پژوهشنامه علم سنجی
پیاپی 18 (پاییز و زمستان 1402)

Alongside research and educational indicators, national ranking systems evaluate the performance of universities in terms of such indicators as social responsibilities, facilities, entrepreneurship, commercialization of technology, university infrastructure, economic impact, technological developments, and quality of education. These indicators are typically not utilized in global rianking systems because of the difficulties associated with collecting data on a global level. The Iranian National University Ranking System, which evaluates the performance of Iranian universities, differs from global university ranking systems. These differences may result from its methodology. While it incorporates some criteria and indicators from the global university ranking systems, it has added some new ones to compensate for the shortcomings of the global university ranking systems and, thereby, achieve a more realistic picture of the performance of universities. These innovations necessitate comparing the two approaches in order to understand how they affect the results of the university ranking systems. This research aimed to identify the criteria and indicators leading to the divergence of the results of the Iranian National University Ranking System (as a national university ranking) and several well-known global university ranking systems. The latter included Times Higher Education (THE), Shanghai Academic Ranking of World Universities (ARWU), University Ranking by Academic Performance (URAP), and Islamic World Science Citation (ISC WUR). 

The present study was a descriptive scientometric research based on a library method. It examined a sample of top Iranian universities commonly ranked by the Iranian system and each of the aforementioned global systems in 2020. Using semi-partial correlation analysis in SPSS 23, we analyzed the correlations between the universities' total scores reported by the Iranian and global systems through controlling the criteria and indicators (un)common between these systems. 

The findings showed that some criteria and indicators of the Iranian system account for the divergence of its results from those of the THE system. These include education, faculty member hierarchy, employment of graduates, effectiveness of research outputs, scientific publications, international mobility of students and faculty members, university budget, university income, and facilities. In addition, the citation criterion in THE contributes to the difference between the results of the two systems. Besides, it has been found that the criterion of economic impact partially contributes to the divergence of results between the Iranian National Ranking System and the ARWU system. Moreover, graduates' employment is an indicator that contributes to the divergence between the results of the Iranian system of ranking universities and the results of the URAP system. On the other hand, none of the indicators of the Iranian National Ranking System lead to a divergence of its results from those of the ISC WUR system. 

The results showed that some innovative criteria and indicators used in the Iranian National University Ranking System play a role in the divergence of its results and those of the global university ranking systems. This implies that methodological innovations have enriched this national system, enabling it to measure different dimensions of a university's performance. However, there are inconsistencies. Some innovative criteria and indicators led to the convergence of the results or showed no effect on the correlations, while some similar factors unexpectedly contributed to the divergence. The divergence in the ranking results caused by similar criteria and indicators is likely to stem from differences in the calculation method, periods, and data sources. Overall, it appears that the methodology used in the Iranian National University Ranking System leads to variations in the ranking outcomes. However, the differences cannot necessarily be interpreted as conductive to a more realistic evaluation. Consequently, there is a need for further investigations, particularly qualitative ones, to assess the conformity of the results with a gold standard benchmark.

The current research compares the representative elements (title, abstract, and keywords) of the articles that existed in the proposal references' citation network with the proposals’ elements. The other goal of this research is to calculate representative elements’ weighted average (title, abstract, and keywords) from a textual similarity perspective.

This is an applied and quantitative research that uses citation analysis and content analysis. The research sample is 3019 articles extracted from the citation network of 31 graduated students’ proposals (M.Sc. and Ph.D.) in Chemistry at Shiraz University. All English articles' titles in the proposals' references were searched on the Web of Science database, and each article's file and all articles’ files in its citation network were saved in Excel format. All retrieved files were merged into one file and sorted based on citation count to have the unit citation network for each user's proposal. Because some of the proposals had an extended citation network with more than a thousand articles, 100 articles with the greatest citation count of each network were analyzed to create uniformity and balance among the proposals’ citation networks. Next, the scale of textual similarity of 100 articles' representative elements with the greatest citation count in the citation network, was calculated with the proposal’s title, the proposal’s text, and the titles of the proposal’s references. The scale of textual similarity was checked using designed software based on the Python programming language and measuring the cosine similarity. 

The results of the Kruskal-Wallis test showed that there was a significant difference between the articles’ representative elements and the title, text, and references’ titles of the proposals from a textual similarity viewpoint; and in all three cases articles’ abstracts had the most textual similarity with the proposal elements, then, the title and keywords of the articles' citation network were in the second and third ranks; In addition, the representative elements’ weighted average was calculated. The obtained value was 0.62 for the abstract, 0.5 for the title, and 0.22 for the keywords, respectively.

Despite the use of different platforms to measure the similarity between the documents searched and the documents desired by the user, there is still a distance to reach the ideal level. Until now, no research had used the representative elements of the articles that existed in the proposal references' citation network to measure the textual similarity with the proposal elements and had not evaluated their capability. The confirmation of textual similarity among the representative elements of the articles that existed in the proposal references' citation network with proposals’ elements, indicates that the student's proposal can be used as a platform for recommending related articles. Hence, the designers of scientific recommender systems, scientific information retrieval systems, digital libraries, and scientific social networks such as LinkedIn, Academia, and ResearchGate can use the elements of articles' citation networks to recommend related articles. In addition, considering the articles’ representative elements as independent units is important not only for similarity measurement but also for keyword expansion and suggesting the appropriate journal to the authors for publishing their articles. According to the determined weight of representative elements and to increase the efficiency of information systems, it is suggested that designers of such systems use the abstracts and the titles of the articles to measure the similarity and avoid calculating the similarity of the texts as a whole unit. This saves time, resources, and energy, presents better results, and users can reach their target and desired information more easily and faster than before. In addition, for indexing articles in databases and search engines, the articles' abstracts and titles can be prioritized to save financial resources and energy.

Linked data was developed and introduced as the best practice for publishing and linking structured data on the Web. In studies related to the scientific collaboration network, which is defined by co-authorship relationships, Social Network Analysis (SNA) is applied. Identifying influential researchers in co-authorship networks across different scientific fields can be considered one of the goals of scientometric studies. The purpose of the current research is to determine the productivity and efficiency of researchers in the field of linked data. Additionally, it aims to identify and analyze the most influential researchers in linked data using the Scholarly Influence Model. 

The current research is an applied study and has been conducted using common techniques in Scientometrics, specifically co-authorship and network analysis methods. To obtain the primary data, the keyword "Linked data" was searched in the Web of Science Database, which contains 4612 records from 1983 to 2019. The data was saved in plaintext format and then processed by Bibexcel software. Based on co-authorship, the number of unique researchers was determined to be 48,643. The names of the researchers were transferred from Bibexcel to Excel software and sorted alphabetically. Then, they were edited, modified, and unified into preferred names. In the following, Bradford's Law was applied to determine the sample size with a cutoff of 38 in order to facilitate easier analysis in the co-authorship network. The sample size was determined to be 174 researchers. BibExcel has been used to calculate the productivity (number of articles), efficiency (number of citations), and H-index of the researchers. After creating a co-authorship matrix of researchers in BibExcel, it was converted into a correlation matrix using UCINET in order to calculate the degree, betweenness, and closeness centrality. In addition, the g-index and hc-index of 174 researchers were manually calculated using Excel software. Next, the relationship between productivity and efficiency, as well as the impact of social and environmental influences on ideational influence, were investigated using Lisrel Software. 

The findings showed that Bizer C. and Berners-Lee T. are considered to be the most influential researchers in the field of linked data, with the highest productivity and efficiency. In terms of co-authorship, "Auer S" and "Klyne G" have the highest degree centrality. In terms of closeness centrality, "Fellegi I" and "Zhang Y" have the highest scores, while "Fellegi I" and "Rubin D" have the highest scores in betweenness centrality. Regarding the hypotheses, there is a significant relationship between the productivity and efficiency of researchers in the field of linked data. Also, the findings showed that higher productivity is associated with higher betweenness and degree centrality. However, there is no significant relationship between closeness centrality score. Specifically, "Bizer C", "Berners-Lee T", "Hogan A", and "Auer S" have the highest scores in the indicators of social and ideational influences. Furthermore, it was found that social influence has a positive effect on venue and ideational influence, meaning that researchers with higher social influence also have higher venue and ideational influence. In addition, social influence has a positive effect on ideational influence, meaning that researchers with higher social influence also possess higher ideational influence. 

The favorable status of researchers in terms of productivity and efficiency, as well as their high scores in the indicators of degree and betweenness centrality in the co-authorship network, can indicate their significant scientific influence in this field. This finding confirms the positive and significant impact of triple relationships in the Scholarly Influence Model. Generally, the results can provide a deeper understanding of the quantitative and qualitative status of scientific publications and leading researchers in this field. Using a combination of productivity and efficiency indicators, along with the components of the Scholarly Influence Model, can help identify top researchers in each scientific field.

In addition to the distinction between the field of humanities and other sciences in terms of their nature and research goals, different disciplines within the humanities also possess distinct characteristics. This study aims to identify the disadvantages of evaluating humanities research outputs, with a specific focus on the fields of language and literature. It also aims to provide solutions to improve the evaluation of researchers in these areas. 

