The evolution of data science and big data research: A bibliometric analysis

Abstract

In this study the evolution of Big Data (BD) and Data Science (DS) literatures and the relationship between the two are analyzed by bibliometric indicators that help establish the course taken by publications on these research areas before and after forming concepts. We observe a surge in BD publications along a gradual increase in DS publications. Interestingly, a new publications course emerges combining the BD and DS concepts. We evaluate the three literature streams using various bibliometric indicators including research areas and their origin, central journals, the countries producing and funding research and startup organizations, citation dynamics, dispersion and author commitment. We find that BD and DS have differing academic origin and different leading publications. Of the two terms, BD is more salient, possibly catalyzed by the strong acceptance of the pre-coordinated term by the research community, intensive citation activity, and also, we observe, by generous funding from Chinese sources. Overall, DS literature serves as a theory-base for BD publications.

Environmental Sustainability in Higher Education: Mapping the Field

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Leading Research by Institutions and Authors: A Modern Research Analysis

“…made a comparative study of research competitiveness of institutions/countries in one or more disciplines, including DS. Raban and Gordon (2020) focused on the evolution of bid data and DS literature, and author commitment was only one of the bibliometric indicators they used.…”

An entropy-based measure for the evolution of h index research

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