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1.
Knowledge diffusion is a significant driving force behind discipline development and technological innovation. Keyword is a unique knowledge diffusion trajectory, in which the sleeping beauty phenomenon sometimes appears. In this paper, we first put forward the concept of Keyword Sleeping Beauties (KSBs) on the basis of the scientific literature phenomenon of sleeping beauties. Then, we construct a parameter-free identification method to distinguish KSBs based on beauty coefficient criteria. Furthermore, we analyze the intrinsic and extrinsic influencing factors to explore the awakening mechanism of KSBs. The experiment results show that sleeping beauty phenomena also exist in the keyword diffusion trajectory and 284 KSBs are identified. The depth of sleep has a positive correlation with awakening intensity, while the length of sleep has a negative correlation with awakening intensity. In the two years of pre-awakening, KSBs tend to appear in the journals with a higher impact factor. In addition, the adoption frequency and the number of KSBs both increase obviously in the one year of pre-awakening. The findings of this paper enrich the patterns of knowledge diffusion and extend academic thinking on the sleeping beauty in science.  相似文献   

2.
[目的/意义] 睡美人文献是指那些发表初期遭遇长时间的冷遇,随后突然引起广泛关注的文献。这些文献往往蕴含着重大科学发现,对其进行识别研究具有重大意义。通过对现有识别方法的梳理和分析,提出识别睡美人文献方法的改进思路和做法。[方法/过程] 明确以高被引文献作为睡美人文献识别研究的对象,选取文献年被引频次累积变化趋势作为研究切入点,提出累积被引速度和累积被引加速度两个指标,明确自引的处理方法。[结果/结论] 提出睡美人文献识别方法的改进思路与实证路线,在一定程度上优化了识别方法,同时为睡美人文献预测方法的研究提供一定的基础。  相似文献   

3.
Inspired by “sleeping beauties in science”, we proposed that the awakening effect in knowledge diffusion is ubiquitous, whereas the “prince” paper has the strongest effect. To test this hypothesis, a three-layer super-network model depicting the knowledge diffusion trajectory is designed and the diffusion path of the awakening effect (defined on the basis of influential strength) is simulated. In detail, the model is built based on the citation network and collaboration network of 63785 publications in the library and information science domain. Through meta-paths in this super-network, the influential strength of a paper and the awakening effect from neighboring papers can be quantified into 36 numerical features. By testing the effectiveness of these features in citation counts prediction, we try to prove our hypothesis. Thus an effective predictor in machine learning is trained upon these features. Using this predictor, we showed that most neighboring papers in the super-network had effects on future citation counts. The effectiveness of these features is again demonstrated through experiments on papers with different publication years. We also did a case study on papers that were significantly affected by the awakening effect, and found that the model proposed in this paper can also be used to explain some common phenomena in knowledge diffusion. All results show that the awakening effect could be not only ubiquitous but also quantifiable.  相似文献   

4.
[目的/意义]回顾现有的睡美人文献识别方法,梳理不同方法的优缺点,尝试兼顾准确性与易操作性来改进睡美人文献的识别方法。[方法/过程]基于目前发展较为成熟的Bcp指数识别法,借鉴其利用引文曲线"离散程度"进行识别这一核心思想,引入统计学中的"变异系数"概念,将其应用于不同引文曲线类型的区分,从而提出用以识别睡美人文献的PCV指数。[结果/结论]识别结果显示,PCV指数能够较为简单、准确地识别睡美人文献,且该方法对总被引次数具有较低的依赖性。  相似文献   

5.
高被引论文与“睡美人”论文引用曲线及影响因素研究   总被引：2，自引：0，他引：2  
[目的/意义]通过对潜在“睡美人”论文的引用分布分析,提炼其特征,以期为“睡美人”论文的预判研究提供思路。[方法/过程]采用引用曲线这一更为直观的反映论文引用分布的方法,以“天文学和天体物理”这一领域为例,构建其10的高被引论文、“睡美人”论文的10-20年被引用数据并进行引文分布的对比分析。[结果/结论]研究发现两类文献的引用曲线模式及特点——高被引论文的持续增长型、显峰型、双峰型、振荡型,“睡美人”论文的持续增长型、显峰型、双峰型、振荡型、稳定型等被引用曲线模式;针对施引文献、研究主题演化方向探讨了各模式引用曲线形成的相关因素,发现两类文献达到引用高峰的时间存在差异。  相似文献   

6.
We first introduced interesting definitions of “heartbeat” and “heartbeat spectrum” for “sleeping beauties”, based on van Raan's variables. Then, we investigated 58,963 papers of Nobel laureates during 1900–2000 and found 758 sleeping beauties. By proposing and using Gs index, an adjustment of Gini coefficient, to measure the inequality of “heartbeat spectrum”, we observed that publications which possess “late heartbeats” (most citations were received in the second half of sleeping period) have higher awakening probability than those have “early heartbeats” (most citations were received in the first half of sleeping period). The awakening probability appears the highest if an article's Gs index exists in the interval [0.2, 0.6).  相似文献   

