Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.020
Atul Kumar, Jaiprakash M. Paliwal, Vinaydeep Brar, Mahesh Singh, Prashant R Tambe Patil, S. Raibagkar
Over the last few years, CiteScore has emerged as a popular metric to measure the performance of Journals. In this paper, we analyze CiteScores of the top 400 Scopus-indexed journals of 2021 for years from 2011 to 2021. Some interesting observations emerged from the analysis. The average CiteScore of the top 400 journals doubled from 16.48 in 2011 to 31.83 in 2021. At the same time, the standard deviation has almost trebled from 13.53 in 2011 to 38.18 in 2021. The CiteScores also show sizable increases for skewness and kurtosis, implying major variations in the CiteScores of the journals for a year. Importantly, the previous year’s CiteScores strongly predict the next year’s scores. This has been observed consistently for the last ten years. The average Pearson correlation coefficient between the preceding and succeeding years’ CiteScores for the ten years is 0.98. We also show that it is easily possible for even people with just basic knowledge of computers to forecast the CiteScore. Researchers can predict CiteScores based on the past year’s CiteScores and decide better about publishing their current research in a journal with an idea about its likely CiteScore. Such a forecast can be useful to publishers, editorial staff, indexing services, university authorities, and funding agencies.
{"title":"Previous Year’s Cite Score Strongly Predicts the Next Year’s Score: Ten Years of Evidence for the Top 400 Scopus-indexed Journals of 2021","authors":"Atul Kumar, Jaiprakash M. Paliwal, Vinaydeep Brar, Mahesh Singh, Prashant R Tambe Patil, S. Raibagkar","doi":"10.5530/jscires.12.2.020","DOIUrl":"https://doi.org/10.5530/jscires.12.2.020","url":null,"abstract":"Over the last few years, CiteScore has emerged as a popular metric to measure the performance of Journals. In this paper, we analyze CiteScores of the top 400 Scopus-indexed journals of 2021 for years from 2011 to 2021. Some interesting observations emerged from the analysis. The average CiteScore of the top 400 journals doubled from 16.48 in 2011 to 31.83 in 2021. At the same time, the standard deviation has almost trebled from 13.53 in 2011 to 38.18 in 2021. The CiteScores also show sizable increases for skewness and kurtosis, implying major variations in the CiteScores of the journals for a year. Importantly, the previous year’s CiteScores strongly predict the next year’s scores. This has been observed consistently for the last ten years. The average Pearson correlation coefficient between the preceding and succeeding years’ CiteScores for the ten years is 0.98. We also show that it is easily possible for even people with just basic knowledge of computers to forecast the CiteScore. Researchers can predict CiteScores based on the past year’s CiteScores and decide better about publishing their current research in a journal with an idea about its likely CiteScore. Such a forecast can be useful to publishers, editorial staff, indexing services, university authorities, and funding agencies.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80642455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.036
Anjali Yadav, Arpana Pandey, Chanchal Chanchal
Menstrual health has reaped much attention with a swift increase in the related literature. This study intended to map the knowledge landscape of menstrual health research in India using a scientometric and information visualization approach. The scientometric analysis of Scopus data on parameters like publication output, publication share, growth rate, prolific authors, authorship pattern, scientific fields, citation analysis, international collaboration, etc., has been conducted. 52257 publications were produced globally during the study period, with 2668 papers from India. The majority of these research output is collaborative and multi-authored. America is the most productive country and India's top collaborative associate in menstrual studies. All India Institute of Medical Sciences and Clinical and Diagnostic Research journal is the most efficient institute and journal. Moreover, menstrual health, menstrual cycle and menstrual hygiene, menstrual syndrome, and studies on the function of hormones in menstruation were diagnosed as the mainstream topics in the fields of menstrual health. The study's findings will offer proof of the current status and trends in menstrual health. They will assist researchers and policymakers in understanding the panorama of menstrual health and expecting the dynamic research guidelines.
