An Empirical Study on the Landscape of Mining and Mineral Processing (MMP) With Big Data

An Empirical Study on the Landscape of Mining and Mineral Processing (MMP) With Big Data

Ruiyun Duan
DOI: 10.4018/IJITSA.318041
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Abstract

Over the last two decades, mining and mineral processing (MMP) has changed dramatically. Little is known about the bibliometric analysis of MMP. To this end, this study used the big data analysis to investigate the quantity and quality of scientific outputs in MMP over the past 21-year timespan. This study used IBM SPSS Statistics 25.0 and VOSviewer software to research on the 20 journals from Science Citation Index Expanded (SCI-Expanded) of Web of Science Core Collection (WOSCC). VOSviewer software was used to identify the visualization contributions of scientific outputs over 21-year timespan. Totally, the big data analysis shows people of China have the highest cumulative IFs, but their mean IFs are relatively low and are ranked in fourth place. Visually, people of Chin ranked the first in total link strength (2967), but not in links (86), which is the third place among Top15 countries. From the perspective of quality, it cannot rank the first. Thus, people of China should put more effort into improving the quality of scientific outputs.
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Introduction

Mining and mineral processing (MMP) have significantly contributed to the advancement of human civilization and national economies, but they also have the potential to cause serious environmental degradation (Adusumilli et al., 2005). Thus, sustainable mining and mineral processing is of paramount importance worldwide. Therefore, the studies of MMP are significantly growing and have made great progress over the last 21 years.

MMP consists of a wide scale of aspects connected with economic development, social needs, ecological balance, and environmental problems, thus the research on MMP involves many aspects. The development of MMP plays a vital role to ensure reliable energy supply, reduce environmental pollution, and mitigate greenhouse gas emissions (Allahabadi et al., 2021).

Over the past 21 years, the study of MMP has made remarkable progress all over the world. However, no literature review in this field has been conducted on the comparison of the quantity and quality of scientific outputs using bibliometric analysis from 2000 to 2020, although Rojas-Sola and Aguilera-García (2015) analyzed MMP from a very different perspective (Oelrich et al., 2007). The quantity and quality of the scientific papers can reflect not only the level of individual research, but also the comprehensive national strength of a country (Bhattacharya, M. et al., 2015). For a country, the quantity and quality of publications of that country can reflect its research level in a specific scientific field (Briones-Bitar et al., 2020). Multidimensional data analysis can provide a completely new perspective by and for scholars (Najjar and Dahabiyeh, 2021; Gu et al., 2021; Zhu, 2021).

Bibliometrics is an important tool to analyze the literature of a certain scientific domain and to assess the trends in research activity over time (Brown, 2007). Bibliometric analysis is also a powerful and important tool in evaluating the scientific performance and development of a research field (Cherubini, 2008; Eck & Waltman, 2010; Fu et al., 2011; Fu & Ho, 2010; Journal of Citation Reports, 2022). In view of this situation, this study uses a bibliometric analysis to systematically evaluate the scientific outputs of MMP in the comparison of quantity and quality worldwide and among top ranking countries to provide a new perspective for future research directions. Impact factor (IF) for each journal and each year, total scientific outputs from years 2000-2020, and the numbers of scientific outputs in 20 MMP journals in the studied years were collected from the Science Citation Index Expanded (SCI-Expanded) of the Web of Science Core Collection (WOSCC). Cumulative IFs (CIFs), mean CIFs, citations, and average citations were analyzed in detail and the visualization contribution was investigated in this study. These criteria could be considered as indicators of the quantity and quality of research productivity, although limitations of the criteria, such as the IF or citation analysis should always be taken into account (Garrigos-Simon et al., 2019; Grange, 1999; Herrera-Franco et al., 2021; Huai & Chai, 2016).

For availability and completeness of data, only the 21-year timespan was included for evaluation.

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