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.
Highlights
Science research keeps expanding over the years and “new specialisms arise from old areas all the time” (Meadows 1998)
While Data Science (DS) was more significant in early years for about five decades, Big Data (BD) made a leap during the recent decade
Research areas related to DS and BD
Summary
Science research keeps expanding over the years and “new specialisms arise from old areas all the time” (Meadows 1998). The normal interdisciplinary trends of disciplines’ creation in the past occurred when a new unifying concept brought together a wide range of knowledge (Ibid.:). Meadows (1998) brings cybernetics as an example of a field arising from aggregation of a wide range of social science and engineering ideas. Scientometrics (2020) 122:1563–1581 a cohesive one (Gordon 2004). Another evolutionary change may occur when a field that existed for many years was absorbed by a larger research field because of lack of researchers’ commitment to the field (Creager 2010; Mullins 1972). Disciplines exhibit dynamics from fragmentation to unification as time and necessities dictate (Balietti et al 2015). Glänzel and Thijs (2012:196) set several criteria for detecting an emerging research area, including: the existence of a critical mass of publications to form a coherent cluster, emerging topic identification, and cognitive description of the new topic by analyzing articles’ titles and/or keywords
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