{"title":"Identification and Visualization of the Knowledge Landscape of Menstrual Health Research in India: 1996-2020","authors":"Anjali Yadav, Arpana Pandey, Chanchal Chanchal","doi":"10.5530/jscires.12.2.036","DOIUrl":"https://doi.org/10.5530/jscires.12.2.036","url":null,"abstract":"Menstrual health has reaped much attention with a swift increase in the related literature. This study intended to map the knowledge landscape of menstrual health research in India using a scientometric and information visualization approach. The scientometric analysis of Scopus data on parameters like publication output, publication share, growth rate, prolific authors, authorship pattern, scientific fields, citation analysis, international collaboration, etc., has been conducted. 52257 publications were produced globally during the study period, with 2668 papers from India. The majority of these research output is collaborative and multi-authored. America is the most productive country and India's top collaborative associate in menstrual studies. All India Institute of Medical Sciences and Clinical and Diagnostic Research journal is the most efficient institute and journal. Moreover, menstrual health, menstrual cycle and menstrual hygiene, menstrual syndrome, and studies on the function of hormones in menstruation were diagnosed as the mainstream topics in the fields of menstrual health. The study's findings will offer proof of the current status and trends in menstrual health. They will assist researchers and policymakers in understanding the panorama of menstrual health and expecting the dynamic research guidelines.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89615495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.025
Ma. Elena Luna-Morales, M. A. Pérez-Angón, Evelia Luna-Morales
We carried out a bibliometric analysis of the research production in the field of evolutionary computation in Latin America (LA) for the period 1980-2020. The bibliometric method is applied with a quantitative review of the published literature. The search for publications was carried out in the Web of Science database through the terms that are most commonly used to identify this field of study. The data analysis the data analysis used Microsoft Office tools (excel and Access) to organize our data were used to organize our data: authors, institutions, journals, countries and thematic categories. It was completed with VOS Viewer 1.8.16 to generate a co-authorship network map of authors, and the development of base maps for collaboration by countries. We have identified the first Latin American publications in the journals Archivos de Biologia y Medicina Experimentales and Desarrollo Economico-Revista de Ciencias Sociales; this research field reached a consolidation in the 2000s with the opening of the first graduate programs in this geographical region; there is an extraordinary number of LA scholars active in this research field and an increasing number of academic institutions mainly from Brazil, Mexico, Argentina, Chile and Colombia; while the Asian and European production in this research field is about 30%, the respective LA contribution is just 4.9%. The present study attempts to document the progress of evolutionary computation in Latin America, an issue that has gained relevance for society, especially in recent years. No studies have been generated that cover the Latin American region, and therefore it is hoped that these findings will be useful for the development of scientific and public policies and also for other future work.
我们对拉丁美洲(LA) 1980-2020年进化计算领域的研究成果进行了文献计量分析。应用文献计量学方法对已发表的文献进行定量回顾。出版物的搜索是在Web of Science数据库中通过最常用的术语进行的，这些术语用于识别该研究领域。数据分析数据分析使用Microsoft Office工具(excel和Access)来组织我们的数据使用作者，机构，期刊，国家和专题类别来组织我们的数据。它是用VOS Viewer 1.8.16完成的，以生成作者的合作网络地图，并开发供各国合作的基础地图。我们已经在《生物学与医学实验档案》和《社会科学经济评论》杂志上确定了第一批拉丁美洲出版物;这一研究领域在2000年代随着该地理区域第一批研究生课程的开设而得到巩固;活跃在这一研究领域的洛杉矶学者数量惊人，越来越多的学术机构主要来自巴西、墨西哥、阿根廷、智利和哥伦比亚;亚洲和欧洲在这一研究领域的贡献约为30%，而各自的LA贡献仅为4.9%。本研究试图记录拉丁美洲进化计算的进展，这是一个与社会相关的问题，特别是近年来。目前还没有关于拉丁美洲区域的研究报告，因此希望这些研究结果将有助于制定科学和公共政策，也有助于今后的其他工作。
{"title":"Strengthen of a Scientific Field in Latin America: Evolutionary Computation","authors":"Ma. Elena Luna-Morales, M. A. Pérez-Angón, Evelia Luna-Morales","doi":"10.5530/jscires.12.2.025","DOIUrl":"https://doi.org/10.5530/jscires.12.2.025","url":null,"abstract":"We carried out a bibliometric analysis of the research production in the field of evolutionary computation in Latin America (LA) for the period 1980-2020. The bibliometric method is applied with a quantitative review of the published literature. The search for publications was carried out in the Web of Science database through the terms that are most commonly used to identify this field of study. The data analysis the data analysis used Microsoft Office tools (excel