پژوهشنامه علم سنجی
پیاپی 15 (بهار و تابستان 1401)

This study aimes to develop and present a model for evaluating the impact of humanities research in Iran.  In the present study, the mixed method approach, which is a combination of quantitative and qualitative methods, was used. The research was conducted in two phases. The first phase used the Meta-Synthesis method to identify the dimensions and indicators of the impact of humanities research. In the second phase, the goal was to validate the indicators from the perspective of experts and a model was developed via the fuzzy Delphi method. The statistical population in the first phase included 58 documents related to measuring and evaluating the impact of humanities research, and in the second phase, it was experts in the field of research assessment based on sampling.  Using seven Meta-Synthesis steps, 6 dimensions, 25 criteria and 123 indicators were identified as model elements for evaluating the impact of humanities research. Fuzzy Delphi was performed in two stages to validate the model elements at the level of dimensions, criteria and indicators and the opinions of the experts were applied. Analyzing the results of the fuzzy Delphi demonstrated that the experts agreed on all dimensions, criteria and indicators, while eight indicators scored very low and were thus removed at this stage. Accordingly, a multidimensional model was designed. Analyzing the results of the fuzzy Delphi demonstrated the policy dimension had the highest, and the social and cultural dimensions had the lowest level of agreement among others. Prioritizing the criteria, in the scientific dimension, “scientific contribution" was the most important followed by, "education" in the dimension of capacity, "innovation" in the economic dimension, "social justice" in the social and cultural, "production of goods and services" in the performance, and "demand" in the policy dimensions were the most important criteria, respectively.Conclustion: This multidimensional model provides an efficient coherent system for evaluating the impact of research in the field of humanities for policymakers and researchers, and facilitates decision making regarding scientific investments.

The present study has investigated the presence of articles in Iranian Journals indexed by Scopus via social media during 2010-2018. In this study, the articles with the highest altmetric scores were identified.

This is a descriptive study and has used altmetric indicators for evaluating the articles of Iranian Journals. Altmetrics databases such as altmetric Explorer and Scopus were used in this respect. Data analysis was performed using Microsoft Excel and SPSS software.

The results showed that out of the total articles of the research community, 1595 articles have altmetric coverage equal to 2.1% which are mostly in the field of medicine. Mendeley's social network (95.8%), Twitter (80.1%), Citations Dimensions (70%) and Facebook (13.9%) were the most important social media used.

The present study showed that the articles published in Iranian journals indexed by Scopus in the field of medicine had relatively good altimetric coverage and were more widely shared on social media and received more attention from users of these media.

Probabilistic topic modeling methods consist of a set of algorithms whose main purpose is to discover the hidden subject structure in a large volume of documents. The purpose of this study is to thematically model the articles of Iranian researchers in the field of endocrinology and metabolism in the citation database of Web of Science.

The present research is of applied type and has been done by text mining and content analysis method. In this study, all required data were retrieved from the Web of Science Citation Database using the keywords registered in the medical subject heading without a time limit until November 6, 2018. Then, using a hidden allocation algorithm, the whole set of documents in MATLAB was analyzed.  

Subject categories were extracted as groups of 20 words in 10 subject categories. Then, by endocrinologists, the subject categories were named based on their relationship to various topics in the field of  endocrinology and metabolism, and each category was assigned a subject title.

The results indicate that the implementation of the latent Dirichlet allocation model has an acceptable performance in presenting the categories of endocrinology and metabolism. The extracted subject categories have good homogeneity and thematic relevance with each  other.

This study aims to investigate the keywords similarities of documents in three areas of electronic, digital, and virtual libraries based on cluster analysis.

This scientometric study is conducted using co-word analysis and clustering techniques. Research materials consist of all relevant documents related to electronic, digital, and virtual libraries that have been indexed in Web of Science, during the period 1990-2018. In addition to the superficial similarity observed, the rate of similarities is calculated by the Jaccard index.

An examination of the growth trends indicates a negative and slight increase in the number of documents related to electronic and virtual libraries (with growth coefficients of -0.68 and 36.1). However, the number of digital libraries is higher (-0.68, 1.36, and 13.11 respectively). The topics of "Electronic Resources", "Information Retrieval", and "Internet "are placed at the top of core keywords in the fields of electronic, digital, and virtual libraries, respectively. The clustering of keywords in the three areas of the research led to the formation of 8, 14, and 7 clusters.

Results indicate little overlap of keywords in the three areas in terms of clustering. It reveals an obvious distinction between these three phenomena within the scientific community and can be viewed as evidence of the researchers' proper understanding of the concepts associated with different types of libraries.

