Alvaro Cabezas-Clavijo, Yusnelkis Milanés-Guisado, Ruben Alba-Ruiz, Ángel M. Delgado-Vázquez
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Findings An average of 2.6 shortcomings per paper was found for the whole sample, with an almost identical number of flaws in both journals. Sustainability has more flaws than Scientometrics in four of the seven parameters studied, while Scientometrics has more shortcomings in the remaining three variables. Research limitations The first limitation of this work is that it is a study of two scientific journals, so the results cannot be directly extrapolated to the set of thematic bibliometric analyses published in journals from all fields. Practical implications We propose the adoption of protocols, guidelines, and other similar tools, adapted to bibliometric practice, which could increase the thoroughness, transparency, and reproducibility of this type of research. Originality/value These results show considerable room for improvement in terms of the adequate use and breakdown of methodological procedures in thematic bibliometric research, both in journals in the Information Science area and journals outside the discipline.","PeriodicalId":44622,"journal":{"name":"Journal of Data and Information Science","volume":"59 1","pages":"0"},"PeriodicalIF":1.5000,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics\",\"authors\":\"Alvaro Cabezas-Clavijo, Yusnelkis Milanés-Guisado, Ruben Alba-Ruiz, Ángel M. 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The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics
Abstract Purpose The aim of this article is to explore up to seven parameters related to the methodological quality and reproducibility of thematic bibliometric research published in the two most productive journals in bibliometrics, Sustainability (a journal outside the discipline) and Scientometrics, the flagship journal in the field. Design/methodology/approach The study identifies the need for developing tailored tools for improving the quality of thematic bibliometric analyses, and presents a framework that can guide the development of such tools. A total of 508 papers are analysed, 77% of Sustainability, and 23% published in Scientometrics, for the 2019-2021 period. Findings An average of 2.6 shortcomings per paper was found for the whole sample, with an almost identical number of flaws in both journals. Sustainability has more flaws than Scientometrics in four of the seven parameters studied, while Scientometrics has more shortcomings in the remaining three variables. Research limitations The first limitation of this work is that it is a study of two scientific journals, so the results cannot be directly extrapolated to the set of thematic bibliometric analyses published in journals from all fields. Practical implications We propose the adoption of protocols, guidelines, and other similar tools, adapted to bibliometric practice, which could increase the thoroughness, transparency, and reproducibility of this type of research. Originality/value These results show considerable room for improvement in terms of the adequate use and breakdown of methodological procedures in thematic bibliometric research, both in journals in the Information Science area and journals outside the discipline.
期刊介绍:
JDIS devotes itself to the study and application of the theories, methods, techniques, services, infrastructural facilities using big data to support knowledge discovery for decision & policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. The special effort is on the knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions and disruptions in research, innovation, business, politics, security, media and communications, and social development, where the big data may include metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, and dynamic or interactive data.
The main areas of interest are:
(1) New theories, methods, and techniques of big data based data mining, knowledge discovery, and informatics, including but not limited to scientometrics, communication analysis, social network analysis, tech & industry analysis, competitive intelligence, knowledge mapping, evidence based policy analysis, and predictive analysis.
(2) New methods, architectures, and facilities to develop or improve knowledge infrastructure capable to support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain specific knowledge infrastructure, and semantic sciences.
(3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge assisted scientific discovery, data mining driven intelligent workflows in learning, communications, and management.
Specific topic areas may include:
Knowledge organization
Knowledge discovery and data mining
Knowledge integration and fusion
Semantic Web metrics
Scientometrics
Analytic and diagnostic informetrics
Competitive intelligence
Predictive analysis
Social network analysis and metrics
Semantic and interactively analytic retrieval
Evidence-based policy analysis
Intelligent knowledge production
Knowledge-driven workflow management and decision-making
Knowledge-driven collaboration and its management
Domain knowledge infrastructure with knowledge fusion and analytics
Development of data and information services