7.
[目的/意义]"睡美人"文献是对科学论文中存在的迟滞认可现象的描述，而延迟发现与延迟关注的现象也同样存在于技术文献中。在梳理文献中的睡美人、专利沉睡现象及专利引文分析的相关研究后，将此概念引入到专利信息分析中，揭示专利文献中存在的睡美人现象。[方法/过程]以美国专利商标局和美国国家经济研究局发布的专利及其引证信息为基础，使用睡美人文献经典识别方法识别出睡美人专利，对其进行特征分布分析，并选取典型案例进行研究。[结果/结论]结果证明专利文献中也存在睡美人现象，且拥有专利文献特有的特征，为后续睡美人专利的识别与唤醒奠定基础，进而为及早发现并利用此类有价值的专利文献提供解决方案，促进知识流动和技术迭代，提高科研效率，加速科学发现。  相似文献   

8.
The disruption (D) index is a network-based indicator to quantify the extent to which a focal paper disrupts its predecessors. This study focuses on what disruption means by examining example articles related to “sleeping beauties in science” and frequency-inverse document frequency (TF-IDF). We investigated the structure of the citation network and subsequent papers’ motivations for citing the focal papers. Based on the observation that conceptual work is more likely to disrupt science than technical work, we hypothesize that disruption reflects the mechanism of how paradigms shift in the development of science. We also assume that the disruption identified by the D index indicates more than generating a new direction. Disruptive contributions include revolutionary studies such as Nobel-prize-winning papers, as suggested in previous work. However, disruptive contributions also include scientific dissemination of new terminology created by popular proposals, such as “sleeping beauties in science.” Such contributions redefine and popularize phenomena in science.  相似文献   

9.
睡美人与王子文献的识别方法研究   总被引：1，自引：0，他引：1  
[目的/意义] 研究睡美人与王子文献的识别方法。分析唤醒机制,为未来在学术交流体系中发现"王子"作者,发掘、唤醒低被引和零被引文献的潜在价值提供理论依据。[方法/过程] 采用被引速率指标和睡美人指数两种客观指标识别1970-2005年临床医学四大名刊上发表的睡美人文献;基于以下4个原则寻找唤醒睡美人的王子文献:①发表于被引突增的附近年份;②本身被引次数较高;③与睡美人文献的同被引次数高;④在年度被引次数曲线上,王子文献对睡美人文献的"牵引或拉动"作用非常显著,即至少在睡美人文献引用突增的附近年份,王子文献的年度被引次数应高于睡美人文献。[结果/结论] 由于考虑了全部引文窗的引文曲线,被引速率指标能够识别出那些被引生命周期长、至今仍持续不断高频被引的论文;睡美人指数能够快速识别出睡美人文献,但却无法反映年度被引次数达到峰值之后的引文曲线;将被引速率+发表最初5年年均被引次数两个指标结合起来能够更好地识别睡美人文献。分析发现,综述、指南、著作等"共识型"的文献对于引发那些提出了新思想但尚未被认可的睡美人文献的被引突增起到了关键作用。建议事后识别睡美人文献可采用客观指标与主观界定相结合的方法,事前预测睡美人文献要注意追踪其是否被"共识性"文献推荐和引用,学术评价要特别关注被引速率低的论文。  相似文献   

10.
[目的/意义]鉴于国内对"睡美人"文献研究的不足,介绍一种新的识别"睡美人"文献的方法——K值算法,并利用该算法识别图书情报学（Information Science&Library Science,ISLS）领域的"睡美人"文献。研究对发现ISLS领域科技成果的发明人和倡导者、保护并促进重大科学发现的推广应用等均具有重要意义。[方法/过程]以Web of Science数据库中1988-2007年ISLS领域的3460篇文献为例,构成识别"睡美人"文献的数据集,利用K值算法识别其中的"睡美人"文献,总结ISLS领域"睡美人"文献的特征,并分析其唤醒机制。[结果/结论]结果表明,K值算法能够较好地识别ISLS领域的"睡美人"文献,用该算法从ISLS领域文献中共识别出6篇"睡美人"文献,这些文献的沉睡时长从7-14年不等,其研究内容主要是新方法、新系统在医学上的应用。唤醒上述"睡美人"文献的动因包括：理论和技术的后续发展、系统的商业化、作者后来赢得的声誉、知名学者的引用等。  相似文献   

