scopus-BIT
Scopus使用指南
网络信息(通过 检索引擎) 网络信息(通过Scirus检索引擎) 检索引擎
整合 Scirus 网络检索
专利检索
专利信息: 专利信息:来自 USPTO/EPO/WIPO/JPO
检索演示) 精练检索结果(检索演示)
排除以下范围
限于以下范围
摘要+ 摘要+参考文献页面
被引次数 作者 作者机构 摘要
选择显示格式: 选择显示格式: 基本和扩展
Hale Waihona Puke 摘要和参考文献页面的定制链接
标准链接和个性化链接
个性化服务
检索保存 检索提示 引文提示
注册
个人注册界面
个性化设置
我的提示
系统根据你设置的检索式定期给您发送最新 被Scopus收录的相关文献; 收录的相关文献
您设置一篇文章,如果该文章被别的文章引用,系统则自动给您发送提示信息. 您设置一篇文章,如果该文章被别的文章引用,系统则自动给您发送提示信息.
优越的检索功能
相关的支持服务
Scopus 信息网站 () ) 图书馆员: 图书馆员: 如何在图书馆设置Scopus 链接 如何在图书馆设置Scopus 了解 Scopus及相关新闻 Scopus及相关新闻 最终用户 快速参考指南 常见问题解答 联系爱思唯尔(Elsevier)北京代表处 联系爱思唯尔(Elsevier) 电话:85185800电邮: 电话:85185800-209 电邮: cninfo@
截词算符
?替代单个字母,如:wom?n 可检出 woman, women; 替代单个字母, 替代任意个字母, pharmacology等 * 替代任意个字母,如:pharmac* 可检出 pharmacy, pharmacology等; sul*ur 可检出 sulfur, sulphur
英文数据库与英文文献检索通用课件
These databases combine elements of structured and unstructured databases They provide some structure for organizing information, but not as strictly as fully structured databases
English Database and English Literature Retrieval
2023
REPORTING
Overview of English databasesIntroduction to commonly used English databasesEnglish Literature Retrieval SkillsReading and organizing English literaturePractice of English Database and Literature RetrievalAdvanced English Database and Literature Retrieval
PART
03
English Literature Retrieval Skills
2023
REPORTING
Choosing the appropriate keywords
The selection of keywords is crucial for the search results. Choosing keywords that are relevant to the topic and representative can improve the accuracy and efficiency of retrieval.
scopus位置算符
scopus位置算符Scopus是一种用于评估学术研究影响力的工具，它提供了全球范围内的学术文献检索和分析功能。
作为一种基于文献引用的数据库，Scopus可以帮助研究人员找到相关的文献、评估研究的影响力以及追踪学术趋势。
本文将介绍Scopus的位置算符功能，并探讨其在学术研究中的应用。
位置算符是Scopus中的一项重要功能，它可以帮助研究人员更准确地定位和筛选相关的文献。
在Scopus中，位置算符使用特定的符号和运算符来指定搜索条件。
这样一来，研究人员可以通过在搜索字符串中使用位置算符来限定关键词的位置和顺序，从而获得更精确的搜索结果。
Scopus中常用的位置算符有“AND”、“OR”和“NOT”。
其中，“AND”表示两个关键词必须同时出现在文献中，“OR”表示两个关键词中的任意一个出现在文献中，而“NOT”表示排除包含某个关键词的文献。
通过巧妙地组合这些位置算符，研究人员可以根据自己的需要进行精确的文献检索。
以一个具体的例子来说明位置算符的应用。
假设研究人员对“人工智能”和“医疗”领域中的文献感兴趣。
他们想要找到同时涉及这两个领域的文献，于是可以使用“AND”位置算符来限定关键词的位置和顺序。
在Scopus中，他们可以输入“AI AND healthcare”来进行检索。
这样一来，他们就能够找到同时涉及“人工智能”和“医疗”领域的文献，从而更好地了解这两个领域的交叉研究。
除了基本的位置算符，Scopus还提供了一些高级的位置算符功能，如“NEAR”和“SAME”。
这些功能可以帮助研究人员更进一步地精确搜索。
例如，研究人员可以使用“NEAR”位置算符来限定两个关键词之间的距离，以便找到它们在文献中相对靠近的位置。
而使用“SAME”位置算符，则可以确保两个关键词在文献中的顺序不变。
在学术研究中，Scopus的位置算符功能可以帮助研究人员更快速、准确地找到相关的文献。
通过合理地使用位置算符，研究人员可以缩小搜索范围，避免大量无关的文献，从而节省时间和精力。
CiteSpace使用手册
The CiteSpace ManualVersion 1.01Chaomei ChenCollege of Computing and InformaticsDrexel UniversityHow to cite:Chen, Chaomei (2014) The CiteSpace Manual. /~cchen/citespace/CiteSpaceManual.pdfContents1How can I find the latest version of the CiteSpace Manual? (5)2What can I use CiteSpace for? (5)2.1What if I have Questions (7)2.2How should I cite CiteSpace? (7)2.3Where are the Users of CiteSpace? (8)3Requirements to Run CiteSpace (10)3.1Java Runtime (JRE) (10)3.2How do I check whether Java is on my computer? (10)3.3Do I have a 32-bit or 64-bit Computer? (12)4How to Install and Configure CiteSpace (12)4.1Where Can I download CiteSpace from the Web? (12)4.2What is the maximum number of records that I can handle with CiteSpace? (13)4.3How to configure the memory allocation for CiteSpace? (13)4.4How to uninstall CiteSpace (14)4.5On Mac or Unix-based Systems (15)5Get Started with CiteSpace (19)5.1Try it with a demonstrative dataset (19)5.1.1The Demo Project (20)5.1.2Clustering (23)5.1.3Generate Cluster Labels (25)5.1.4Where are the major areas of research based on the input dataset? (27)5.1.5How are these major areas connected? (28)5.1.6Where are the most active areas? (28)5.1.7What is each major area about? Which/where are the key papers for a given area?365.1.8Timeline View (38)5.2Try it with a dataset of your own (39)5.2.1Collecting Data (39)5.2.2Working with a CiteSpace Project (43)5.2.3Data Sources in Chinese (44)5.2.4How to handle search results containing irrelevant topics (45)6Configure a CiteSpace Run (47)6.1Time Slicing (47)6.3Configure the Networks (48)6.3.1Bibliographic Coupling (49)6.4Node Selection Criteria (49)6.4.1Do I have the right network? (50)6.5Pruning, or Link Reduction (50)6.6Visualization (51)7Interacting with CiteSpace (51)7.1How to Show or Hide Link Strengths (51)7.2Adding a Persistent Label to a Node (52)7.3Using Aliases to Merge Nodes (53)7.4How to Exclude a Node from the Network (55)7.5How to Use the Fisheye View