The present study is an applied type research with a qualitative content analysis method. The study's statistical population included faculty members from language and literature fields with various Persian and non-Persian orientations (English, Arabic, French, German, and Chinese) in Iran. The research sample consisted of 24 individuals, including 19 men and 5 women who were at least Assistant professors from 8 universities: Isfahan, Tarbiat Modarres, Tehran, Al-Zahra, Shahid Chamran Ahvaz, Shiraz, Allameh Tabatabaei, and Ferdowsi of Mashhad. The participants were selected using targeted sampling and the snowball method. The semi-structured interview was used as a research tool, and MAXQDA 2020 qualitative data analysis software was utilized for coding. Theoretical coding involves three stages: open coding, axial coding, and selective coding. To ensure credibility, the opinions of two coders were sought. Also, the results of the coding were sent to three interviewees who played a significant role in expressing injuries and proposing solutions. They were asked to comment on the compatibility of the extracted codes with their own experiences. The terms and details of the work are explained in detail within the plan for the transferability criterion. In order to measure dependability, all the stages and processes of the research were recorded and reported accurately from the beginning to the end, mentioning the details of the data analysis, and excerpts from the text of the interviews were presented. For confirmability, the findings were provided to 5 interviewees and peers to express their opinions about the results. 

Evaluation harms were identified through 62 codes. The concept of science production and the promotion of knowledge foundations were investigated in two general areas (humanities) and a specific area (language and literature) with different components. The general area includes the establishment of policies for evaluating research output, the inclusion of research output ratings in promotion regulations, and considerations of research ethics. The specific area includes the evaluation of research outputs in the promotion regulations, publications, international article publications, theoretical contributions, research style of faculty members, and literary and creative works. The concept of practicality and addressing societal issues also encompasses two aspects: disconnecting the relationship between language and literature fields and society, and teaching language and literature fields. The solutions were presented in the form of 34 codes. The concept of science production and the promotion of knowledge foundations were investigated in two general areas (humanities) and a specific area (language and literature) with different components. The general area includes the development of policies for evaluating research output, the inclusion of research output ratings in promotion regulations, and the improvement of research facilities and resources. The specific area includes the evaluation of research outputs in the promotion regulations, the research approach of faculty members, and research facilities. The concept of practicality and addressing societal issues also encompasses two aspects of applied research: the connection between the disciplines of language and literature and society. 

If the evaluation is conducted accurately and comprehensively, and it clearly indicates the gap between the current situation and the desired outcome, it will minimize trial and error in this field and reduce costs associated with ineffective and unnecessary tasks. It accelerates the steps towards success and progress. The essence of evaluation is optimizing performance. The competition to obtain the best scientific position among researchers, educational groups, and research institutes has gained great importance and momentum. The health of such a competition and its correct orientation will be possible through planning for accurate evaluation, and a review of the evaluation policy of academic faculty members of universities and higher education is one of the fundamental needs in this direction. The results of the present study can be utilized as an effective measure to enhance the current situation.

Analyzing the subject structure of research in this field can provide valuable information about the current state of the field, including its issues and needs, strengths, gaps, and shortcomings. The present study aims to analyze the global research topics in information science and library science, while also reviewing research topics from previous years and identifying thematic gaps. This analysis will provide a foundation for planning and policy research in this field.

The present study utilized an automated classification technique. Data analysis was performed on 85,403 articles indexed on the Web of Science website between 1945 and 2019 in the field of library and information science. The source and tool used for extracting research data was Web of Science. After preparing and pre-processing the data and formulating the classification plan for articles across 31 thematic categories, the logistic regression algorithm has been employed for the automatic classification of articles. This algorithm assigns articles to their respective thematic categories using the Cosine similarity criterion. The various stages of the research were performed using the Python 3.7 programming language and the Pandas, Numpy, NLTK, and Plotly software packages.

Most of the global research in this field is dedicated to information technology, while the field of organizational management has the least amount of research. The growth trend of research in many thematic areas is increasing. However, there is further growth in areas such as web studies, information technology, information behavior, user studies, information retrieval, and search engines. After the 1990s, popular terms such as social media, big data, virtual library, IoT (Internet of Things), machine learning, deep learning, data mining, open data, artificial intelligence, natural language processing, cloud computing, virtual reality, augmented reality, and machine translation have emerged in research in this field. It appears that the utilization of keywords pertaining to traditional subjects in the field has declined, while the usage of concepts encompassing new topics related to technology and the web has increased. The rapid growth of fields such as web development, technology, artificial intelligence, and data mining has led to the evolution of research topics and increased connections with computer science and information technology, particularly in recent years. The changes in the subjects of this field can be attributed to the development of the web and advancements in technology. Additionally, the concepts and research topics in this field have had an impact on globalization, communication progress, and overall development in various economic, social, and cultural dimensions. 

The conclusion of the study highlights the thematic trends in library and information science research over different time periods. It also identifies the weaknesses in the scientific output of this field, while outlining the evolutionary process and the path of progress and development in the field of science. Due to the fact that scientific outputs are derived from the issues, needs, and priorities of a society at any given time, studying them can help clarify the most important issues and research needs in the field of library and information science. This, in turn, can provide a foundation for identifying future research needs and subjects in the field. In other words, having a clear understanding of the subject areas of library and information science will help researchers in this field identify the most important issues and needs. This, in turn, will enable them to take appropriate actions when selecting topics for future research. By conducting needs-based research, we can lay the groundwork for policy development and research planning in this field. This approach will also help address potential challenges and problems that may arise. This is despite the fact that if the needs and issues are ignored, research resources and credits will be spent on topics that do not have the necessary priority and importance. It cannot be expected that the findings from these topics will solve the problems and pave the way for the advancement of this field.

Considering the importance of knowledge and activities related to knowledge management in the knowledge-based era, as well as the existence of issues such as desire, motivation, and participation of people, it is necessary to design and use tools and mechanisms, such as gamification, to motivate people to participate in activities and processes related to knowledge management. Additionally, there has been a significant amount of research on the combination of knowledge management and gamification. Therefore, the aim of this research is to answer the following questions: 1. What is the status of studies related to gamification and knowledge management in terms of document format, publication language, countries, participating institutions and researchers, participating research areas, and top publications? What are the main subjects that frequently appear together, have a significant impact (receiving citations), and are widely discussed in research on gamification and knowledge management, as determined through co-word analysis and social network analysis? What are the most frequent co-word pairs in these studies? 3. What is the hierarchical clustering of study subjects related to gamification and knowledge management based on co-occurrence analysis? 4. In terms of the maturity and development level of the clusters resulting from the co-word analysis, what is the situation in the strategic diagram related to the studies of gamification and knowledge management? 5. Is there a significant difference between the scientific fields participating in studies related to gamification and knowledge management in terms of the number of citations and records produced? 

This descriptive-analytical study was conducted using the scientometrics approach and employed various techniques including co-word analysis, social network analysis, hierarchical clustering, and strategic diagram techniques. The study population includes research studies on gamification and knowledge management from the Web of Science database, spanning from 1991 to 2022. In order to analyze data and draw graphs, Excel, Bibexcel, Histcite, UCINET, VOSViewer, and SPSS are used. 

Among the 449 related studies, Fernandez-Manjon, Bee, and Martinez-Ortiz from the United States and the United Kingdom, along with researchers from the Complutense University of Madrid and the University of Taiwan, have made significant contributions to the field of knowledge management and gamification. Notably, the journals Computer & Education and Sustainability have published many influential articles in this area. Publications in this area have shown a steady growth rate, with the highest rate achieved in 2020. Gamification, education, and game-based learning are the keywords with the highest frequencies, with 104, 88, and 66 occurrences, respectively. In terms of citations, the subjects of gender differences and online memory games have the most citations, with 331 and 229 references, respectively. The studies were hierarchically clustered into five clusters. In the knowledge management process through gamification, cluster 3, which focuses on user centricity, has the highest centrality value of 21. Cluster 2, which aims to enhance the efficiency of knowledge management through smartization, has the highest density value of 1.071. Among contributors in various scientific fields, education has the highest level of productivity compared to other areas. 

Analyzing studies related to gamification and knowledge management can provide valuable insights into the key contributors and subjects, leading to a better understanding of the visions, issues, and dominant discourse. This understanding can ultimately enhance the quality and quantity of products and the practical application of these studies.On the other hand, mapping and identifying the intellectual structure of knowledge in this field helps researchers to focus their studies purposefully and align them with practical topics. This enables them to advance their knowledge in the field. Additionally, the visual representation of the intellectual structure of knowledge in the aforementioned scientific field can offer a scientific perspective on the significant and emerging issues within these fields. It can also highlight thematic gaps and discourage redundant and underutilized research in Iran.

The aerospace industry and technology are always considered one of the most important and valuable industries due to their special and unique features and applications. The field of aerospace research is a priority in the grand strategies of science and technology development, and it is essential to focus on it. Aerospace researchers and experts play critical roles in advancing aerospace science and industry. They are responsible for conducting scientific and industrial activities as well as research. Evaluating the research performance and quality of aerospace researchers at the international level is crucial. The current research aims to study scientometrics and analyze the centrality metrics of the co-authorship network of aerospace researchers at the international level. This will be done using data available on the Web of Science Core Collection (WOSCC).

