Citation context analysis of a co-citation cluster: Recombinant-DNA

The techniques of co-citation clustering and citation context analysis are combined to concretely define the shared knowledge within a research specialty. The cluster for a large and fast moving biomedical specialty, recombinant-DNA, is presented in terms of the highly cited documents comprising it and their co-citation links. By examining citation contexts in the papers citing the highly cited documents, it is possible to label each of the documents in the cluster with its specific cognitive meaning for the citing authors. Co-citation contexts are used to reveal the relationships among the concepts symbolized by the highly cited documents, providing a cognitive equivalent of the co-citation links. This may open a new way to the investigation of the logic of conceptual change at the specialty level.     

Exploring the analytical potential of comparing citing and cited source items

Summary Comparing properties of citing and cited source items opens a wide variety of analytical possibilities. In a study of citations among papers in the journal Scientometrics a number of analytical themes are identified. The analysis shows: the way in which a citation graph can be decomposed into different subparts; country specific citation patterns; the effects of self-citations and domestic citations; the mapping of cited author relationships using direct citation and co-citation links; and time slicing effects on impact ranking of countries and papers.     

Knowledge-transfer analysis based on co-citation clustering

Based on co-citation cluster analysis, we propose a knowledge-transfer analysis model for any technology field. In this model, patent data with backward citations to non-patent literature and forward citations by later patents would be analyzed. Co-citation clustering of the cited articles defines scientific knowledge sources, while that of the patents themselves defines technology fronts. According to the citation between the article and patent clusters, the landscape of knowledge-transfer including route and strength between scientific knowledge sources and technology fronts can be mapped out. The model has been applied to the field of transgenic rice. As a result of the analysis, ten scientific knowledge sources and eight technology fronts have emerged, and reasonable links between them have been established, which clearly show how knowledge has been transferred in this field.     

Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage

With the development of the era of big data, research data are accumulating, and various research directions emerge endlessly. It is difficult for researchers to grasp the hotspots and development trends of the discipline. Therefore, exploring methods to quickly and accurately identify research fronts is of great significance to scientific and technological innovation. This paper proposes a research front identification method integrating machine learning and main path analysis in conjunction with papers and patents based on the existing research. The innovation of this method is the combination of citation analysis and semantic analysis to identify research front from the perspective of science-technology linkage. This article takes the Internet of Things in supply chain as an example to verify the feasibility and effectiveness of the method. It is of great significance to identify important scientific and technological research fronts in a specific domain by intuitively revealing knowledge diffusion and text mining. The proposed method enriches the application of MPA and helps scholars grasp the latest information, mainstreams and future directions.

     

Mapping the Intellectual Structure of Research on ‘Born Global’ Firms and INVs: A Citation/Co-citation Analysis

The present research paper shows the results of an analysis of the existing literature on one of the topics that has sparked the most interest among scholars and researchers in the fields of international management and entrepreneurship: born global firms or international new ventures. Concretely, with the aim of identifying and visualising the intellectual structure of research on this phenomenon, a total of 124 research papers whose titles contain the above terms are analysed. The methodology is mainly based on the bibliometric techniques of document citation and co-citation analyses and the analysis of social networks.     

Individual co-citation clusters as nuclei of complete and dynamic informetric models of scientific and technological areas

This paper describes the construction of improved informetric models of individual scientific and technological areas on the basis of individual co-citation clusters. The developed methodology of replenishment of research front with accidentally absent papers describes more completely the model. The simple method of cluster dynamisation is proposed for the study of evolution of a research area. The transition under consideration from co-citation clusters to lexical maps of papers and patents allows to monitor the relationship between R and D in a given technological area. Pressure-swing adsorption (PSA) as modern chemical engineering, is given as an example.     

Highly dynamic specialities in climate research

In this paper the identification and analysis of highly dynamic, rapidly developing research fronts of climate research are demonstrated. The underlying method based on co-citation analysis is described, and two selected highly dynamic specialities (HDS) are analysed using journal profiles, co-citation maps, and actor profiles as information elements. The two examples demonstrate how co-citation analysis can be used to identify and analyse highly dynamic specialities.     

Tracking and predicting growth areas in science

Summary We explore the possibility of using co-citation clusters over three time periods to track the emergence and growth of research areas, and predict their near term change. Data sets are from three overlapping six-year periods: 1996-2001, 1997-2002 and 1998-2003. The methodologies of co-citation clustering, mapping, and string formation are reviewed, and a measure of cluster currency is defined as the average age of highly cited papers relative to the year span of the data set. An association is found between the currency variable in a prior period and the percentage change in cluster size and citation frequency in the following period. The conflating factor of “single-issue clusters” is discussed and dealt with using a new metric called in-group citation.     

