This research aimed to investigate the global development of social media and mental health research and analyze publishing trends within the esteemed Scopus and Web of Science (WoS) databases, shedding light on the growing significance of this interdisciplinary field for understanding the interplay between digital technologies and mental well-being. Leveraging ScientoPy, the study analyzed factors such as publication numbers, primary research themes, top countries, subject areas, frequently used author keywords, preferred sources, and institutional data. Visualization maps and content analysis were created using VOSviewer and Biblioshiny, respectively. The analysis encompassed 3,119 entries from the Scopus and WoS databases, revealing a notable upward trajectory in social media and mental health research. Psychology emerged as the most prominent subject area, with the United States being the most productive country. Keywords such as "social media," "depression," and "mental health" saw a significant surge in popularity during 2021 and 2022. This study offers readers and future researchers a comprehensive global perspective on key topics in social media and mental health, facilitating the structuring of data for the development of robust theories and practices in this domain.
{"title":"Exploring the Role of Social Media in Mental Health Research: A Bibliometric and Content Analysis","authors":"Azliyana Azizan","doi":"10.5530/jscires.13.1.1","DOIUrl":"https://doi.org/10.5530/jscires.13.1.1","url":null,"abstract":"This research aimed to investigate the global development of social media and mental health research and analyze publishing trends within the esteemed Scopus and Web of Science (WoS) databases, shedding light on the growing significance of this interdisciplinary field for understanding the interplay between digital technologies and mental well-being. Leveraging ScientoPy, the study analyzed factors such as publication numbers, primary research themes, top countries, subject areas, frequently used author keywords, preferred sources, and institutional data. Visualization maps and content analysis were created using VOSviewer and Biblioshiny, respectively. The analysis encompassed 3,119 entries from the Scopus and WoS databases, revealing a notable upward trajectory in social media and mental health research. Psychology emerged as the most prominent subject area, with the United States being the most productive country. Keywords such as \"social media,\" \"depression,\" and \"mental health\" saw a significant surge in popularity during 2021 and 2022. This study offers readers and future researchers a comprehensive global perspective on key topics in social media and mental health, facilitating the structuring of data for the development of robust theories and practices in this domain.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Relationship between Insider Research and Time from Submission to Acceptance in Turkish Dentistry Journals","authors":"Baris Baser, M. Alpaydin, S. Buyuk","doi":"10.5530/jscires.13.1.19","DOIUrl":"https://doi.org/10.5530/jscires.13.1.19","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elahe Ebrahimi Dorcheh, Ali Mansouri, Mitra Pashootanizadeh, A. Mirbagherifard, Ahmad Shabani
With regard to the specific nature and variety of the humanities fields and disciplines and the need to evaluate the humanities research outputs according to their nature and intrinsic characteristics, two questions has been posed and answered in this study as follows: “What are the criteria and indicators for evaluating the research outputs of humanities?” and “What is the prioritizing of the evaluation criteria according to the research approaches and goals in humanities?” Considering the differences in the fields of humanities, a case study of language and literature was conducted. This research was done with a mixed method (qualitative and quantitative stages). The first stage was carried out using a library research method to extract the criteria and indicators for the evaluation of the research outputs in the fields of language and literature. In the second stage, in order to finalize and prioritize the criteria, a questionnaire was designed and distributed among a number of experts in the fields of language and literature in two rounds of fuzzy Delphi. In the first stage, 42 indicators were identified and divided into 8 categories of criteria: 1) platform for creation, presentation and publication, 2) writing structure, 3) content, 4) impact in online environment, 5) scientific impact, 6) social impact, 7) economic impact, and 8) cultural impact. The prioritizing of the criteria was also based on their average obtained in the second round of fuzzy Delphi, which shows the impact of research approaches and goals on the priority of using the criteria.