The research conducted is of an applied nature, employing an analytical approach. In this article, the technique of network analysis has been employed to visualize the network of co-authorship at both the micro and macro levels. This includes analyzing the social network of co-authorship among researchers and their organizations, as well as examining centrality indicators and conducting network analysis of researchers' research topics. The current research community includes all aerospace researchers, with 153,994 records indexed on the Web of Science Core Collection (WOSCC) from 1945 to 2021. There are 161,156 aerospace researchers, of which 6,706 were anonymous and were excluded from the research population. Therefore, 154,450 researchers were included in the study. Data Lab was used to accurately extract data for aerospace researchers. Ravar PreMap was also used to standardize data and prepare a square matrix for researchers. The symmetric correlation matrix of researchers (AU) was obtained using Bibexcel and Netdraw. Then, the required centralities were calculated. Co-authorship maps were also created using NetDraw. Co-authorship network analysis technique was used for data analysis. A 157×157 matrix was considered to identify keywords that appeared with a frequency of 70 or more. This matrix was used to create a network of commonly researched topics among researchers. VOSviewer version 1.6.18 was used to visualize co-authorship networks.

The density of the co-authorship network among aerospace researchers is low, and the network exhibits low cohesion. In the current research, five clusters of collaboration were identified, with the center consisting of prominent researchers in the field of aerospace. "David A. Fulghum" of the Maritime Center in America published 863 articles in the field of aerospace between 1983 and 2003. "Florian Menter" from Ensys Germany has the highest number of citations (excluding self-citations) for published articles in the aerospace field. Out of 87,778 keywords identified in the Web of Science Core Collection (WOSC C) in aerospace, 9712 were associated with Florian Menter. Additionally, a map was prepared using 157 keywords that had a frequency of 70 or more. The co-word clusters of the aerospace network consist of seven topic clusters, 157 nodes, 2679 edges, and have a density of 0.11. The first cluster was a hot topic discussed in the aerospace industry, and the most frequently mentioned topic, "Aircraft," is associated with cluster 3. The most prominent topics are aerodynamics, flight control, and vibrations. The most significant scientific collaboration of aerospace researchers is between Giovanni Mengali and Alessandro A. Quarta from the University of Pisa, Italy. The most scientific advancements in aerospace research have been published in the fields of aerodynamics, flight mechanics, control, and vibrations. After the United States, China had the most scholarly communications with other countries.

Developing science policy and advancing strategic plans and programs for aerospace research require comprehensive and accurate information about researchers' potential scientific and technical abilities. The involvement of prominent aerospace researchers in communication and scientific collaborations has resulted in the establishment of significant international partnerships in the aerospace industry. In order to effectively participate in robust and cohesive scientific collaboration networks, it is necessary to enhance communication among researchers, research centers, and countries, and leverage their synergistic capabilities. The present research results are utilized in the science, technology, and innovation policies of the aerospace industry. It is also used in the planning and direction of applied research, as well as in the application of research conducted by aerospace scientific associations, universities, research institutes, and aerospace industry organizations. Additionally, the results obtained from this research can be used to expand international cooperation among aerospace researchers. Another application of the results presented in this article is the optimal utilization of experts and meticulous planning for the establishment and growth of specialized clusters of aerospace researchers. Prominent aerospace researchers have facilitated the establishment of scientific collaborations and significant partnerships at the international level. Nevertheless, in order to establish stronger and more cohesive scientific collaboration networks, it is essential to prioritize the exploration of potential connections among researchers, research centers, universities, and countries, as well as their synergistic capabilities.

The main objective of this research is to rank Iranian-indexed journals in the Journal Citation Reports (JCR) 2020 based on the Journal Citation Index (JCI). Additionally, the study aims to investigate the significant correlation between the JCI and other metrics such as Eigenfactor, Article Influence Score (AIS), Quartile, and Journal Impact Factor (JIF). Among them, forty Iranian journals are indexed in three main citation indexes and have received Journal Impact Factor (JIF) and Quartile rankings for the first time, which also reported their Journal Citation Index (JCI). However, the ranking of these Iranian journals based on the new JCI is not specified. More importantly, the study does not examine the significant correlation between JCI and other indices such as Eigenfactor, AIS, Quartile, and JIF. Therefore, this research aims to rank Iranian journals indexed in JCR 2020 based on the Journal Citation Indicator (JCI) and investigate the significant correlation of this index with Eigenfactor, Article Influence Score (AIS), Quartile, and Journal Impact Factor (JIF). The main focus of this research is to determine whether changes in the value of the new index (JCI) will correspond to changes in the old indices (Eigenfactor, AIS, Quartile, and JIF). Therefore, this article aims to investigate the mentioned issues and provide scientific and accurate answers. 

The present research is applied and has been conducted using scientometric indices and an analytical approach. The required data for the research was extracted from JCR 2020. All Iranian journals in JCR 2020 with an Impact Factor (IF) and Quartile, totaling forty journals, formed the research population. In this article, five research questions have been posed. Iranian journals and their corresponding indices were extracted from JCR 2020. They were then ranked and reported in a table based on JCI to address the first question. To address the second to fifth research questions, which investigate the significant relationship between JCI and Eigenfactor, AIS, Quartile, and JIF, the Shapiro-Wilk test was initially used to assess the normality of the distribution of the relevant data for Eigenfactor, AIS, Quartile, and JIF. The results of the Shapiro-Wilk test indicated that the data related to Eigenfactor and AIS followed a normal distribution, while the data related to JIF did not. Therefore, to investigate the significant relationship between JCI and Eigenfactor and AIS, the Pearson correlation coefficient was used. Additionally, to study the significant relationship between JCI and JIF, Spearman's rho correlation coefficient was employed. Additionally, when examining the relationship between JCI and Quartile, it is important to note that Quartile only has four levels (Q1, Q2, Q3, and Q4). Therefore, the Kendall's tau b test was used. This test is used when the data under investigation is on an ordinal scale. 

"Iranian Journal of Fuzzy Systems," "International Journal of Health Policy and Management," and "BioImpacts" ranked first, second, and third, respectively, in JCI with scores of 65.1, 15.1, and 99.0. A positive and significant correlation between JCI and Eigenfactor (R=44.0, P<0.006) indicates a statistical relationship. There is a positive and significant correlation between JCI and Article Influence Score (AIS) (R=73.0, P<0.0001), suggesting that an increase in JCI is associated with an increase in AIS. There is a negative and significant correlation between JCI and Q (R=-64.0, P<0.0001), implying that an increase in JCI is associated with a decrease in Q. In summary, the results suggest that JCI is positively related to Eigenfactor and AIS but negatively related to Q. These findings can have implications for evaluating the impact and influence of academic journals. 

The JCI can be a complementary indicator with the JIF. Many indicators have been introduced to cover JIF's weaknesses, but JIF is still the most famous indicator. The passage of time will show that the JCI will also suffer the fate of the previous indicators or, as a new and unique indicator, will remove the authority and acceptance of the JIF after half a century.

The COVID-19 pandemic has presented the world with significant challenges. The infodemic surrounding this disease encompasses four main subject areas where people are seeking reliable information, but also encountering a significant amount of misinformation and rumors. These areas include: the cause and origin of the virus and disease; the signs and patterns of transmission; available treatments, prevention, and treatment options; and the effectiveness and impact of interventions by health authorities or other institutions. This also causes harm to the people in society and jeopardizes the stability of the global health system, making it challenging to address the epidemic. Access to a vast amount of false information about the Covid-19 virus can contribute to the spread of misunderstandings about the disease in society and negatively impact various aspects of people's lives, particularly their mental health. The public health crisis of the COVID-19 pandemic has been exacerbated by the effects of misinformation. In light of the spread of the coronavirus, false information has proliferated worldwide, making it challenging to discern between true and false information. The spread of fake news and dissemination of false information, much like the transmission of the coronavirus, can have far-reaching consequences on various aspects of people's lives. It can cause significant harm by perpetuating misunderstandings about the disease within society, and, conversely, contribute to the escalation of the epidemic. Considering the significant increase in research on Covid-19, it would be helpful for researchers to identify the subject areas, structure, and scientific map of articles related to the disease. This will allow them to gain a better understanding of the international subject areas and related issues in this field. Therefore, it seems necessary to expand and strengthen scientific and research studies in the field of the COVID-19 virus in order to reduce the risks and fully contain this disease. Therefore, the purpose of this study is to conduct a thematic analysis and examine scientific collaborations in research related to misinformation and disinformation in the field of Covid-19, based on studies from the Web of Science. 

The present study is a descriptive-analytical study with a scientometric approach. It utilizes the method of content analysis, hierarchical clustering techniques, strategic diagrams, co-authorship analysis, and social network analysis. The research focuses on articles related to misinformation and disinformation in the field of Covid-19. HistCite, Bibexcel, UCINET, and SPSS software are used for data analysis. 

