Search (77 results, page 1 of 4)

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Content

Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.

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Date

21. 1.2015 14:55:22

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33

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Abstract

Using data from the Web of Science (WoS), we analyze the mutual information among university, industry, and government addresses (U-I-G) at the country level for a number of countries. The dynamic evolution of the Triple Helix can thus be compared among developed and developing nations in terms of cross-sectional coauthorship relations. The results show that the Triple Helix interactions among the three subsystems U-I-G become less intensive over time, but unequally for different countries. We suggest that globalization erodes local Triple Helix relations and thus can be expected to have increased differentiation in national systems since the mid-1990s. This effect of globalization is more pronounced in developed countries than in developing ones. In the dynamic analysis, we focus on a more detailed comparison between China and the United States. Specifically, the Chinese Academy of the (Social) Sciences is changing increasingly from a public research institute to an academic one, and this has a measurable effect on China's position in the globalization.

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Abstract

The h-index has been shown to increase in many cases mostly because of citations to rather old publications. This inertia can be circumvented by restricting the evaluation to a citation time window. Here I report results of an empirical study analyzing the evolution of the thus defined timed h-index in dependence on the length of the citation time window.

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Abstract

The research question studied in this contribution is how to find an adequate representation to describe the diffusion of scientific ideas over time. We claim that citation data, at least of articles that act as concept symbols, can be considered to contain this information. As a case study we show how the founding article by Nobel Prize winner Kao illustrates the evolution of the field of fiber optics communication. We use a continuous description of discrete citation data in order to accentuate turning points and breakthroughs in the history of this field. Applying the principles explained in this contribution informetrics may reveal the trajectories along which science is developing.

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Abstract

Purpose Science is a continuum of experiences consisting of authors and their publications, and the authors' experience is an integral part of their work that gets reflected through self-citations. Thus, self-citations can be employed in measuring the relevance between publications and tracking the evolution of research. The paper aims to discuss this issue. Design/methodology/approach Based on the bibliographic data obtained from Scopus, this study constructs and visualizes the self-citation networks of ten Nobel laureates 2018, in the fields of Physiology or Medicine, Physics, Chemistry and Economic Science, to demonstrate the evolving process of each laureate's research across his or her scholarly career. Findings Statistics indicate that prominent scientists, such as Nobel laureates, have also frequently cited their own publications. However, their self-cited rates are quite low. Self-citations constitute an indispensable part of the citation system but contribute little to authors' scientific impact, regardless of artificial self-citations. Self-citation networks present a trajectory that shows the evolving process of research across a scientist's long-term scholarly career. There are obvious differences in self-citation patterns and network structures of different laureates without a disciplinary difference observed. The structures of self-citation networks are significantly influenced by laureates' productivity. In addition, it is laureates' own research patterns and citation habits that lead to the diversified patterns and structures of self-citation networks. Research limitations/implications Only scientific achievements presented in the form of publications are investigated and other kinds of scientific output, such as patents, are not included. Moreover, this approach is fit for scientists who have had a longer career and higher productivity. Originality/value This study proves the feasibility and effectiveness of self-citation analysis as a new way to examine research evolution.

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Abstract

Purpose - This paper aims to provide a new insight into the reasons why authors cite. Design/methodology/approach The authors argue that, based on philosophical ideas about the essence of things, pure rational thinking about the role of citations leads to the answer. Findings - Citations originate from the interestingness of the investigated phenomenon. The essence of citation lies in the interaction between different ideas or perspectives on a phenomenon addressed in the citing as well as in the cited articles. Research limitations/implications - The findings only apply to ethical (not whimsical or self-serving) citations. As such citations reflect interactions of scientific ideas, they can reveal the evolution of science, revive the cognitive process of an investigated scientific phenomenon and reveal political and economic factors influencing the development of science. Originality/value - This article is the first to propose interestingness and the interaction of ideas as the basic reason for citing. This view on citations allows reverse engineering from citations to ideas and hence becomes useful for science policy.

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Abstract

In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.

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Abstract

In a range of scientific coauthorship networks, transitions emerge in degree distribution, in the correlation between degree and local clustering coefficient, etc. The existence of those transitions could be regarded because of the diversity in collaboration behaviors of scientific fields. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two specific collaboration behaviors, namely the behaviors of research team leaders and those of the other team members. The model successfully predicts the transitions, as well as many common features of coauthorship networks. Particularly, it realizes a process of deriving the complex "scale-free" property from the simple "yes/no" decisions. Moreover, it provides a reasonable explanation for the emergence of transitions with the difference of collaboration behaviors between leaders and other members. The difference emerges in the evolution of research teams, which synthetically addresses several specific factors of generating collaborations, namely the communications between research teams, academic impacts and homophily of authors.

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Abstract

Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.

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Abstract

The fact that patents are documents highly constrained by law and structured by international treaties make them a unique body of publications for tracing the history and evolution of technology. The distinctiveness of prior art patent citations compared to bibliographic references in the nonpatent literature is discussed. Starting from these observations and using the patent classification scheme as a framework of reference, we have identified a data structure, the "technology footprint," derived from the patents cited as prior art for a selected set of patents. This data structure will provide us with dynamic information about the technological components of the selected set of patents, which represents a technology, company, or inventor. Two case studies are presented in order to illustrate the visualization of the technology footprint: one concerning an inventor-Mr. Engelbart, the inventor of the "computer mouse"-and another concerning the early years of a technology-computerized tomography.

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Abstract

The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fast-evolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific-collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance-based computational network growth schemes, namely the distance-based growth model (DG) and the hybrid degree and distance-based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.

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Abstract

Previous analyses identified research on environmental tobacco smoke to be subject to strong fluctuations as measured by both quantitative and qualitative indicators. The evolution of search algorithms (based on the Web of Science and Web of Knowledge database platforms) was used to show the impact of errors of omission and commission in the outcomes of scientometric research. Optimization of the search algorithm led to the complete reassessment of previously published findings on the performance of environmental tobacco smoke research. Instead of strong continuous growth, the field of environmental tobacco smoke research was shown to experience stagnation or slow growth since mid-1990s when evaluated quantitatively. Qualitative analysis revealed steady but slow increase in the citation rate and decrease in uncitedness. Country analysis revealed the North-European countries as leaders in environmental tobacco smoke research (when the normalized results were evaluated both quantitatively and qualitatively), whereas the United States ranked first only when assessing the total number of papers produced. Scientometric research artifacts, including both errors of omission and commission, were shown to be capable of completely obscuring the real output of the chosen research field.

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Abstract

This article analyzes the context of citations within the full text of research articles. It studies articles published in a single journal: the Journal of Informetrics (JOI), in the first year the journal was published, 2007. The analysis classified the citations into in- and out-disciplinary content and looked at their use within the articles' sections such as introduction, literature review, methodology, findings, discussion, and conclusions. In addition, it took into account the age of cited articles. A thematic analysis of these citations was performed in order to identify the evolution of topics within the articles sections and the journal's content. A matrix describing the relationships between the citations' use, and their in- and out-disciplinary focus within the articles' sections is presented. The findings show that an analysis of citations based on their in- and out-disciplinary orientation within the context of the articles' sections can be an indication of the manner by which cross-disciplinary science works, and reveals the connections between the issues, methods, analysis, and conclusions coming from different research disciplines.

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Abstract

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

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