2019-2020 年诺贝尔物理学奖、化学奖和医学奖得主的研究经费和论文引用情况

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

Journal of Data and Information Science Pub Date : 2024-02-19 DOI:10.2478/jdis-2024-0006

Mario Coccia, Saeed Roshani

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引用次数: 0

摘要

目的本研究旨在比较分析 2019-2020 年物理学、化学和医学诺贝尔奖获得者论文的经费（科学研究的主要驱动力）与引用率之间的关系，以及这些研究领域作为一个整体的相同关系。设计/方法/途径本研究利用幂律模型来探讨科研经费与相关论文引用率之间的关系。本研究分析了物理学、化学和医学领域诺贝尔奖获得者的 3,539 篇记录文献，以及 Web of Science 数据库中与物理学、医学和化学领域相关的 183,016 篇更广泛的数据集。研究结果研究结果显示，在化学和医学领域，诺贝尔奖获得者论文中发表的受资助研究的引用率高于文章中发表的未受资助研究的引用率；反之，物理学领域诺贝尔奖获得者论文中发表的未受资助研究的引用率较高。相反，如果对物理学、化学和医学的论文发表和引用的整体数据进行分析，所有基于资助研究的论文都比未获资助的论文引用率高。原创性/价值研究结果阐明了科研经费对科学传播的推动作用，其系统化的一般特性是：a) 与物理、化学和医学科学论文中发表的（未获）资助的研究相比，与资助研究相关的文章获得了更多的引用，从而产生了较高的马太效应（随着论文数量的增加，引用的增长也更高）；b) 与基础研究领域（如物理）相比，科研经费更能增加应用研究领域（如化学和医学）文章的引用。实际意义本文的研究结果解释了科学发展和传播的一些特点，强调了研究经费在促进引用和科学知识扩展方面的关键作用。这一发现可以为政策制定者和研发管理人员的决策提供支持，从而提高科学政策中财政资源分配的有效性，产生更积极的科学和社会影响。

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Research funding and citations in papers of Nobel Laureates in Physics, Chemistry and Medicine, 2019-2020

Purpose The goal of this study is a comparative analysis of the relation between funding (a main driver for scientific research) and citations in papers of Nobel Laureates in physics, chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole. Design/Methodology/Approach This study utilizes a power law model to explore the relationship between research funding and citations of related papers. The study here analyzes 3,539 recorded documents by Nobel Laureates in physics, chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics, medicine, and chemistry recorded in the Web of Science database. Findings Results reveal that in chemistry and medicine, funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles; vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers. Instead, when overall data of publications and citations in physics, chemistry and medicine are analyzed, all papers based on funded researches show higher citations than unfunded ones. Originality/Value Results clarify the driving role of research funding for science diffusion that are systematized in general properties: a) articles concerning funded researches receive more citations than (un)funded studies published in papers of physics, chemistry and medicine sciences, generating a high Matthew effect (a higher growth of citations with the increase in the number of papers); b) research funding increases the citations of articles in fields oriented to applied research (e.g., chemistry and medicine) more than fields oriented towards basic research (e.g., physics). Practical Implications The results here explain some characteristics of scientific development and diffusion, highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge. This finding can support decisionmaking of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.

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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