Among the studies in this field, 3688 studies related to misinformation and disinformation in the field of Covid-19 were found. The keywords "social media," "vaccination," and "general health" ranked first, second, and third, respectively, with frequencies of 332, 158, and 145. On the other hand, topics such as "Covid-19 and social media" with 334 instances of co-occurrence have been assigned as study topics in this field. Additionally, the majority of works were published in 2021. Kumar S. has published the highest number of works among the authors, with 11 works. Godman B. and Haque M. are in second and third place, respectively, with 8 works each. America is in first place with 1068 works, while England and China are in second and third place with 476 and 323 works, respectively. Most of the studies have been published in English. "The International Journal of Environmental Research and Public Health" has the highest number of published studies among other publications, with 96 articles. "The Journal of Medical Internet Research" and "PLOS ONE" are ranked second and third, with 74 and 44 articles, respectively. Among the institutions, the "University of Oxford" is at the top with 49 works and holds the first place, while the "University of Toronto" and the "London School of Hygiene & Tropical Medicine" are in second and third place with 40 and 34 works, respectively. The results obtained from the hierarchical diagram form two clusters: the first cluster includes factors affecting the creation of misinformation and disinformation in the field of Covid-19, and the second cluster focuses on the effects of misinformation and disinformation in the same field. Clusters are present in the first and third regions. 

Understanding the findings of scientometric studies in strategic areas like the Covid-19 pandemic can greatly assist researchers and policymakers in the healthcare and treatment field in making informed decisions and developing effective solutions. It can also help in creating a knowledge map and conducting valuable studies. Analysis and comparison of studies related to misinformation and disinformation in the field of Covid-19 lead to a better understanding of current trends, gaps, and dominant discourse, with the aim of improving both the quantity and quality of information. The results of this study can also assist managers and policy makers in making more informed decisions.

Considering the emergence and increasing expansion of various subject domains and the lack of a valid codified thesaurus, the main aim of this study is to provide a semantic model of information security based on a conceptual network for use in domain ontologies, so it is applied research.

The research method is a combination of co-word analysis, library, and Delphi methods. In the first stage, the conceptual network was extracted from 7547 scientific documents on "information security" using the co-words analysis method. These documents were indexed in the Scopus databases and WOS from 2013 to 2017. Pre-processing operations on 19648 keywords and tags were done in a completely targeted manner by using five dictionaries in information security, and three dictionaries in computer science. With a minimum co-occurrence of 5 for each word in "VOS Viewer", 207 preferred concepts were selected based on the latest version of the information security dictionary, and its conceptual network was mapped. By "Gephi", betweenness centrality, density, and clustering coefficient indices were checked. Then in the second stage, for extracting a new semantic model, used the library method. So, seven related semantic models: Security ontology, information security ontology, attack ontology, vulnerability ontology, existence - Ontosec mapping, and threat taxonomy as well as the conceptual model of information systems security in libraries. These entities, classes, subclasses, relationships between them, concepts, and examples attributed to each class and subclass were studied and examined carefully. Then, 207 conceptual network concepts were adapted to the common components of these models, and a new model was presented. Finally, in third stage, using the fuzzy Delphi technique, the consensus of experts in both fields of Knowledge and Information Science (KIS) and Computer Sciences was examined. Using SPSS and Kendall's non-parametric test, the experts' agreement coefficient about the classes and sub-classes, as well as their associated concepts, were investigated. 5 classes, 6 subclasses and also 71 concepts out of 97 common concepts with an agreement coefficient above 0.7 were obtained. Finally, confirmatory factor analysis and Smart PLS structural modeling were used to check the correctness of the relationships governing the classes and subclasses in the conceptual model.

The main nodes and strong links in the conceptual network of information security include: "information security," "security," "information system," "privacy," "telecommunication," "information," "intrusion detection system," "cryptography," "cyber security," "authentication," "network," "risk," "threat," and "risk management framework." The extracted semantic model has a goodness of fitting (GOF) of 0.710 and confirms 11 semantic relationships. These relationships include: "Requires level," "Diminish," "Threatens," "Exploited by," "has Source," "Uses of," "Lead to," "Attack," "Vulnerability on," "Implemented by," and "Reduce." Also, it has 5 main classes, including "Information Asset," "Security Attribution," "Threat," "Vulnerability," and "Countermeasure." There are also 6 subclasses, which include "Threat Source," "Access Path (influence way)," "Threat Tools," and "Attack," all related to the Threat class. Additionally, there are Technological countermeasures and Organizational countermeasures, which are related to the Countermeasure class. Also, it was discovered that there are 71 attributive concepts, some of which include: Password, Smart card, User, Integrity, Hacker, Malicious code, Virus, Distributed Denial Of Service (DDOS), Risk management, Backup, Digital signature, Penetration testing, Antivirus, Firewall, and so on.

The conceptual network and semantic model can be inferred in semantic systems and databases. This research can provide a new method for creating high-level ontologies to optimize search engines and reduce false dropping, as well as recover unwanted information.

This study aims to analyze the intellectual structure of knowledge in information retrieval based on Co-Word Analysis. Nowadays, the field of information retrieval has primarily shifted from information science to computer science. In information science, information retrieval refers to the interaction between individuals, and the information retrieval system refers to making judgments related to the results obtained from the selection of search strategies. While in computer science, information retrieval is the extraction of relevant information that meets user needs from a large amount of unstructured information stored in a computer. Co-Word analysis is a method of content analysis that is obtained through the co-occurrence of words or concepts in texts and sources, and through which the main concepts of a scientific field or field, and through this knowledge, conceptual patterns and events, scientific structure, conceptual network, hierarchical relations of concepts, and conceptual categories of that field can be discovered, drawn, and managed. Co-word analysis is a tool for discovering hidden patterns and emerging conceptual events. 

As an applied bibliometrics study, the research uses co-word analysis and clustering techniques. The research data consists of information retrieval of 13490 articles which were indexed in the Library, Information Science & Technology Abstracts (LISTA) database during 1967-2018. RavarpreMAP, bibExcel, SPSS, and VOSviewer were used for analysis. 

The results showed that during 1967-2018, the trend of publishing articles in the first two decades was steady. However, in the following two decades, there was a pattern of fluctuation, with occasional declines and rises. The Journal of the American Society for Information Science & Technology published 687 articles and has made the greatest contribution to research publication in this field. The publications in Information Processing & Management and Journal of the American Society for Information Science are ranked second and third, with 667 and 637 articles, respectively. The most frequently mentioned keyword in the text is "information storage and retrieval system," which appears 3652 times. Other frequently mentioned topics include the World Wide Web, information resource management, information science, information services, search engines, websites, and the search for electronic information resources. These topics ranked second to tenth in terms of frequency and attention. Additionally, there is a strong co-occurrence between the following pairs: website - World Wide Web, Internet - World Wide Web, and information resource management - information storage and retrieval system. The use of hierarchical clustering by the Ward method resulted in the creation of 13 subject clusters, including computer network and multimedia systems; academic library and access to information; database and information retrieval; search strategy; data mining cluster and database; online library catalog; tutorials and web versions 1, 2, and 3; catalog and subject heading; Madeline; ontology and machine learning; library and information technology services; information organization and knowledge management; and artificial intelligence and medical informatics.

The results showed that recent developments in information storage and retrieval systems, as well as users' need for quick access to up-to-date and sufficient information, have led to changes in the subject areas of information retrieval. These changes include the transformation of traditional libraries into virtual, digital, and automated libraries, the evolution of the one-dimensional web to the interactive web or Web2, and the management of electronic resources, databases, digital libraries, ontology, data mining, social networks, machine learning, natural language processing, knowledge management, and artificial intelligence. Information retrieval is now widely used in various fields such as medicine, social networking, image recovery, music, and more. The field of information retrieval is a multidisciplinary field and has extensive connections with other fields. The results of cluster analysis, illustration, and examination of high-frequency topics clearly demonstrate this relationship.

Today, with the advancement of communication technologies, particularly the Internet, we are witnessing the generation of a vast amount of information. In academic research, it is crucial to identify the most frequently studied topics and challenges in each field, as well as to determine their significance. One way to evaluate scientific research in any field is by analyzing its scientific map. One of the most effective methods for visualizing and analyzing a scientific map is to utilize network analysis approaches. This technique can effectively demonstrate the structure of scientific networks. 

In this study, we visualized and analyzed the scientific network of articles published in the journal "Research in School and Virtual Learning" using network and co-word analysis methods. A total of 227 articles were included in the analysis. The general research approach includes collecting and cleaning data, constructing matrices of scientific networks, and analyzing and evaluating the results. Various scientific networks of articles, including the co-authorship network, co-organization network, semantic network of articles, and co-occurrence network of words, have been analyzed. PHP language was used for data crawling and processing, while Python language and Gephi software were utilized for network-based analysis and visualization of different networks. In addition, a proposed approach based on the TF-IDF method has been used to calculate the adjacency matrices of each network. This approach includes ten steps. 1) Integrating the title, keywords, and abstract of each article. 