The purpose of the present study is to analyze the publication       patterns of the humanities and social sciences domain (HSSD) in the top 50 leading countries in comparison with their total publications at the Web of    Science (WoS) during the years of 1990-2017.

This study was conducted by scientometrics approach. Data were collected from WoS and entered into Excel software and analyzed with descriptive and inferential statistics.

The findings showed that out of 48329064 publications in 50 under-studied countries, 8517586 records (17.62%) have been published in the HSSD. The average growth rate of publication for the 50 countries in all disciplines was about 37% and in the HSSD it was about 45%. In all scientific disciplines, the Iranian publications’ growth rate was 117%, which is 3.16 times larger than the average global growth rate. Also, the investigation of growth rate in the HSSD showed that China is at the top with 103%, while Iran with 100 % and Indonesia with 98% ranked second and third respectively.

The humanities and social sciences publications in the 50% of under-studied countries is lower than publications of Pure Science, this shows the need for serious attention of these countries to HSSD researches.

The purpose is to analyze the structure of social network Co-occurrence and Co-authorship in quality of life using social network analysis indicators in the Clarivate analytics WoS database.

This research is a scientometric study and has been done using co-occurrence and co-authorship analysis. Social network analysis (SNA) was used to obtain an understanding of the nodes on quality of life areas. MESH subject heading was used to extract related terms on quality of life. The population included 1421 documents which were extracted from Clarivate analytics WoS from 2000 to 2017. Descriptive statistics and Scientometric methods have been used to analyze the data.

The results showed that among researchers, Montazeri has been the most prolific author with 97 documents. Among the Institutions, Tehran University of Medical Sciences with 831 documents ranked first. The total documents for the year 2014 were 284 documents, the area in this research was internal and general medicine with 235 documents, and among documents, the highest type of documents was related to articles with 1111 records. In the study of co-occurrence network in the field of quality of life, 9 thematic clusters were identified: Motor Control (109), Obesity (54), Lifestyle (53), Meta-analysis (52), Risk factors (39), Women (33), Risk (20), Eating (13) and Coronary disease (13). The Motion Control cluster is the largest cluster, accounting for 84% of the total network volume.

The results of the co-occurrence analysis show that this area consists of 9 clusters, the top three clusters were Motor Control, Obesity, and Lifestyle.  Analysis of centrality indicators shows that most of the top-ranking authors are mainly working at the top universities of the country, and the top authors are mostly concentrated on the universities located in the capital rather than the other cities. Also, the study of macro indicators of co-authorship network shows that this structure is considered as a Small-world network due to its average short path length (2.77), network diameter equal to 5, and relatively high clustering coefficient (3.75).

This study aims to identify the extent of reflection of English-language  articles by Iranian researchers of the Agricultural Research, Education and Extension    Organization on social media based on Altmetric indicators.

This research is applied in terms of purpose, a scientometrics study in terms of method which was done with a descriptive-analytical approach. The research population included 3251 articles published by the organization's researchers in international journals indexed in the Scopus database from 2015-2018. The main tools used to collect the data were the Scientometric System and the Bookmarklet plugin of the    Altmetric site.  

The findings show that 528 articles (16.24%) out of the total articles have been reflected at least once in various social media. The average Altmetrix score for all articles was 3.15. The findings also show that out of a total of 17,688 events on social media, Mendeley's share with 10,241 (57.9%) and Twitter with 3,613 (20.43%) have the highest events than other media. In addition, there is no strong significant correlation between the number of citations and the Altmetric score, or between the citation rate and reflection in the Mendeley and Twitter media. Thematic analysis of the reflections also shows that the field of the Environment with a total Altmetrix score of 3596 and then the fields of Botany and Animal sciences with the scores of 641 and 450 have   received the most attention from social media.

Although the level of reflection of international articles of agricultural researchers of the organization is acceptable compared to similar studies, the first authors of the best articles reflected in these media are none of the researchers of the organization and have no Iranian origin. Also, the reflection of articles on social media has had little effect on increasing the citation rate.

The purpose of this study is a topical analysis of references from and citations to Iranian international patents.  

This study is applied research that has been done using the library method, citation analysis, and Reverse citation relations analysis. Required data was collected through the USPTO and Scopus databases. The statistical population of this study includes 5806 patents and 895 articles. The tool used to analyze the findings of this research is Excel software.  

The highest number of references in Iranian international patents, base on the CPC, belongs to Human Necessities field (A). Iranian international patents have the highest references to patents in physics field (G) and the articles in the field of Biochemistry, Genetics, and Molecular Biology. They have also been cited by patents in Human Necessities (A) and articles in the field of engineering and Chemical Engineering.

In the last 14 years, the number of Iranian international patents in the USPTO database had an ascending growth. Patents in various subject classifications have more references and citations than scientific articles.

