Pub Date : 2025-12-13DOI: 10.1016/j.joi.2025.101757
Tolga Yuret
We asked academics from the economics, political science, psychology, and sociology departments of the top 500 universities to state their top 3 publications. 2331 researchers who work in 42 countries responded to the survey. We collected the respondents’ Google Scholar (GS) and Scopus profiles to identify the publications which they did and did not select as their top 3 publications. Therefore, our study links researchers’ self-evaluated top publications with detailed bibliometric profiles, enabling a comparison between subjective assessments and bibliometric statistics. Around 30 % of respondents’ top 3 publications in political science and sociology are not indexed in Scopus, largely because many are books or non-English works. The top 3 publications demonstrate greater citation performance and are published in higher-impact journals compared to the average output. However, only 40 % of the publications identified as top 3 in self-evaluations also rank among the top 3 by citation performance. Self-evaluations offer additional insights that complement bibliometric measures, helping to address issues such as limited coverage, neglect of locally relevant research, and underrepresentation of certain academic fields.
{"title":"Assessing scientific output through self-evaluation: Evidence from four social science fields","authors":"Tolga Yuret","doi":"10.1016/j.joi.2025.101757","DOIUrl":"10.1016/j.joi.2025.101757","url":null,"abstract":"<div><div>We asked academics from the economics, political science, psychology, and sociology departments of the top 500 universities to state their top 3 publications. 2331 researchers who work in 42 countries responded to the survey. We collected the respondents’ Google Scholar (GS) and Scopus profiles to identify the publications which they did and did not select as their top 3 publications. Therefore, our study links researchers’ self-evaluated top publications with detailed bibliometric profiles, enabling a comparison between subjective assessments and bibliometric statistics. Around 30 % of respondents’ top 3 publications in political science and sociology are not indexed in Scopus, largely because many are books or non-English works. The top 3 publications demonstrate greater citation performance and are published in higher-impact journals compared to the average output. However, only 40 % of the publications identified as top 3 in self-evaluations also rank among the top 3 by citation performance. Self-evaluations offer additional insights that complement bibliometric measures, helping to address issues such as limited coverage, neglect of locally relevant research, and underrepresentation of certain academic fields.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"20 1","pages":"Article 101757"},"PeriodicalIF":3.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.joi.2025.101759
Ashkan Ebadi , Alain Auger , Yvan Gauthier
The landscape of science and technology is characterized by its dynamic and evolving nature, constantly reshaped by discoveries, innovations, and paradigm shifts. Moreover, science is undergoing a remarkable shift towards increasing interdisciplinary collaboration, where the convergence of diverse fields fosters innovative solutions to complex problems. Detecting emerging scientific topics is paramount as it enables industries, policymakers, and innovators to adapt their strategies, investments, and regulations proactively. As the common approach for detecting emerging technologies, despite being useful, bibliometric analyses may suffer from oversimplification and/or misinterpretation of complex interdisciplinary trends. In addition, relying solely on domain experts to pinpoint emerging technologies from science and technology trends might restrict the ability to systematically analyze extensive information and introduce subjective judgments into the interpretations. To overcome these drawbacks, in this work, we present an automated artificial intelligence-enabled framework, called WISDOM, for (1) detecting emerging research themes using advanced topic modelling and weak signal analysis, and (2) generating candidate labels for identified themes to assist experts. The proposed approach can assist strategic planners and domain experts in more effectively recognizing and tracking trends related to emerging topics by swiftly processing and analyzing vast volumes of data, uncovering hidden cross-disciplinary patterns, and offering unbiased insights, thereby enhancing the efficiency and objectivity of the detection process. As the case technology, we assess WISDOM's performance in identifying emerging research as well as its trends, in the field of underwater sensing technologies using scientific papers published between 2004 and 2021.
