Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Co-Word中图文混排的文档一致性研究

近年来,人们设计各类算法来实现协同编辑中的文本一致性维护,而关于Word中图文混排的研究相对较少。对Word中的图片对象和文字对象重新建模、分别存储,以行作为单位设置文档中对象的摆放位置,并通过算法消解各类图片对象与文字对象间的位置冲突,实现图文混排与各站点的一致性。在Co-Word原型系统中增加了图文混排支持,并通过实例分析来讨论策略的执行流程。

Co-word clustering analysis for nursing safety management research focuses by PubMed

Objective： To analyze hot research areas and the present research status of nursing safety management in PubMed. Methods： PubMed was searched using ＂safety management＂ for the literature on nursing safety management. BICOMB 2.0 and SPSS 20.0 software were used to analyze high-frequency keywords and conduct co-word clustering analysis. Results： We searched for totally 2353 articles related to our topic and extracted 19 high-frequency keywords （27.50%）. Five research focuses were concluded, including： study on nursing safety culture; team work to promote nursing safety; practice of nursing safety management; workplace violence against nursing staffs; nursing safety and quality evaluation standard. Conclusion： Analysis of the hotspots of nursing safety management in the past 10 years will contribute to understanding the research emphases and trend of development, and provide reference for the study and practice of nursing safety management.

The hot issues of studies in China on digital information resources: Based on co-word analysis

With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.

Structures and dynamics of scientific knowledge networks:An empirical analysis based on a co-word network

Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.

Research status and hotspots of economic evaluation in nursing by co-word clustering analysis

Objective:The aim of this study is to discover research status and hotspots of economic evaluation(EE)in nursing area using co-word cluster analysis.Methods:Medical Subject Heading(MeSH)term“cost–benefit analysis”was searched in PubMed and nursing journals were limited by the function of filter.The information of author,country,year,journal,and keywords of collected paper was extracted and exported to Bicomb 2.0 system,where high-frequency terms and other data could be further mined.SPSS 19.0 was used for cluster analysis to generate dendrogram.Results:In all,3,020 articles were found and 10,573 MeSH terms were detected;among them,1,909 were MeSH major topics and generated 42 high-frequency terms.The consequence of dendrogram showed seven clusters,representing seven research hotspots:skin administration,infection prevention,education program,nurse education and management,EE research,neoplasm patient,and extension of nurse function.Conclusions:Nursing EE research involved multiple aspects in nursing area,which is an important indicator for decision-making.Although the number of papers is increasing,the quality of study is not promising.Therefore,further study may be required to detect nurses’knowledge of economic analysis method and their attitude to apply it into nursing research.More nursing economics course could carry out in nursing school or hospitals.

基于改进Co-word分析法的脆弱性数据分析技术研究

针对脆弱性信息系统中脆弱性数据质量保证问题,研究基于改进Co-word分析法的脆弱性数据分析技术。依据脆弱性分类法分类特征确定分析关键字,通过对Co-word分析法的改进设计了脆弱性数据分析算法,实现了脆弱性信息系统中脆弱性数据的验证、完善和补充。

融合WK和CSK Co-word Model的共词分析法

基于关键词的共词分析方法是利用关键词在文献中出现的频次高低来确定该领域研究热点的方法。传统的基于关键词的共词分析方法只是简单的统计关键词出现的绝对次数,忽略了关键词以及文献的内在特性以及关键词在概念上的重复性,造成结果的不准确性。文中提出了一种融合关键词加权模型(WK Co-word Model)和同义关键词合并模型(CSK Co-word Model)的共词分析法,该方法根据关键词自身的特征以及关键词所在载体文献的特征对关键词进行加权处理,同时以同义词词林为基础,计算关键词之间的词语相似度,合并同义关键词。该方法既强调了关键词之间权重的不同,又消除了同义词对结果准确性造成的影响。仿真实验表明,该方法提高了共词分析的准确性。

Bibliometric analysis of ChatGPT and plastic surgery research:Insights from diverse search strategies and co-word analysis

Background:The rise of artificial intelligence in healthcare,particularly the development of large language models like ChatGPT,has opened new avenues for innovation in medical fields,including plastic surgery.ChatGPT offers potential applications in patient education,surgical planning,and decision-making support,making it an important research subject.However,there has been limited investigation into its impact on plastic surgery.The objective of this study was to investigate the progress of research on ChatGPT and plastic surgery,focusing on key contributors and emerging topics within the field.Methods:Five distinct search strategies were employed to analyze relevant publications from the Scopus database.Results:The analysis identified and presented the top authors,universities,countries,sponsors,and journals(within each search strategy).The co-authorship networks of authors,universities,and countries are graphically presented.The authors’performance was depicted by various indicators,such as total publications,citations,hindex,g-index,and m-index.A co-word analysis revealed the focus of the papers,which were presented in 15 groups.This multifaceted approach provides a detailed understanding of key themes in the field.Conclusion:This report offers a comprehensive overview of the current state of research at the intersection of ChatGPT and plastic surgery.