Citation analysis of research performer quality

Background: Citation analysis for evaluative purposes typically requires normalization against some control group of similar papers. Selection of this control group is an open question. Objectives: Gain a better understanding of control group requirements for credible normalization. Approach: Performed citation analysis on prior publications of two proposing research units, to help estimate team research quality. Compared citations of each unit"s publications to citations received by thematically and temporally similar papers. Results: Identification of thematically similar papers was very complex and labor intensive, even with relatively few control papers selected. Conclusions: A credible citation analysis for determining performer or team quality should have the following components: – Multiple technical experts to average out individual bias and subjectivity; – A process for comparing performer or team output papers with a normalization base of similar papers; – A process for retrieving a substantial fraction of candidate normalization base papers; Manual evaluation of many candidate normalization base papers to obtain high thematic similarity and statistical representation.     

Citation and co-citation indicators of a phased impact of the BCS theory in the physics of superconductivity

An intellectual account of of the physics of superconductivity was compared with citation and co-citation data during two historical periods that coincided with the introduction of its central explanatory theory (BCS). Factor analysis is used to investigate the co-citation data. The results give preliminary support to a hypothesis that distinguishes impact phases in the effect of the theory on the cognitive organization of the specialty. It is also observed that citation and co-citation data are separate types of information which, under some historical conditions, give differing results.This research was supported in part by a grant from the National Science Foundation to the Columbia Program in the Sociology of Science NSF SOC 72 05326     

A comparison of two bibliometric methods for mapping of the research front

Summary This paper builds on previous research concerned with the classification and specialty mapping of research fields. Two methods are put to test in order to decide if significant differences as to mapping results of the research front of a science field occur when compared. The first method was based on document co-citation analysis where papers citing co-citation clusters were assumed to reflect the research front. The second method was bibliographic coupling where likewise citing papers were assumed to reflect the research front. The application of these methods resulted in two different types of aggregations of papers: (1) groups of papers citing clusters of co-cited works and (2) clusters of bibliographically coupled papers. The comparision of the two methods as to mapping results was pursued by matching word profiles of groups of papers citing a particular co-citation cluster with word profiles of clusters of bibliographically coupled papers. Findings suggested that the research front was portrayed in two considerably different ways by the methods applied. It was concluded that the results in this study would support a further comparative study of these methods on a more detailed and qualitative ground. The original data set encompassed 73,379 articles from the fifty most cited environmental science journals listed in Journal Citation Report, science edition downloaded from the Science Citation Index on CD-ROM.     

Defining triadic patent families as a measure of technological strength

Objective  This paper aimed to examine the reliability of co-citation clustering analysis in representing the research history of subject by comparing the results from co-citation clustering analysis with a review written by authorities. Methods  Firstly, the treatment of traumatic spinal cord injury was chosen as an investigated subject to be retrieved the resource articles and their references were downloaded from Science Citation Index CD-ROM between 1992 and 2002. Then, the highly cited papers were arranged chronologically and clustered with the method of co-citation clustering. After mapping the time line visualization, the history and structure of treatment of spinal cord injury were presented clearly. At last, the results and the review were compared according the time period, and then the recall and the precision were calculated. Results  The recall was 37.5%, and the precision was 54.5%. The research history of traumatic spinal cord injury treatment analyzed by co-citation clustering was nearly consistent with authoritative review, although some clusters had shorter period than which was summarized by professionals. Conclusion  This paper concluded that co-citation clustering analysis was a useful method in representing the research history of subject, especially for the information researchers, who do not have enough professional knowledge. Its demerit of low recall could be offset by combination this method with other analytic techniques.     

Female researchers in Russia: have they become more visible?

This study is based on the fact that the surnames of many Russian scientists have gender endings, with “a” denoting a female, so that the sex of most of them can be readily determined from the listing of authors in the Web of Science (WoS). A comparison was made between the proportion of females in 1985, 1995, and 2005, with a corresponding analysis of the major fields in which they worked, their propensity to co-author papers internationally (which often necessitates having the opportunity to travel to conferences abroad to meet possible colleagues), and their citation records. We found, as expected, that women had a higher presence in the biological sciences and a very low presence in engineering, mathematics, and physics. Their citation scores, on a fractionated basis, were lower than those for men in almost all fields and years, and were not explained by their writing of fewer reviews and papers in English (both of which lead to higher citations), or their lower amount of international collaboration in 1995 and 2005 after Russia had become a more open society.     