{"title":"WISDOM: An AI-powered framework for emerging research detection using weak signal analysis and advanced topic modelling","authors":"Ashkan Ebadi , Alain Auger , Yvan Gauthier","doi":"10.1016/j.joi.2025.101759","DOIUrl":"10.1016/j.joi.2025.101759","url":null,"abstract":"<div><div>The landscape of science and technology is characterized by its dynamic and evolving nature, constantly reshaped by discoveries, innovations, and paradigm shifts. Moreover, science is undergoing a remarkable shift towards increasing interdisciplinary collaboration, where the convergence of diverse fields fosters innovative solutions to complex problems. Detecting emerging scientific topics is paramount as it enables industries, policymakers, and innovators to adapt their strategies, investments, and regulations proactively. As the common approach for detecting emerging technologies, despite being useful, bibliometric analyses may suffer from oversimplification and/or misinterpretation of complex interdisciplinary trends. In addition, relying solely on domain experts to pinpoint emerging technologies from science and technology trends might restrict the ability to systematically analyze extensive information and introduce subjective judgments into the interpretations. To overcome these drawbacks, in this work, we present an automated artificial intelligence-enabled framework, called WISDOM, for (1) detecting emerging research themes using advanced topic modelling and weak signal analysis, and (2) generating candidate labels for identified themes to assist experts. The proposed approach can assist strategic planners and domain experts in more effectively recognizing and tracking trends related to emerging topics by swiftly processing and analyzing vast volumes of data, uncovering hidden cross-disciplinary patterns, and offering unbiased insights, thereby enhancing the efficiency and objectivity of the detection process. As the case technology, we assess WISDOM's performance in identifying emerging research as well as its trends, in the field of underwater sensing technologies using scientific papers published between 2004 and 2021.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"20 1","pages":"Article 101759"},"PeriodicalIF":3.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1016/j.joi.2025.101761
Weiye Gu, Chaoqun Ni
Understanding how universities contribute to policymaking is crucial for assessing the societal impact of research. This study analyzes 80,650 U.S. policy documents (2017–2022) that cite 295,428 US-affiliated academic publications, linking Overton policy data with Scopus affiliation records and federal research funding from the National Center for Science and Engineering Statistics. Universities account for over 70 % of all policy-cited research, confirming their central role in evidence-informed governance. Yet this influence is uneven: a small number of research-intensive institutions dominate, while most universities contribute modestly. Health sciences account for the largest share of university-cited research, with additional contributions in economics, education, and social sciences. Federal agencies cite university research more heavily than local governments or think tanks, and policy visibility is geographically concentrated in states with strong research ecosystems. Correlation analyses indicate that productivity, federal funding, and institutional prestige strongly predict policy influence, whereas government involvement is not consistently associated.
{"title":"Universities as central actors in evidence-based policymaking in the US","authors":"Weiye Gu, Chaoqun Ni","doi":"10.1016/j.joi.2025.101761","DOIUrl":"10.1016/j.joi.2025.101761","url":null,"abstract":"<div><div>Understanding how universities contribute to policymaking is crucial for assessing the societal impact of research. This study analyzes 80,650 U.S. policy documents (2017–2022) that cite 295,428 US-affiliated academic publications, linking Overton policy data with Scopus affiliation records and federal research funding from the National Center for Science and Engineering Statistics. Universities account for over 70 % of all policy-cited research, confirming their central role in evidence-informed governance. Yet this influence is uneven: a small number of research-intensive institutions dominate, while most universities contribute modestly. Health sciences account for the largest share of university-cited research, with additional contributions in economics, education, and social sciences. Federal agencies cite university research more heavily than local governments or think tanks, and policy visibility is geographically concentrated in states with strong research ecosystems. Correlation analyses indicate that productivity, federal funding, and institutional prestige strongly predict policy influence, whereas government involvement is not consistently associated.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"20 1","pages":"Article 101761"},"PeriodicalIF":3.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101744
Aliakbar Akbaritabar , Robin Haunschild , Lutz Bornmann
Identifying potentially talented academics worldwide using publication data has been proven to be successful with other performance measures based on citations and funding data in previous studies. In this study, we investigate the scientific mobility and immobility among academics as an additional performance measure. We reconstruct the mobility trajectory of potentially talented researchers throughout their scientific careers to study whether they have a different propensity to be mobile or non-mobile than other researchers in the group for comparison. Since the researchers’ gender may play an important role in scientific careers, we delve into gender differences. Our results indicate that potentially talented researchers have a higher propensity to be mobile than other researchers in the group for comparison – more so among male than female talented researchers. Women are overrepresented among non-mobile researchers in the other researchers group. We conclude – based on our findings – that the proposed method for identifying potentially talented individuals seems to select researchers who are more successful in their academic careers than the researchers in the group for comparison. The results agree with the findings of the previous studies based on citation and funding data. In the interpretation of our study results, one should consider yet that higher mobility is a privilege (that may be independent of talent). Specific groups, such as those with fewer caring responsibilities and visa restrictions, could have better access to this privilege. Further research is necessary thus on the trade-off between higher mobility's potential advantages and disadvantages as a strategy to build a successful academic career and unequal access to mobility.