基于共词分析法的我国骨科日间手术主题热点研究

目的:对国内骨科日间手术领域已发表文献进行信息计量学可视化研究,深入挖掘其热点研究主题与方向。方法:主要基于万方数据库和中国知网(CNKI),综合运用信息计量学手段从发文量、作者、研究机构等角度进行常规分析,利用共词分析法构建高频关键词共现网络进行数据挖掘。结果:从分析结果上看,2011年—2024年骨科日间手术发文量呈现上升趋势;166篇文献中涉及660位作者,人均发文0.25篇,其中最高产作者的发文量为8篇,核心作者共有16位;192家研究机构中四川大学华西医院发文量排在第一;确定词频≥3的35个关键词为高频关键词,通过中心性分析发现日间手术、半月板、全膝关节置换术、睡眠质量等关键词为研究热点,疼痛控制、脊柱结核、中药外敷疗法等为相对边缘的研究主题,根据中心性差异对高频关键词进行核心区、中间区和边缘区的划分;经凝聚子群分析我国目前骨科日间手术主要有腰椎间盘突出、膝关节和髋关节置换、患者心理状况和睡眠质量评估、术后并发症安全管理4个研究方向。结论:近年来国内骨科日间手术研究呈现上升趋势,但整体发文量偏少;研究人员较为分散,国内研究力量有待壮大;研究前沿分析,研究内容上存在一些薄弱点,需要加强研究的广度和深度。

浙江省科技创新政策文本分析:2000—2022

通过对浙江省2000—2022年颁布的科技创新政策进行文本分析,探讨了其在不同阶段的特征和政策关注重点。利用词频分析和共词网络分析方法,研究发现浙江省科技创新政策的关注重点已从最初的单一科技项目和企业合作,逐步向更加多元化的科技创新、产业发展、法规建设、人才培养等方面转变,其科技创新政策日渐成熟,更强调构建完善的科技创新生态系统。基于分析结果,提出加强政策执行效果监督评估、进一步细化政策内容、加强政策宣传解读、优化政策体系等建议,旨在推动浙江省科技创新事业持续健康发展。

基于语义加权网络的重点领域科学基金资助态势识别研究

[目的/意义]科学基金制度是各国(地区)提升科学研究水平的重要方式,准确识别科学基金的资助方向和资助成效是感知科技发展态势的关键。[方法/过程]为识别科学基金资助态势,在提出关键短语抽取规则后,利用语义相似度消除同、近义词的歧义影响,利用动态滑动窗口和语义相似度构建语义加权的词共现网络,并基于社团划分方法识别基金主题。在集成电路领域比较1812项中国国家自然科学基金和2807项美国国家科学基金的资助主题分布、资助力度变化和资助效果,该方法能够准确识别基金主题分布和资助成效。[结果/结论]中国国家自然科学基金和美国国家科学基金均涵盖了集成电路领域的主要研究主题,在重点资助方向和资助力度上有较大差异;中国国家自然科学基金资助的论文平均被引频次相对较低,受资助机构相对单一。

基于主路径分析和引文文本挖掘的关键核心技术基础研究结构识别

准确识别关键核心技术的基础研究结构,为聚力突破“卡脖子”关键核心技术提供参考。提出了一套用以识别关键核心技术基础研究结构的理论框架——科技知识树,构建了针对“纯基础研究—应用型基础研究—关键核心技术—常规技术”关联的分析方法,通过综合应用复杂网络分析与文本挖掘技术,理清了基础研究与应用研究间的知识流动关系,识别出了关键核心技术的基础研究结构。以集成电路技术为例进行实证分析,识别得到关键核心技术分布于21个领域,涵盖11类应用型基础研究,包括Petri网、算法优化、光子学原理等;14类纯基础研究,包括半导体原理、数字电路建构原理等。经验证,提出的方法能够有效识别支撑技术研发的基础研究结构,为成果转化、技术攻关与基础研究布局提供了参考。

广西自然资源低碳政策演进过程及特征研究——基于2007—2022年政策文本量化分析

科学认识和把握广西自然资源低碳政策演进过程和结构特征,对统筹推进新时期广西自然资源低碳管理工作具有重要意义。以广西2007—2022年自治区级部门出台的自然资源低碳政策为研究对象,运用政策效力评估模型、政策工具分类、共词分析法对政策文本进行量化分析。研究表明:(1)广西自然资源低碳政策发展历程分为生态文明探索期、生态文明建设期、低碳战略布局期、生态价值转化期4个时期;(2)广西自然资源低碳政策的政策力度较强,政策目标多为定性且覆盖面窄,政策措施多样但描述笼统,政策反馈机制待健全;(3)政策工具以强制型为主,种类丰富但配比失衡,同质化问题突出。可多措并举提高政策效力,丰富政策工具组合,以提高广西自然资源低碳政策质量。

