Journal of Informetrics最新文献

{"title":"Multi-agent simulation of team stability evolution: A complexity science perspective","authors":"Liang Yaqi,&nbsp;Hou Guisheng,&nbsp;Jiang Xiujuan","doi":"10.1016/j.joi.2025.101655","DOIUrl":"10.1016/j.joi.2025.101655","url":null,"abstract":"<div><div>Drawing on the theory of complex adaptive systems, this study develops a multi-agent model of a research innovation team through the NetLogo simulation platform. The operational mechanisms of the research innovation team are delineated into three distinct processes: demand-driven collaborative mechanism, objectives-driven knowledge sharing mechanism, and outcome-driven dynamic trust mechanism. These processes describe the individual decision-making of team members and the complex interactions among them. By analyzing the evolutionary patterns of research innovation team stability under various influencing factors, this study shows that: (1) While the effects on team stability vary across different parameter settings, the underlying evolutionary patterns remain largely consistent. (2) The influences of different factors on team stability exhibit nonlinear characteristics. These findings offer theoretical insights and decision-making support for fostering stable development within research innovation teams.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101655"},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549900","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}

{"title":"The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database","authors":"Gerson Pech ,&nbsp;Aleksandra Mreła ,&nbsp;Veslava Osińska ,&nbsp;Oleksandr Sokolov","doi":"10.1016/j.joi.2025.101648","DOIUrl":"10.1016/j.joi.2025.101648","url":null,"abstract":"<div><div>Processing scientific literature metadata allows us to verify the assignment of articles to predefined categories. The Triangle of Biomedicine (TB) is a convenient space for considering the positions of biomedical papers according to human, animal, and molecular-cellular subdisciplines. The placement of PubMed papers in the TB using citations and, what is more interesting, the dynamics of the changing positions of papers (because of citations) have not been examined to date. This research presents a method for finding the article citation vectors of directly cited papers whose components are the MeSH terms shares offered by the PubMed database. The citation vectors allow finding the paper's position in the TB and comparing it with the original position of the publication. The analysis of sets of citation vectors enables locating their position on the translational line to show the distance between human research and animal-molecular studies. Moreover, applying information entropy, the dynamics of entropies in four different sets of articles are studied.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101648"},"PeriodicalIF":3.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New paper-by-paper classification for Scopus based on references reclassified by the origin of the papers citing them","authors":"Jesús M. Álvarez-Llorente ,&nbsp;Vicente P. Guerrero-Bote ,&nbsp;Félix Moya-Anegón","doi":"10.1016/j.joi.2025.101647","DOIUrl":"10.1016/j.joi.2025.101647","url":null,"abstract":"<div><div>A reference-based classification system for individual Scopus publications is presented which takes into account the categories of the papers citing those references instead of the journals in which those cited papers are published. It supports multiple assignments of up to 5 categories within the Scopus ASJC structure, but eliminates the Multidisciplinary Area and the miscellaneous categories, and it allows for the reclassification of a greater number of publications (potentially 100%) than traditional reference-based systems. Twelve variants of the system were obtained by adjusting different parameters, which were applied to the more than 3.2 million citable papers from the active Scientific Journals in 2020 indexed in Scopus. The results were analyzed and compared with other classification systems such as the original journal-based Scopus ASJC, the 2 generation-reference based M3-AWC-0.8 (Álvarez-Llorente et al., 2024), and the corresponding authors' assignment based AAC (Álvarez-Llorente et al., 2023). The different variants obtained of the classification give results that improve those used as references in multiple scientometric fields. The variation called U1-F-0.8 seems especially promising due to its restraint in assigning multiple categories, consistency with reference classifications and the fact of applying normalization processes to avoid the overinfluence of articles that have a greater number of references.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101647"},"PeriodicalIF":3.4,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445864","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}

