Search (172 results, page 2 of 9)

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Abstract

The construction of virtual science landscapes based on citation networks and the strategic use of the information therein shed new light on the issues of the evolution of the science system and possibilities for control. Leydesdorff's approach to citation theory described in his 1998 article (see this issue of LISA) takes into account the dual layered character of communication networks and the second order nature of the science system. This perspective may help to sharpen the awareness of scientists and science policy makers for possible feedback loops within actions and activities in the science system, and probably nonlinear phenomena resulting therefrom. Sketches an additional link to geometrically oriented evolutionary theories and uses a specific landscape concept as a framework for some comments

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Abstract

Citations support the communication of specialist knowledge by allowing authors and readers to make specific selections in several contexts at the same time. In the interactions between the social network of authors and the network of their reflexive communications, a sub textual code of communication with a distributed character has emerged. Citation analysis reflects on citation practices. Reference lists are aggregated in scientometric analysis using one of the available contexts to reduce the complexity: geometrical representations of dynamic operations are reflected in corresponding theories of citation. The specific contexts represented in the modern citation can be deconstructed from the perspective of the cultural evolution of scientific communication

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Abstract

Like most activities in the world, scientific evolution has its own rhythm. How can this evolutionary rhythm be described and made visible? Do different fields have different rhythms, and how can they be measured? In order to answer these questions a relative indicator, called R-sequence, was designed. This indicator is time dependent, derived from publication and citation data, but independent of the absolute number of publications, as weIl as the absolute number of citations, and can therefore be used in a comparison of different scientific fields, nations, Institutes, or journals. Two caiculation methods of the R-sequence-the triangle method and the parallelogram method-are introduced. As a case study JASIS(T)'s R-sequence has been obtained.

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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

One aim of science evaluation studies is to determine quantitatively the contribution of different players (authors, departments, countries) to the whole system. This information is then used to study the evolution of the system, for instance to gauge the results of special national or international programs. Taking articles as our basic data, we want to determine the exact relative contribution of each coauthor or each country. These numbers are brought together to obtain country scores, or department scores, etc. It turns out, as we will show in this article, that different scoring methods can yield totally different rankings. Conseqeuntly, a ranking between countries, universities, research groups or authors, based on one particular accrediting methods does not contain an absolute truth about their relative importance

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Abstract

According to the bibliographical data included in the Web of Science, SCOPUS, Chemical Abstracts, and other specialized information services covering the period 1900-1950, the first publications in mainstream journals by Mexican researchers appeared only in the first decades of the 20th century. Contrary to expectations, we find that the academic community was not the protagonist in the early stages of Mexican scientific practices, but that there was a strong contribution coming from researchers associated with the public-health sector and the chemical and mining industries. We were able to identify in this half century four different modes of scientific production: amateur, institutional, academic, and industrial, which in turn correspond to distinct stages in the evolution of the Mexican scientific production. We characterize these modes of production with a variety of indicators: publication and citation patterns, author output, journal and subject categories, institutional collaborations, and geographical distribution.

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Abstract

Webometrics is an information science field concerned with measuring aspects of the World Wide Web (WWW) for a variety of information science research goals. It came into existence about five years after the Web was formed and has since grown to become a significant aspect of information science, at least in terms of published research. Although some webometrics research has focused on the structure or evolution of the Web itself or the performance of commercial search engines, most has used data from the Web to shed light on information provision or online communication in various contexts. Most prominently, techniques have been developed to track, map, and assess Web-based informal scholarly communication, for example, in terms of the hyperlinks between academic Web sites or the online impact of digital repositories. In addition, a range of nonacademic issues and groups of Web users have also been analyzed.

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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

We present evidence that in some research fields, research published in journals and reported on the Web may collectively represent different evolutionary stages of the field, with journals lagging a few years behind the Web on average, and that a "two-tier" scholarly communication system may therefore be evolving. We conclude that in such fields, (a) for detecting current research fronts, author co-citation analyses (ACA) using articles published on the Web as a data source can outperform traditional ACAs using articles published in journals as data, and that (b) as a result, it is important to use multiple data sources in citation analysis studies of scholarly communication for a complete picture of communication patterns. Our evidence stems from comparing the respective intellectual structures of the XML research field, a subfield of computer science, as revealed from three sets of ACA covering two time periods: (a) from the field's beginnings in 1996 to 2001, and (b) from 2001 to 2006. For the first time period, we analyze research articles both from journals as indexed by the Science Citation Index (SCI) and from the Web as indexed by CiteSeer. We follow up by an ACA of SCI data for the second time period. We find that most trends in the evolution of this field from the first to the second time period that we find when comparing ACA results from the SCI between the two time periods already were apparent in the ACA results from CiteSeer during the first time period.

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Abstract

Despite a very large number of studies on the aging and obsolescence of scientific literature, no study has yet measured, over a very long time period, the changes in the rates at which scientific literature becomes obsolete. This article studies the evolution of the aging phenomenon and, in particular, how the age of cited literature has changed over more than 100 years of scientific activity. It shows that the average and median ages of cited literature have undergone several changes over the period. Specifically, both World War I and World War II had the effect of significantly increasing the age of the cited literature. The major finding of this article is that contrary to a widely held belief, the age of cited material has risen continuously since the mid-1960s. In other words, during that period, researchers were relying on an increasingly old body of literature. Our data suggest that this phenomenon is a direct response to the steady-state dynamics of modern science that followed its exponential growth; however, we also have observed that online preprint archives such as arXiv have had the opposite effect in some subfields.