Data Science Publication: Thirty-Six Years Lesson of Scientometric Review

Abstract

Data science as part of technological development is growing and needed. It has been research yet the notion about data science review publication which showed the big picture using data from all countries. This research aims to study the position of the international publication map of data science indexed by Scopus using scientometric review. Scientometric methods and analyzed research data was used to analyze search results service from Scopus and the VOSviewer application. The research data of 5,202 documents published from 1983 to 2019 were obtained from the Scopus database. Most countries, subject areas, and type documents in data science publications were the United States, computer science; and conference paper. There were ten collaborative researchers' group patterns. This research proposes a convergence axis classification consisting of data science publication to characterize the body of knowledge generated from three decades of publication: Machine learning, Organism, Data mining, and Data analysis, abbreviated as MODD themes.

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Stored only when both papers are in the corpus — 36 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.