{"title":"Data-enhanced revealing of trends in Geoscience","authors":"Yu Zhao, Meng Wang, Jiaxin Ding, Jiexing Qi, Lyuwen Wu, Sibo Zhang, Luoyi Fu, Xinbing Wang, Li Cheng","doi":"10.2478/jdis-2024-0023","DOIUrl":null,"url":null,"abstract":"Purpose This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023. By integrating bibliometric analysis with expert insights from the Deeptime Digital Earth (DDE) initiative, this article identifies key emerging themes shaping the landscape of Earth Sciences<jats:sup>①</jats:sup>. Design/methodology/approach The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database. To map relationships between articles, citation networks were constructed, and spectral clustering algorithms were then employed to identify groups of related research, resulting in 407 clusters. Relevant research terms were extracted using the Log-Likelihood Ratio (LLR) algorithm, followed by statistical analyses on the volume of papers, average publication year, and average citation count within each cluster. Additionally, expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation, relevance, and impact within Geosciences, and finalize naming of these top trends with consideration of the content and implications of the associated research. This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists. Findings Thirty significant trends were identified in the field of Geosciences, spanning five domains: deep space, deep time, deep Earth, habitable Earth, and big data. These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society, science, and technology. Research limitations The analyzed data of this study only contain those were included in the Web of Science. Practical implications This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science, especially on solid earth. The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study. Originality/value This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.","PeriodicalId":44622,"journal":{"name":"Journal of Data and Information Science","volume":"80 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data and Information Science","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.2478/jdis-2024-0023","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023. By integrating bibliometric analysis with expert insights from the Deeptime Digital Earth (DDE) initiative, this article identifies key emerging themes shaping the landscape of Earth Sciences①. Design/methodology/approach The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database. To map relationships between articles, citation networks were constructed, and spectral clustering algorithms were then employed to identify groups of related research, resulting in 407 clusters. Relevant research terms were extracted using the Log-Likelihood Ratio (LLR) algorithm, followed by statistical analyses on the volume of papers, average publication year, and average citation count within each cluster. Additionally, expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation, relevance, and impact within Geosciences, and finalize naming of these top trends with consideration of the content and implications of the associated research. This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists. Findings Thirty significant trends were identified in the field of Geosciences, spanning five domains: deep space, deep time, deep Earth, habitable Earth, and big data. These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society, science, and technology. Research limitations The analyzed data of this study only contain those were included in the Web of Science. Practical implications This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science, especially on solid earth. The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study. Originality/value This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.
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