Journal of Informetrics最新文献

英文中文

The role of preprints in open science: Accelerating knowledge transfer from science to technology

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-08 DOI: 10.1016/j.joi.2025.101663

Zhiqi Wang , Yue Chen , Chun Yang

Preprints have become increasingly essential in the landscape of open science, facilitating not only the exchange of knowledge within the scientific community but also bridging the gap between science and technology. However, the impact of preprints on technological innovation, given their unreviewed nature, remains unclear. This study fills this gap by conducting a comprehensive scientometric analysis of patent citations to bioRxiv preprints submitted between 2013 and 2021, measuring and accessing the contribution of preprints in accelerating knowledge transfer from science to technology. Our findings reveal a growing trend of patent citations to bioRxiv preprints, with a notable surge in 2020, primarily driven by the COVID-19 pandemic. Preprints play a critical role in accelerating innovation, not only expedite the dissemination of scientific knowledge into technological innovation but also enhance the visibility of early research results in the patenting process, while journals remain essential for academic rigor and reliability. The substantial number of post-online-publication patent citations highlights the critical role of the open science model—particularly the “open access” effect of preprints—in amplifying the impact of science on technological innovation. This study provides empirical evidence that open science policies encouraging the early sharing of research outputs, such as preprints, contribute to more efficient linkage between science and technology, suggesting an acceleration in the pace of innovation, higher innovation quality, and economic benefits.

{"title":"The role of preprints in open science: Accelerating knowledge transfer from science to technology","authors":"Zhiqi Wang , Yue Chen , Chun Yang","doi":"10.1016/j.joi.2025.101663","DOIUrl":"10.1016/j.joi.2025.101663","url":null,"abstract":"<div><div>Preprints have become increasingly essential in the landscape of open science, facilitating not only the exchange of knowledge within the scientific community but also bridging the gap between science and technology. However, the impact of preprints on technological innovation, given their unreviewed nature, remains unclear. This study fills this gap by conducting a comprehensive scientometric analysis of patent citations to bioRxiv preprints submitted between 2013 and 2021, measuring and accessing the contribution of preprints in accelerating knowledge transfer from science to technology. Our findings reveal a growing trend of patent citations to bioRxiv preprints, with a notable surge in 2020, primarily driven by the COVID-19 pandemic. Preprints play a critical role in accelerating innovation, not only expedite the dissemination of scientific knowledge into technological innovation but also enhance the visibility of early research results in the patenting process, while journals remain essential for academic rigor and reliability. The substantial number of post-online-publication patent citations highlights the critical role of the open science model—particularly the “open access” effect of preprints—in amplifying the impact of science on technological innovation. This study provides empirical evidence that open science policies encouraging the early sharing of research outputs, such as preprints, contribute to more efficient linkage between science and technology, suggesting an acceleration in the pace of innovation, higher innovation quality, and economic benefits.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101663"},"PeriodicalIF":3.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791546","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}

引用次数: 0

Is higher team gender diversity correlated with better scientific impact?

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-05 DOI: 10.1016/j.joi.2025.101662

Chengzhi Zhang, Jiaqi Zeng, Yi Zhao

Collaborative research involving scholars of various genders constitutes a prominent theme in scientific research that has garnered substantial attention. While several studies have investigated the connection between gender-specific collaboration patterns and the scientific impact of paper, the specific gender diversity factors that contribute to enhanced scientific impact remain largely unexplored. In this study, we analyze the correlation between gender diversity and the scientific impact of papers using the examples of Natural Language Processing (NLP) and Library and Information Science (LIS) domains. Our findings reveal three key observations: First, significant gender disparities exist in both NLP and LIS domains, with underrepresentation of female scholars. The gender disparity is more pronounced in the NLP domain compared to the LIS domain. Second, based on papers from the NLP and LIS domains, we find that papers with different gender compositions achieve varying numbers of citations, with mixed-gender collaborations gradually obtaining higher average citation counts compared to same-gender collaborations. Lastly, there is an inverted U-shaped relationship between the gender diversity of paper collaborations and the number of citations received by those papers. Based on the most impactful gender diversity calculations, the ideal gender ratio for NLP and LIS teams within a range where one gender constitutes 5% to 15% of the total number of authors. This paper contributes to the exploration of the most impactful gender diversity in collaborative research and offers insights to guide more effective scientific paper collaboration.