{"title":"Determining and Prioritizing the Evaluation Criteria of Humanities Scientific Outputs: A Case Study of Language and Literature Fields","authors":"Elahe Ebrahimi Dorcheh, Ali Mansouri, Mitra Pashootanizadeh, A. Mirbagherifard, Ahmad Shabani","doi":"10.5530/jscires.13.1.13","DOIUrl":"https://doi.org/10.5530/jscires.13.1.13","url":null,"abstract":"With regard to the specific nature and variety of the humanities fields and disciplines and the need to evaluate the humanities research outputs according to their nature and intrinsic characteristics, two questions has been posed and answered in this study as follows: “What are the criteria and indicators for evaluating the research outputs of humanities?” and “What is the prioritizing of the evaluation criteria according to the research approaches and goals in humanities?” Considering the differences in the fields of humanities, a case study of language and literature was conducted. This research was done with a mixed method (qualitative and quantitative stages). The first stage was carried out using a library research method to extract the criteria and indicators for the evaluation of the research outputs in the fields of language and literature. In the second stage, in order to finalize and prioritize the criteria, a questionnaire was designed and distributed among a number of experts in the fields of language and literature in two rounds of fuzzy Delphi. In the first stage, 42 indicators were identified and divided into 8 categories of criteria: 1) platform for creation, presentation and publication, 2) writing structure, 3) content, 4) impact in online environment, 5) scientific impact, 6) social impact, 7) economic impact, and 8) cultural impact. The prioritizing of the criteria was also based on their average obtained in the second round of fuzzy Delphi, which shows the impact of research approaches and goals on the priority of using the criteria.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualising Knowledge, Research Hotspots and Trends of Literacy Studies in the Context of Library, 1969-2021","authors":"Anupta Jana, Rosalien Rout","doi":"10.5530/jscires.13.1.14","DOIUrl":"https://doi.org/10.5530/jscires.13.1.14","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online credit card fraud (OCCF) is the malicious act of using credit card details belonging to another person to complete fraudulent transactions over the Internet. Naturally, masses of researchers have engaged in the imperative search for effective solutions across a wide range of disciplines. The result is a rich tapestry of methodologies, models, frameworks, and inventions exhibiting dramatic spread and growth. However, this also results in an unorganized research domain. In this state, a bibliometric analysis is a useful technique for establishing a reconciled snapshot of the OCCF research domain. This paper has particular interest in determining the intellectual structure of the knowledge of machine learning, deep learning, and ensemble learning models for early detection of OCCF. This bibliometric analysis is conducted using 524 publications between 2013 and 2022 extracted from the SCOPUS core collection database. Microsoft Excel, VOSViewer, and Biblioshiny software tools were used for data analysis. The findings indicate that ensemble learning models are trending and the three most authoritative authors have been exposed in this study. There is a sharp rise in global publications annually and India has the most publications with the most impactful authors. Five broad clusters of knowledge are imbalanced data, anomaly detection, machine learning, decision trees, and ensemble learning. Intellectual collaboration across regions is strong amongst Asia, Europe, and North America with weak associations between Africa and South America. This is the first bibliometric analysis in the domain of OCCF detection to the best of the author’s ability. The findings significantly contribute to the application of OCCF detection through the creation of intellectual patterns in existing literature. The results bring about synthesis within a domain of research that is currently disorganized. This in turn helps researchers to identify research gaps, and areas for further research and formulate a curriculum.
