Research funding and citations in papers of Nobel Laureates in Physics, Chemistry and Medicine, 2019-2020

Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.

General laws of funding for scientific citations:how citations change in funded and unfunded research between basic and applied sciences

Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.Design/methodology/approach:A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.Findings:The original results reveal general characteristics of the diffusion of science in research fields:a)Funded articles receive higher citations compared to unfunded papers in journals;b)Funded articles exhibit a super-linear growth in citations,surpassing the increase seen in unfunded articles.This finding reveals a higher diffusion of scientific knowledge in funded articles.Moreover,c)funded articles in both basic and applied sciences demonstrate a similar expected change in citations,equivalent to about 1.23%,when the number of funded papers increases by 1%in journals.This result suggests,for the first time,that funding effect of scientific research is an invariant driver,irrespective of the nature of the basic or applied sciences.Originality/value:This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society.These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.Practical implications:This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

Citation distributions and research evaluations:The impossibility of formulating a universal indicator

Purpose:To analyze the diversity of citation distributions to publications in different research topics to investigate the accuracy of size-independent,rank-based indicators.The top percentile-based indicators are the most common indicators of this type,and the evaluations of Japan are the most evident misjudgments.Design/methodology/approach:The distributions of citations to publications from countries and journals in several research topics were analyzed along with the corresponding global publications using histograms with logarithmic binning,double rank plots,and normal probability plots of log-transformed numbers of citations.Findings:Size-independent,top percentile-based indicators are accurate when the global ranks of local publications fit a power law,but deviations in the least cited papers are frequent in countries and occur in all journals with high impact factors.In these cases,a single indicator is misleading.Comparisons of the proportions of uncited papers are the best way to predict these deviations.Research limitations:This study is fundamentally analytical,and its results describe mathematical facts that are self-evident.Practical implications:Respectable institutions,such as the OECD,the European Commission,and the U.S.National Science Board,produce research country rankings and individual evaluations using size-independent percentile indicators that are misleading in many countries.These misleading evaluations should be discontinued because they can cause confusion among research policymakers and lead to incorrect research policies.Originality/value:Studies linking the lower tail of citation distribution,including uncited papers,to percentile research indicators have not been performed previously.The present results demonstrate that studies of this type are necessary to find reliable procedures for research assessments.

Tracking direct and indirect impact on technology and policy of transformative research via ego citation network

Purpose:The disseminating of academic knowledge to nonacademic audiences partly relies on the transition of subsequent citing papers.This study aims to investigate direct and indirect impact on technology and policy originating from transformative research based on ego citation network.Design/methodology/approach:Key Nobel Prize-winning publications(NPs)in fields of gene engineering and astrophysics are regarded as a proxy for transformative research.In this contribution,we introduce a network-structural indicator of citing patents to measure technological impact of a target article and use policy citations as a preliminary tool for policy impact.Findings:The results show that the impact on technology and policy of NPs are higher than that of their subsequent citation generations in gene engineering but not in astrophysics.Research limitations:The selection of Nobel Prizes is not balanced and the database used in this study,Dimensions,suffers from incompleteness and inaccuracy of citation links.Practical implications:Our findings provide useful clues to better understand the characteristics of transformative research in technological and policy impact.Originality/value:This study proposes a new framework to explore the direct and indirect impact on technology and policy originating from transformative research.

