Structure and patterns of cross-national Big Data research collaborations
| Published date | 09 October 2017 |
| Pages | 1119-1136 |
| Date | 09 October 2017 |
| DOI | https://doi.org/10.1108/JD-12-2016-0146 |
| Author | Jiming Hu,Yin Zhang |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
Structure and patterns of
cross-national Big Data
research collaborations
Jiming Hu
School of Information Management, Wuhan University, Wuhan, China, and
Yin Zhang
School of Information, Kent State University, Kent, Ohio, USA
Abstract
Purpose –The purpose of this paper is to reveal the structure and patternsof cross-national collaborationsin
Big Data research through application of various social network analysis and geographical visualization methods.
Design/methodology/approach –The sample includes articles containing Big Data research, covering all
years, in the Web of Science Core Collection as of December 2015. First, co-occurrence data representing
collaborations among nations were extracted from author affiliations. Second, the descriptive statistics,
network indicators of collaborations, and research communities were calculated. Third, topological network
maps, geographical maps integrated with topological network projections, and proportional maps were
produced for visualization.
Findings –The results show that the scope of international collaborations in Big Data research is broad, but
the distribution among nations is unbalanced and fragmented. The USA, China, and the UK were identified as
the major contributors to this research area. Five research communities are identified, led by the USA, China,
Italy, South Korea, and Brazil. Collaborations within each community vary, reflecting different levels of
research development. The visualizations show that nations advance in Big Data research are centralized in
North America, Europe, and Asia-Pacific.
Originality/value –This study applied various informetric methods and tools to reveal the collaboration
structure and patterns among nations in Big Data research. Visualized maps help shed new light on global
research efforts.
Keywords Research networks, Maps, Big Data research, Geographical visualization, International collaboration,
Network structure and patterns
Paper type Research paper
1. Introduction
Big Data has become a critical research frontier, with the potential to revolutionize many
fields, including business, science, and public administration (Savitz, 2012). Nature
Publishing Group (2008) and Science/AAAS (2011), two premier scientific journals,
produced special issues to analyze the significance, challenges, and impacts of Big Data.
It is evident that Big Data has drawn huge attention from many nations in recent years
because of its potential for increasing business productivity and breakthroughs in scientific
research (Chen and Zhang, 2014).
Big Data is still a fast evolving field of both research and practice. It poses a challenge
to comprehensively define Big Data, as the term has been used interchangeably to refer to
many different and yet often intertwined aspects of studies, such as the characteristics of
the data source, a class of analytic approaches –Big Data methods, and/or an overall
approach to problem solving (Tonidandel et al., 2016). Drawing on an extensive review of
literature, Kitchin (2014) summarizes the major characteristics of Big Data as huge in
volume, high in velocity, diverse in variety, exhaustive in scope, fine-grained in resolution
and uniquely indexical in identification, relational in nature, flexible in holding the traits
of extensionality, and scalability. Big Data requires innovative techniques and
technologies to perform its capture, curation, analysis, visualization, and application
(Casado and Younas, 2015).
Journal of Documentation
Vol. 73 No. 6, 2017
pp. 1119-1136
© Emerald PublishingLimited
0022-0418
DOI 10.1108/JD-12-2016-0146
Received 1 December 2016
Revised 17 June 2017
Accepted 26 June 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0022-0418.htm
1119
Cross-national
Big Data
research
collaborations
Since the beginning of this new century, there have been a large number of international
theoretical and technical studies on Big Data involving many nations, regions, institutions,
and researchers (Chen et al., 2014; Emani et al., 2015; Fang et al., 2015). Many nations and
governmental bodies play a vital role in promoting Big Data research and applications.
In recent decades, the USA, European Union, and China have advanced in development
planning of Big Data (Emani et al., 2015). For example, in March 2012, the US Government
announced the investment of 200 million dollars to launch the “Big Data Initiative”with a
focus on foundational technology and public sector applications, aimed at promoting
theoretical and technological research and strengthening scientific research, education, and
national security (Kalil, 2015). Similarly, in 2013, the UK proposed a series of initiatives
supporting the development of Big Data, primarily for the high-technology field, and
government and public sector applications. In addition, China, Japan, Australia, Singapore,
and several other nations issued related development strategies and accelerated research
and application.
In recent years, many governments and institutions have promoted international
collaborations in Big Data (Fang et al., 2015). Previous research has shown that as
researchers are encouraged to work and study collaboratively, and as international
co-authorship has become mainstream in Big Data research (George et al., 2014), there has
been an increase in papers involving these collaborations published in journals (Michael and
Miller, 2013). As a result, there is a great need to reveal the structure and patterns of
international collaboration at the national level.
This study expands on previous research exploring patterns and characteristics of
international collaborations in Big Data research. Different from the traditional
co-authorship research, this study is the first step toward a greater understanding of the
collaborations among nations as extracted from author affiliation addresses.
The publication records retrieved from the Web of Science (WoS) database allow us to
examine the extent of cross-national collaborations geographically. This research aims to
reveal the collaboration patterns and network structures among nations, detect the
structural communities of nations in terms of cross-national collaborations, and then
visualize these collaborations using social network and geographical mapping.
Furthermore, the characteristics of collaboration networks are examined in order to
reveal the general status and position of each nation in Big Data research.
This research fills the gap with a detailed and systematic visual mapping of the
collaborations in Big Data research, and examination of the characteristics of the overall
international collaboration network and its sub-networks. The results of this study will
contribute to a greater understanding of international collaboration in Big Data research
and shed light on where each nation is positioned in this relatively new and evolving global
research effort. The results may suggest potential opportunities for international
community development in this research frontier that benefit all involved.
2. Literature review
2.1 The landscape of Big Data development
The past decade has seen an explosive, global increase in what is described as “Big Data,”
the large and complex data sets that cannot be processed by traditional means (Kalil, 2015).
While Big Data creates business and research value, it also generates significant
challenges (e.g. Marx, 2013; Chen et al., 2012) in terms of networking, storage,
management, analytics, and even ethics (Fang et al., 2015). Therefore, researchers stress
the urgent need to develop and innovate new techniques and technologies to process this
data and benefit various specified purposes (Chen and Zhang, 2014). Big Data research
has covered many different but yet often overlapped areas such as data sources, methods,
and approaches to problem solving (Tonidandel et al., 2016). It is covered as an abstract
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