and Access) to organize our data were used to organize our data: authors, institutions, journals, countries and thematic categories. It was completed with VOS Viewer 1.8.16 to generate a co-authorship network map of authors, and the development of base maps for collaboration by countries. We have identified the first Latin American publications in the journals Archivos de Biologia y Medicina Experimentales and Desarrollo Economico-Revista de Ciencias Sociales; this research field reached a consolidation in the 2000s with the opening of the first graduate programs in this geographical region; there is an extraordinary number of LA scholars active in this research field and an increasing number of academic institutions mainly from Brazil, Mexico, Argentina, Chile and Colombia; while the Asian and European production in this research field is about 30%, the respective LA contribution is just 4.9%. The present study attempts to document the progress of evolutionary computation in Latin America, an issue that has gained relevance for society, especially in recent years. No studies have been generated that cover the Latin American region, and therefore it is hoped that these findings will be useful for the development of scientific and public policies and also for other future work.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84211964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article aimed to develop a systematic review of the scientific literature about indicators for the evaluation of science, technology and innovation activities. For this, the Web of Science, Scopus and Google Scholar databases were used. Through the application of the SysteRe-HSS methodology, 96 publications were selected that formed the basis for a descriptive model of the science, technology and innovation indicators. The results of the research showed that there is a predominance of indicators related to the evaluation of innovation activities, human resources allocated to the activity of science, technology and innovation, financial resources and investments in research plus development, and indicators related to bibliometrics and scientometrics. However, challenges are faced related to measuring indicators of social innovation, linking insights from existing innovation measurement approaches with the essential features of social innovation, measuring the impact of social appropriation practices of science and technology, and the next generation metrics, responsible metrics and evaluation for open science, as well as alternative indicators for the evaluation of the social impact of research in web 2.0.
本文旨在对科学、技术和创新活动评价指标的科学文献进行系统的综述。为此，我们使用了Web of Science、Scopus和Google Scholar数据库。通过应用system - hss方法，选择了96份出版物，这些出版物构成了科学、技术和创新指标描述性模型的基础。研究结果表明，创新活动评价指标、科技创新活动人力资源配置指标、研发投入指标、文献计量学指标和科学计量学指标占主导地位。然而，在衡量社会创新指标、将现有创新测量方法的见解与社会创新的本质特征联系起来、衡量科学技术的社会占有实践的影响、下一代指标、开放科学的负责任指标和评估以及评估web 2.0下研究的社会影响的替代指标方面面临着挑战。
{"title":"Indicators for the Evaluation of Science, Technology and Innovation Activities: A Systematized Review","authors":"Roelvis Ortiz-Núñez, Stephany Novo-Castro, Ricardo Casate-Fernández","doi":"10.5530/jscires.12.2.041","DOIUrl":"https://doi.org/10.5530/jscires.12.2.041","url":null,"abstract":"The article aimed to develop a systematic review of the scientific literature about indicators for the evaluation of science, technology and innovation activities. For this, the Web of Science, Scopus and Google Scholar databases were used. Through the application of the SysteRe-HSS methodology, 96 publications were selected that formed the basis for a descriptive model of the science, technology and innovation indicators. The results of the research showed that there is a predominance of indicators related to the evaluation of innovation activities, human resources allocated to the activity of science, technology and innovation, financial resources and investments in research plus development, and indicators related to bibliometrics and scientometrics. However, challenges are faced related to measuring indicators of social innovation, linking insights from existing innovation measurement approaches with the essential features of social innovation, measuring the impact of social appropriation practices of science and technology, and the next generation metrics, responsible metrics and evaluation for open science, as well as alternative indicators for the evaluation of the social impact of research in web 2.0.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78278245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.024
Mayur Makawana, Rupa G. Mehta