The findings reveal the extent of semantic connection among the published articles in the semantic network. In order to plot and analyze the semantic network of articles, the semantic matrix is obtained by multiplying the word-article matrix with the article-word matrix. The final semantic graph was clustered using the Grivan-Newman algorithm. The top six communities were evaluated based on various metrics. Also, the results showed that there are few articles with high and low degrees, and they are mostly located in the middle of the distribution chart. Therefore, the semantic network of the articles can be classified as a free scale type. The results of the co-occurrence analysis of words show that satisfaction, psychology, and emotion have been addressed more frequently than other topics. The result of the co-authorship network analysis showed that it has a degree of 2.881. Each author has contributed to the writing of the article with three other authors. The average clustering coefficient of the authors in the co-authored network was 0.685. The network exhibited a compactness value of 0.936 and consisted of 83 communities. Additionally, the results showed that the majority of articles were written collaboratively by three authors, followed by four and five authors. The largest component of the co-authorship network of articles was also extracted. Also, the individual network of the top authors in the co-authorship network was drawn and analyzed up to a depth of 3. Personal networks describe a person's relationships in the network with other authors. The structural characteristics of individual networks determine many aspects of a person's cooperative behavior, including the willingness to cooperate and share resources. The analysis of the intra-university collaboration network showed that the researchers of Payam-e-Noor University had the highest number of intra-university collaborations, with 42 articles. After that, Islamic Azad University ranks next with 23 articles. The analysis of the co-university cooperation network showed that each educational institution has participated in the publication of articles with at least three other educational institutions. 

Upon analyzing the word count, it is evident that the topics published in the journal exhibit a wide range of topic distribution. Also, the investigation of the authors' co-authorship network and its clustering showed that the authors of the articles were more inclined to form small scientific groups within their respective organizations, such as universities or research institutes. Also, Payam-e-Noor and Islamic Azad universities have the largest number of co-authored articles within the university. The co-authorship network of Payam-e-Noor University exhibits an interesting and significant structure, indicating a higher willingness to cooperate among university members compared to independent groups. While the authors at Azad University are more inclined to conduct research as independent groups.

In recent years, university ranking systems have garnered the attention of numerous students, researchers, universities, and research policy makers. In addition, such systems affect the budget allocation of universities. The presence of a large number of Iranian universities in global ranking systems demonstrates the credibility of these institutions and underscores the importance of these ranking systems. Times Higher Education (THE) is one of the leading ranking systems that is considered by Iranian universities. This study aims to compare the performance of Kerman University of Medical Sciences with selected universities in THE rankings. Additionally, it aims to explore the relationship between universities' research performance and their position in the THE ranking system. The findings of this research can provide valuable information for policymakers in higher education. 

This study employs an applied research approach with an analytical focus. The method used in this study is comparative, and the data collection tool is a checklist that includes research indicators from the THE ranking system and bibliometric data extracted from the SciVal database between 2016 and 2020. Research indicators include the "research score" and "citation score" of universities in THE, while bibliometrics data include metrics such as "scholarly output", "citation count", "citation per paper", "% of cited publications", and "publications in top 25% journal percentiles" as measured by "CiteScore Percentile", "SNIP Percentile", and "SJR Percentile". The research population included 34 universities, comprising of 15 Iranian medical universities ranked in the THE ranking system 2022, and 19 foreign universities with the same ranking as Kerman University of Medical Sciences. The data was analyzed using Microsoft Excel and SPSS Statistics software. Spearman's correlation test was used to compare the research performance of universities and their overall rank and citation score in the THE ranking system. The independent t-test was used to compare the five criteria of the THE ranking system between Iranian and foreign universities. Kerman University of Medical Sciences has been ranked 601-800 in the 2022 edition of the THE ranking system, which indicates an improvement compared to its previous ranking in the 2021 edition (801-1000). Overall, the research indicators of Kerman University of Medical Sciences were relatively good. Although this university has been ranked 12th and last in the research score (with a score of 10.1) among domestic and foreign universities, achieving the 4th and 6th rank in citation criteria among domestic and foreign universities respectively shows the effectiveness of the outputs of this university. Independent t-tests revealed statistically significant differences between the research scores, educational scores, and international reputations of domestic and foreign universities. There was a positive relationship between the "citation per paper" and the rank of universities, as well as between the "number of documents," "total number of citations," and "citation per paper" with the research score. But there was no linear relationship between the indicators of the "number of documents" of the universities and their ranking in THE. According to the findings, universities that have published more documents will not necessarily have a higher rank in the THE ranking system. Moreover, the Mann-Whitney test shows that there was no statistically significant difference between domestic and foreign universities in citation score (P > 0.05). 

Kerman University of Sciences should pay more attention to publishing international papers indexed in Scopus. This university is currently ranked only in the THE ranking system. However, based on the suggestions of the present study, it has the potential to achieve a better position through more coherent planning. This university can also participate in other ranking systems, such as the Leiden and Shanghai rankings. In general, improving the overall ranking of a university can be achieved through various methods. These include ensuring transparency in the data provided to ranking systems, publishing in reputable databases such as Scopus, attracting international faculty members and students, and focusing on trending research topics. Besides, universities should pay attention to criteria other than research indicators, such as the effectiveness of their scientific outputs and citations per paper.

Human thought in the field of crisis management is focused on preventing, preparing for, and dealing with crises in various areas such as organizational crises and natural disasters. However, accessing information on crisis management and related areas can be challenging due to the interdisciplinary nature of the concept of crisis, and there are differing opinions on this matter. One of the most critical and essential tasks of crisis management is to take measures, within the framework of urban management, to eliminate the problems caused by natural disasters, reduce their adverse effects, and prepare for relief and the improvement of the situation. Considering the significance of crisis management worldwide and the need to mitigate its impact, it is crucial to regularly analyze and advance crisis research. This will ensure that research in this field progresses with accuracy and relevance. Therefore, the objective of this study is to identify research topics in the field of crisis and risk management using the Scopus database from 1973 to 2020.

The present research is a scientometric study that utilizes content analysis techniques, specifically co-word analysis and network analysis. The research community encompasses all documents in the field of crisis and risk management from the inception to 2020 in the Scopus database. In this research, the documentary method was used to collect data. First, the relevant keywords were identified, and the search was conducted using Boolean operators and truncation techniques. Finally, on November 7, 2021, 2,330 documents were retrieved and entered into Excel software. Using Bibexcel software, the names of organizations, countries, and keywords were extracted and analyzed. Centrality indices, such as closeness centrality, betweenness centrality, and degree centrality, were analyzed using Gephi software. Additionally, a vocabulary co-occurrence network was created using VOSviewer software.

According to the increasing trend of scientific publications in the field of crisis and risk management, there has been a rise in the production of works. The highest number of publications was recorded in the year 2020, with 222 documents. Most of the documents in terms of the type of work are research articles, and the majority of the published documents are in English. The United States, with 467 documents, Runciman, W.B. The author with 15 documents and the social science field with 694 documents were the most active in terms of country, author, and field. The topics of crisis management and risk can be categorized into five clusters of issues. The first cluster focuses on risk management and disasters caused by crises. The second cluster addresses crises, vulnerability, and disaster management during crisis situations. The third cluster examines the assessment of potential risks. The fourth cluster explores risk factors and interventions in crisis occurrence, as well as training individuals to prevent such accidents. The fifth cluster discusses post-accident management, healthcare, and providing relief to victims. The vocabulary co-occurrence network also shows that the keywords "crisis management," "risk management," and "risk assessment" have a degree centrality of 196, 194, and 192, respectively. The keywords "risk management," "vulnerability," and "water management" have an equal value of 1 and the highest value of closeness centrality. The keywords "human," "risk management," and "risk assessment" have the highest value of betweenness centrality, with values of 8.653, 491, and 3.398, respectively. These three nodes have the shortest path connecting them to other pairs of nodes, indicating that they have more influence and occupy a vital position in the network.

  The key to crisis management lies in proper planning and the implementation of continuous structured processes that are designed before a crisis occurs in order to fully equip and prepare. Crisis management is a process that involves evaluating crisis signals and taking necessary measures before, during, and after the crisis to effectively overcome it and minimize the resulting damage. Conducting research in this field is one of the practical methods that can assist countries in crisis control and management. The results of previous research in the field of crisis management and natural hazards indicate that the majority of studies focus on crisis management, risk management, and human health. This highlights the significance of global research on human health during accidents. However, despite this, Iran, known as one of the accident-prone countries, has a low level of scientific output and limited international collaboration in this area. Policymakers and crisis management planners in countries can take a significant step towards identifying the principles of crisis prevention and mitigating the resulting damages by prioritizing the crisis issue and conducting further research on the identified hot issues in the present study.

Currently, the issue of children's physical activity in primary schools has been neglected in the field of scientometrics. So far, no combined bibliometric analysis has examined the physical activity of children in the open space of schools in Iran or the world. Therefore, the aim of this study is to evaluate scientific products in physical activity in schools and physical characteristics of open spaces using keyword co-occurrence of analysis.

