Large Scopus Data Sets and Its Analysis for Decision Making

Q1 Decision Sciences Annals of Data Science Pub Date : 2022-08-02 DOI:10.1007/s40745-022-00435-3
Prem Kumar Singh
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引用次数: 0

Abstract

Recently several authors paid attention towards Scopus Data analysis for intellectual measurement of institutes or authors. It is well known that the SCOPUS contains more than 34,346 peer reviewed Journals from different subjects with 3 lakh conferences. It is difficult to measure the performance or expertise of any institute or author in the given domain for admission, job, and ranking or other decision making process. The reason is several manipulations started in document and citation count via strategic authors or institute which can be measured via average author publications, number of funding, collaborations and retracted papers. It is happening due to rogue editor or business strategic of educationalist for profit. However these types of misconduct impacts lot to real researcher which forces brain drain. To resolve this issue, the current paper provides a way to measure the intellectual achievement of an institute or author based on several metrics. The proposed method is illustrated using the SCOPUS data sets and it’s metric for critical understanding.

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大型Scopus数据集及其决策分析
最近,一些作者开始关注 Scopus 数据分析,以衡量机构或作者的智力水平。众所周知,SCOPUS 包含超过 34346 种不同学科的同行评审期刊和 30 万个会议。在招生、就业、排名或其他决策过程中,很难衡量任何机构或作者在特定领域的表现或专业技能。究其原因,是一些人开始通过战略作者或机构对文献和引用次数进行操纵,而这可以通过作者平均发表论文量、资助数量、合作和被撤论文数量来衡量。这种情况的发生是由于流氓编辑或教育家的商业策略牟利所致。然而,这些类型的不当行为对真正的研究人员造成了很大影响,迫使人才流失。为了解决这个问题,本文提供了一种基于多个指标来衡量机构或作者智力成就的方法。本文使用 SCOPUS 数据集及其度量标准对所提出的方法进行了说明,以帮助读者更好地理解。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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