{"title":"A study of gender and regional differences in scientific mobility and immobility among researchers identified as potentially talented","authors":"Aliakbar Akbaritabar , Robin Haunschild , Lutz Bornmann","doi":"10.1016/j.joi.2025.101744","DOIUrl":"10.1016/j.joi.2025.101744","url":null,"abstract":"<div><div>Identifying potentially talented academics worldwide using publication data has been proven to be successful with other performance measures based on citations and funding data in previous studies. In this study, we investigate the scientific mobility and immobility among academics as an additional performance measure. We reconstruct the mobility trajectory of potentially talented researchers throughout their scientific careers to study whether they have a different propensity to be mobile or non-mobile than other researchers in the group for comparison. Since the researchers’ gender may play an important role in scientific careers, we delve into gender differences. Our results indicate that potentially talented researchers have a higher propensity to be mobile than other researchers in the group for comparison – more so among male than female talented researchers. Women are overrepresented among non-mobile researchers in the other researchers group. We conclude – based on our findings – that the proposed method for identifying potentially talented individuals seems to select researchers who are more successful in their academic careers than the researchers in the group for comparison. The results agree with the findings of the previous studies based on citation and funding data. In the interpretation of our study results, one should consider yet that higher mobility is a privilege (that may be independent of talent). Specific groups, such as those with fewer caring responsibilities and visa restrictions, could have better access to this privilege. Further research is necessary thus on the trade-off between higher mobility's potential advantages and disadvantages as a strategy to build a successful academic career and unequal access to mobility.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101744"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101740
Giulio Giacomo Cantone , Paul Nightingale
There are many operational definitions and many indicators of interdisciplinary research. As a consequence, claims about its scientific impact may suffer from high model uncertainty and low credibility, since they can be based on specific results highly dependent on implicit choices of the modelling process. This study addresses the issue with Multiverse Analysis, a protocol for multi-model inference that specifies the modelling factors that can generate variability in the results. Combinations of these factors are fit and analysed simultaneously, so that inference cannot be selective in reporting results. 1,344 regression models are fit to a sample of 5,828 articles from journals of Business Studies. The Multiverse Analysis does not find support for the claim that interdisciplinary articles of Business are more cited. Even though a minority of the models reach statistical significance, the median estimates for the effect size are close to zero. Through an analysis of variance it is established that the choice of the indicator is the most influential, reproducing 45% of the total variance. This result confirms that claims on the scientific impact of IDR are highly dependent on the operational definition. The choice of the sources of metadata on articles, a factor previously overlooked in literature, reproduce 10% of the total variance. Findings suggest caution in accepting generic claims about the scientific impact of interdisciplinary research in social sciences.
{"title":"Model uncertainty in the evaluation of the impact of interdisciplinary research in Business Studies: A Multiverse Analysis","authors":"Giulio Giacomo Cantone , Paul Nightingale","doi":"10.1016/j.joi.2025.101740","DOIUrl":"10.1016/j.joi.2025.101740","url":null,"abstract":"<div><div>There are many operational definitions and many indicators of interdisciplinary research. As a consequence, claims about its scientific impact may suffer from high model uncertainty and low credibility, since they can be based on specific results highly dependent on implicit choices of the modelling process. This study addresses the issue with Multiverse Analysis, a protocol for multi-model inference that specifies the modelling factors that can generate variability in the results. Combinations of these factors are fit and analysed simultaneously, so that inference cannot be selective in reporting results. 1,344 regression models are fit to a sample of 5,828 articles from journals of Business Studies. The Multiverse Analysis does not find support for the claim that interdisciplinary articles of Business are more cited. Even though a minority of the models reach statistical significance, the median estimates for the effect size are close to zero. Through an analysis of variance it is established that the choice of the indicator is the most influential, reproducing 45% of the total variance. This result confirms that claims on the scientific impact of IDR are highly dependent on the operational definition. The choice of the sources of metadata on articles, a factor previously overlooked in literature, reproduce 10% of the total variance. Findings suggest caution in accepting generic claims about the scientific impact of interdisciplinary research in social sciences.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101740"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101747
Kyoungmi Lee , Kyungran Jung , Jae-Suk Yang
Weak signals have received growing attention as early indicators of future change, yet prior research has treated them as static indicators. Given their fragmented and uncertain nature, weak signals do not consistently evolve into strong signals. This study investigates whether weak signals can transition into strong ones, with particular attention to the role of research equipment. Building on TF-DoV–based analytical methods, we develop a longitudinal framework and analyze a decade of nationally funded R&D projects in the chemical sector. The results show that projects involving equipment exhibit a higher transition rate from weak to strong signals. This finding highlights the enabling role of equipment, functioning both as material infrastructure and as a cognitive foundation that facilitates the recognition and maturation of emerging topics. Overall, this study reconceptualizes weak signals as dynamic trajectories and demonstrates the catalytic influence of research equipment in their evolution. Further, it utilizes nationally funded R&D projects as a novel policy-relevant data source that captures emerging technological trajectories. These contributions advance the theoretical understanding of weak signals, extend methods for analyzing signal transitions, and provide practical guidance for R&D policy and strategy.
