Detecting emerging research fronts based on topological measures in citation networks of scientific publications

Shibata, Naoki; Kajikawa, Yuya; Takeda, Yoshiyuki; Matsushima, Katsumori

doi:10.1016/j.technovation.2008.03.009

Cited by 288 publications

(174 citation statements)

References 63 publications

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“…Massive cultural digital objects include large-scale corpus like the millions of books scanned by Google and the ones produced by numerous other digitization initiatives (Jacquesson 2010), the millions of photos and micro-message shared on social network services (Thusoo et al 2010), giant geographical information systems like Google Earth (Butler 2006), or the ever expanding networks of academic papers citing one another (Shibata et al 2008). These interconnected objects -either digitally born or reconstructed through digitization pipelines -are too big to be read or watched.…”

Section: Big Cultural Datasetsmentioning

confidence: 99%

A Map for Big Data Research in Digital Humanities

Kaplan

2015

Front. Digit. Humanit.

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This article is an attempt to represent Big Data research in digital humanities as a structured research field. A division in three concentric areas of study is presented. Challenges in the first circlefocusing on the processing and interpretations of large cultural datasets -can be organized linearly following the data processing pipeline. Challenges in the second circle -concerning digital culture at large -can be structured around the different relations linking massive datasets, large communities, collective discourses, global actors, and the software medium. Challenges in the third circledealing with the experience of big data -can be described within a continuous space of possible interfaces organized around three poles: immersion, abstraction, and language. By identifying research challenges in all these domains, the article illustrates how this initial cartography could be helpful to organize the exploration of the various dimensions of Big Data Digital Humanities research.

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Section: Big Cultural Datasetsmentioning

confidence: 99%

A Map for Big Data Research in Digital Humanities

Kaplan

2015

Front. Digit. Humanit.

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“…Small and Griffith (1974) considered co-citation clusters as research fronts; Vlachý (1984) summarized prior research on scientometric studies on research fronts detection and pointed out that "science grows from a very thin skin of its research front" and "a core body of seminal literature" constitutes "a sort of epidermal layer, an active research front" (p. 95). Garfield (1994) pointed out that research fronts are co-citation clusters plus citing articles; Morris et al (2003) applied bibliographic coupling methods to identify the research fronts; Shibata et al (2008) proposed that research fronts are direct citation clusters. Presently, Chen (2006), Braam, Moed, and van Raan (1991), and Persson's (1994) views are the mainstream in research fronts detection.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the large time lag, there is a problem in using times cited as an indicator in research fronts identification. It might take up to two years for a paper to become highly cited (Shibata et al, 2008), and the situation varies among disciplines. Besides, times cited is affected by authors' different citing motivations, articles accessibility (Bollen et al, 2005), etc.…”

Section: Introductionmentioning

confidence: 99%

Usage Count: A New Indicator to Detect Research Fronts

Qiu

Yuan

2017

Journal of Data and Information Science

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Purpose: Research fronts build on recent work, but using times cited as a traditional indicator to detect research fronts will inevitably result in a certain time lag. This study attempts to explore the effects of usage count as a new indicator to detect research fronts in shortening the time lag of classic indicators in research fronts detection.Design/methodology/approach: An exploratory study was conducted where the new indicator "usage count" was compared to the traditional citation count, "times cited," in detecting research fronts of the regenerative medicine domain. An initial topic search of the term "regenerative medicine" returned 10,553 records published between 2000 and 2015 in the Web of Science (WoS). We first ranked these records with usage count and times cited, respectively, and selected the top 2,000 records for each. We then performed a co-citation analysis in order to obtain the citing papers of the co-citation clusters as the research fronts. Finally, we compared the average publication year of the citing papers as well as the mean cited year of the co-citation clusters. F indings:T he citing articles detected by usage count tend to be published more recently compared with times cited within the same research front. Moreover, research fronts detected by usage count tend to be within the last two years, which presents a higher immediacy and real-time feature compared to times cited. There is approximately a three-year time span among the mean cited years (known as "intellectual base") of all clusters generated by usage count and this figure is about four years in the network of times cited. In comparison to times cited, usage count is a dynamic and instant indicator. Research limitations:We are trying to find the cutting-edge research fronts, but those generated based on co-citations may refer to the hot research fronts. The usage count of older highly cited papers was not taken into consideration, because the usage count indicator released by WoS only reflects usage logs after February 2013. Practical implications:The article provides a new perspective on using usage count as a new indicator to detect research fronts.

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“…Instead of any known co-publication (Aberer and Hauswirth, 2001) and citation networks analysis (Chen, 1999) and emerging research front detection methods (Shibata et al, 2008), this methodological approach was used for clustering because the author hypothesized a particular cognitive framework in advance and it was taken as a starting point for processing the related literature. After briefly addressing definitional issues and referring to important previous work, the content of the proposed reasoning model will be discussed in the next section.…”

Section: Introductionmentioning

confidence: 99%

An initial categorization of foundational research in complex technical systems

Horváth

2015

J. Zhejiang Univ. Sci. A

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There has been intense foundational research in complex technical systems (CTSs) over the last half century. These systems are exemplified by advanced mechatronics systems, embedded control systems, real-time systems, agent-based smart systems, distributed software systems, internet of things systems, and cyber-physical systems. The objective of this paper is to offer an initial cataloguing of the various research domains and to identify the major research issues. The paper has an ontological flavour, because it concentrates on what research has been and is being done, rather than on why and how research is done. The underpinning study has been done in three stages: (i) intuition-driven exploration of a reference set of related academic publications, (ii) evidence-based specification of a categorization of the domains and subdomains of research, and (iii) refinement and validation of the proposed classification based on a control set of related academic publications. The proposed reasoning model identifies three categories of research domains. The 'intellectualizations' category includes research domains such as: (i) philosophy, (ii) ontology, and (iii) epistemology of CTSs. The research domains included in the 'realizations' category are: (iv) methodology, and (v) creation of CTSs. The domains considered in the 'influences' category are: (vi) manifestations, and (vii) axiology of CTSs. Based on the proposed reasoning model a landscape of foundational research in CTSs is proposed for public debate. Our follow-up study focuses on the extension of the proposed classification to other families of complex engineered systems such as sociotechnical systems and social ecosystems.

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Detecting emerging research fronts based on topological measures in citation networks of scientific publications

Cited by 288 publications

References 63 publications

A Map for Big Data Research in Digital Humanities

A Map for Big Data Research in Digital Humanities

Usage Count: A New Indicator to Detect Research Fronts

An initial categorization of foundational research in complex technical systems

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