Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research

Unlike most bibliometric studies focusing on publications, taking Big Data research as a case study, we introduce a novel bibliometric approach to unfold the status of a given scientific community from an individual-level perspective. We study the academic age, production, and research focus of the community of authors active in Big Data research. Artificial Intelligence (AI) is selected as a reference area for comparative purposes. Results show that the academic realm of “Big Data” is a growing topic with an expanding community of authors, particularly of new authors every year. Compared to AI, Big Data attracts authors with a longer academic age, who can be regarded to have accumulated some publishing experience before entering the community. Despite the highly skewed distribution of productivity amongst researchers in both communities, Big Data authors have higher values of both research focus and production than those of AI. Considering the community size, overall academic age, and persistence of publishing on the topic, our results support the idea of Big Data as a research topic with attractiveness for researchers. We argue that the community-focused indicators proposed in this study could be generalized to investigate the development and dynamics of other research fields and topics.

[1]  Lutz Bornmann,et al.  How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations , 2013, Scientometrics.

[2]  Ruixian Yang.  Bibliometrical Analysis on the Big Data Research in China , 2013, J. Digit. Inf. Manag..

[3]  D. Barker.  Global gender disparities in science , 2013 .

[4]  Deepak Gupta,et al.  A study of big data evolution and research challenges , 2018, J. Inf. Sci..

[5]  K. Brad Wray,et al.  The Future of The Structure of Scientific Revolutions , 2013 .

[6]  Jacky Akoka,et al.  Research on Big Data - A systematic mapping study , 2017, Comput. Stand. Interfaces.

[7]  Nees Jan van Eck,et al.  Large scale author name disambiguation using rule-based scoring and clustering , 2014 .

[8]  Ludo Waltman,et al.  A new methodology for constructing a publication-level classification system of science , 2012, J. Assoc. Inf. Sci. Technol..

[9]  Daniel B. Larremore,et al.  The misleading narrative of the canonical faculty productivity trajectory , 2016, Proceedings of the National Academy of Sciences.

[10]  Alan L. Porter,et al.  A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’ , 2015, Scientometrics.

[11]  Giovanni Abramo,et al.  The effects of gender, age and academic rank on research diversification , 2018, Scientometrics.

[12]  Ludo Waltman,et al.  Citation-based clustering of publications using CitNetExplorer and VOSviewer , 2017, Scientometrics.

[13]  Alfred J. Lotka,et al.  The frequency distribution of scientific productivity , 1926 .

[14]  Aldo Geuna,et al.  Global mobility of research scientists : the economics of who goes where and why , 2015 .

[15]  D. Price.  Little Science, Big Science , 1965 .

[16]  Giovanni Abramo,et al.  The combined effects of age and seniority on research performance of full professors , 2016, ArXiv.

[17]  Rodrigo Costas,et al.  Do age and professional rank influence the order of authorship in scientific publications? Some evidence from a micro-level perspective , 2011, Scientometrics.

[18]  Birger Larsen,et al.  A review of the characteristics of 108 author-level bibliometric indicators , 2014, Scientometrics.

[19]  Lutz Bornmann,et al.  Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches1 , 2019, Quantitative Science Studies.

[20]  Marcelo Fantinato,et al.  A study on the author collaboration network in big data* , 2017, Inf. Syst. Frontiers.

[21]  Dag W. Aksnes,et al.  Publication rate expressed by age, gender and academic position - A large-scale analysis of Norwegian academic staff , 2015, J. Informetrics.

[22]  Marcos André Gonçalves,et al.  Incremental author name disambiguation by exploiting domain‐specific heuristics , 2017, J. Assoc. Inf. Sci. Technol..

[23]  Filippo Radicchi,et al.  Changing demographics of scientific careers: The rise of the temporary workforce , 2018, Proceedings of the National Academy of Sciences.

[24]  Alan L. Porter,et al.  How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China , 2016, PloS one.

[25]  Andreas M. Kaplan,et al.  Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence , 2019, Business Horizons.

[26]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[27]  Giovanni Abramo,et al.  National-scale research performance assessment at the individual level , 2011, Scientometrics.

[28]  B. Reskin.  Scientific Productivity and the Reward Structure of Science , 1977 .

[29]  Aldo Geuna,et al.  Moving Out of Academic Research: Why Scientists Stop Doing Research? , 2015 .

[30]  Rodrigo Costas,et al.  The skewness of scientific productivity , 2014, J. Informetrics.

[31]  P. Gaulé,et al.  An Advisor Like Me? Advisor Gender and Post-Graduate Careers in Science , 2017, SSRN Electronic Journal.

[32]  Jinseok Kim,et al.  Evaluating author name disambiguation for digital libraries: a case of DBLP , 2018, Scientometrics.

[33]  Yin Zhang,et al.  Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization , 2017, Scientometrics.

[34]  D. Hicks.  The Four Literatures of Social Science , 2004 .

[35]  Vincent Larivière,et al.  redicting the age of researchers using bibliometric data , 2017 .

[36]  Y. Gingras,et al.  The Effects of Aging on Researchers' Publication and Citation Patterns , 2008, PloS one.

[37]  Kwan-Liu Ma,et al.  A Visual Analytics Approach to Author Name Disambiguation , 2016, 2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT).

[38]  Paula E. Stephan,et al.  Research Productivity over the Life Cycle: Evidence for Academic Scientists , 1991 .

[39]  Thed N. van Leeuwen,et al.  A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact , 2010, J. Assoc. Inf. Sci. Technol..

[40]  P. Allison,et al.  Productivity Differences Among Scientists: Evidence for Accumulative Advantage , 1974 .

[41]  Cassidy R. Sugimoto,et al.  Bibliometrics: Global gender disparities in science , 2013, Nature.

[42]  Nader Ale Ebrahim,et al.  A bibliometric approach to tracking big data research trends , 2017, Journal of Big Data.

[43]  P. Geurts,et al.  The impact of the autonomy and interdependence of individual researchers on their production of knowledge and its impact: An empirical study of a nanotechnology institute , 2010 .

[44]  Yin Zhang,et al.  Structure and patterns of cross-national Big Data research collaborations , 2017, J. Documentation.

[45]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[46]  Adele Paul-Hus,et al.  Predicting the age of researchers using bibliometric data , 2017 .

[47]  Struan Jacobs.  Models of scientific community: Charles Sanders Peirce to Thomas Kuhn , 2006 .

[48]  Sumit Kumar Banshal,et al.  Scientometric mapping of research on ‘Big Data’ , 2015, Scientometrics.