{"title":"Bibliometric Analysis of Recent Trends in Machine Learning for Online Credit Card Fraud Detection","authors":"Dickson Hove, O. Olugbara, Alveen Singh","doi":"10.5530/jscires.13.1.4","DOIUrl":"https://doi.org/10.5530/jscires.13.1.4","url":null,"abstract":"Online credit card fraud (OCCF) is the malicious act of using credit card details belonging to another person to complete fraudulent transactions over the Internet. Naturally, masses of researchers have engaged in the imperative search for effective solutions across a wide range of disciplines. The result is a rich tapestry of methodologies, models, frameworks, and inventions exhibiting dramatic spread and growth. However, this also results in an unorganized research domain. In this state, a bibliometric analysis is a useful technique for establishing a reconciled snapshot of the OCCF research domain. This paper has particular interest in determining the intellectual structure of the knowledge of machine learning, deep learning, and ensemble learning models for early detection of OCCF. This bibliometric analysis is conducted using 524 publications between 2013 and 2022 extracted from the SCOPUS core collection database. Microsoft Excel, VOSViewer, and Biblioshiny software tools were used for data analysis. The findings indicate that ensemble learning models are trending and the three most authoritative authors have been exposed in this study. There is a sharp rise in global publications annually and India has the most publications with the most impactful authors. Five broad clusters of knowledge are imbalanced data, anomaly detection, machine learning, decision trees, and ensemble learning. Intellectual collaboration across regions is strong amongst Asia, Europe, and North America with weak associations between Africa and South America. This is the first bibliometric analysis in the domain of OCCF detection to the best of the author’s ability. The findings significantly contribute to the application of OCCF detection through the creation of intellectual patterns in existing literature. The results bring about synthesis within a domain of research that is currently disorganized. This in turn helps researchers to identify research gaps, and areas for further research and formulate a curriculum.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140704069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mapping the Landscape of Sustainability in Social Media: A Bibliometric Analysis and Research Trends","authors":"Sarita Nagvanshi, Neha Gupta","doi":"10.5530/jscires.13.1.21","DOIUrl":"https://doi.org/10.5530/jscires.13.1.21","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mapping of research has become important across the world and any new technology requires a new institutional framework for mapping appropriate outcomes of research. It involves analyzing linkages between various actors, stakeholders, agencies, and institutions to map potential research domains. Over the years, Biotechnology Applied Microbiology has emerged as a niche area, and this sector is recognized as the key driver for economic growth and development. Biotechnology has emerged as a promising area of research in selected African countries but requires expanding its S&T base. To enhance S&T-based and capacity building Africa has initiated to expand its collaborative efforts with other countries including Europe, Asia, the US, the Middle East, and Africa with promising results in different areas like nanotechnology, biotechnology, agriculture, pharmaceuticals, etc. Biotechnology is one of the emerging areas and African biotechnology has the potential to transform the economy. Therefore, this paper presents an analysis of the emerging pattern of research areas in selected African countries and in particular biotechnology research activities in Africa. More than 56000 research articles were analyzed, using SPSS software, indicates that R&D collaboration and national as well as international networks could be helpful in enhancing publication output and research competency in the field of biotechnology research in Africa.
{"title":"Analysis of Emerging Research Areas in Selected African Countries: A Case of Biotechnology-Applied Microbiology Discipline","authors":"Tahany Abdel Ghafar Ahmed Aly, Naresh Kumar","doi":"10.5530/jscires.13.1.10","DOIUrl":"https://doi.org/10.5530/jscires.13.1.10","url":null,"abstract":"Mapping of research has become important across the world and any new technology requires a new institutional framework for mapping appropriate outcomes of research. It involves analyzing linkages between various actors, stakeholders, agencies, and institutions to map potential research domains. Over the years, Biotechnology Applied Microbiology has emerged as a niche area, and this sector is recognized as the key driver for economic growth and development. Biotechnology has emerged as a promising area of research in selected African countries but requires expanding its S&T base. To enhance S&T-based and capacity building Africa has initiated to expand its collaborative efforts with other countries including Europe, Asia, the US, the Middle East, and Africa with promising results in different areas like nanotechnology, biotechnology, agriculture, pharmaceuticals, etc. Biotechnology is one of the emerging areas and African biotechnology has the potential to transform the economy. Therefore, this paper presents an analysis of the emerging pattern of research areas in selected African countries and in particular biotechnology research activities in Africa. More than 56000 research articles were analyzed, using SPSS software, indicates that R&D collaboration and national as well as international networks could be helpful in enhancing publication output and research competency in the field of biotechnology research in Africa.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jyotirmoi Jena, S. K. Biswal, Rashmiranjan Panigrahi, A. Shrivastava
In recent years, there has been widespread interest in the applications of Artificial Intelligence (AI) techniques to the financial sector and in the development of new financial products and services. AI methods are widely regarded as the most important methods in the emerging market for providing not only cutting-edge financial services, but also an innovative approach to business process automation, a solution to the challenges of reducing service costs associated with managing low-income and rural customers and a method of identifying and evaluating the creditworthiness of those customers. No clear reviews are identified in the areas of AI and its contribution to Financial Innovations (FI) research in finance. To address the above gap, the present study provides a systematic literature review and bibliometric view of AI and FI research in finance. Co-citation, co-occurrence and bibliographic coupling analysis techniques are being used to make inferences about the structure of AI and FI research in finance from 1987 to 2022. The study used 237 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through “R” to justify study objectives. Through bibliometric analysis, this study unveils influential authors, journals and institutions, emphasizing top-cited research articles and unveiling six emerging thematic clusters. The novelty lies in the identification of prominent keywords linked to AI and financial innovation research, accompanied by a comprehensive analysis of globally and locally cited articles. Employing an analytical approach, the study identifies research gaps to contribute to the existing body of knowledge.
