Uncovering the Statistical Foundations of Bibliometric Techniques and Their Role in Exploring the Data Science Research Evolution

Abstract

Sceinometric and bibliometric techniques are widely used in assessing the quality and quantity of research production in all aspects. In sceinometrics techniques, we focus on evaluating the scientific progress in all fields of science, while in bibliometrics, which is basically is a subpart of sceinometrics, we mainly focus on the quality and quantity of the research production. Both techniques are very important for finding the current research trends. These techniques are widely used by many research-funded institutions, decision makers. and the government. These help them with research funding allocations and identify the emerging research areas. These techniques are very useful in evaluating the journal impact factor, ranking universities, countries, and enhancing research efficiency. Because all research production activity creates data so these techniques are very closely related to data science. In Data Science, we collect, analyze, and process the largest sets of research data. We can find its application in every field, such as healthcare, education, and environmental studies, etc. In this study, we are focusing on the statistical foundation of bibliometric techniques and see that what is their role in exploring the data science research field. Many statistical models, such as discriminant analysis, cluster analysis, and VOSviewer software, are used for viewing the scientific landscape of this research. The present research gives us in-depth insight into the progression and evolution of data science as a prominent research field, after using advanced bibliometric techniques on the dataset of all 4424 journal articles till 31 December 2023. This study concludes that data science is evolving and has a potential research domain.