{"title":"From weak to strong signals: Exploring R&D projects with research equipment","authors":"Kyoungmi Lee , Kyungran Jung , Jae-Suk Yang","doi":"10.1016/j.joi.2025.101747","DOIUrl":"10.1016/j.joi.2025.101747","url":null,"abstract":"<div><div>Weak signals have received growing attention as early indicators of future change, yet prior research has treated them as static indicators. Given their fragmented and uncertain nature, weak signals do not consistently evolve into strong signals. This study investigates whether weak signals can transition into strong ones, with particular attention to the role of research equipment. Building on TF-DoV–based analytical methods, we develop a longitudinal framework and analyze a decade of nationally funded R&D projects in the chemical sector. The results show that projects involving equipment exhibit a higher transition rate from weak to strong signals. This finding highlights the enabling role of equipment, functioning both as material infrastructure and as a cognitive foundation that facilitates the recognition and maturation of emerging topics. Overall, this study reconceptualizes weak signals as dynamic trajectories and demonstrates the catalytic influence of research equipment in their evolution. Further, it utilizes nationally funded R&D projects as a novel policy-relevant data source that captures emerging technological trajectories. These contributions advance the theoretical understanding of weak signals, extend methods for analyzing signal transitions, and provide practical guidance for R&D policy and strategy.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101747"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101742
Huanan Wei , Xiwen Liu , Jielan Ding , Zihao Qu
Do papers with high recognition from reviewers gain higher attention and impact in subsequent dissemination? Studying the gap between these two aspects helps better understand the dissemination mechanism of paper impact and inspire thoughts on the effectiveness of peer review. Existing research has only focused on the single review behavior and has been conducted from overall perspective, with insufficient exploration of fine-grained content dimensions. This paper takes 28,885 papers published in the open peer review journal PLoS One from 2019 to 2024, which include peer review reports, as the research objects. It focuses on the fine-grained content dimensions (Methods & Data, Experiment & Analysis, Openness & Transparency, Rigor & Correctness) and studies the relationship between reviewers’ review behaviors—including confidence level (Anonymity), level of recognition (Quality Score), and degree of consensus (Consistency)—and attention that papers have acquired. Research results indicate that when reviewers are non-anonymous, the results of recognition in all content dimensions are more positive than those under anonymous conditions, and the phenomenon of “Evaluating in a more positive way in public scenario” is widely prevalent. The fine-grained recognition from reviewers significantly associated with the paper’s dissemination in the academic dissemination chain, and as the dissemination process progresses, the requirements for quality gradually emerge and become increasingly stringent. During peer review, reviewers exhibit a high degree of consensus regarding the recognition of paper quality, papers with complete consensus are more likely to attract high-attention, while those papers with significant controversy can also acquire a certain level of high-attention. In the process of scientific knowledge dissemination, shallow attention behaviors tend to be accompanied by evaluation consensus, while deep dissemination behaviors have a stronger correlation with the intrinsic quality of the paper.