As part of the research process, relevant documents are identified to keep up with the latest advancements in the domain. Document recommendation systems are used by researchers as a means of accomplishing this goal. Textual content, collaborative filtering, and citation information-based approaches are among the proposed approaches for the recommendation systems. Content-based techniques take advantage of the entire text of papers and produce more promising results, but comparing input document text data to every document in the dataset is not practical for the content-based recommender system. This study looks into the possibility of using bibliographic data to reduce the number of comparisons. The proposed system is based on the assumption that two scientific papers are semantically connected if they are co-cited more frequently than by chance. The likelihood of co-citation, also known as semantic relatedness, can be used to quantify this connection. This work presents a new way to distribute the weight among connected scholarly documents based on a semantic relatedness score. Our proposed solution eliminates a substantial amount of needless text comparisons for the system by gathering scholarly document pairs with high likelihood values and using them as a search area for the content-based recommender system. By spreading the co-citation relationship out to certain distances, the proposed approach can find relevant documents that are not found by traditional co-citation searches. The results reveal that the system is capable of reducing computations by a significant margin and of detecting false positive situations in content comparison using Doc2vec.
{"title":"Discovering Search Space Using M-distance Clustering of Semantic Relatedness Based Weighted Network for the Content-based Recommender System","authors":"Mayur Makawana, Rupa G. Mehta","doi":"10.5530/jscires.12.2.024","DOIUrl":"https://doi.org/10.5530/jscires.12.2.024","url":null,"abstract":"As part of the research process, relevant documents are identified to keep up with the latest advancements in the domain. Document recommendation systems are used by researchers as a means of accomplishing this goal. Textual content, collaborative filtering, and citation information-based approaches are among the proposed approaches for the recommendation systems. Content-based techniques take advantage of the entire text of papers and produce more promising results, but comparing input document text data to every document in the dataset is not practical for the content-based recommender system. This study looks into the possibility of using bibliographic data to reduce the number of comparisons. The proposed system is based on the assumption that two scientific papers are semantically connected if they are co-cited more frequently than by chance. The likelihood of co-citation, also known as semantic relatedness, can be used to quantify this connection. This work presents a new way to distribute the weight among connected scholarly documents based on a semantic relatedness score. Our proposed solution eliminates a substantial amount of needless text comparisons for the system by gathering scholarly document pairs with high likelihood values and using them as a search area for the content-based recommender system. By spreading the co-citation relationship out to certain distances, the proposed approach can find relevant documents that are not found by traditional co-citation searches. The results reveal that the system is capable of reducing computations by a significant margin and of detecting false positive situations in content comparison using Doc2vec.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85691159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.031
Paula Marques Borges Vinhas Porto, Sabrina de Oliveira Anício, Rodrigo Nogueira Vasconcelos, T. Malheiros, Washington de Jesus Sant’Anna da Franca Rocha
Biogas, a by-product of effluent treatment, is increasingly no longer seen as a passive, taking on the role of an asset. This work carried out a bibliometric study of the world production of biogas from domestic wastewater, focusing on the evolution of knowledge over the decades. For this purpose, a search for scientific articles was carried out in the Scopus database and, from the documents obtained, a review of literature was developed to access information and reveal quantification patterns. The analysis of the graphs and networks generated proved efficient in the exploratory study of the scientific and technological evolution of biogas from domestic sewage, making it possible to observe a highlight for the recovery of dissolved methane, as well as for the gains in reducing emissions of GHG from biogas reuse, in addition to the focus on nutrient and energy recovery, which underscores the importance of anaerobic processes for obtaining energy and nutrient conservation, as well as their potential contribution to achieving the goals related to the Sustainable Development Goals.