{"title":"Investigating the Potential Areas in Artificial Intelligence and Financial Innovation: A Bibliometric Analysis","authors":"Jyotirmoi Jena, S. K. Biswal, Rashmiranjan Panigrahi, A. Shrivastava","doi":"10.5530/jscires.13.1.6","DOIUrl":"https://doi.org/10.5530/jscires.13.1.6","url":null,"abstract":"In recent years, there has been widespread interest in the applications of Artificial Intelligence (AI) techniques to the financial sector and in the development of new financial products and services. AI methods are widely regarded as the most important methods in the emerging market for providing not only cutting-edge financial services, but also an innovative approach to business process automation, a solution to the challenges of reducing service costs associated with managing low-income and rural customers and a method of identifying and evaluating the creditworthiness of those customers. No clear reviews are identified in the areas of AI and its contribution to Financial Innovations (FI) research in finance. To address the above gap, the present study provides a systematic literature review and bibliometric view of AI and FI research in finance. Co-citation, co-occurrence and bibliographic coupling analysis techniques are being used to make inferences about the structure of AI and FI research in finance from 1987 to 2022. The study used 237 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through “R” to justify study objectives. Through bibliometric analysis, this study unveils influential authors, journals and institutions, emphasizing top-cited research articles and unveiling six emerging thematic clusters. The novelty lies in the identification of prominent keywords linked to AI and financial innovation research, accompanied by a comprehensive analysis of globally and locally cited articles. Employing an analytical approach, the study identifies research gaps to contribute to the existing body of knowledge.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Sustainable Development Goal 7 (SDG-7) promises to ensure the affordable and clean energy to the world. The United Nations (UN) has set a target for 2030, which can only be achieved through academic excellence. The present study aims to analyze the academic research support of SDG 7 from a global perspective by using bibliometric analysis and topic modelling approaches using Orange Python-based software. The present study extracts the scholarly publications from the lens database from 2015 to 2022 and the dataset consisted of 918 publications with 18,377 citations related to the SDG 7. These including 121 single-author and 797 multiple-authors publications. Most of the papers have been published in open-access journals. Environmental Science and Pollution Research International (5343 citations; 225 publications and CPP 23.74) was the most impactful journal, Muntasir Murshed (13 publications, 421 citations, CPP 32.3) was the most influential author, and China was the most productive country. Under co-occurrence analysis, Clean Energy, Environmental Economics, Health, Affordable Energy, Climate Change, and Business, six different denoted clusters were found, while in the topic modeling approach, six key topics were identified, in which three topics were related to economics and the other were energy-related and climate change. Environmental, renewable energy, and economics were the top words used in SDG 7, and six key documents on each topic were identified according to the distribution and weighting of the topics. The Implications of the research findings and addressing research gaps can inform researchers, policymakers, and funding agencies involved in advancing SDG 7 to help accelerate the achievement of the SDGs in the decision-making process.
