of scientific publications, which makes it possible to calculate
publication activities and citation parameters, which are used to define the value and demand for the results of scientific
work in various fields of domestic science. 相似文献
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《Journal of Informetrics》2020,14(1):101005
Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 network-based metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation metrics are strongly affected by the interplay between the age distribution of the milestone items and age biases of the evaluated metrics. Outcomes of these metrics are therefore not representative of the metrics’ ranking ability. We argue in favor of a modified evaluation procedure that explicitly penalizes biased metrics and allows us to reveal metrics’ performance patterns that are consistent across the datasets. PageRank and LeaderRank turn out to be the best-performing ranking metrics when their age bias is suppressed by a simple transformation of the scores that they produce, whereas other popular metrics, including citation count, HITS and Collective Influence, produce significantly worse ranking results. 相似文献
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［目的/意义］ 基于专利的全代引证网络对专利进行分类,对高影响力专利的知识扩散特征进行分析,为专利影响力的认识和评估提供重要参考。［方法/过程］ 以生物学家悉尼·布伦纳的专利为例,研究其专利和前向引证专利生成的专利全代引证网络,根据专利的直接引证数量和引证长度两个对专利扩散发挥重要作用的因素将专利分为四类,将具有高被引数量长引证路径的专利定义为高影响力专利,对这种专利的知识扩散特征进行分析。［结果/结论］ 研究发现,在专利的全代引证网络中"关键专利""重要专利"和"隐藏的高影响力专利"对专利的扩散影响巨大,全代引证网络中专利的领域变化也体现了知识的流动现象,知识扩散速度可以通过数字直接刻画专利时序网络特点。结合研究结果,对高影响力专利的特点有了更具体的认识,并为高影响力专利的评价提出新的思路。 相似文献
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[目的/意义]altmetrics指标是对传统文献计量指标的有力补充。随着altmetrics研究的发展,国际上许多机构知识库已经应用相关工具,取得了一定的效果。那么,altmetrics工具在我国机构知识库中的应用是否必要和可行是一个现阶段极受关注的问题。[方法/过程]在altmetrics应用背景扫描和可行性分析基础上,分别从应用工具的选择、嵌入步骤和插件类型等方面梳理altmetrics工具在机构知识库中应用的相关问题;总结国外基金、科研、医院和高校等机构在整合altmetrics工具后取得的实际效果。[结果/结论]研究发现,在我国已建立的机构知识库中嵌入PlumX插件是一个十分切实可行的方法。国外的PlumX相关实践已显示出6个方面的成效:可视化展示基金投入的产出情况;帮助年轻的科研人员展示成果影响力;基于学术交流指标预测引文影响力;激励科研成果的缴存,提供重要的决策支持信息;更好地满足对小学科小专业科研成果评价的需要;提升机构成果的国际可见度和同行认可度。 相似文献
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This article outlines a methodology to generate a list of local core journal titles by doing a citation analysis and details the process for retrieving and downloading data from Scopus. It analyzes correlations among citation count, journal rankings, and journal usage. The results of this study reveal significant correlations between journal rankings and journal usage. No correlation with citation count has been found. Limitations and implications for collection development and outreach are also discussed. 相似文献
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For comparisons of citation impacts across fields and over time, bibliometricians normalize the observed citation counts with reference to an expected citation value. Percentile-based approaches have been proposed as a non-parametric alternative to parametric central-tendency statistics. Percentiles are based on an ordered set of citation counts in a reference set, whereby the fraction of papers at or below the citation counts of a focal paper is used as an indicator for its relative citation impact in the set. In this study, we pursue two related objectives: (1) although different percentile-based approaches have been developed, an approach is hitherto missing that satisfies a number of criteria such as scaling of the percentile ranks from zero (all other papers perform better) to 100 (all other papers perform worse), and solving the problem with tied citation ranks unambiguously. We introduce a new citation-rank approach having these properties, namely P100; (2) we compare the reliability of P100 empirically with other percentile-based approaches, such as the approaches developed by the SCImago group, the Centre for Science and Technology Studies (CWTS), and Thomson Reuters (InCites), using all papers published in 1980 in Thomson Reuters Web of Science (WoS). How accurately can the different approaches predict the long-term citation impact in 2010 (in year 31) using citation impact measured in previous time windows (years 1–30)? The comparison of the approaches shows that the method used by InCites overestimates citation impact (because of using the highest percentile rank when papers are assigned to more than a single subject category) whereas the SCImago indicator shows higher power in predicting the long-term citation impact on the basis of citation rates in early years. Since the results show a disadvantage in this predictive ability for P100 against the other approaches, there is still room for further improvements. 相似文献