基于政策工具-政策议题视角下老年教育政策研究——以北京市为例

基于政策工具和政策议题视角,审视进入新时代以来北京市发布的老年教育相关政策文件,运用ROSTCM6和NetDraw2.084软件开展共词分析,对完善老年教育服务体系、优化老年教育投入、提升老年教育服务能力和凸显老年教育成果等四个议题,采用命令性工具、激励工具、能力建设工具和系统变革工具进行文本量化分析,发现北京市老年教育在政策工具使用上存在政策工具结构失衡、政策工具内部运用失调、不同政策议题下政策工具缺少系统变革工具、政策工具选择性单一等问题;同时提出“调整政策工具结构,优化政策工具的组合与占比”“优化政策工具与政策议题配置,提升政策与老年教育发展的契合度”“加强统筹规划与示范实践,进一步发展系统变革与能力建设的覆盖面”“加强施策的精准度,持续提升政府公共治理水平”等建议,助力老年教育高质量发展。

基于战略坐标图的我国“互联网+护理服务”研究主题分析

目的:分析国内“互联网+护理服务”研究主题和发展态势,以期为“互联网+护理服务”高质量、可持续发展提供参考。方法:以“互联网+护理服务”为主题,检索建库至2022年12月期间中国知网收录的文献,使用文献计量学分析软件BICOMB进行计量分析,使用统计分析软件SPSS进行聚类分析,绘制研究主题战略坐标图,揭示核心主题及其发展态势。结果:共纳入327篇文献,论文发文量快速增加,期刊分布相对集中。通过关键词聚类出9大研究主题,其中核心且成熟的主题是“互联网+护理服务”护患体验、“互联网+护理服务”护患双方认知意愿需求以及老年慢性病延续护理;核心不成熟主题是“互联网+护理服务”质量;待发展主题是上门服务、“互联网+护理服务”现状及影响因素、“互联网+护理服务”风险防控以及护士培训。结论:国内“互联网+护理服务”研究目前处于快速发展阶段,受到众多研究者的关注。各研究主题发展阶段不同,逐渐向“互联网+护理服务”内涵发展。研究者应关注热点主题的进展,进一步推动风险防控、质量评价等发展潜力主题的研究。

基于细粒度知识图谱的科技文献主题发现与热点分析

[目的/意义]利用知识图谱进行细粒度的知识组织并识别科技文献的热点主题,有助于科研工作者把握领域的研究现状和学科前沿,进而为科研资源优化配置提供有力支持。[方法/过程]提出一套基于知识图谱的主题发现和热点分析方法。首先,识别科技文献中的知识元,再抽取知识元中的语义实体进行主题发现;其次,构建“文献—知识元—主题”知识图谱,通过知识图谱中的共现关系网络识别领域的研究热点;最后,以农学领域的中文学术论文为实验数据,进行实证研究。[结果/结论]所构建的细粒度知识图谱不仅能够揭示科学知识与主题的潜在关联,而且能够实现科技文献主题的热点分析。

国内智慧教育领域研究热点与趋势

近年来,智慧教育的兴起引起人们的广泛关注。本文以2013—2022年CSSCI数据库中关于智慧教育的572篇文献的高频关键词为原始数据,运用Bibexcel、Ucinet和SPSS软件对高频关键词数据进行共词分析、社会网络分析、多维尺度分析和聚类分析,揭示智慧教育领域的研究热点和发展趋势。研究结果显示智慧教育研究的热点主要体现在:教育信息化、教育现代化等政策、战略;智慧教育的内涵、特征、功能等;智慧教育的体系架构与智慧环境建设等;智慧教育的技术支撑及实践。本文还基于智慧教育研究现状,提出未来发展需要关注的四个方向。

基于共词分析的中国科技成果转化政策变迁研究

[目的/意义]科技成果转化是科技创新体系的重要组成部分,是实现中国科技自立自强的重要基础。[方法/过程]以1985—2022年中国政府发布的167份政策文本为研究对象,应用共词分析方法和社会网络研究方法,深入探究政策变迁的阶段性政策特征和总体变迁规律。[结果/结论]中国科技成果转化政策具有明显的阶段性特征:探索阶段注重科技体制改革、发展阶段注重科技创新、完善阶段注重科技成果转化。政策侧重点随政策变迁发生转移,体现国家推进科技成果转化的过程。政策供给不断增强,政策主题得到极大拓展,主题词互动增强且趋于稳定,政策主体趋向多元,政策趋于同时关注宏观规划和微观实施。

基于隐含空间模型降维和LDA模型的学科主题识别研究

【目的/意义】现有学科研究主题的梳理多为领域专家的定性分析和学科学者的文献梳理,一定程度会由于研究思维的局限性和获取知识的片面性造成学科研究主题误判,为有效避免漏判误判现象的发生,提出识别模型。【方法/过程】首先,运用传统LDA模型分析主题特征词;其次,结合上下文语义信息进行中文分词,形成学科主题词库;最后,结合隐含位置聚类算法发现潜在社区,提高主题识别效果。【结果/结论】提出的方法一定程度上优化了主题挖掘算法在识别短文本主题的效果,消除主观意愿。由计算机自行分类并实现科学研究前沿主题的预测,揭示前沿领域的研究热点,为致力于研究前沿学科的新兴学者提供参考价值。