科学引文资助的一般规律：基础科学和应用科学之间有资助和无资助研究的引文如何变化

IF 1.5 3区管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

Journal of Data and Information Science Pub Date : 2024-02-26 DOI:10.2478/jdis-2024-0005

Mario Coccia, Saeed Roshani

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引用次数: 0

摘要

目的本研究旨在分析基础科学和应用科学领域中获得资助和未获资助的论文及其引用率之间的关系。设计/方法/手段利用 Web of Science 数据库中记录的 831,337 篇文献，采用幂律模型分析了研究经费与论文引用率之间的关系。研究结果原始结果揭示了科学在研究领域传播的一般特征：a) 与未获资助的论文相比，获得资助的文章在期刊中获得的引用率更高；b) 获得资助的文章在引用率方面呈现超线性增长，超过了未获资助文章的增幅。这一发现表明，受资助文章的科学知识传播率更高。此外，c) 基础科学和应用科学领域的受资助文章在期刊中的受资助论文数量增加 1%时，引文量也会出现类似的预期变化，约为 1.23%。这一结果首次表明，无论基础科学或应用科学的性质如何，科学研究的资助效应都是一个不变的驱动因素。原创性/价值这一证据提出了科学引文资助的经验规律，解释了健全的资助机制对于在科学和社会领域取得有影响力的研究成果的重要性。这些发现还强调，科研经费是支持基础科学和应用科学领域记录文献中科学知识的引用和传播的重要推动力。实践意义这一综合结果提供了科学研究经费与引文绩效之间关系的整体视角，可指导政策制定者和研发管理者制定科学政策，引导科研经费用于促进科学发展和成果传播，从而推动人类社会的进步。

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General laws of funding for scientific citations: how citations change in funded and unfunded research between basic and applied sciences

Purpose The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences. Design/methodology/approach A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database. Findings The original results reveal general characteristics of the diffusion of science in research fields: a) Funded articles receive higher citations compared to unfunded papers in journals; b) Funded articles exhibit a super-linear growth in citations, surpassing the increase seen in unfunded articles. This finding reveals a higher diffusion of scientific knowledge in funded articles. Moreover, c) funded articles in both basic and applied sciences demonstrate a similar expected change in citations, equivalent to about 1.23%, when the number of funded papers increases by 1% in journals. This result suggests, for the first time, that funding effect of scientific research is an invariant driver, irrespective of the nature of the basic or applied sciences. Originality/value This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society. These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences. Practical implications This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.

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来源期刊

Journal of Data and Information Science INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

3.50

自引率

6.70%

发文量

495

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