{"title":"Is higher team gender diversity correlated with better scientific impact?","authors":"Chengzhi Zhang, Jiaqi Zeng, Yi Zhao","doi":"10.1016/j.joi.2025.101662","DOIUrl":"10.1016/j.joi.2025.101662","url":null,"abstract":"<div><div>Collaborative research involving scholars of various genders constitutes a prominent theme in scientific research that has garnered substantial attention. While several studies have investigated the connection between gender-specific collaboration patterns and the scientific impact of paper, the specific gender diversity factors that contribute to enhanced scientific impact remain largely unexplored. In this study, we analyze the correlation between gender diversity and the scientific impact of papers using the examples of Natural Language Processing (NLP) and Library and Information Science (LIS) domains. Our findings reveal three key observations: First, significant gender disparities exist in both NLP and LIS domains, with underrepresentation of female scholars. The gender disparity is more pronounced in the NLP domain compared to the LIS domain. Second, based on papers from the NLP and LIS domains, we find that papers with different gender compositions achieve varying numbers of citations, with mixed-gender collaborations gradually obtaining higher average citation counts compared to same-gender collaborations. Lastly, there is an inverted U-shaped relationship between the gender diversity of paper collaborations and the number of citations received by those papers. Based on the most impactful gender diversity calculations, the ideal gender ratio for NLP and LIS teams within a range where one gender constitutes 5% to 15% of the total number of authors. This paper contributes to the exploration of the most impactful gender diversity in collaborative research and offers insights to guide more effective scientific paper collaboration.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101662"},"PeriodicalIF":3.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777675","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}

引用次数: 0

Annotating scientific uncertainty: A comprehensive model using linguistic patterns and comparison with existing approaches

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-03 DOI: 10.1016/j.joi.2025.101661

Panggih Kusuma Ningrum , Philipp Mayr , Nina Smirnova , Iana Atanassova

We present UnScientify,¹ a system designed to detect scientific uncertainty in scholarly full text. The system utilizes a weakly supervised technique to identify verbally expressed uncertainty in scientific texts and their authorial references. The core methodology of UnScientify is based on a multi-faceted pipeline that integrates span pattern matching, complex sentence analysis and author reference checking. This approach streamlines the labeling and annotation processes essential for identifying scientific uncertainty, covering a variety of uncertainty expression types to support diverse applications including information retrieval, text mining and scientific document processing. The evaluation results highlight the trade-offs between modern large language models (LLMs) and the UnScientify system. UnScientify, which employs more traditional techniques, achieved superior performance in the scientific uncertainty detection task, attaining an accuracy score of 0.808. This finding underscores the continued relevance and efficiency of UnScientify's simple rule-based and pattern matching strategy for this specific application. The results demonstrate that in scenarios where resource efficiency, interpretability, and domain-specific adaptability are critical, traditional methods can still offer significant advantages.

{"title":"Annotating scientific uncertainty: A comprehensive model using linguistic patterns and comparison with existing approaches","authors":"Panggih Kusuma Ningrum , Philipp Mayr , Nina Smirnova , Iana Atanassova","doi":"10.1016/j.joi.2025.101661","DOIUrl":"10.1016/j.joi.2025.101661","url":null,"abstract":"<div><div>We present UnScientify,<span><span><sup>1</sup></span></span> a system designed to detect scientific uncertainty in scholarly full text. The system utilizes a weakly supervised technique to identify verbally expressed uncertainty in scientific texts and their authorial references. The core methodology of UnScientify is based on a multi-faceted pipeline that integrates span pattern matching, complex sentence analysis and author reference checking. This approach streamlines the labeling and annotation processes essential for identifying scientific uncertainty, covering a variety of uncertainty expression types to support diverse applications including information retrieval, text mining and scientific document processing. The evaluation results highlight the trade-offs between modern large language models (LLMs) and the UnScientify system. UnScientify, which employs more traditional techniques, achieved superior performance in the scientific uncertainty detection task, attaining an accuracy score of 0.808. This finding underscores the continued relevance and efficiency of UnScientify's simple rule-based and pattern matching strategy for this specific application. The results demonstrate that in scenarios where resource efficiency, interpretability, and domain-specific adaptability are critical, traditional methods can still offer significant advantages.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101661"},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759742","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}

引用次数: 0

Hyperprolific authorship: Unveiling the extent of extreme publishing in the ‘publish or perish’ era

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-01 DOI: 10.1016/j.joi.2025.101658