11.
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   

12.
What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics.  相似文献   

13.
This paper introduces a new approach to describe the spread of research topics across disciplines using epidemic models. The approach is based on applying individual-based models from mathematical epidemiology to the diffusion of a research topic over a contact network that represents knowledge flows over the map of science—as obtained from citations between ISI Subject Categories. Using research publications on the protein class kinesin as a case study, we report a better fit between model and empirical data when using the citation-based contact network. Incubation periods on the order of 4–15.5 years support the view that, whilst research topics may grow very quickly, they face difficulties to overcome disciplinary boundaries.  相似文献   

14.
In this study, we investigate the extent to which patent citations to papers can serve as early signs for predicting delayed recognized knowledge in science using a comparative study with a control group, i.e., instant recognition papers. We identify the two opposite groups of papers by the Bcp measure, a parameter-free index for identifying papers which were recognized with delay. We provide a macro (Science/Nature papers dataset) and micro (a case chosen from the dataset) evidence on paper-patent citation linkages as early signs for predicting delayed recognized knowledge in science. It appears that papers with delayed recognition show a stronger and longer technical impact than instant recognition papers. We provide indication that in the more recent years papers with delayed recognition are awakened more often and earlier by a patent rather than by a scientific paper (also called “prince”). We also found that patent citations seem to play an important role to avoid instant recognition papers to level off or to become a so called “flash in the pan”, i.e., instant recognition. It also appears that the sleeping beauties may firstly encounter negative citations and then patent citations and finally get widely recognized. In contrast to the two focused fields (biology and chemistry) for instant recognition papers, delayed recognition papers are rather evenly distributed in biology, chemistry, psychology, geology, materials science, and physics. We discovered several pairs of “science sleeping”-“technology inducing”, such as “biology-biotechnology/pharmaceuticals”, “chemistry-chemical engineering”, as well as some trans-fields science-technology interactions, such as “psychology - computer technology/control technology/audio-visual technology”, “physics - computer technology”, and “mathematics-computer technology”. We propose in further research to discover the potential ahead of time and transformative research by using citation delay analysis, patent & NPL analysis, and citation context analysis.  相似文献   

15.
Towards an explanatory and computational theory of scientific discovery   总被引：1，自引：0，他引：1  
We propose an explanatory and computational theory of transformative discoveries in science. The theory is derived from a recurring theme found in a diverse range of scientific change, scientific discovery, and knowledge diffusion theories in philosophy of science, sociology of science, social network analysis, and information science. The theory extends the concept of structural holes from social networks to a broader range of associative networks found in science studies, especially including networks that reflect underlying intellectual structures such as co-citation networks and collaboration networks. The central premise is that connecting otherwise disparate patches of knowledge is a valuable mechanism of creative thinking in general and transformative scientific discovery in particular. In addition, the premise consistently explains the value of connecting people from different disciplinary specialties. The theory not only explains the nature of transformative discoveries in terms of the brokerage mechanism but also characterizes the subsequent diffusion process as optimal information foraging in a problem space. Complementary to epidemiological models of diffusion, foraging-based conceptualizations offer a unified framework for arriving at insightful discoveries and optimizing subsequent pathways of search in a problem space. Structural and temporal properties of potentially high-impact scientific discoveries are derived from the theory to characterize the emergence and evolution of intellectual networks of a field. Two Nobel Prize winning discoveries, the discovery of Helicobacter pylori and gene targeting techniques, and a discovery in string theory demonstrated such properties. Connections to and differences from existing approaches are discussed. The primary value of the theory is that it provides not only a computational model of intellectual growth, but also concrete and constructive explanations of where one may find insightful inspirations for transformative scientific discoveries.  相似文献   

16.
In science-technology research, papers and patents are used to represent science and technology, respectively. Detecting sleeping beauty papers and their princes in technology (patent field) could uncover dynamic knowledge contributions from science (paper field) to technology (patent field). However, previous studies have mainly focused on sleeping beauty in science. Some studies have examined SB patents in technology, but SB papers in patents are rarely studied and need to be further discussed. In addition, knowledge could flow along citations. Thus, if one paper is cited by one patent's reference (indirect citation), it also contributes to the patent, even though the patent does not directly cite it. At the same time, indirect citations are rarely discussed in sleeping beauty studies. This could lead to a loss of significant information. Therefore, to reveal the dynamic knowledge contribution from science to technology considering indirect citations, this study proposed a new method of mining sleeping beauty papers in technology and their princes. The lithium-ion battery domain is selected as a case study. The findings are as follows: (1) Most papers do not contribute knowledge to technology continuously, even when considering indirect citations, and the time-varying knowledge contribution strength changes significantly overtime. (2) The knowledge contribution strength with a time delay of more than 11 years occupies 80% of the total knowledge contribution strength. It is suggested that the window period of paper publication evaluation be extended. (3) 22 sleeping beauty papers in technology are detected. Nine papers are among the top 10 regarding the total knowledge contribution strength. (4) The princes of 9 typical sleeping beauty papers in technology are all papers. This implies that the awakening of these papers in technology was all provoked by scientific development.  相似文献   

