Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research
Xiaozan Lyu, Rodrigo Costas

TL;DR
This study introduces a novel bibliometric approach to analyze scientific communities at the individual level, focusing on Big Data research to reveal community characteristics and dynamics compared to AI.
Contribution
It presents new community-focused bibliometric indicators and applies them to Big Data, offering insights into community growth, author experience, and research focus.
Findings
Big Data community is growing rapidly with many new authors annually.
Authors in Big Data tend to have longer academic careers before publishing.
Big Data researchers show higher research focus and productivity than AI researchers.
Abstract
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…
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