The purpose of this research is to study the components of the information analysis in information retrieval systems.

The current study has been done using systematic review and qualitative content analysis. The statistical population includes research articles in the field of information analysis in information retrieval systems, which have been found by search strategies from the beginning to 2019 in the scientific databases (Scopus, Science Direct, Pubmed, and IEEE). PRISMA Statement was used for sampling. First 2048 articles were identified and then after four steps (Identification, Screening, Eligibility, and Inclusion), 83 articles were selected for final analysis.  

For answering the research question about what is effective components of information analysis in Information retrieval systems, there are five basic categories was obtained Analytics (“Benchmarking”, “Collaboration and Scientometrics”, “Text analysis”), Description (“Analyzed fields”, “Representation levels”, “Presentation”), Human-computer interaction (“Navigation”, “Searching”, “Filtering”, “Sorting”, “user-system dialog”, “Links”, “Personalization”), Information management (“System management”, “User management”, “Helps”) and Information analysis results (“Exploration”, “Psychological aspects”).

The obtained categorization in current research can be noticed for information retrieval systems design more analytically and evaluate them.

The present study aims to identify the dimensions and factors affecting the scientific production from the perspective of faculty members of Iranian Public Universities and provide a suitable model for implementation in universities and scientific centers of the country.

This research was a mixture of consecutive exploratory types and was performed by combining two methods qualitative (interview based on grounded theory) and quantitative (descriptive survey). The statistical population of the study in two qualitative and quantitative sections includes 15 experts, and 385 faculty members of knowledge and information science of Iranian public universities who were selected by theoretical and cluster sampling method, respectively. The data collection tool was a semi-structured interview in the qualitative part and an organized questionnaire with 161 items in the quantitative part. Data analysis in the qualitative stage was performed by qualitative data coding method with MAXQDA.2018 software. In the quantitative stage, based on the principal component analysis method as well as structural equation modeling (SEM)  the analysis was performed in SPSS and PLS software.

Based on the research findings, dimensions, and factors affecting the accelerators of scientific production from the perspective of the population in 6 dimensions include: central phenomenon, causal conditions, strategies, underlying conditions, interference factors, and its consequences. Then, with the approval of experts, the final model is presented.

The results indicate that for balanced and effective scientific development in the universities of country, serious and systematic attention to all aspects of accelerating systems of science production among faculty members is required.

The aim of this study was to investigate the level of attention paid to the scientific outputs of researchers focusing on the Coronavirus and family viruses on social media.

The present research is a descriptive study that has been conducted using scientometric and altmetrics indicators. The research population includes 968 scientific outputs, which have been written by Iranian researchers in different years about COVID 19 or its family viruses.

38% of scientific outputs have been considered 3322 times in social media, 206 times in news and blogs, 39 times in policy and patents, 12 times in other sources, and three times in scientific sources. The average Altmetric scores of Iranian articles published on the Coronavirus are 9.6, and the most attention to these articles shared via Twitter. Also, no correlation was found between the Altmetric score of papers and the number of citations.

Articles on coronary artery disease using stem cells, Iranians fighting Covid 19 despite sanctions, how people function when they get Covid 19, and the effect of Covid 19 on stroke in patients which has been noticed by doctors and researchers and even the public in recent months, has had the most presence on social networks.

The purpose of this study is to identify the characteristics of scientific products in the field of small-data indexed in the Web of Science database and to explain its application based on identifying the words of scientific products related to this subject separately by scientific fields.

This research is a descriptive study based on the scientometric approach and content analysis method, which has been done by using the common techniques of co-word analysis and social network analysis. Data analysis was performed by HistCite, Bibexecl, Gephi, and SPSS software; and the data mapping is done by VOSviewer.

Over the past decades, the rate of publications in the field of small data has had an increasing trend with an average annual growth rate of 15.59%. The main language of these works is in English. Although the National Cheng Kung University (Taiwan) ranked the first of organizations in this field, the United States, China and Germany recognized the top countries in this field, overall. More than 90% of these products are in the fields of Computer Science (8 clusters), Engineering (6 clusters), Mathematics (7 clusters), Telecommunications (5 clusters), and Physics (3 clusters). The greatest degree of centrality belongs to Machine Learning, the Internet of Things, and Universal existence; the most closeness centrality belongs to Adaptation, Bipartite Graph, and Machine Learning; and the most betweenness centrality belongs to Machine Learning, Long-Term Evolution Technology, and Global Existence.

The pattern of dissemination of scientific products in the field of small data indicates a continuous growth situation. Theoretical discussions of microdata have further evolved in mathematics and physics, and its applications in computer science and other fields are expanding.