 Using the scientometrics approach, content analysis method, and systematic review and co-occurrence of keywords technique, Scopus and Pubmed databases were examined using VOSViewer software. The method used in this research was done in two steps. In the first step, the most frequent words and identification of the areas addressed to this topic and the areas with little reference, a comprehensive search strategy for selecting scientific products in consultation with a child psychology and physical education expert from the "Scopos" database by entering the keywords of physical activity, children, elementary /primary school, schoolyard and open space/outdoor without restrictions in the field and until the 21 November 2021. In order to identify the contexts related to the topic, after removing the unrelated words, the remaining 76 keywords were drawn and analyzed by the co-occurrence network of words in "VOS Viewer" version 16.6.1 software. The obtained 10 clusters (domains) were named with the assistance of an expert in the field of architecture of educational space architecture. Additionally, high-frequency words and areas related to the subject that were , highly cited and less cited areas related to the subject were identified. In the second step, after identifying the field of "school bodies" as one of the less cited fields in the scientific productions of children's physical activity, with the aim of focusing on the field of "elementary school body", the desired scientific products from the two databases "Scopos" and "PubMed", 165 sources were extracted. Highly cited journals, countries, highly authored scholars, and their co-authorship network, as well as the methods and tools used in this 125 scientific production, were evaluated.

 "VOS Viewer" software identified 10 clusters by analyzing the keywords of abstracts of scientific productions about physical activity in primary schools. "Nutrition and Health" and "Physical Education" were identified as the most domains , while "Schoolyard Body" fell into the category of less paid domains. The systematic review of scientific studieson the physical characteristics of primary schools and physical activity revealed  that the increase in emissions after 2019 has resulted in a significant decline.Also, the top 5 magazines with the most publications in the field of physical activity among students and the physical characteristics of the open spaces of primary schools were identified. "Preventive Medicine" and "BMC Public Health" magazines ranked forst, while the "Journal of Physical Activity and Health" ranked second. The United States and the "United Kingdom" have the largest number of publications, while the share of Asian countries and Iran has been minimal. The co-authorship network revealed that several English authors dominate the literature in this field. Among the methods used in the reviewed studies, the survey method was the most commonly used. Out of the total, 53% of the articles utilized the survey method, with 67 articles employing the cross-sectional survey  method and 27 articles utilising the longitudinal survey method. After that, the quasi-experimental method (control group), correlation method, and quasi-experimental method (pre and post tests) were ranked third, fourth, and fifth. Also, the findings from the review of the tools used in the studies showed that the device was assigned the highest rank among the tools for measuring children's physical activity in 76 studies (18.44%). Meanwhile, in 64 studies, accelerometers were used, while Global Positioning System (GPS) was used in 14 studies. Heart Rate telemetry (HR) was used in 9 studies, pedometers in 8 studies, and Geographic Information System (GIS) in 3 articles. After that, the observation system was used in 56 articles (55.32%). The System for Observing Play and Leisure Activity in Youth (SOPLAY) had the highest frequency among the observation tools used in the reviewed studies.

The conclusion of this study highlights the importance of addressing the state of education in Iran, as there is a low trend of scientific production. These findings will assist future researchers in identifying research gaps, saving time by avoiding repetitive topics, and taking necessary steps to address these gaps.

The purpose of this study is to evaluate the scientific performance of authors in Iranian and Turkish journals in the Web of Science (WOS). The study also aims to analyze the citation behavior of authors and map their co-authorship.

The present study is a scientometric study that utilizes social network analysis. Data analysis is conducted using Microsoft Excel, while social network mapping is performed using VOSviewer.

This research provides a general overview of the scientific publications from two prominent countries, Iran and Turkey, within the D-8 group of developing Islamic countries. The data was collected from the WoSa database. The research findings show that 50% of the records in Iranian journals were produced by Iranian authors, while 60% of the records in Turkish journals were produced by Turkish authors. Among these two countries' journals, the most international scientific cooperation has taken place among Iran, Turkey, China, India and the United States.the conclusion reveals that a total of 1,624 degrees from Iranian publications and 15,464 degrees from Turkish publications have been indexed in the database between 2017 and 2019. The dispersion distribution of Iranian and Turkish publications is in accordance with Bradford's law, that is, a small number of publications (three Iranian and Turkish publications) published the largest number of scientific documents (thirty percent in Iranian publications and fifteen percent in Turkish publications). This issue also applies to the most cited publications. So that 33 percent of the citations of Iranian publications and 24 percent of the citations of Turkish publications belong to 3 Iranian and Turkish publications. In Iranian publications, 50% of scientific documents are produced by Iranian authors, and in Turkish publications, about 60% of scientific documents are produced by Turkish authors.Also, writers from China, India, and America have a significant and active presence in the publications of both countries. The results of the surveys of the most prolific authors showed that in Turkish publications, all prolific authors are national writers from Turkey, while this issue is different in Iranian publications, and among the 11 prolific authors of Iranian publications, 2 Chinese authors are It can be seen that this shows the strong presence of Chinese writers in Iranian publications. The ratio of citations to articles in Iranian publications is about 8.3 citations per article, and 4.2 citations per article in Turkish publications. Also, the average number of citations to articles in Iranian publications is equal to 2 and in Turkish publications is equal to 1. As a result, the citation status of Iranian publications is more favorable than Turkish publications, both in terms of the ratio of citations to articles and the average of citations. In the publications of both countries, the articles from 2017 received the most citations. The articles of Iranian publications have received the highest number of citations from the countries of Iran, China, India, America, Turkey, and England, respectively. Additionally, in Turkish publications, the highest number of citations came from Turkey, China, the United States, India, Iran, and Italy, in that order.By examining foreign quoting institutions and countries, it was found that institutions affiliated with China and the United States cited articles from Iranian and Turkish publications the most. This supports Lotka's law, which states that the distribution of articles produced by partner institutions in Iranian and Turkish publications follows a pattern where a limited number of institutions produce the majority of the documents. In Iranian publications, ten prolific institutions account for forty-three percent of the documents, while in Turkish publications, ten prolific institutions account for twenty-five percent of the documents.It is worth mentioning that the growing willingness of researchers to collaborate with other authors in producing joint scientific documents can have a significant impact on increasing visibility and receiving more citations over time. This is something that researchers should take into consideration. The results related to scientific cooperation in Iranian publications show that America, Iran, China, and India had the highest amount of scientific cooperation with other countries, with 60, 58, 39, and 39 cooperation links, respectively. Meanwhile, the authors from Iran and America had the most extensive and robust scientific cooperation with each other. America is known as the primary partner of Iranian researchers in international cooperation. In Turkish publications, Turkey, America and England established the most scientific cooperation with other countries with 66, 61 and 59 links respectively. Mapping the social network in countries' co-authorship shows that authors from the United States are recognized as the main collaborators of Iranian and Turkish researchers in scientific and international co-authorship. Also, the status of study citations shows that Iranian journals have more favorable citation rates and median citations compared to Turkish journals.

Service industries are recognized as one of the largest sectors of the economy globally, and it has the most prominent role in the countries' economic growth. To create an essential change that represents a revolutionary change in the technology of a product or service, there is a need to acquire, extract and develop knowledge to achieve a competitive advantage. Therefore, this study aims to analyze the knowledge development clusters in the service industry's knowledge extraction field. In the knowledge management process, knowledge extraction is the main phase of knowledge acquisition. Knowledge acquisition is one of the important aspects of knowledge discovery in databases to help managers make timely decisions by extracting correct knowledge.

Bibliometrics and scientific mapping techniques have been used in this applied research. Research data were collected from the Scopus database from 1986 to 2022. VOSviewer and Bibliometrix R were used to analyze and visualize data and scientific maps. Furthermore, to ensure the accuracy and validity of the results, Bibliometrix and Excel tools have been used to integrate data and remove duplicate data.

The research findings show the knowledge extraction application among 434 documents in 5 clusters of knowledge extraction, artificial intelligence, information retrieval, semantics, and forecasting. In the research, knowledge extraction and data mining are the most widely used words in a single cluster and have the most centrality and betweenness. Likewise, the bibliometric analysis of the data in The Multiple Correspondence Analysis (MCA) shows that the Internet, natural language processing, and machine learning are among the topics that are important next to the healthcare sector. This shows the importance of natural language and machine learning in extracting knowledge in healthcare services. Since 2006, the importance of knowledge extraction has received more attention. The co-occurrence of keywords shows that knowledge extraction is widely used with data mining, extraction, and artificial intelligence. The keywords of knowledge extraction and data mining in cluster 1, semantics, knowledge management, and information services in cluster 2, and information retrieval, internet, and human in cluster 3 have the highest centrality. The theme mapping shows that forecasting, multi-agent systems, and planning are themes with high density and low centrality, which are called niche themes. Semantics, web services, and knowledge-based systems are the main themes with low density and high centrality. Also, artificial intelligence, information management, and decision support systems are themes with low density and centrality, which are also known as emerging or declining themes. The forecasting cluster is located in the strategic knowledge cluster group. Information retrieval, knowledge extraction, and artificial intelligence are included in the cluster of practical knowledge. Semantics as a cluster including various experts and specialists such as domain experts, knowledge engineers, and programmers is in the collaborative cluster.

Knowledge extraction is an emerging interdisciplinary field in knowledge management and has a direct and significant impact on the country's economy. Knowledge development and integration of key issues in knowledge extraction are essential. According to the findings of this study, for the promotion and advancement of this process in the service industry, it is suggested to provide a strategic view in the use of metadata analysis of the context of activity and success of the service industry in knowledge extraction. Moreover, knowledge management as the primary discipline and domain can guarantee success in this process. The clusters identified in this study are also divided into three practical, strategic, and collaborative knowledge clusters. Moreover, the results of this research can help managers of organizations, especially their knowledge managers, to plan and make decisions in the field of service industries to facilitate optimal knowledge extraction and maintain competitive advantage.

