WORLD-CLASS UNIVERSITIES AND SUBJECTS EVALUATION REPORT(2023-2024)

1.AboutCASEE The Chinese Academy of Science and Education Evaluation(CASEE),also known as the Chinese Academy of Evaluation Sciences(CAES),is a key evaluation and research institute established by Hangzhou Dianzi University in 2017.Prof.Junping Qiu,a well-known Chinese bibliometrican who has expertise in evaluation management and subsidized by the State Council of China,is the founder and the first president of CASEE.

WORLD CLASS UNIVERSITY RANKING: GLOBAL VIEW OF CHINESE SCIENTIFIC EVALUATION

On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.

高等教育质量发展指数的国内外比较研究

借鉴已有关于教育发展指数的研究,结合当今时代高等教育发展现状,系统设计了由4个一级指标和13个二级指标组成的高等教育质量发展指数指标体系。选取中国、美国、英国、德国、日本、澳大利亚六国作为考察国,以2011-2017年作为考察期,比较分析各国高等教育的人才培养质量发展指数、科学研究质量发展指数、社会服务质量发展指数和支撑条件质量发展指数。研究发现,中国高等教育发展迅速、成绩斐然,教育规模远超其他五国,但也存在一些问题,如教育质量发展不均衡,国际顶尖的大学和学科不多,人才培养质量距离发达国家有较大距离;高水平科研成果少,科研成果影响力亟待提高;科研成果转化率有待提升,社会服务能力亟需加强;基础设施落后,支撑条件有待改善。

The Quantitative Law of Network Information Growth

Based on the assumptions of ＂information transfer＂ and ＂information creation＂, this paper educes the multiplied growth mechanism of network information： that the gross quantity of network information （Im） is about n times as much as the quantity of real network information （Ir）. According to this theoretical model, we give a uniform explanation to all kinds of information growth models in existence, and put forward some proposals, such as ＂forbidding information transfer＂ and ＂building up the central information base＂, to control the repeated information flooding on the network and facilitate the full use of network information.

A case study on knowledge communication based on friends-list links in the science blogging community at Sciencenet.cn

Sciencenet.cn is the leading online portal serving the Chinese scientific community. This paper intends to analyze the interdisciplinary and intradisciplinary knowledge communication patterns based on friends-list links in the blog community at Sciencenet.cn by using hyperlink analysis and social network analysis. The major findings are: 1) More bloggers have an academic background in management science and life science; 2) there are some core actors in co-inlink network and co-outlink network, who take the lead in engaging with knowledge exchange activities and produce a great influence on interdisciplinary communication; 3) interactive relationships commonly exist between a blogger and those on his/her friends list, and the most linked-to blogs usually play a key role in generating interactive communication; 4) management science has the highest co-inlink count with life science or information science and it has the highest co-outlink count with life science or mathematical and physical science; 5) management science and life science have the greatest impact on information science and the interdisciplinary knowledge communication will also produce relatively significant influence on the development of information science discipline. It is our hope that this research can serve as a reference source for the future studies of academic virtual communities, and the development of mechanisms for facilitating increased engagement in knowledge exchange activities in academic virtual communities.

WORLD-CLASSUNIVERSITIESANDSUBJECTS EVALUATION REPORT (2022-2023)

1.About CASEE The Chinese Academy of Science and Education Evaluation(CASEE),also known as the Chinese Academy of Evaluation Sciences(CAES),is a key evaluation and research institute established by Hangzhou Dianzi University in 2017.Prof.Junping Qiu,a well-known Chinese bibliometrican who has expertise in evaluation management and subsidized by the State Council of China,is the founder and the first president of CASEE.

WORLD CLASS UNIVERSITY RANKING: GLOBAL VIEW OF CHINESE SCIENTIFIC EVALUATION

On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.

WORLD CLASS UNIVERSITY RANKING:GLOBAL VIEW OF CHINESE SCIENTIFIC EVALUATION

On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.

Open sharing ofgovernment data and enterprise decision making

Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The article first constructs an evolutionary game model of government data opening and sharing(with local governments and enterprises as game participants)by combining realistic scenarios and evolutionary game models.Then,it discusses the evolutionary stabilization strategies under different scenarios in a categorical manner.Finally,it uses MATLAB to conduct numerical simulations to verify the accuracy of the model and analyze the key influencing factors.Several results were obtained.(1)the optimal evolutionary path to promote government data opening and sharing is for enterprises to choose to"use data"and for local governments to choose the"positive sharing"strategy,and the enterprises'decision is the internal driver.(2)The value of data assets provided by local governments when applying the"positive sharing"strategy,the cost of data used by enterprises,and the data value conversion rate of enterprises are the key factors influencing the decisions of both parties.To promote open sharing and exploitation of government data,enterprises should enhance their independent innovation capabilities,while governments should enhance the value of data assets and continuously optimize their business environments.