{"title":"Energy Recovery from Biogas in Domestic Waste Water Treatment Plant in the Last 5 Decades: A Bibliometric Analysis","authors":"Paula Marques Borges Vinhas Porto, Sabrina de Oliveira Anício, Rodrigo Nogueira Vasconcelos, T. Malheiros, Washington de Jesus Sant’Anna da Franca Rocha","doi":"10.5530/jscires.12.2.031","DOIUrl":"https://doi.org/10.5530/jscires.12.2.031","url":null,"abstract":"Biogas, a by-product of effluent treatment, is increasingly no longer seen as a passive, taking on the role of an asset. This work carried out a bibliometric study of the world production of biogas from domestic wastewater, focusing on the evolution of knowledge over the decades. For this purpose, a search for scientific articles was carried out in the Scopus database and, from the documents obtained, a review of literature was developed to access information and reveal quantification patterns. The analysis of the graphs and networks generated proved efficient in the exploratory study of the scientific and technological evolution of biogas from domestic sewage, making it possible to observe a highlight for the recovery of dissolved methane, as well as for the gains in reducing emissions of GHG from biogas reuse, in addition to the focus on nutrient and energy recovery, which underscores the importance of anaerobic processes for obtaining energy and nutrient conservation, as well as their potential contribution to achieving the goals related to the Sustainable Development Goals.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78651924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.037
Jalal Mardaneh, Reza Ahmadi, M. Dastani
Drug-resistant tuberculosis is a form of tuberculosis that is resistant to at least one of the standard first-line anti-tuberculosis drugs. DR-TB can occur when patients do not complete their full course of TB medication, leading to the development of drug resistance. Improved diagnostics and more effective treatments are urgently needed to address this global health challenge, So This study uses bibliometric and text mining techniques to conduct a topical analysis of scientific publications on drug-resistant tuberculosis. WOS Core Collection citation database was used to extract data from the beginning until April 25, 2022. Afterward, the data was analyzed using Python and Microsoft Excel. The results revealed that scientific publications on drug-resistant tuberculosis have increased in recent years, with the majority of the publications consisting of articles and reviews. The USA, India, and South Africa, on the other hand, account for the majority of the publications. Furthermore, the findings demonstrated that publications related to drug-resistant tuberculosis had the highest publication rate in the following subjects: Drug Resistance, Care, Treatment, Drug Activity, Patient, and Drug Dose Therapy Regimen. The findings of the present study showed that the interest in drug-resistant tuberculosis is increasing and controlling its prevalence is becoming one of the key health preferences in the world.
{"title":"Topical Analysis of Scientific Publications on Drug-Resistant Tuberculosis Using Bibliometric and Text Mining Techniques","authors":"Jalal Mardaneh, Reza Ahmadi, M. Dastani","doi":"10.5530/jscires.12.2.037","DOIUrl":"https://doi.org/10.5530/jscires.12.2.037","url":null,"abstract":"Drug-resistant tuberculosis is a form of tuberculosis that is resistant to at least one of the standard first-line anti-tuberculosis drugs. DR-TB can occur when patients do not complete their full course of TB medication, leading to the development of drug resistance. Improved diagnostics and more effective treatments are urgently needed to address this global health challenge, So This study uses bibliometric and text mining techniques to conduct a topical analysis of scientific publications on drug-resistant tuberculosis. WOS Core Collection citation database was used to extract data from the beginning until April 25, 2022. Afterward, the data was analyzed using Python and Microsoft Excel. The results revealed that scientific publications on drug-resistant tuberculosis have increased in recent years, with the majority of the publications consisting of articles and reviews. The USA, India, and South Africa, on the other hand, account for the majority of the publications. Furthermore, the findings demonstrated that publications related to drug-resistant tuberculosis had the highest publication rate in the following subjects: Drug Resistance, Care, Treatment, Drug Activity, Patient, and Drug Dose Therapy Regimen. The findings of the present study showed that the interest in drug-resistant tuberculosis is increasing and controlling its prevalence is becoming one of the key health preferences in the world.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73858739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.035