{"title":"Sequential citation counts prediction enhanced by dynamic contents","authors":"Guoxiu He ,&nbsp;Sichen Gu ,&nbsp;Zhikai Xue ,&nbsp;Yufeng Duan ,&nbsp;Xiaomin Zhu","doi":"10.1016/j.joi.2025.101645","DOIUrl":"10.1016/j.joi.2025.101645","url":null,"abstract":"<div><div>The assessment of the impact of scholarly publications has garnered significant attention among researchers, particularly in predicting the future sequence of citation counts. However, current studies predominantly regard academic papers as static entities, failing to acknowledge the dynamic nature of their fixed content, which can undergo shifts in focus over time. To this end, we implement dynamic representations of the content to mirror chronological changes within the given paper, facilitating the sequential prediction of citation counts. Specifically, we propose a novel deep neural network called <strong>D</strong>ynam<strong>I</strong>c <strong>C</strong>ontent-aware <strong>T</strong>r<strong>A</strong>nsformer (DICTA). The proposed model incorporates a dynamic content module that leverages the power of a sequential module to effectively capture the evolving focus information within each paper. To account for dependencies between the historical and future citation counts, our model utilizes a transformer-based framework as the backbone. With the encoder-decoder structure, it can effectively handle previous citation accumulations and then predict future citation potentials. Extensive experiments conducted on two scientific datasets demonstrate that DICTA achieves impressive performance and outperforms all baseline approaches. Further analyses underscore the significance of the dynamic content module. The code is available: <span><span>https://github.com/ECNU-Text-Computing/DICTA</span><svg><path></path></svg></span></div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101645"},"PeriodicalIF":3.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395914","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}

{"title":"Identifying potential sleeping beauties based on dynamic time warping algorithm and citation curve benchmarking","authors":"Zewen Hu,&nbsp;Yu Chen,&nbsp;Jingjing Cui","doi":"10.1016/j.joi.2025.101646","DOIUrl":"10.1016/j.joi.2025.101646","url":null,"abstract":"<div><div>Sleeping beauty (SB) is recognised as delayed, highly cited, or high-impact literature. The precise and efficient identification of potential sleeping beauties from the massive literature can maximise their value in science and technology development. Therefore, in this study, a new time-series similarity method, called the dynamic time warping (DTW) algorithm, was designed to efficiently identify sleeping beauties. First, the top 1 % of highly cited publications (5423 publications) in the field of Artificial Intelligence (AI) between 1990 and 2010 were identified based on data collected from the Web of Science database. Then, the DTW algorithm was designed and implemented to identify potential sleeping beauties based on the benchmarking sleeping beauty citation curve. Subsequently, the DTW method was combined with three indicators defined by Van Raan (2004) to design the DTW* method. Finally, the newly designed DTW and DTW* methods were used alongside quadratic function fitting (QFF), beauty coefficient (B), and modified beauty coefficient (Bcp) to identify sleeping beauties and evaluate the effect of the DTW algorithm. Among the findings: (1) The DTW algorithm can quickly and effectively identify potential sleeping beauties with help from the citation trajectory of different benchmarking sleeping beauties. These benchmarking sleeping beauties are identified by Raan's criteria from physics, as well as the B and Bcp index from the AI field, indicating the robustness of the DTW method, which is less reliant on methods and disciplinary factors of selecting benchmarking sleeping beauties. The DTW algorithm can automatically and accurately identify different types of highly influential publications, including sleeping beauties and highly cited publications, based on publication citation trajectories, further indicating robustness and application prospects. (2) The DTW* method improves the DTW recognition accuracy using the sleeping time defined by Van Raan, to accurately identify standardised sleeping beauties conforming to the standardised citation curve of benchmarking sleeping beauty, thereby ensuring exclusion of false sleeping beauties with extremely short sleeping time. (3) Using the DTW* method, 39 sleeping beauties with sleeping time greater than five years were identified, among which, 38 met Raan's three criteria with an accuracy of 97 %. Of the 39 sleeping beauties, 62 % were conference publications, suggesting that conference publications have an extremely high probability of becoming a sleeping beauty in the AI field. In content analysis, the 39 sleeping beauties were associated with innovative algorithms, methods, and related applications. (4) The DTW algorithm can be extended to another significant different field as ‘Social Sciences, Interdisciplinary’ category for sleeping beauty identification, further verifying the effectiveness and robustness of the DTW approach.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101646"},"PeriodicalIF":3.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395913","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}