Citation and bibliographic coupling between authors in the field of social network analysis

Purpose:We analyzed the structure of a community of authors working in the field of social network analysis(SNA)based on citation indicators:direct citation and bibliographic coupling metrics.We observed patterns at the micro,meso,and macro levels of analysis.Design/methodology/approach:We used bibliometric network analysis,including the“temporal quantities”approach proposed to study temporal networks.Using a two-mode network linking publications with authors and a one-mode network of citations between the works,we constructed and analyzed the networks of citation and bibliographic coupling among authors.We used an iterated saturation data collection approach.Findings:At the macro-level,we observed the global structural features of citations between authors,showing that 80%of authors have not more than 15 citations from other works.At the meso-level,we extracted the groups of authors citing each other and similar to each other according to their citation patterns.We have seen a division of authors in SNA into groups of social scientists and physicists,as well as into other groups of authors from different disciplines.We found some examples of brokerage between different groups that maintained the common identity of the field.At the micro-level,we extracted authors with extremely high values of received citations,who can be considered as the most prominent authors in the field.We examined the temporal properties of the most popular authors.Research limitations:The main challenge in this approach is the resolution of the author’s name(synonyms and homonyms).We faced the author disambiguation,or“multiple personalities”(Harzing,2015)problem.To remain consistent and comparable with our previously published articles,we used the same SNA data collected up to 2018.The analysis and conclusions on the activity,productivity,and visibility of the authors are relative only to the field of SNA.Practical implications:The proposed approach can be utilized for similar objectives and identifying key structures and characteristics in other disciplines.This may potentially inspire the application of network approaches in other research areas,creating more authors collaborating in the field of SNA.Originality/value:We identified and applied an innovative approach and methods to study the structure of scientific communities,which allowed us to get the findings going beyond those obtained with other methods.We used a new approach to temporal network analysis,which is an important addition to the analysis as it provides detailed information on different measures for the authors and pairs of authors over time.

A Bibliometric Analysis of Positive Mental Health Research and Development in the Social Science Citation Index

Background:This study aimed to conduct a bibliometric analysis of positive mental health,focusing on citation performance,article title,abstract,author keywords,Keyword Plus,and their development trends.The novelty of this study is a pioneer within the field of positive mental health.Therefore,it delivered new ideas for researchers and practitioners who had concerns about positive mental health in terms of trends research which covered recommended articles and the research focus in recent years.Methods:The data were retrieved on 30 April 2024 from the Social Sciences Citation Index(SSCI)of Clarivate Analytics’Web of Science Core Collection for studies published between 1992 and 2023.Results:The distribution of keywords in the article title and keywords chosen by the authors were used to assess research trends.1391 documents in SSCI were found during the search;1221 of these were document-type“articles.”524 journals published these publications.The most frequently used keywords by the writers,according to the articles’analysis,are“depression,”“resilience,”“COVID-19,”“anxiety,”and“social support.”Kristin D.Neff wrote the most frequently cited paper in 2003.Most articles came from Europe(five countries),America(two countries),Asia(two countries),and Oceania(one country),and were published in English.The majority of the research in the field of positive mental health is conducted in Europe and America,two regions where English is the primary language.The main research topics in positive mental health were related to adolescents,children,and college students.Conclusion:Trends research through bibliometric analysis by using data from Web of Science Core Collection should be followed by manual inspection to avoid errors.Therefore,scientists need more careful data examination in bibliometric analysis.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.

基于Citation Topic的领域期刊主题演化研究——以行为医学为例

基于论文层面的Citation Topic分类体系,通过测度行为医学领域SCI及SSCI期刊引文主题变动情况,展现国际行为医学领域研究重点以及相关支撑学科的演化。

Research on overlapping structures and evolution properties of co-citation network

Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.

Evolution of the Socio-cognitive Structure of Knowledge Management(1986–2015): An Author Co-citation Analysis

Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submitted to author co-citation analysis(ACA) following a longitudinal perspective as of the following time slices: 1986–1996, 1997–2006, and 2007–2015. The top 10% of most cited first authors by sub-periods were mapped in bibliometric networks in order to interpret the communities formed and their relationships.Findings: KM is a homogeneous field as indicated by networks results. Nine classical authors are identified since they are highly co-cited in each sub-period, highlighting Ikujiro Nonaka as the most influential authors in the field. The most significant communities in KM are devoted to strategic management, KM foundations, organisational learning and behaviour, and organisational theories. Major trends in the evolution of the intellectual structure of KM evidence a technological influence in 1986–1996, a strategic influence in 1997–2006, and finally a sociological influence in 2007–2015.Research limitations: Describing a field from a single database can offer biases in terms of output coverage. Likewise, the conference proceedings and books were not used and the analysis was only based on first authors. However, the results obtained can be very useful to understand the evolution of KM research.Practical implications: These results might be useful for managers and academicians to understand the evolution of KM field and to(re)define research activities and organisational projects.Originality/value: The novelty of this paper lies in considering ACA as a bibliometric technique to study KM research. In addition, our investigation has a wider time coverage than earlier articles.

WIMS/CITATION在IHNI-1堆芯物理计算中的应用

建立了利用WIMS+CITATION计算医院中子照射器Ⅰ型堆堆芯中子学参数的模型,计算了堆芯的功率分布、顶铍反应性价值、控制棒价值、温度系数、堆芯燃耗等中子学参数,计算结果与文献数据一致,表明文章所建立的计算模型可用于医院中子照射器I型堆堆芯的物理计算。

基于局部加权的Citation-kNN算法

Citation-kNN算法对传统的kNN算法进行了改进,使其可以应用于多示例学习问题,但其0-1决策方式具有一定的局限性,没有充分考虑样本的分布情况。为解决该问题,该文提出局部加权的Citation-kNN算法,综合考虑样本的分布情况,提出基于样本距离加权、基于样本离散度加权的方法,并对各种组合情况进行了实验。在标准数据集MUSK和乳腺超声图像数据库上的实验结果表明,该文提出的方法与Citation-kNN相比,性能有明显提高,并具有良好的适应性。

基于改进Citation-KNN算法的性别识别研究

为了简化系统模型训练方法,提高性别识别系统的整体效率,提出了一种基于改进Citation-KNN算法的说话人性别识别方法。该方法将连续语音切分,训练每段语音的高斯混合模型(Gaussian Mixture Model,GMM)作为多示例包,其所有混合元为相应包中示例;采用改进的Hausdorff距离作为包与包之间的距离测度,通过Citation-KNN算法进行性别识别。该方法以多示例包间距离为分类依据,简化了系统训练,且识别率优于一些传统算法。

镁合金表面热喷涂WC-10Co-4Cr粒子沉积及分布特性

为了探究高速空气燃料热喷涂(activated combustion-high velocity air fuel,AC-HVAF)过程中喷涂粒子撞击基材后的沉积特性。采用AC-HVAF热喷涂技术在AZ80镁合金基体上沉积WC-10Co-4Cr硬质涂层。通过离散沉积实验获得薄层沉积粒子,探讨各种沉积形貌的种类、形成原因、结合机制及射流中粒子的径向和轴向分布。结果表明:在AC-HVAF粒子沉积过程中,嵌入型沉积为主要的沉积形貌,同时包含少量的破碎型与空腔型沉积粒子。在涂层的形成过程中,嵌入型沉积对涂层/基体结合性能起重要作用;空腔型沉积的小颗粒及破碎型沉积的大颗粒是造成沉积效率下降的主要原因。喷涂粒子主要集中在射流中心,越靠近射流边缘,空腔型沉积粒子越多,最终导致AC-HVAF粒子射流呈现出空间分布特征。

Science Citation Index Expanded (SCI-E)及其检索技巧

简要介绍了SCI-E网络数据库及特点,并结合实例说明SCI-E数据库的检索技巧及分析功能的应用。

Writer Stance in Citations: An Ethnolinguistic and Paradigmatic Perspective

This study examines how writer stance is projected in citation from a comparative perspective.The dataset consisted of 120 qualitative and quantitative research articles(RAs)that were authored by Anglophone and Chinese applied linguists.ANO?VA tests revealed marked cross-language and cross-paradigmatic differences in stance-based dialogic engagement.