Slider (55)7.6How to Configure When to Calculate Centrality Scores Automatically (56)7.7How to Save the Visualization as a PNG File (57)7.8Filters: Match Records with Pubmed (58)8Additional Functions (62)8.1Menu: Data (62)8.1.1CiteSpace Built-in Database (62)8.1.2Utility Functions for the Web of Science Format (65)8.1.3Scopus (66)8.1.4PubMed (67)8.2Menu: Network (69)8.2.1Batch Export to Pajek .net Files (69)8.3Menu: Geographical (69)8.3.1Generate Google Earth Maps (69)8.4Menu: Overlay Maps (72)8.4.1Add an Overlay (73)8.4.2Further Reading and Terms of Use (75)8.5Menu: Text (75)8.5.1Concept Trees and Predicate Trees (75)8.5.2List Terms by Clumping Properties (78)8.5.3Latent Semantic Analysis (79)9Selected Examples (80)10.1Information Theoretic (82)10.1.1Information Entropy (82)10.2Structural (82)10.2.1Betweenness Centrality (82)10.2.2Modularity (82)10.2.3Silhouette (82)10.3Temporal (82)10.3.1Burstness (82)10.4Combined (82)10.4.1Sigma (82)10.5Cluster Labeling (83)10.5.1Term Frequency by Inversed Document Frequency (83)10.5.2Log-Likelihood Ratio (83)10.5.3Mutual Information (83)11References (83)1How can I find the latest version of the CiteSpace Manual?The latest version of the CiteSpace Manual is always at the following location:/~cchen/citespace/CiteSpaceManual.pdfYou can also access the manual from CiteSpace: Help ►View the CiteSpace Manual (PDF). It will open up the PDF file in a new browser window.Figure 1. The latest version of the CiteSpace Manual is accessible from CiteSpace itself.2What can I use CiteSpace for?CiteSpace is designed to answer questions about a knowledge domain, which is a broadly defined concept that covers a scientific field, a research area, or a scientific discipline. A knowledge domain is typically represented by a set of bibliographic records of relevant publications. It is your responsibility to prepare the most appropriate and representative dataset that contains adequate information to answer your questions.CiteSpace is designed to make it easy for you to answer questions about the structure and dynamics of a knowledge domain. Here are some typical questions:•What are the major areas of research based on the input dataset?•How are these major areas connected, i.e. through which specific articles?•Where are the most active areas?•What is each major area about? Which/where are the key papers for a given area?•Are there critical transitions in the history of the development of the field? Where are the ‘turning points’?The design of CiteSpace is inspired by Thomas Kuhn’s structure of scientific revolutions. The central idea is that centers of research focus change over time, sometime incrementally and other times drastically. The development of science can be traced by studying their footprints revealed by scholarly publications.Members of the contemporary scientific community make their contributions. Their contributions form a dynamic and self-organizing system of knowledge. The system contains consensus, disputes, uncertainties, hypotheses, mysteries, unsolved problems, and unanswered questions. It is not enough to study a single school of thought. In fact, a better understanding of a specific topic often relies on an understanding of how it is related to other topics.The foundation of the CiteSpace is network analysis and visualization. Through network modeling and visualization, you can explore the intellectual landscape of a knowledge domain and discern what questions researchers have been trying to answer and what methods and tools they have developed to reach their goals.This is not a simple task. Rather it is often conceptually demanding and complex. If you are about to write a novel, the word processor or a text editor can make the task easier, but it cannot help you to create the plot or enrich the character of your hero. Similarly, and probably to a greater extent, CiteSpace can generate X-ray photos of a knowledge domain, but to interpret what these X-ray photos mean, you need to have some knowledge of various elements involved. The role of CiteSpace is to shift some of the traditionally labor-some burdens to computer algorithms and interactive visualizations so that you can concentrate on what human users are most good at in problem solving and truth finding. However, it is probably easier to generate some mysterious looking visualizations with CiteSpace than to fully understand