Muhammad Farid Azlan Halmi, Mohd Amirul Faiz Zulkifli, Kamal Hisham Kamarul Zaman
The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) is a promising molecular tool that has revolutionised genome editing and was recognised with the Nobel Prize in Chemistry in 2020. This study assesses the scientific productivity and knowledge structure in the scientific domain of CRISPR-Cas9 genome editing up to 2022. A total of 12,799 publications were retrieved from the Science Citation Index Expanded (SCIE) database within the Web of Science (WoS), employing related keyword searches. The records were published by authors from 107 countries in 1,731 journals. Of the total scientific publications, 499,895 total citations were found, with 39.06 average citations per publication. The United States of America dominated the research and is currently the global leader in this area with the most publications and prolific top institutions. Visualisation analysis for mapping research trends based on co-occurrences of keywords was done using VOSviewer revealing six clusters of research themes comprising; 1) conception and fundamental development; 2) gene therapy and drug delivery; 3) cancer biology; 4) plant biotechnology; 5) livestock breeding, and; 6) synthetic biology and metabolic engineering. Nanoparticle-based delivery of CRISPR-Cas9 is gaining academic attention, while CRISPR-Cas9 application in synthetic biology and metabolic engineering has progressed recently and becoming the current research interest.
簇状规则间隔短回文重复序列(CRISPR)相关蛋白9 (CRISPR- cas9)是一种很有前途的分子工具，它彻底改变了基因组编辑，并在2020年获得了诺贝尔化学奖。本研究评估了截至2022年CRISPR-Cas9基因组编辑科学领域的科学生产力和知识结构。采用相关关键字检索方法从Web of Science (WoS)的SCIE数据库中检索到12799篇文献。这些记录由来自107个国家的作者发表在1731份期刊上。在全部科学出版物中，共被引用499895次，平均被引用39.06次。美国主导了这一研究，目前是该领域的全球领导者，拥有最多的出版物和多产的顶级机构。使用VOSviewer对基于关键词共现的研究趋势进行可视化分析，揭示了六个研究主题集群，包括;1)概念和基本发展;2)基因治疗与药物传递;3)癌症生物学;4)植物生物技术;(五)牲畜养殖;6)合成生物学与代谢工程。近年来，CRISPR-Cas9在合成生物学和代谢工程领域的应用取得了新的进展，成为当前的研究热点。
{"title":"CRISPR-Cas9 Genome Editing: A Brief Scientometric Insight on Scientific Production and Knowledge Structure","authors":"Muhammad Farid Azlan Halmi, Mohd Amirul Faiz Zulkifli, Kamal Hisham Kamarul Zaman","doi":"10.5530/jscires.12.2.035","DOIUrl":"https://doi.org/10.5530/jscires.12.2.035","url":null,"abstract":"The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) is a promising molecular tool that has revolutionised genome editing and was recognised with the Nobel Prize in Chemistry in 2020. This study assesses the scientific productivity and knowledge structure in the scientific domain of CRISPR-Cas9 genome editing up to 2022. A total of 12,799 publications were retrieved from the Science Citation Index Expanded (SCIE) database within the Web of Science (WoS), employing related keyword searches. The records were published by authors from 107 countries in 1,731 journals. Of the total scientific publications, 499,895 total citations were found, with 39.06 average citations per publication. The United States of America dominated the research and is currently the global leader in this area with the most publications and prolific top institutions. Visualisation analysis for mapping research trends based on co-occurrences of keywords was done using VOSviewer revealing six clusters of research themes comprising; 1) conception and fundamental development; 2) gene therapy and drug delivery; 3) cancer biology; 4) plant biotechnology; 5) livestock breeding, and; 6) synthetic biology and metabolic engineering. Nanoparticle-based delivery of CRISPR-Cas9 is gaining academic attention, while CRISPR-Cas9 application in synthetic biology and metabolic engineering has progressed recently and becoming the current research interest.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85777225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.040
Fati Tahiru, Steven. Parbanath, Samuel Agbesi