{"title":"Does higher recognition from reviewers lead to greater attention to research papers? A combined analysis of review behavior and content","authors":"Huanan Wei , Xiwen Liu , Jielan Ding , Zihao Qu","doi":"10.1016/j.joi.2025.101742","DOIUrl":"10.1016/j.joi.2025.101742","url":null,"abstract":"<div><div>Do papers with high recognition from reviewers gain higher attention and impact in subsequent dissemination? Studying the gap between these two aspects helps better understand the dissemination mechanism of paper impact and inspire thoughts on the effectiveness of peer review. Existing research has only focused on the single review behavior and has been conducted from overall perspective, with insufficient exploration of fine-grained content dimensions. This paper takes 28,885 papers published in the open peer review journal <em>PLoS One</em> from 2019 to 2024, which include peer review reports, as the research objects. It focuses on the fine-grained content dimensions (Methods & Data, Experiment & Analysis, Openness & Transparency, Rigor & Correctness) and studies the relationship between reviewers’ review behaviors—including confidence level (Anonymity), level of recognition (Quality Score), and degree of consensus (Consistency)—and attention that papers have acquired. Research results indicate that when reviewers are non-anonymous, the results of recognition in all content dimensions are more positive than those under anonymous conditions, and the phenomenon of “Evaluating in a more positive way in public scenario” is widely prevalent. The fine-grained recognition from reviewers significantly associated with the paper’s dissemination in the academic dissemination chain, and as the dissemination process progresses, the requirements for quality gradually emerge and become increasingly stringent. During peer review, reviewers exhibit a high degree of consensus regarding the recognition of paper quality, papers with complete consensus are more likely to attract high-attention, while those papers with significant controversy can also acquire a certain level of high-attention. In the process of scientific knowledge dissemination, shallow attention behaviors tend to be accompanied by evaluation consensus, while deep dissemination behaviors have a stronger correlation with the intrinsic quality of the paper.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101742"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101743
Zhe Cao , Lin Zhang , Ronald Rousseau , Gunnar Sivertsen
With the rapid globalization of scientific research and the increasing tendency of countries to co-fund research programs and projects, measuring international collaboration has become a key focus in the field of scientometrics and in policy documents. The relative intensity of collaboration () has been introduced as a new approach to measure the activity in bilateral relations relative to all such relations within a network. This study proposes the application of fractional counting as an alternative to full counting methods to calculate the index, thereby shifting the emphasis from the perspective of participation in collaboration to the perspective of contribution to collaboration. At the level of countries, the change of perspective corresponds to a policy perspective in which questions about the prioritization of partners and resources spent on international scientific collaboration might be asked. To illustrate the new approach, we present examples of collaboration between China, the United States, and other countries within the global research network.
{"title":"Measuring the relative intensity of collaboration with fractional counting methods","authors":"Zhe Cao , Lin Zhang , Ronald Rousseau , Gunnar Sivertsen","doi":"10.1016/j.joi.2025.101743","DOIUrl":"10.1016/j.joi.2025.101743","url":null,"abstract":"<div><div>With the rapid globalization of scientific research and the increasing tendency of countries to co-fund research programs and projects, measuring international collaboration has become a key focus in the field of scientometrics and in policy documents. The relative intensity of collaboration (<span><math><mrow><mi>R</mi><mi>I</mi><mi>C</mi></mrow></math></span>) has been introduced as a new approach to measure the activity in bilateral relations relative to all such relations within a network. This study proposes the application of fractional counting as an alternative to full counting methods to calculate the <span><math><mrow><mi>R</mi><mi>I</mi><mi>C</mi></mrow></math></span> index, thereby shifting the emphasis from the perspective of <em>participation</em> in collaboration to the perspective of <em>contribution</em> to collaboration. At the level of countries, the change of perspective corresponds to a policy perspective in which questions about the prioritization of partners and resources spent on international scientific collaboration might be asked. To illustrate the new approach, we present examples of collaboration between China, the United States, and other countries within the global research network.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101743"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101746
Sarah Bratt , Danushka Bandara , Qiaoyi Liu , Mrudang Langalia , Abhishek Nanoti
Despite the significant role of datasets in advancing genomics and biomedicine, few metrics exist for assessing team integration of dataset contributors into the publication. Drawing from set theory, this study develops several measures of data-intensive team integration. We develop and describe 'data labor integration ratio' and variants and apply them two case studies in the biomedical sciences and genomics by analyzing the metadata of scientific datasets and their associated publications deposited to GenBank over 29 years (1992–2021). Findings indicate that using these measures, we can newly estimate whether teams with highly integrated dataset contributors are strongly correlated with higher citation impact. We also find that tightly integrated teams tend to publish more quickly. Results suggest that a higher data-and-publication team integration promotes conditions for the exchange of expertise and access to resources, thereby bolstering research capacity. The study offers effective metrics for quantifying team integration in biomedicine and genomics. We argue that these measures of dataset contributor integration are superior to approaches that rely solely on publication information advancing the assessment of data-intensive team integration on citation impact and scientific capacity-building in international collaborations.