Giovanni Abramo , Ciriaco Andrea D'Angelo

The increasing pressure of the “publish or perish” academic culture has contributed to the rise of hyperprolific authors—researchers who produce an exceptionally high number of publications. This study investigates the global phenomenon of hyperprolific authorship by analyzing the bibliometric data of over two million scholars across various disciplines from 2017 to 2019. Using field-specific thresholds to identify hyperprolific authors, we explore their geographic and disciplinary distributions, the impact of their publications, and their collaboration patterns. The results reveal that hyperprolific authors are concentrated in fields such as Clinical Medicine, Biomedical Research, and Chemistry, and in countries with substantial research investments, including China, the United States, and Germany. Contrary to concerns about a trade-off between quantity and quality, hyperprolific authors tend to produce higher-impact publications on average compared to their peers. Their output is strongly associated with extensive co-authorship networks, reflecting the role of collaboration in enabling prolific publishing. The findings underscore the need for balanced evaluation metrics that prioritize both quality and integrity in academic publishing. This study contributes to understanding the drivers and consequences of hyperprolific behavior, offering insights for research policy and evaluation practices.

{"title":"Hyperprolific authorship: Unveiling the extent of extreme publishing in the ‘publish or perish’ era","authors":"Giovanni Abramo , Ciriaco Andrea D'Angelo","doi":"10.1016/j.joi.2025.101658","DOIUrl":"10.1016/j.joi.2025.101658","url":null,"abstract":"<div><div>The increasing pressure of the “publish or perish” academic culture has contributed to the rise of hyperprolific authors—researchers who produce an exceptionally high number of publications. This study investigates the global phenomenon of hyperprolific authorship by analyzing the bibliometric data of over two million scholars across various disciplines from 2017 to 2019. Using field-specific thresholds to identify hyperprolific authors, we explore their geographic and disciplinary distributions, the impact of their publications, and their collaboration patterns. The results reveal that hyperprolific authors are concentrated in fields such as Clinical Medicine, Biomedical Research, and Chemistry, and in countries with substantial research investments, including China, the United States, and Germany. Contrary to concerns about a trade-off between quantity and quality, hyperprolific authors tend to produce higher-impact publications on average compared to their peers. Their output is strongly associated with extensive co-authorship networks, reflecting the role of collaboration in enabling prolific publishing. The findings underscore the need for balanced evaluation metrics that prioritize both quality and integrity in academic publishing. This study contributes to understanding the drivers and consequences of hyperprolific behavior, offering insights for research policy and evaluation practices.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101658"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738960","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}

引用次数: 0

How do academic gatherings promote knowledge production and dissemination?

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-26 DOI: 10.1016/j.joi.2025.101659

Li Hou , Ruilu Yang

Academic gatherings, such as conferences, are one of the most important types of activities in academia, yet they are understudied in the literature. Using a sample of 5379 articles published between 2000 and 2019 in the top five Economics journals, we identify whether the article was presented in academic gatherings based on wording in the paper's Acknowledgement sections, and explore its effect on citations. We find that papers acknowledging academic gatherings are associated with higher citations, and this relationship is mediated by a broader knowledge base and the more attention; that is, acknowledging academic gatherings corresponds to the paper having a broader knowledge base and being viewed more frequently, thus receive higher citations. We also find that papers authored by junior scholars with limited knowledge, skills, and experience benefit more from academic gatherings than those by senior scholars. Our findings deepen the understanding of academic gatherings and knowledge production and dissemination.

学术会议等学术集会是学术界最重要的活动类型之一，但文献对其研究不足。我们以 2000 年至 2019 年间发表在五大经济学期刊上的 5379 篇文章为样本，根据论文 "致谢 "部分的措辞识别文章是否在学术集会上发表，并探讨其对引用率的影响。我们发现，鸣谢学术集会的论文与更高的引用率相关，而这种关系是以更广泛的知识基础和更高的关注度为中介的；也就是说，鸣谢学术集会相当于论文拥有更广泛的知识基础和更高的浏览频率，从而获得更高的引用率。我们还发现，与资深学者的论文相比，知识、技能和经验有限的初级学者的论文从学术集会中获益更多。我们的发现加深了人们对学术集会和知识生产与传播的理解。

引用次数: 0

A simple equation for rank-citation profiles

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-25 DOI: 10.1016/j.joi.2025.101660

Y.C. Tay , Akarsh Srivastava , Mostafa Rezazad , Hamid Sarbazi-Azad

There is considerable interest in the citation count for an author's publications. This has led to many proposals for citation indices to characterize rank-citation profiles, which order an author's publications by their citation count. However, there is so far no tractable model to facilitate the analysis of these profiles and the design of their indices. This paper presents a simple equation for such design and analysis.