{"title":"Mapping the Global Academic Support for Sustainable Development Goal 7: A Bibliometric Analysis and Topic Modelling Approach","authors":"Rajkumar Natarajan, Manoj Kumar Verma, Surulinathi Muthuraj","doi":"10.5530/jscires.13.1.24","DOIUrl":"https://doi.org/10.5530/jscires.13.1.24","url":null,"abstract":"The Sustainable Development Goal 7 (SDG-7) promises to ensure the affordable and clean energy to the world. The United Nations (UN) has set a target for 2030, which can only be achieved through academic excellence. The present study aims to analyze the academic research support of SDG 7 from a global perspective by using bibliometric analysis and topic modelling approaches using Orange Python-based software. The present study extracts the scholarly publications from the lens database from 2015 to 2022 and the dataset consisted of 918 publications with 18,377 citations related to the SDG 7. These including 121 single-author and 797 multiple-authors publications. Most of the papers have been published in open-access journals. Environmental Science and Pollution Research International (5343 citations; 225 publications and CPP 23.74) was the most impactful journal, Muntasir Murshed (13 publications, 421 citations, CPP 32.3) was the most influential author, and China was the most productive country. Under co-occurrence analysis, Clean Energy, Environmental Economics, Health, Affordable Energy, Climate Change, and Business, six different denoted clusters were found, while in the topic modeling approach, six key topics were identified, in which three topics were related to economics and the other were energy-related and climate change. Environmental, renewable energy, and economics were the top words used in SDG 7, and six key documents on each topic were identified according to the distribution and weighting of the topics. The Implications of the research findings and addressing research gaps can inform researchers, policymakers, and funding agencies involved in advancing SDG 7 to help accelerate the achievement of the SDGs in the decision-making process.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article we analyze the cited references in 1.38 million papers by Russian (co-)authors indexed in the Web of Science database until May 2022. Similarly, to the established processes in the so-called Reference Publication Year Spectroscopy (RPYS), we study the distribution of the references across the cited years and seek to identify the peak years with the publications that attracted the most attention of Russian scholars. In this way, the historical roots of Russian science may be traced and we take a closer look at these most influential works. In addition, we investigate the evolution of the mean age of references and of their average number per paper over time and inspect the most frequently cited sources. The results show that the average number of references in Russian papers has been steadily increasing, but the mean age of references has been declining in the most recent years. Also, the foundations of Russian science seem to be physics of particles and electrochemistry and have recently become based more internationally than in the past. This study is the first of its kind and may help better understand the character of Russian research.
在本文中，我们分析了截至 2022 年 5 月被 Web of Science 数据库收录的 138 万篇俄罗斯（合作）作者论文中的引用参考文献。与所谓 "参考文献发表年份光谱学"（RPYS）的既定流程类似，我们研究了参考文献在各引用年份的分布情况，并试图找出最受俄罗斯学者关注的出版物的高峰年份。通过这种方法，我们可以追溯俄罗斯科学的历史根源，并对这些最具影响力的著作进行更深入的研究。此外，我们还研究了参考文献的平均年龄和每篇论文的平均数量随时间推移而发生的变化，并考察了最常被引用的资料来源。结果表明，俄罗斯论文中的平均参考文献数量一直在稳步增长，但参考文献的平均年龄在最近几年却在下降。此外，俄罗斯科学的基础似乎是粒子物理学和电化学，与过去相比，最近的基础更加国际化。这项研究是同类研究中的首次，可能有助于更好地了解俄罗斯研究的特点。
{"title":"Analysis of Cited References in Russian Publications on Web of Science","authors":"Dalibor Fiala, Daria Maltseva","doi":"10.5530/jscires.13.1.8","DOIUrl":"https://doi.org/10.5530/jscires.13.1.8","url":null,"abstract":"In this article we analyze the cited references in 1.38 million papers by Russian (co-)authors indexed in the Web of Science database until May 2022. Similarly, to the established processes in the so-called Reference Publication Year Spectroscopy (RPYS), we study the distribution of the references across the cited years and seek to identify the peak years with the publications that attracted the most attention of Russian scholars. In this way, the historical roots of Russian science may be traced and we take a closer look at these most influential works. In addition, we investigate the evolution of the mean age of references and of their average number per paper over time and inspect the most frequently cited sources. The results show that the average number of references in Russian papers has been steadily increasing, but the mean age of references has been declining in the most recent years. Also, the foundations of Russian science seem to be physics of particles and electrochemistry and have recently become based more internationally than in the past. This study is the first of its kind and may help better understand the character of Russian research.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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