Search (9 results, page 1 of 1)

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Abstract

This study provides evidence of data set mentions and citations in multiple disciplines based on a content analysis of 600 publications in PLoS One. We find that data set mentions and citations varied greatly among disciplines in terms of how data sets were collected, referenced, and curated. While a majority of articles provided free access to data, formal ways of data attribution such as DOIs and data citations were used in a limited number of articles. In addition, data reuse took place in less than 30% of the publications that used data, suggesting that researchers are still inclined to create and use their own data sets, rather than reusing previously curated data. This paper provides a comprehensive understanding of how data sets are used in science and helps institutions and publishers make useful data policies.

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Abstract

In informetrics, journals have been used as a standard unit to analyze research impact, productivity, and scholarship. The increasing practice of interdisciplinary research challenges the effectiveness of journal-based assessments. The aim of this article is to highlight topics as a valuable unit of analysis. A set of topic-based approaches is applied to a data set on library and information science publications. Results show that topic-based approaches are capable of revealing the research dynamics, impact, and dissemination of the selected data set. The article also identifies a nonsignificant relationship between topic popularity and impact and argues for the need to use both variables in describing topic characteristics. Additionally, a flow map illustrates critical topic-level knowledge dissemination channels.

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Abstract

This study examines patterns of dynamic disciplinary knowledge production and diffusion. It uses a citation data set of Scopus-indexed journals and proceedings. The journal-level citation data set is aggregated into 27 subject areas and these subjects are selected as the unit of analysis. A 3-step approach is employed: the first step examines disciplines' citation characteristics through scientific trading dimensions; the second step analyzes citation flows between pairs of disciplines; and the third step uses egocentric citation networks to assess individual disciplines' citation flow diversity through Shannon entropy. The results show that measured by scientific impact, the subjects of Chemical Engineering, Energy, and Environmental Science have the fastest growth. Furthermore, most subjects are carrying out more diversified knowledge trading practices by importing higher volumes of knowledge from a greater number of subjects. The study also finds that the growth rates of disciplinary citations align with the growth rates of global research and development (R&D) expenditures, thus providing evidence to support the impact of R&D expenditures on knowledge production.

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Abstract

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.

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Source

Information processing and management. 47(2011) no.1, S.125-134

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Abstract

The objective of this research is to examine the interaction of institutions, based on their citation and collaboration networks. The domain of library and information science is examined, using data from 1965-2010. A linear model is formulated to explore the factors that are associated with institutional citation behaviors, using the number of citations as the dependent variable, and the number of collaborations, physical distance, and topical distance as independent variables. It is found that institutional citation behaviors are associated with social, topical, and geographical factors. Dynamically, the number of citations is becoming more associated with collaboration intensity and less dependent on the country boundary and/or physical distance. This research is informative for scientometricians and policy makers.

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Abstract

The objective of this paper is to identify the dynamic structure of several time-dependent, discipline-level citation networks through a path-based method. A network data set is prepared that comprises 27 subjects and their citations aggregated from more than 27,000 journals and proceedings indexed in the Scopus database. A maximum spanning tree method is employed to extract paths in the weighted, directed, and cyclic networks. This paper finds that subjects such as Medicine, Biochemistry, Chemistry, Materials Science, Physics, and Social Sciences are the ones with multiple branches in the spanning tree. This paper also finds that most paths connect science, technology, engineering, and mathematics (STEM) fields; 2 critical paths connecting STEM and non-STEM fields are the one from Mathematics to Decision Sciences and the one from Medicine to Social Sciences.

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Abstract

This study examines the relationship between team's gender composition and outputs of funded projects using a large data set of National Institutes of Health (NIH) R01 grants and their associated publications between 1990 and 2017. This study finds that while the women investigators' presence in NIH grants is generally low, higher women investigator presence is on average related to slightly lower number of publications. This study finds empirically that women investigators elect to work in fields in which fewer publications per million-dollar funding is the norm. For fields where women investigators are relatively well represented, they are as productive as men. The overall lower productivity of women investigators may be attributed to the low representation of women in high productivity fields dominated by men investigators. The findings shed light on possible reasons for gender disparity in grant productivity.

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Date

26.10.2014 20:22:22