{"title":"Research funding and citations in papers of Nobel Laureates in Physics, Chemistry and Medicine, 2019-2020","authors":"Mario Coccia, Saeed Roshani","doi":"10.2478/jdis-2024-0006","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":44622,"journal":{"name":"Journal of Data and Information Science","volume":"37 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-02-19","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-2024-0006","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 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.
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
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