This paper aims to comprehensively review the present state and research trends in predictive systems in higher education. It also addresses the research contribution of countries in Machine Learning-based predictive systems in higher education to depict the research landscape given the growing number of related publications. A bibliometric analysis of publications on predictive systems in education published in the Scopus Database from 2015 to 2022 was conducted. The dataset obtained covered the contribution of authors, affiliations, countries, themes and trends in the field of Machine Learning-based predictive systems in higher education. A total of 72 publications with 3408 cited references were collected from Scopus for the bibliometric analysis. The technique used for the bibliometric analysis included performance analysis and science mapping. Research on Machine Learning-based predictive systems has been widely published from 2020 to 2022. Researchers in China, Belgium, Spain, India, and Korea were most active in researching Machine Learning-based predictive systems in education. However, international collaborations have remained infrequent except for the few involving Australia, Belgium, and Canada. There is a lack of research in the subject area in Africa. This study illustrates the intellectual landscape of Machine Learning-based predictive systems in higher education and the field's evolution and emerging trends. The findings highlight the area of research concentration and the most recent developments and suggest future research collaborations on a larger scale as well as additional research on the implementation of
{"title":"Machine Learning-based Predictive Systems in Higher Education: A Bibliometric Analysis","authors":"Fati Tahiru, Steven. Parbanath, Samuel Agbesi","doi":"10.5530/jscires.12.2.040","DOIUrl":"https://doi.org/10.5530/jscires.12.2.040","url":null,"abstract":"This paper aims to comprehensively review the present state and research trends in predictive systems in higher education. It also addresses the research contribution of countries in Machine Learning-based predictive systems in higher education to depict the research landscape given the growing number of related publications. A bibliometric analysis of publications on predictive systems in education published in the Scopus Database from 2015 to 2022 was conducted. The dataset obtained covered the contribution of authors, affiliations, countries, themes and trends in the field of Machine Learning-based predictive systems in higher education. A total of 72 publications with 3408 cited references were collected from Scopus for the bibliometric analysis. The technique used for the bibliometric analysis included performance analysis and science mapping. Research on Machine Learning-based predictive systems has been widely published from 2020 to 2022. Researchers in China, Belgium, Spain, India, and Korea were most active in researching Machine Learning-based predictive systems in education. However, international collaborations have remained infrequent except for the few involving Australia, Belgium, and Canada. There is a lack of research in the subject area in Africa. This study illustrates the intellectual landscape of Machine Learning-based predictive systems in higher education and the field's evolution and emerging trends. The findings highlight the area of research concentration and the most recent developments and suggest future research collaborations on a larger scale as well as additional research on the implementation of","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89721993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.023
Solanki Gupta, Vivek Kumar Singh
The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas.
人工智能(AI)在人类生活各个领域的普遍应用导致了人工智能研究的惊人增长。人工智能的研究成果在过去十年中迅速增长。虽然一些科学计量学研究已经注意到出版物的增长，但实际上没有研究可以从开展和发表人工智能研究的领域和期刊的增长方面描述出版物的增长。本文试图利用Bradford生产率定律的Leimkuhler模型对人工智能研究发表进行定量估计，以填补这一研究空白。2011年至2020年期间在Web of Science索引的出版物用于分析。分析利用生产力分布及其特征解释了人工智能研究语料库的变化。定量研究结果支持了这样一种观点，即人工智能研究不仅在数量上有所增加，而且在更广泛的领域得到了应用。
{"title":"Quantitative Estimation of Trends in Artificial Intelligence Research: A Study of Bradford Distributions using Leimkuhler Model","authors":"Solanki Gupta, Vivek Kumar Singh","doi":"10.5530/jscires.12.2.023","DOIUrl":"https://doi.org/10.5530/jscires.12.2.023","url":null,"abstract":"The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90784249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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