This study aims to analyze the scientific cooperation network of ontology researchers using social network indicators. It also investigates the correlation between centrality indicators and the productivity and efficiency of researchers in this field.

The present study is an applied research project that utilizes scientometric techniques and indicators. The social network analysis method was employed to illustrate and analyze the scientific cooperation network in the field of ontology. In order to retrieve the outputs related to the field of ontology, a search was conducted for the term "Ontolog*" in the title and subject fields of the Web of Science database from 1990 to July 2, 2021. The search strategy used is TI=(Ontolog*) OR TS=(Ontolog*). In the next step, the recovered findings were limited to research conducted in the fields of computer science, information science, and librarianship. This is because ontology is a subject that is also used in other fields such as philosophy, religious studies, biology, and so on. Despite the fact that the concept of ontology in this research is "a tool that specifies the concepts of the related field, their characteristics, and the relationships between concepts and characteristics, thereby increasing the semantic interaction between documents and sources to process complex, advanced, and text-sensitive questions." Among the retrieved data, original articles, conference articles, review articles, and editorials with more credibility were selected. In the end, 29,611 research articles were obtained. The retrieved records were entered into VOSviewer version 16.6.1 software for matrix design and visual map design. To standardize the names, Gephi software was used. The matrix designed in Gephi software was then imported, and micro-indexes of social network analysis were calculated. These measures included degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The calculation of macro indicators for the co-authorship network in the field of ontology outputs was also performed using the UCINET software. To test the research hypotheses, SPSS software Version 24 was utilized. Due to the non-normal distribution of the data, a non-parametric test (Spearman's correlation) was used to test the hypotheses.

Examination of the four measures of centrality, which include degree centrality, closeness centrality, betweenness centrality, and special vector centrality, revealed that Pascal Hitzler from Kansas State University, USA; Stefano Borgo from the Italian National Research Council; Jeff Z. Pan from the University of Edinburgh, Scotland; Stefan Schulz from the Medical University of Graz, Austria; Barry Smith from the University of Buffalo, USA; Nicola Guarino from the National Research Council of Italy; Ian Horrock from the University of Oxford, England; Bernardo Cuenca Grau from the University of Oxford, England; Heiner Stuckenschmidt from the University of Mannheim, Germany; and Jerome Euzenat from the Diderot University of Paris, France are the most influential researchers in the co-authorship network in the field of ontology. The analysis of the scientific cooperation network in the field of ontology using macro indicators of social network analysis revealed that the network is not cohesive. This is evident from the density value, which is less than one, and the high clustering coefficient of the network. Of course, the flow of information in this network is fast, based on the network's diameter and average distance. Additionally, the findings revealed a significant and positive correlation between centrality measures (such as degree, closeness, betweenness, and special vector centrality) and both the number of scientific productions and the number of citations. However, it is worth noting that the relationship between closeness centrality and scientific productions was found to be not significant.

The results showed that the cooperation between researchers who know each other has led to a decrease in the diameter of the network and the average distance in the web of ontology co-authorship. As a result, the information flow in this network has intensified, overcoming the weaknesses of low density and high clustering coefficient. According to the results, when authors collaborate with colleagues they know in the field of ontology, the network diameter and average distance decrease, resulting in increased information flow in the ontology co-authorship network. This holds true even when the density and clustering coefficient are high.

This study was conducted with the aim of "identifying and analyzing the research activities of faculty members in the field of political science of public universities in Tehran within the framework of the regulations for promoting the rank of faculty members. 

The current research is a type of analytical evaluative research, which was carried out using the library study method. The population of this research includes all the members of the political science faculty of the government universities located in Tehran, whose number was 174 at the time of the research. To select the sample, the stratified sampling method is used according to the volume. Among the statistical population, 119 people were estimated as a statistical sample using Cochran's sampling formula. The data collection of this research is related to the time efficiency of 2015 from the time of the implementation of the latest scientific promotion regulations until the end of 2019. The data has been analyzed using SPSS version 26 and Excel software. 

The results of research activities based on the promotion regulations showed that the number of research articles in the study period with a maximum of 43 articles, with the number of 43 articles indexed in the ISC database, 8 articles indexed in Scopus 2 articles are indexed in Web of Sciences and 2 articles are indexed in JCR. During the study period, a maximum of 7 articles were analyzed, with 7 of them being related to promotional articles and 2 related to promotional articles. The study period yielded a total of 19 complete articles from valid scientific conferences. Out of these, 12 articles were related to domestic conferences, while 17 articles were related to international conferences. Carrying out research projects in the study period with a maximum of 4 research projects, of which the number of projects inside and outside the institution is 3 and 4, respectively. The publication of books in the studied period is with a maximum of 37 book volumes, of which the authorship and composition of the book is the most with the number of 12, followed by the translation of the book with a numerical number of 8 and the title of book correction with a numerical value.contract Guidance for theses and doctoral dissertations with a maximum of 41 items, which are 32 and 15 respectively for the master's thesis and doctoral dissertation. The maximum presentation of theorizing chairs of the course under study is with a numerical value of 2, which is scientific-promotional chairs of criticism and scientific innovation in the scientific field, theorizing in the scientific field with a numerical value of 2 and presenting the results of the chairs in conferences with a numerical value of 0. Also, the ratio of books to articles in the study period is one fifth. The frequency of article publication, based on the academic rank of professors, associate professors, and assistant professors in the course under review, is 43, 33, and 17, respectively. In the field of specialization, almost the most areas of lack of expertise and international relations among faculty members in the field of political science are related to these two fields. The meaning of the field lacks research expertise, lack of order, intellectual coherence and a research procedure and focus, and a special and central specialized field with a numerical value of 24, which means that (20.16%) faculty members are in no specialized research field. They do not have them and also the field of international relations with a numerical value of 24 (20.16%) followed by political thought in Islam and Iran with a numerical value of 6 (5.04%) has the highest specialization field.

The promotion system of faculty members plays a crucial role in organizing their performance and is considered the most objective manifestation of scientific board. The literature suggests that improving the scientific order requires attention to both the quantity and quality of joint efforts. The study's results indicate that the promotion system includes multiple elements, but the imbalance in evaluating activities and the excessive weight given to research papers without any limit undermines its effectiveness.

The present research aims to identify the usefulness of the FWCI in evaluating the research activities of the researchers of the Agricultural Research, Education, and Extension Organization. In addition, the comparison of different subject fields of the organization in terms of the FWCI scores and also the comparison of the researchers' FWCI score of the organization with their H-index have been among other goals.

The current research was conducted with a scientometric approach and using the citation analysis method. The research community consists of 13,066 international articles from 1,589 researchers of the Agricultural Research, Education, and Extension Organization, whose data was extracted from the Scopus database and the Scientometric System of the organization. For the current research, only those researchers who simultaneously had an H-index of 1 and whose articles also had the FWC score (1589 people in total) were extracted from this collection and their data for this study. Were analyzed. The calculation of the average FWCI of the researchers was based solely on the articles with the mentioned score, and the articles without this score were excluded from the scope of the review. Data analysis was done using Excel and SPSS software.

The findings of the research show that in the investigated period, an average of 8 articles were published by each researcher, and these articles received a total of 206,175 citations. The average H-index of these researchers was 4 and their average FWCI score was 0.398. The highest score obtained in the FWCI index was 9.85. Researchers with the rank of professor and assistant professor have equally obtained an average of 0.46 in this index, and researchers with the rank of associated professor have also obtained a score of 0.42. The average of this index for all eleven subject groups is less than 1, and at the same time, the subject group of forest and pasture and natural resources with a score of 0.61 is higher than the other groups and the subject group of education is lower than the other groups with a score of 0.35. At the same time, the subject group of forest and pasture is ranked fourth in the H-index with an average of 4.07. The results also show that there is no direct relationship between the researchers' H-index and their FWCI scores as a whole, or such a relationship is very weak. According to the findings of the research, in some areas (such as agricultural biotechnology), a higher average H-index does not necessarily mean a higher quality of the articles, and the weighted subject average of these articles is at a lower level compared to their peers globally. On the contrary, in some areas such as soil protection, water, and watershed management, although it has a lower H-index average, it has far better conditions in terms of FWCI average score.

In total, the score of the organization's researchers in the FWCI is much lower than the global average, both in general and in terms of subject groups. In addition, despite the lack of wide differences between FWCI and H-indexes, using FWCI in evaluating the articles of the organization's researchers can be more reliable.

The main objective of this paper is to analyze the intellectual framework of seismology in two distinct domains: materials and energy, from 2010 to 2020, using a science mapping technique. These two fields were selected based on their investment rate by international institutions compared to other areas of seismology. Moreover, this study also focused on the evolution of different clusters and subclusters that were formed or transformed into other clusters during the time span of the study. 