The equation has three parameters that are calibrated by three geometrical characteristics of a rank-citation profile, namely the maximum number of citations for a publication (M), the number of cited publications (N), and the Hirsch index (h). The equation's simple form makes it tractable for analyzing rank-citation profiles and indices.

To demonstrate, the equation is used to derive closed-form approximations (in terms of M, N and h) for various indices; these expressions provide new insight into previous index analyses, the influence of a profile's tail, and the effect of time.

引用次数: 0

Researching deeply or broadly? The effects of scientists’ research strategies on disruptive performance over their careers

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-22 DOI: 10.1016/j.joi.2025.101657

Weiyi Ao , Libo Sheng , Xuanmin Ruan , Dongqing Lyu , Jiang Li , Ying Cheng

The research strategies scientists use can affect the efficiency and direction of scientific discovery. This study focuses on the relationships between scientists’ knowledge breadth and depth strategies and disruptive performance as well as the role career age plays in these relationships. The data were from 651,831 publications authored by 12,278 biomedical scientists from the PubMed Knowledge Graph (PKG) dataset. The two main findings are as follows: (1) U-shaped correlations were found between scientists’ knowledge breadth, depth, and disruptive performance; and (2) career age influences the relationship between knowledge depth and disruptive performance, with different impacts across various stages of a scientist's career. The findings imply that future research must consider the key role scientists’ career age plays in the relationship between research strategies and scientific performance.

引用次数: 0

Navigating disruptions: The effects of the pandemic on scientific collaboration and research novelty in Hong Kong

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-12 DOI: 10.1016/j.joi.2025.101656

Rong Ni , Jue Wang

Scientific collaboration and novelty are fundamental drivers of progress in knowledge advancement and innovation. However, the emergence of the COVID-19 pandemic disrupted traditional channels of scholarly communication, potentially influencing both short- and long-term scientific endeavors. Using data from 116,942 WoS publications from Hong Kong between 2017 and 2022, this study investigates the pandemic's impact on both scientific collaboration and research novelty. Our analysis reveals that the pandemic reduced the proportion of international collaborative papers and led to a contraction in the scale of international collaboration. On the other hand, domestic collaboration increased in both scope and scale. Additionally, while the pandemic negatively affected research novelty, international collaboration significantly mitigated this impact. However, no moderating effect of collaboration was observed on high-novelty research. This study underscores the importance of maintaining a collaborative scientific community and highlights the resilience and adaptability of academic communities when facing unprecedented challenges.

{"title":"Navigating disruptions: The effects of the pandemic on scientific collaboration and research novelty in Hong Kong","authors":"Rong Ni , Jue Wang","doi":"10.1016/j.joi.2025.101656","DOIUrl":"10.1016/j.joi.2025.101656","url":null,"abstract":"<div><div>Scientific collaboration and novelty are fundamental drivers of progress in knowledge advancement and innovation. However, the emergence of the COVID-19 pandemic disrupted traditional channels of scholarly communication, potentially influencing both short- and long-term scientific endeavors. Using data from 116,942 WoS publications from Hong Kong between 2017 and 2022, this study investigates the pandemic's impact on both scientific collaboration and research novelty. Our analysis reveals that the pandemic reduced the proportion of international collaborative papers and led to a contraction in the scale of international collaboration. On the other hand, domestic collaboration increased in both scope and scale. Additionally, while the pandemic negatively affected research novelty, international collaboration significantly mitigated this impact. However, no moderating effect of collaboration was observed on high-novelty research. This study underscores the importance of maintaining a collaborative scientific community and highlights the resilience and adaptability of academic communities when facing unprecedented challenges.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101656"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600858","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}

引用次数: 0

Multi-agent simulation of team stability evolution: A complexity science perspective

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-06 DOI: 10.1016/j.joi.2025.101655

Liang Yaqi, Hou Guisheng, Jiang Xiujuan

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.

引用次数: 0

The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-02-25 DOI: 10.1016/j.joi.2025.101648

Gerson Pech , Aleksandra Mreła , Veslava Osińska , Oleksandr Sokolov

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.

{"title":"The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database","authors":"Gerson Pech , Aleksandra Mreła , Veslava Osińska , 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}

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Journal of Informetrics

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