Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/m...Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.展开更多
Purpose:This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approach:The li...Purpose:This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approach:The literatures on single cell research were extracted from Clarivate Analytic’s Web of Science Core Collection between 2009 and 2019.Firstly,bibliometric analyses were performed with Thomson Data Analyzer(TDA).Secondly,topic identification and evolution trends of single cell research was conducted through the LDA topic model.Thirdly,taking the post-discretized method which is used for topic evolution analysis for reference,the topics were also be dispersed to countries to detect the spatial distribution.Findings:The publication of single cell research shows significantly increasing tendency in the last decade.The topics of single cell research field can be divided into three categories,which respectively refers to single cell research methods,mechanism of biological process,and clinical application of single cell technologies.The different trends of these categories indicate that technological innovation drives the development of applied research.The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years.The topic distributions of some countries are relatively balanced,while for the other countries,several topics show significant superiority.Research limitations:The analyzed data of this study only contain those were included in the Web of Science Core Collection.Practical implications:This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges.The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.Originality/value:This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field.The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.展开更多
[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以...[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以图书情报领域中2011—2022年Web of Science数据库中的机器学习研究论文为例,融合LDA和Word2vec方法进行主题建模和主题演化分析,引入主题强度、主题影响力、主题关注度与主题新颖性指标识别热点主题与新兴热点主题。[结果/结论]研究结果表明,(1)Word2vec语义处理能力与LDA主题演化能力的结合能够更加准确地识别研究主题,直观展示研究主题的分阶段演化规律;(2)图书情报领域的机器学习研究主题主要分为自然语言处理与文本分析、数据挖掘与分析、信息与知识服务三大类范畴。各类主题之间的关联性较强,且具有主题关联演化特征;(3)设计的主题强度、主题影响力和主题关注度指标及综合指标能够较好地识别出2011—2014年、2015—2018年和2019—2022年3个不同周期阶段的热点主题。展开更多
基金supported by the National Natural Science Foundation of China,Grant numbers:71974167 and 71573225。
文摘Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.
基金the Chinese Academy of Sciences literature information capability construction project of 2020“Construction of strategic information research and consultation system in science and technology field”(Grant No.E290001)。
文摘Purpose:This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approach:The literatures on single cell research were extracted from Clarivate Analytic’s Web of Science Core Collection between 2009 and 2019.Firstly,bibliometric analyses were performed with Thomson Data Analyzer(TDA).Secondly,topic identification and evolution trends of single cell research was conducted through the LDA topic model.Thirdly,taking the post-discretized method which is used for topic evolution analysis for reference,the topics were also be dispersed to countries to detect the spatial distribution.Findings:The publication of single cell research shows significantly increasing tendency in the last decade.The topics of single cell research field can be divided into three categories,which respectively refers to single cell research methods,mechanism of biological process,and clinical application of single cell technologies.The different trends of these categories indicate that technological innovation drives the development of applied research.The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years.The topic distributions of some countries are relatively balanced,while for the other countries,several topics show significant superiority.Research limitations:The analyzed data of this study only contain those were included in the Web of Science Core Collection.Practical implications:This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges.The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.Originality/value:This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field.The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.
文摘[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以图书情报领域中2011—2022年Web of Science数据库中的机器学习研究论文为例,融合LDA和Word2vec方法进行主题建模和主题演化分析,引入主题强度、主题影响力、主题关注度与主题新颖性指标识别热点主题与新兴热点主题。[结果/结论]研究结果表明,(1)Word2vec语义处理能力与LDA主题演化能力的结合能够更加准确地识别研究主题,直观展示研究主题的分阶段演化规律;(2)图书情报领域的机器学习研究主题主要分为自然语言处理与文本分析、数据挖掘与分析、信息与知识服务三大类范畴。各类主题之间的关联性较强,且具有主题关联演化特征;(3)设计的主题强度、主题影响力和主题关注度指标及综合指标能够较好地识别出2011—2014年、2015—2018年和2019—2022年3个不同周期阶段的热点主题。