17.
As science is becoming more interdisciplinary and potentially more data driven over time, it is important to investigate the changing specialty structures and the emerging intellectual patterns of research fields and domains. By employing a clustering-based network approach, we map the contours of a novel interdisciplinary domain – research using social media data – and analyze how the specialty structures and intellectual contributions are organized and evolve. We construct and validate a large-scale (N = 12,732) dataset of research papers using social media data from the Web of Science (WoS) database, complementing it with citation relationships from the Microsoft Academic Graph (MAG) database. We conduct cluster analyses in three types of citation-based empirical networks and compare the observed features with those generated by null network models. Overall, we find three core thematic research subfields – interdisciplinary socio-cultural sciences, health sciences, and geo-informatics – that designate the main epicenter of research interests recognized by this domain itself. Nevertheless, at the global topological level of all networks, we observe an increasingly interdisciplinary trend over the years, fueled by publications not only from core fields such as communication and computer science, but also from a wide variety of fields in the social sciences, natural sciences, and technology. Our results characterize the specialty structures of this domain at a time of growing emphasis on big social data, and we discuss the implications for indicating interdisciplinarity.  相似文献   

18.
[目的/意义] 随着社交媒体和电子出版平台的兴起,利用期刊在Twitter上的关注度来评价期刊能够对传统的期刊评价方式进行补充,发现该指标与传统指标之间的相关性关系,并以期最终构建合理的期刊社交网络影响力评价指标。[方法/过程] 根据《期刊引用报告》(Journal Citation Reports,JCR)的社会科学版,选取国际图书情报学领域影响因子前30位的期刊作为该领域的国际顶级期刊。为了研究altmetrics指标与传统的基于引文的评价指标间的相关关系,利用Spearman非参数相关性分析对期刊Twitter提及频次与8个传统指标(总被引数、影响因子、5年期影响因子、即年指标、论文数、引文半衰期、特征因子和论文影响分值)之间的相关性进行分析。[结果/结论] 统计结果显示,JASIST、College & Research Libraries和Scientometrics是本领域中在Twitter上受关注度最高的期刊。期刊Twitter提及频次仅与期刊特征因子间存在中等的显著相关性,与其他指标间存在较弱的相关性。值得注意的是,相比其他期刊,在Twitter上设有官方账号的期刊明显得到更高的关注度。  相似文献   

19.
Duplicate tweeting is a kind of behavior that a user posts the same or substantially similar original tweets to one or several topics multiple times in a row, which is commonly used to boost social media exposure to online information. However, how this behavior boosts social media exposure to online information, especially to scientific publications, is still unclear. In this study, we use the number of retweets an article received as a proxy for social media exposure to the article. Based on the duplicate tweeting records of 12,263 users on Twitter from 2011 to 2017, we find that social media exposure to scholarly articles has a significant marginal effect as the number of duplicate tweeting (k) increases. It ramps up a peak value when k is between 2 and 4 and goes down when k takes a greater value. We also find that a longer posting interval can sharply reduce social media exposure to scholarly articles, and posting non-duplicate content can significantly improve the exposure. Our findings not only help scientists and journal publishers effectively improve the social impact of their research outputs but also advance the understanding of the diffusion processes of scholarly articles.  相似文献   

20.
Journal metrics are employed for the assessment of scientific scholar journals from a general bibliometric perspective. In this context, the Thomson Reuters journal impact factors (JIFs) are the citation-based indicators most used. The 2-year journal impact factor (2-JIF) counts citations to one and two year old articles, while the 5-year journal impact factor (5-JIF) counts citations from one to five year old articles. Nevertheless, these indicators are not comparable among fields of science for two reasons: (i) each field has a different impact maturity time, and (ii) because of systematic differences in publication and citation behavior across disciplines. In fact, the 5-JIF firstly appeared in the Journal Citation Reports (JCR) in 2007 with the purpose of making more comparable impacts in fields in which impact matures slowly. However, there is not an optimal fixed impact maturity time valid for all the fields. In some of them two years provides a good performance whereas in others three or more years are necessary. Therefore, there is a problem when comparing a journal from a field in which impact matures slowly with a journal from a field in which impact matures rapidly. In this work, we propose the 2-year maximum journal impact factor (2M-JIF), a new impact indicator that considers the 2-year rolling citation time window of maximum impact instead of the previous 2-year time window. Finally, an empirical application comparing 2-JIF, 5-JIF, and 2M-JIF shows that the maximum rolling target window reduces the between-group variance with respect to the within-group variance in a random sample of about six hundred journals from eight different fields.  相似文献   

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