To this end, scientometrics approach and science mapping technique used for creating an intellectual structure of seismology through a co-word analysis. Both strategic and theme evolution diagrams were prepared using R's Bibliometrix package. Strategic diagrams pinpointed the place of different clusters in four areas of Motor themes, Highly developed and isolated themes, Emerging or declining themes, and Basic and transversal themes. Sankey diagrams were also utilized in order to depict the evolution of different clusters through time. The time frames of these graphs were determined automatically by the R Bibliometrix package. 

the results showed that the number of papers in materials' field is higher than energy field and this number is ascending in both fields. 4 clusters were identified in the field of materials and each of them is placed in one of the 4 tiers of the strategic diagram. “Earthquakes” is placed in Motor themes, while “reinforced concretes” cluster is placed somewhere between motor themes and Basic and transversal themes. The "energy dissipation" cluster is classified under Highly advanced and isolated themes, while "walls (structural partitions)" falls under Emerging or declining themes. As for the energy field, six clusters were identified, but they were divided between two different quadrants of the strategic diagram. The clusters "earthquakes," "earthquake event," and "Nuclear power plant" were placed in the Motor themes quadrant, while "Wenchuan earthquake," "forecasting," and "stochastic systems" were fitted in the Emerging or declining themes quadrant. Another interesting finding of this study based on the Sankey diagrams is that during 2010 to 2020 in the field of materials at least 20 different clusters were formed and reformed or dissolved into other clusters which means that this field is somehow active and during 2014 to 2017 has experienced lots of changes and reforms among its sub-clusters. As for the energy field, 21 clusters were identified, each of which experienced some sort of transformation or even devastation. During 2017 and 2018, this field experienced its most active era. If we compare the results of both fields, we can infer that the materials field has undergone more branching than the energy field. In 2010, four clusters were identified in the materials field, whereas in 2020, the number of identified clusters increased to seven. However, in the energy field in 2010, 7 clusters were identified. However, by 2020, the number of clusters had declined to 5. The third part of this study's findings focuses on the highly cited papers in these two fields. The results show that the top ten most cited papers in the materials field are divided into eight different clusters. Among them concretes and earthquake resistance are placed among Motor themes and earthquake engineering and reinforcement clusters are fitted in Basic and transversal themes. While columns(structural) and separation clusters are placed in Emerging or declining themes. As for the energy field, these top 10 cited papers are divided into six clusters. Nuclear energy cluster is considered to be a Motor theme but risk assessment and seismology is placed in Highly developed and isolated themes. However, hydrolic fracturing and deformation clusters are fitted in Basic and transversal themes and earthquakes cluster is among Emerging or declining themes.

based on the results of this study, it is evident that thematic diversity in materials field is more than energy field. This trend is also observed among scientific products with higher citation rates. As for the evolution of clusters in both fields, the results indicate that the materials field has undergone more branching than the energy field.

ResearchGate and LinkedIn are considered as professional research social networks, respectively, with purposes such as scientific communication, promoting the visibility of scientific and technological works, altmetrics studies, being up-to-date with the profession, and are also for job search. However, considering the changes and developments that have taken place in the last decade, we have seen a significant increase in patents registered by Iranians, especially by participation of Iranian women, in international databases such as the USPTO, which indicates the active presence of Iranian inventor women in various technological fields. Therefore, the main objective of this study is to analyze the Iranian inventor women’s scientific presence in scientific-social network LinkedIn and ResearchGate.

This applied research was conducted quantitatively, utilizing the library method and social network analysis in the field of scientometrics, along with the altimetric approach. The statistical research population is, 211 Iranian inventor women who had a patent in the USPTO database in the period of 1971–2021. Also, using the purposive sampling method, a sample of 110 inventors (52.1%) who were members of two social-scientific networks, LinkedIn and ResearchGate, between August 30, 2022, and September 19, 2022, was selected. To collect the research required data, first, in the advanced search section of the USPTO database, three commands "ICN/IR," "ACN/IR" and "AACO/IR" were searched. With these three commands, all patents whose country code of inventor or owner was "IR" were retrieved; but because of the similarity between some countries in this database, such as Ireland's country (Maloney; Ian (Dublin, IR)), many patents that were retrieved were not related to Iran. To solve this problem, the keyword of geographic location was used and three commands "IC/City," "AC/City" and "AACI/City" were also searched. Here, city is referred to the provincial centers of Iran (for example, "IC/Tehran","AC/Tehran" and "AACI/Tehran"). Then, all the data were entered into the Excel file. After merging and refining the data, we extracted the names of 211 Iranian inventor women. This information was based on 184 title patents registered in the USPTO database from 1971 to 2021, which involved at least one Iranian woman inventor. Then, each of these names was searched in two social-scientific networks, LinkedIn and ResearchGate, between 2022/08/30 and 2022/09/19. After that, the required data were collected by referring to the profile of each inventor using a checklist. Finally, the data were analyzed using Excel and Spss software.

More than half of the Iranian inventor women (110 people) were scientific social network members, LinkedIn and ResearchGate. From the profile personal information viewpoint, most of them had an informal personal photo, no biography and unclear language. Most of them were Ph.D. and very few had their contact information listed in their profiles. Also, 42.7% of them mentioned their patents on LinkedIn and 48.2% on ResearchGate. Qudsi Mohammadi Ziarani, with 453 works on ResearchGate and 102 on LinkedIn, has contributed the most compared with other Iranian inventor women in terms of presenting publications and research articles. Additionally, in terms of the new research index interest score and other indices available in ResearchGate, Fatemeh Atyabi has the largest share compared to others, with a research interest score of 5490 and 10374 citations, h-index of 56 and 84801 readings.

This study results indicate that the Iranian inventor women presence importance is in introducing themselves, communication, and scientific research collaboration with colleagues. Even though scientific-social networks LinkedIn and ResearchGate provide a suitable platform for more communication and interactions between users and researchers, inventors, employers, etc., but most Iranian inventor women have not used the maximum facilities and capabilities provided in these networks. Inventors can improve their personal page rank in Google results by completing their profile information in these networks and adding more details to it. They can also attract more researchers and audiences to their scientific and technological work by specifying different ways of communication in their profile. In addition, by publicly presenting their profile, inventors can increase the visibility of their work, leading to more citations and opportunities to commercialize scientific and technological products. This can serve as a pathway for entering the business world and marketing scientific products.

Elites are a national asset and have the power to influence the direction of society. The first and most important step in the elitism process is identifying the elite. If the identification stage is carried out correctly and comprehensively with regard to the skills and abilities of the elites, it will ensure the success of their spiritual education and investment process. Identifying and validating indicators of elitism is one of the most challenging issues that countries face, and it is considered one of the most important concerns in every country. Identifying the indicators of excellence in the field of humanities is no exception to this rule, as humanities provide the roadmap for other scientific fields. It is essential to refine this roadmap by recognizing the elites in this field. Therefore, the current research aims to identify and explain elite indicators and provide a framework for identifying scientific elites in the field of humanities.

This research employs a scientometric approach to identify and categorize elite indicators and validate them based on expert opinion. It aims to identify the most reliable indicators and establish a framework for recognizing scientific elites based on the obtained results. The purpose of the current research is both developmental and applied, utilizing a combination of quantitative and qualitative methods. For certain aspects of this research, the library method was utilized, while the survey method was employed for other parts. The research community consisted of humanities experts who were selected using a non-random purposeful sampling method. The research then continued to expand through a snowball method, ultimately involving 20 people. Based on the analysis of the texts, 157 indicators were identified. After equalization, this number was reduced to 77 indicators, which were then provided to humanities experts. In the continuation of the research, the first step involved analyzing the data. After surveying the experts, the indicators were validated and their average was obtained using SPSS v25 and Excel v2019 software. In the second phase of the research, the indicators that scored higher than the optimal average level of 3, as determined by the experts, were identified as desirable. These indicators were then re-entered into the SPSS software and weighted to establish the research framework.

The results revealed that out of the 26 indicators presented in the elite section, 24 indicators scored above the average. The candidate's achievement value index, with an average of 4.41, had the highest average in this section. Out of the 51 indicators mentioned in the adult section, all the indicators scored above the average. According to the experts, all the indicators were deemed important. In this section, the indicator for presenting a valid scientific theory for the first time in the world (which has gained general acceptance and validity) or the initiation of a new scientific research right in the world (which has achieved international scientific success) had the highest average of 4.65. The results of the second phase of the research regarding the weighting of the indicators revealed that in the youth section, scientific ability (weight: 87.828) was the most significant dimension, followed by economic ability, social ability, and communication ability (weight: 12.171). In the adult indicators identification section, the weighting results showed that the dimension of scientific ability (weight: 61.880) and the dimension of executive ability (weight: 16.849) were more important.

The key to identifying and validating indicators of the elite is the presence of significant and effective scientific measures that can accurately assess individuals' efforts and values. This is essential for promptly identifying the elite and preventing the misuse of national funds. The optimal approach in this field is to analyze indicators and develop models for identifying elites. By extracting and validating the identification indicators of elites and assigning weight to each factor, the proposed operational framework categorizes them into scientific, economic, executive, social, and communication ability factors. The operational framework can effectively improve the decision-making of policymakers and elite evaluators, as well as the quality of identifying scientific elites in the humanities.