{"title":"Measuring the integration of dataset contributors into the publication team: Metrics for assessing team integration in genomics and biomedicine and implications for citation impact and scientific capacity","authors":"Sarah Bratt , Danushka Bandara , Qiaoyi Liu , Mrudang Langalia , Abhishek Nanoti","doi":"10.1016/j.joi.2025.101746","DOIUrl":"10.1016/j.joi.2025.101746","url":null,"abstract":"<div><div>Despite the significant role of datasets in advancing genomics and biomedicine, few metrics exist for assessing team integration of dataset contributors into the publication. Drawing from set theory, this study develops several measures of data-intensive team integration. We develop and describe 'data labor integration ratio' and variants and apply them two case studies in the biomedical sciences and genomics by analyzing the metadata of scientific datasets and their associated publications deposited to GenBank over 29 years (1992–2021). Findings indicate that using these measures, we can newly estimate whether teams with highly integrated dataset contributors are strongly correlated with higher citation impact. We also find that tightly integrated teams tend to publish more quickly. Results suggest that a higher data-and-publication team integration promotes conditions for the exchange of expertise and access to resources, thereby bolstering research capacity. The study offers effective metrics for quantifying team integration in biomedicine and genomics. We argue that these measures of dataset contributor integration are superior to approaches that rely solely on publication information advancing the assessment of data-intensive team integration on citation impact and scientific capacity-building in international collaborations.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101746"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.joi.2025.101745
Fang Han , Yanqing Ren , Ruhao Zhang , Lingzi Feng , Lixue Wang , Junpeng Yuan
This study quantitatively analyzes 373 researchers with retracted papers from 20 leading medical institutions in China and examines their characteristics, retraction drivers, and career impacts based on their publication histories. The results show that: (1) young researchers with retractions show weaker academic performance than their non-retracted peers, while senior researchers exhibit higher productivity, influence, and larger collaboration networks; (2) output-driven incentives strongly correlate with misconduct-related retractions, and younger researchers face higher misconduct risks; (3) peer pressure among researchers within the same institute does not significantly influence the institute’s overall retraction frequency; and (4) retractions significantly reduce citations (–41.5%), collaborations, and career mobility, with early career researchers being the most affected. Midcareer researchers suffer primarily from citation decline. (5) Retractions due to scientific error have a greater negative impact on the authors’ subsequent career development. Their annual citation numbers decrease by 61.8%, and the number of co-authors decreases by 23.6%, which are 1.6 times and 1.4 times the decreases in the academic misconduct group, respectively. These findings provide critical insights into current retraction trends.
{"title":"Drivers and penalties of retraction: An empirical study of Chinese medical researchers","authors":"Fang Han , Yanqing Ren , Ruhao Zhang , Lingzi Feng , Lixue Wang , Junpeng Yuan","doi":"10.1016/j.joi.2025.101745","DOIUrl":"10.1016/j.joi.2025.101745","url":null,"abstract":"<div><div>This study quantitatively analyzes 373 researchers with retracted papers from 20 leading medical institutions in China and examines their characteristics, retraction drivers, and career impacts based on their publication histories. The results show that: (1) young researchers with retractions show weaker academic performance than their non-retracted peers, while senior researchers exhibit higher productivity, influence, and larger collaboration networks; (2) output-driven incentives strongly correlate with misconduct-related retractions, and younger researchers face higher misconduct risks; (3) peer pressure among researchers within the same institute does not significantly influence the institute’s overall retraction frequency; and (4) retractions significantly reduce citations (–41.5%), collaborations, and career mobility, with early career researchers being the most affected. Midcareer researchers suffer primarily from citation decline. (5) Retractions due to scientific error have a greater negative impact on the authors’ subsequent career development. Their annual citation numbers decrease by 61.8%, and the number of co-authors decreases by 23.6%, which are 1.6 times and 1.4 times the decreases in the academic misconduct group, respectively. These findings provide critical insights into current retraction trends.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101745"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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