Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research (2106.05581v1)

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. 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.