Method development has always been and will continue to be a core driving force of microbiome science.In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by ...Method development has always been and will continue to be a core driving force of microbiome science.In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms:(1) a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging;(2) a shift from interrogating a consortium or population of cells to probing individual cells;and(3) a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding 'Made-in-China' tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.展开更多
Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation unde...Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.展开更多
Data with large dimensions will bring various problems to the application of data envelopment analysis(DEA).In this study,we focus on a“big data”problem related to the considerably large dimensions of the input-outp...Data with large dimensions will bring various problems to the application of data envelopment analysis(DEA).In this study,we focus on a“big data”problem related to the considerably large dimensions of the input-output data.The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation,including principal component analysis(PCA-DEA),which is based on the idea of aggregating input and output,efficiency contribution measurement(ECM),average efficiency measure(AEC),and regression-based detection(RB),which is based on the idea of variable selection.We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test.In addition,we discuss the effect of initial variable selection in RB for the first time.Based on the results,we offer guidelines that are more reliable on how to choose an appropriate method.展开更多
随着信息化建设的发展,高等学校积累了海量的教务数据,对该数据进行挖掘,并探讨建立高效的质量评价指标体系显得十分必要。首先,基于某高校学期内所有课程的评教大数据,从5项指标出发,采用熵权法定量计算各项指标的权重,建立了机器学习...随着信息化建设的发展,高等学校积累了海量的教务数据,对该数据进行挖掘,并探讨建立高效的质量评价指标体系显得十分必要。首先,基于某高校学期内所有课程的评教大数据,从5项指标出发,采用熵权法定量计算各项指标的权重,建立了机器学习的TOPSIS(Technique for order preference by similarity to an ideal solution)模型;然后,将大数据按照得分进行聚类分析,得到了相应的教学特征;最后,将课程考核平均绩点与各项指标得分进行相关分析,结果表明课程综合评价得分以及5项指标与课程成绩均呈现统计学上显著的相关性。展开更多
基金We are grateful to the support from the National Natural Science Foundation of China （NSFC） （31425002, 91231205, 81430011, 61303161, 31470220, and 31327001）, and the Frontier Science Research Program, the Soil-Microbe System Function and Regulation Program, and the Science and Technology Service Network Initiative （STS） from the Chinese Academy of Sciences （CAS）.
文摘Method development has always been and will continue to be a core driving force of microbiome science.In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms:(1) a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging;(2) a shift from interrogating a consortium or population of cells to probing individual cells;and(3) a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding 'Made-in-China' tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.
基金general project(No.71672080,72072086)of the National Natural ScienceFoundation of China.
文摘Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.
文摘Data with large dimensions will bring various problems to the application of data envelopment analysis(DEA).In this study,we focus on a“big data”problem related to the considerably large dimensions of the input-output data.The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation,including principal component analysis(PCA-DEA),which is based on the idea of aggregating input and output,efficiency contribution measurement(ECM),average efficiency measure(AEC),and regression-based detection(RB),which is based on the idea of variable selection.We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test.In addition,we discuss the effect of initial variable selection in RB for the first time.Based on the results,we offer guidelines that are more reliable on how to choose an appropriate method.
文摘随着信息化建设的发展,高等学校积累了海量的教务数据,对该数据进行挖掘,并探讨建立高效的质量评价指标体系显得十分必要。首先,基于某高校学期内所有课程的评教大数据,从5项指标出发,采用熵权法定量计算各项指标的权重,建立了机器学习的TOPSIS(Technique for order preference by similarity to an ideal solution)模型;然后,将大数据按照得分进行聚类分析,得到了相应的教学特征;最后,将课程考核平均绩点与各项指标得分进行相关分析,结果表明课程综合评价得分以及5项指标与课程成绩均呈现统计学上显著的相关性。