{"title":"Avoiding the pitfalls of direct linkage: A novelty-driven approach to measuring scientific impact on patents","authors":"Nils M. Denter,&nbsp;Joe Waterstraat,&nbsp;Martin G. Moehrle","doi":"10.1016/j.joi.2025.101644","DOIUrl":"10.1016/j.joi.2025.101644","url":null,"abstract":"<div><div>Scientific knowledge plays a major role in the generation of new technological knowledge. We present a new novelty-driven approach to measure the influence of science on patents. We overcome the weaknesses of previous methods based on either citations or semantic similarities, both representing direct linkages between documents. We combine patent novelty measurement with technology-specific, scientific dictionaries, which allow us to measure a patent's nearness to science by stable indirect linkages. We apply our indicator “science-driven novelty” to the testbed of RFID technology and confirm its validity by conducting an expert survey. Subsequently, we test how science impacts patent value, finding that scientific influence increases the average value of a patent. Our results suggest several implications. For academics, we recommend not relying solely on analyzing direct links between papers and patents to determine the influence of science on technology. For management, we provide a new tool to assess scientific influences in patents and thus the value of their company's own patent portfolio as well as the portfolios of third parties. Using text as data, the tool is viable at a very early stage and can be helpful in go/no-go decisions for technology management.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101644"},"PeriodicalIF":3.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledging the new invisible colleague: Addressing the recognition of Open AI contributions in in scientific publishing","authors":"Juan Gorraiz","doi":"10.1016/j.joi.2025.101642","DOIUrl":"10.1016/j.joi.2025.101642","url":null,"abstract":"<div><div>This study investigates the evolving role of AI tools, such as ChatGPT, in academic research, with a focus on whether these tools are recognized as authors or co-authors, and how their contributions are cited or acknowledged across various fields. Using data from two major bibliometric sources, Web of Science Core Collection and Scopus, the analysis reveals patterns of AI citation, co-authorship, and acknowledgments. While some attempts have been made to credit AI as a co-author, ethical guidelines—such as those from COPE—prevent this due to AI's inability to fulfill the intellectual requirements for authorship. Instead, AI is increasingly cited as a source or mentioned in acknowledgments to ensure transparency in its use. The study further addresses the ethical implications of AI's role in disrupting traditional notions of intellectual reciprocity and bibliometric analysis. The future role of AI in research will depend on how challenges related to access, equity, and intellectual contribution are managed, determining whether AI will democratize research or exacerbate existing inequalities.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101642"},"PeriodicalIF":3.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the predictability of new topics of scholars: A machine learning-based approach using knowledge networks","authors":"Zhixiang Wu ,&nbsp;Hucheng Jiang ,&nbsp;Lianjie Xiao ,&nbsp;Hao Wang ,&nbsp;Jin Mao","doi":"10.1016/j.joi.2025.101637","DOIUrl":"10.1016/j.joi.2025.101637","url":null,"abstract":"<div><div>Scholars continuously explore new research topics to drive personal academic achievements. While factors influencing topic selection exist, the predictability of scholars’ choices regarding new topics is not yet fully understood. To bridge the gap, this study investigates the predictability of <em>new topics of scholars (NTS)</em>. The research task is transformed into a binary classification, predicting whether <em>NTS</em> that appear in the disciplinary knowledge network will be adopted by a scholar in the future. Using PubMed Knowledge Graph (PKG) as the data source, over 17,000 local knowledge networks (LKNs) of individual scholars are constructed, along with a global knowledge network (GKN) of all the scholars in the database. Sixteen features of knowledge network topology and candidate topics are extracted, and seven machine learning algorithms are applied. Our large-scale experiments show that the best prediction model achieves an F1 score of 86.49%. Shapley values provide more interpretable results. A 1-year observation window appears to be sufficient for making predictions. Novel topics and young scholars exhibit good predictability. Our findings provide profound insights into the predictability of scholars' topic selection and offer practical implications for future in-depth studies.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101637"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165448","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}

{"title":"Collaboration networks and radical innovation: Two faces of tie strength and structural holes","authors":"Jia Zhang ,&nbsp;Jian Wang ,&nbsp;Jos Winnink ,&nbsp;Simcha Jong","doi":"10.1016/j.joi.2024.101636","DOIUrl":"10.1016/j.joi.2024.101636","url":null,"abstract":"<div><div>This paper studies how tie strength and structural holes collectively affect innovation radicalness at a location within an innovating firm. We identified 16,011 inventors’ locations of the 93 most innovative U.S. pharmaceuticals and biotechnology companies on the EU Industrial R&amp;D Investment Scoreboard. We tracked their patents from 2001 to 2013 and constructed a panel dataset for analysis. Using firm-location fixed effect models, we found that the average tie strength of a location's egocentric network has a negative effect on innovation radicalness, and this negative effect is stronger when the location's egocentric network is cohesive. This suggests that weak ties have informational advantages for radical innovation, which are more pronounced when there is network cohesion to mitigate the relational disadvantages of weak ties. We also found a negative effect of structural holes on innovation radicalness when tie strength is weak but a positive effect when tie strength is strong. This indicates that strong ties are needed for mobilizing the informational advantages associated with structural holes.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101636"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration vs segregation: Network analysis of interdisciplinarity in funded and unfunded research on infectious diseases","authors":"Anbang Du ,&nbsp;Michael Head ,&nbsp;Markus Brede","doi":"10.1016/j.joi.2024.101634","DOIUrl":"10.1016/j.joi.2024.101634","url":null,"abstract":"<div><div>Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101634"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}