The cognitive structure of current cardiovascular research

This paper presents a citation analysis of the cognitive structure of current cardiovascularresearch. Used methods are co-citation analysis, bibliographic coupling and quantitative analysisof title words. Tables and graphs reveal: (1) The journal co-citation structure; (2) the cognitivecontent and the bibliometric structure of clusters based on co-citation; (3) the cognitive contentand the bibliometric structure of clusters based on bibliographic coupling. A predominance ofdifferent research aspects on coronary artery disease was found in clusters based on co-citations aswell as in clusters based on bibliographic coupling.     

The influence of Elfreda Chatman’s theories: a citation context analysis

The range of theoretical frameworks currently being used by researchers into information behaviour is abundant and diverse. We need to examine thoroughly the contribution of theories and models to further research, as this would help to improve future investigations in the field. This paper adopts this approach, by thoroughly examining the influence that Elfreda Chatman’s three middle-range theories have had on subsequent research. A citation context analysis was carried out on the basis of those received by Information poverty theory, life in the round theory and normative behaviour theory. Analysis covered the year of publication, the type of work and the subject-matter of the citing documents. The cites in context or theoretical incidents were analysed for frequency of citation in citing documents, the content of Chatman’s work being cited, the context co-citation analysis, the citation style and the citation location. The analysis of citation in context has allowed us to draw a distinction between the author and her work, while verifying that not all cites are the same. These differences reflect the unequal relevance of these theories to subsequent research.

     

A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis

Summary Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of “invisible colleges.”     

A scientometric study of global electric vehicle research

A scientometric analysis was applied in this work to evaluate the status and trends of electric vehicle papers published between 1993 and 2012 in any journal of all the subject categories of the Web of Science. Electric vehicle was used as a keyword to search parts of titles, abstracts, or keywords. Publication trends were analyzed by the retrieved results in publication outputs, subject categories and publication pattern, international productivity. The document co-citation analysis was done in CitespaceII to find out the intellectual base and research fronts of electric vehicle. The articles about electric vehicle increased fast in the last 20 years. 11 document types were found in all electric vehicle-related papers and proceedings paper was the most frequently used document type. Language analysis showed that English was the most dominating language. “Engineering electrical electronic”, “Energy fuels” and “Transportation science technology” were the top three most popular subject categories. Journal of Power Sources, IEEE Transaction on Vehicular Technology and IEEE Transaction on Industrial Electronics were the representative journals in the field of electric vehicle. The USA, China and Japan were the most productive countries. University of Michigan, Harbin Institute of Technology and Ohio State University were the most productive countries. Vehicle-to-grid technology, control strategy, combination of power management and traffic information from GPS, plug-in electric vehicle, architectures and modeling, battery and policy about electric vehicle are the research fronts of electric vehicle.     

Reassessment of co-citation methods for science indicators: Effect of methods improving recall rates

Although co-citation techniques are very powerful structuring tools, the use of science policy indicators based on co-citation has often been criticized, especially on ISI research fronts. A major issue is the small fraction of literature retrieved, i.e. the recall rate problem. Our investigations indicate that at the level of micro/meso studies high recall rates can be achieved by (a) the use of appropriate clustering techniques limiting singletons and (b) the enrichment of cocited cores by medium-cited items. This combination of appropriate clustering and extension of recall proves to be efficient, provided that careful trade-offs are sought between the extension and relevance of recall. It leads to a reassessment of the performance of the co-citation approach for structuring scientific fields and providing related indicators not limited to the leading edge. It also opens new opportunities for comparison/combination with other relational methods such as co-word analysis.     

Detecting the knowledge structure of bioinformatics by mining full-text collections

Bioinformatics is a fast-growing, diverse research field that has recently gained much public attention. Even though there are several attempts to understand the field of bioinformatics by bibliometric analysis, the proposed approach in this paper is the first attempt at applying text mining techniques to a large set of full-text articles to detect the knowledge structure of the field. To this end, we use PubMed Central full-text articles for bibliometric analysis instead of relying on citation data provided in Web of Science. In particular, we develop text mining routines to build a custom-made citation database as a result of mining full-text. We present several interesting findings in this study. First, the majority of the papers published in the field of bioinformatics are not cited by others (63 % of papers received less than two citations). Second, there is a linear, consistent increase in the number of publications. Particularly year 2003 is the turning point in terms of publication growth. Third, most researches of bioinformatics are driven by USA-based institutes followed by European institutes. Fourth, the results of topic modeling and word co-occurrence analysis reveal that major topics focus more on biological aspects than on computational aspects of bioinformatics. However, the top 10 ranked articles identified by PageRank are more related to computational aspects. Fifth, visualization of author co-citation analysis indicates that researchers in molecular biology or genomics play a key role in connecting sub-disciplines of bioinformatics.     

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation.