Bibliometric trend analysis on global graphene research

Lv, Peng; Wang, Guifang; Wan, Yong; Liu, Jia; Qing, Liu; Ma, Feicheng

doi:10.1007/s11192-011-0386-x

Cited by 80 publications

(63 citation statements)

References 31 publications

(28 reference statements)

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“…World output in graphene research began to grow exponentially in 2004, as previously reported (Lv et al, 2011). This growth was spurned by a strong pulse in production in the United States from 2004 onward, and in Europe and China from 2006 onward.…”

Section: Resultsmentioning

confidence: 81%

“…Europe and China apparently benefit from encouraging scientific policy that also affects graphene research. It shows exponential growth at the worldwide level from 2004 (Lv et al, 2011) to 2005(Wan and Pan, 2010, demonstrating the great interest awakened among researchers working with this material. Most surprising is the trend seen for China from 2006 onward.…”

Section: Resultsmentioning

confidence: 99%

“…The expression "single layer graphite" has come to largely replace the word graphene (Lv et al, 2011), for which reason we decided to include it in our search equation. It was right-truncated to include the singular and the plural.…”

Section: Methodsmentioning

confidence: 99%

“…Within the framework of carbonbased materials, however, this 2-D material suddenly aroused the attention of scientists (Lv et al, 2011). With the experimental discovery of graphene by Novoselov et al (2004), a rapidly growing stream of scientific literature became fertile land for bibliometric studies: Barth and Marx (2008), Wan and Pan (2010), Shapira et al (2010), Lv et al (2011), Munoz-Sandoval (2014), Small et al (2014), Terekhov (2015), Klincewicz (2016), and Shapira et al (2016).…”

mentioning

confidence: 99%

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Identification and Visualization of the Intellectual Structure in Graphene Research

Vargas-Quesada

Chinchilla‐Rodríguez

Rodríguez

2017

Front. Res. Metr. Anal.

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Since the discovery of the promising properties of graphene, research in the field has attracted numerous grants and sponsors, leading to an exponential rise in the number of papers and applications. This article presents a global map of graphene research and its intellectual structure, drawn using the terms of more than 50,000 documents extracted from Scopus database, years 1998-2015. The unit of analysis consisted of descriptors (including Author Keywords and Indexed Keywords), with the co-occurrence of descriptor as the unit of measure, using fractional counting. The main research lines identified are: Fundamental Research, Functionalization and Biomedical Applications, Technology and Devices, Materials Science, Energy Storage, Optics and Chemical Properties and Sensors. Using overlay maps, we depict the graphene research efforts of the United States, the European Union (Europe-28), and China, and project their evolution through longitudinal maps to facilitate comparison. The United States was initially at the head of world output in graphene research, but was surpassed by China in 2011 and by Europe in 2014, as a result of their respective scientific policies and financial support. The output of China has since been so intense that it can be said to mark graphene research trends. We believe this information may be valuable for the core community involved in this scientific field, as it offers a large-scale analysis showing how research has changed over time. It is therefore also helpful for policy makers and research planners. The resulting maps are a useful and attractive tool for the graphene research community, as they reveal the main lines of exploration at a glance. The methodology described here could be re-created in any other field of science to uncover and display its intellectual structure and evolution over time.

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Section: Resultsmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Identification and Visualization of the Intellectual Structure in Graphene Research

Vargas-Quesada

Chinchilla‐Rodríguez

Rodríguez

2017

Front. Res. Metr. Anal.

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“…Different domains can be well distinguished by the distribution of specific key words as shown by existing bag-of-words approaches [1]- [5]. Further, trend analysis and bibliometric research also show that key word distributions can be used to identify a time period [6]. They trace topic changes over time within a domain.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Feature Classifying and Tracing

Moohebat

Raj

Thorleuchter

et al. 2017

MJCS

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We investigate the identification and analysis of linguistic (lexico-grammatical) features that are characteristically used by articles of a specific year of publication. Linguistic features differ from shallow features because they represent authors' lexico-grammatical writing styles and do not consider well-known bag-of-words model. Current literature focusses on shallow features rather than on linguistic features and existing methods for identifying linguistic features use well-known knowledge-structure based approaches. In contrast to this, we advance these existing methods by applying semantic clustering instead of using knowledge-structure based approaches. For evaluation purpose, a linguistic feature-based prediction model is built to enable an automated assignment of articles to their years of publication. In a case study, the proposed methodology is applied to articles of the Springer book series 'Communications in Computer and Information Science' published from 2009 to 2013. The Case study results show the feasibility of the proposed approach as compared to frequently used baseline.Keywords: Scientific articles, Linguistic features, Latent semantic indexing, Text Mining. INTRODUCTIONWe investigate the occurrence of linguistic (lexico-grammatical) features in articles to show that they can be used for assigning articles to their years of publication. The Literature shows related approaches that can be used to assign articles to a pre-defined class. A domain-specific vocabulary (key words) is often used for this classification task. Different domains can be well distinguished by the distribution of specific key words as shown by existing bag-of-words approaches [1]- [5]. Further, trend analysis and bibliometric research also show that key word distributions can be used to identify a time period [6]. They trace topic changes over time within a domain. Thus, these approaches can estimate an article's publication year based on the used topics.The approaches as mentioned above are based on shallow (bag-of-words) features. They are in contrast to linguistic features such as specific word class distributions that indicate authors' lexico-grammatical writing styles. Literature also shows the possibilities of using linguistic features for classification. [7] investigate the impact of linguistic features on different scientific disciplines and on different points in time. A further approach uses linguistic features for spam detection [8]. Both approaches are based on systemic functional linguistics, in which a knowledge-structure based classifier (e.g. support vector machine) is used.We provide a new approach that identifies articles' linguistic features and that investigates their usage at different points in time. In contrast to previous work, clustering is used instead of classification. Text classification assigns a text to the given pre-defined classes. Classes are normally defined in a way that they cover all known linguistic features that are expected to occur within the given texts. Text clusteri...

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Scholarly data mining: A systematic review of its applications

Dridi

Gaber

Azad

et al. 2020

WIREs Data Min & Knowl

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During the last few decades, the widespread growth of scholarly networks and digital libraries has resulted in an explosion of publicly available scholarly data in various forms such as authors, papers, citations, conferences, and journals. This has created interest in the domain of big scholarly data analysis that analyses worldwide dissemination of scientific findings from different perspectives. Although the study of big scholarly data is relatively new, some studies have emerged on how to investigate scholarly data usage in different disciplines. These studies motivate investigating the scholarly data generated via academic technologies such as scholarly networks and digital libraries for building scalable approaches for retrieving, recommending, and analyzing the scholarly content. We have analyzed these studies following a systematic methodology, classifying them into different applications based on literature features and highlighting the machine learning techniques used for this purpose. We also discuss open challenges that remain unsolved to foster future research in the field of scholarly data mining. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Science and Technology

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Bibliometric trend analysis on global graphene research

Cited by 80 publications

References 31 publications

Identification and Visualization of the Intellectual Structure in Graphene Research

Identification and Visualization of the Intellectual Structure in Graphene Research

Linguistic Feature Classifying and Tracing

Scholarly data mining: A systematic review of its applications

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