平菇^(60)Co-γ诱变孢子杂交新种质的创制及其遗传多样性分析

为了探究不同平菇品种对辐射的敏感性及适宜辐射剂量,以便快速准确地鉴别平菇的杂交后代,创制新平菇种质,从而加快育种进程,本研究利用10个^(60)Co-γ射线剂量分别辐射4种不同平菇的孢子悬浮液,对致死率进行统计,并获得了诱变菌株的孢子单核体。随后配制杂交组合,开展了^(60)Co-γ诱变和杂交育种研究。基于简单序列重复(SSR)标记,对诱变后材料和杂交后代共39份材料进行了遗传多样性分析。结果表明,品种5178的适宜辐射剂量为500 Gy,时间为200 min;9408的适宜辐射剂量为600 Gy,时间为240 min;灰美2号的适宜辐射剂量为700 Gy,时间为280 min;榆黄菇T2的适宜辐射剂量为400 Gy,时间为160 min。筛选获得的35个长势良好的杂交子中,经SSR分子标记验证,这些菌株在8对引物扩增下呈现出不同的特异性条带。综上,本研究可快速、准确地鉴别杂交子,为平菇的辐射诱变和杂交育种提供了技术依据和理论参考。

^(60)Co-γ射线对不同无花果品种一年生枝条的辐射效应分析

【目的】研究^(60)Co-γ射线对不同无花果品种一年生枝条死亡率、抗氧化酶活性等相关理化指标的影响,确定最佳辐射诱变剂量,为利用^(60)Co-γ射线辐射诱变技术提高无花果遗传多样性、创造新种质、培育优良新品种提供科学依据。【方法】以无花果品种布兰瑞克和中华紫果一年生休眠枝为试材,采用^(60)Co-γ射线为辐照源,设置不同剂量0、30、45、60、75 Gy的辐射处理,分析辐照引起枝叶死亡率、酶活性等变化。【结果】随着辐射剂量的增加,2个无花果品种扦插苗成活率呈下降趋势,株高、茎粗、节间长度、叶片长度、叶片宽度和分枝数指标呈下降趋势,叶片长宽比差异不显著。中华紫果品种的SOD、POD、CAT等酶活性均呈现先增后减的趋势,分别在45、60 Gy剂量时达到峰值,MDA含量呈现一直上升趋势,H_(2)O_(2)含量呈现先增后降趋势。布兰瑞克品种的SOD、POD、CAT酶活性,随着辐射剂量的增加均呈现先降后增趋势。MDA、H_(2)O_(2)含量基本保持上升趋势。【结论】辐射导致无花果扦插苗幼苗生长缓慢、株高矮化、茎粗变细、叶片变小、分枝减少。中华紫果的半致死辐射剂量为56 Gy,布兰瑞克为70 Gy,布兰瑞克对辐射的耐受性高于中华紫果。

^(60)Co-γ辐射对2种锦带幼苗生长及生理指标的影响

为了探究辐射诱变对“白花锦带花”和“锦带花”形态及生理特性的影响,采用不同剂量(0~300 Gy)的^(60)Co-γ射线辐射“白花锦带花”与“锦带花”休眠期种子,观测辐射对锦带花生长的影响,测定其生长量及幼苗叶片的叶绿素含量、叶绿素荧光指标、光合作用参数、SOD、POD、MDA和可溶性蛋白含量。研究结果显示,由于辐射剂量的增大,苗高、叶长、叶宽、节间距、叶绿素荧光、光合作用参数、SOD活性、POD活性出现了明显的先升高后降低的趋势,在50 Gy时到达了峰值。同时,随着辐射剂量的增加,叶绿素含量和可溶性蛋白含量呈现出下降的趋势,MDA含量明显升高。综上所述,在(2 Gy/min)剂量率的辐射条件下“白花锦带花”种子较适宜的诱变剂量为50 Gy,而“锦带花”以50~100 Gy最合适。