Big data in education:Themes and trends

With the rapid growth of big data research,the existing research has investigated the research themes and trends of big data in different disciplines,but less has paid attentions on the field of education.Using bibliographic data from the Web of Science(Wos),we conduct bibliometric analysis and science mapping to explore the research themes and trends of big data in education.The results show that though education is not the major producer of big data research,it does have a positive development trend.In addition,we find that big data in education mainly serves as a tool to facilitate educational outcomes.Implications,limitations,and futuredirections arediscussed.

A Cloud Detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

Cloud detection in remote sensing images is a crucial task in various applications,such as meteorological disaster prediction and earth resource exploration,which require accurate cloud identi¯cation.This work proposes a cloud detection model based on the Cloud Detection neural Network(CDNet),incorporating a fusion mechanism of channel and spatial attention.Depthwise separable convolution is adopted to achieve a lightweight network model and enhance the e±ciency of network training and detection.In addition,the Convolutional Block Attention Module(CBAM)is integrated into the network to train the cloud detection model with attention features in channel and spatial dimensions.Experiments were conducted on Landsat 8 imagery to validate the proposed improved CDNet.Averaged over all testing images,the overall accuracy(OA),mean Pixel Accuracy(mPA),Kappa coe±cient and Mean Intersection over Union(MIoU)of improved CDNet were 96.38%,81.18%,96.05%,and 84.69%,respectively.Those results were better than the original CDNet and DeeplabV3+.Experiment results show that the improved CDNet is e®ective and robust for cloud detection in remote sensing images.

Editorial

Welcome to the journal Data Science and Informetrics(DSI).The interdisciplinary fields of data science and informetrics have been extremely active in recent years.DsI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.

Editorial

Welcome to the journal Data Science and Informetrics(DSI).The interdisciplinary fields of data science and informetrics have been extremely active in recent years.DsI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.

China's research contribution in big data

Big data is one of the current and future research frontiers.It has received international attention,and some countries have even upgraded big data research to a national strategy.Therefore,it is interesting to understand the status quo of big data research and identify the status and contribution of a country.Our study is divided into two parts.The first part of this study combines core lexical query and expanded lexical query to get relatively integral publications’data sets on big data.Citation relationships and a maximum connected subgraph algorithm are used to clean and filter unrelated publications.Then the Leiden algorithm is selected to cluster the citation network for big data and VOSviewer is used to map the big data knowledge structure.In the second part of this study,we analyze China’s research contribution in terms of research output and highly-cited papers.In order to better show the distribution of big data research in China,we utilized science overlay mapping to visualize the status quo of China’s research in big data.Our study shows that China is one of the most important countries in big data research and the research covers almost all areas of big data.However,the research performance is relatively low.In terms of knowledge structure with science overlay mapping,China’s research mainly focuses on cloud computing,the Internet of Things(Io T),and social media.However,research topics with a greater rate of highly-cited papers are mainly found in cloud computing,big data medicine,and Industry 4.0.These topics are also the dominant areas of China’s big data research.

Editorial

Welcome to the journal Data Science and In form e tries(DSI).The interdisciplinary fields of data science and informetrics have been extremely active in recent years.DSI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.

Editorial

Welcome to the journal Data Science and Informetrics(DSI).The interdisciplinary fields of data science and informetrics have been extremely active in recent years.DSI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.

Editorial

Welcome to the journal Data Science and Informetrics (DSI). The interdisciplinary fields of data science and informetrics have been extremely active in recent years. DSI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.

Editorial

Welcome to the journal Data Science and Informetrics(DSI).The interdisciplinary fields of data science and informetrics have been extremely active in recent years.DSI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.

Editorial

Welcome to the third issue of the journal Data Science and Informetrics(DSI).The interdisciplinary fields of data science and informetrics have been extremely active in recent years.DSI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.DSI adheres to international standards for scholarly journals with the goal to become an internationally recognized scholarly journal.We employ a double-blind peer review process.

Editorial

Welcome to the second issue of the journal Data Science and Informetrics(DSI). The interdisciplinary fields of data science and informetrics have been extremely active in recent years. DSI is created to publish research results in these fields and to promote innovative data-driven interdisciplinary research across these fields.