what these visualizations tell you and who may benefit from such findings.Figure 2. Hierarchically organized functions of CiteSpace, for example, GUI ►Pruning ►Pathfinder: true.2.1What if I have QuestionsIf you have a question regarding the use of CiteSpace, you should first check the manual whether your question is answered in the manual. You can do a simple search through the PDF file to find out.If the manual does not get you anywhere, you can ask your questions on the Facebook page of CiteSpace:https:///pages/CiteSpace/276625072366558You can also post questions to my blog on sciencenet:/home.php?mod=space&uid=496649Please refrain from sending me emails because you will have a much better chance to get my response from either the Facebook or the sciencenet blog.Generally speaking, thoughtful questions get answered quickly. Questions that you may be able to figure out the answer for yourself if you think a little bit more about it would have a lower priority in the answering queue; it is quite possible that some of them never get answered.2.2How should I cite CiteSpace?The following three publications represent the core ideas of CiteSpace.The 2004 PNAS paper is the initial publication on CiteSpace (Chen 2004). In hindsight, it could have been named CiteSpace I. The 19-page 2006 JASIST paper gives the most thorough and in-depth description of CiteSpace II’s key functions (C. M. Chen, 2006), plus a follow-up study of domain experts identified in the visualizations. The 2010 JASIST paper is even longer with 24 pages (C. Chen, Ibekwe-SanJuan, & Hou, 2010), which is the third of the trilogy. It describes technical details on how cluster labels are selected and how each of the three selection algorithms in comparison with labels chosen by domain experts.ReferenceCitations(Google Scholar)800 Chen, C. (2006). "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature." Journal of the AmericanSociety for Information Science and Technology 57(3): 359-377.394 Chen , C. (2004). "Searching for intellectual turning points: Progressive Knowledge Domain Visualization." Proc. Natl. Acad. Sci. USA101(Suppl.): 5303-5310.157 Chen, C., et al. (2010). "The structure and dynamics of co-citation clusters:A multiple-perspective co-citation analysis." Journal of the AmericanSociety for Information Science and Technology 61(7): 1386-1409.The most recent case study of a topic outside the realm of information science and scientometrics is a scienometric study of regenerative medicine (C. Chen, Hu, Liu, & Tseng, 2012).Chen, C., et al. (2012). "Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace."Expert Opinions on Biological Therapy 12(5): 593-608.2.3Where are the Users of CiteSpace?In terms of the cities where CiteSpace were used, China, the United States, and Europe are prominent. Brazil, Turkey, and Spain also have many cities on the chart.Figure 3. Cities with users of CiteSpace between August 2013 and March 2014 are shown on the map. The colors of markers depict the level of user intensity: green (1-10), yellow (10-100), red (100-1000), and the large red water dropshaped marker (1000+).Figure 4. The use of CiteSpace in China (August 2013 – March 2014).Figure 5. The use of CiteSpace in the United States (August 2013 – March 2014).Figure 6. The use of CiteSpace in Europe (August 2013 – March 2014).3Requirements to Run CiteSpace3.1Java Runtime (JRE)CiteSpace is written in Java. It is a Java application. You should be able to run it on a computer that supports Java, including Windows or Mac.CiteSpace is currently optimized for Windows 64-bit Java 7 (i.e. Java 1.7).To run a Java application on your computer, you need to have Java Runtime (JRE) installed on your computer.3.2How do I check whether Java is on my computer?Figure 7. Select Control Panel.Figure 8. Click into the Programs category to find the Java control panel.Figure 9. Locate the Java control panel.Figure 10. Java Control Panel. Choose the Java tab and press the View button to see more detail.Figure 11. Java Runtime 1.7 is installed.3.3Do I have a 32-bit or 64-bit Computer?You need to find out whether your computer has a 32-bit or a 64-bit operating system.Go to Control Panel ►System and Security ►System. You will see various details about your computer. Under the System type, you will see whether you have a 32-bit or a 64-bit operating system.Follow the link below for further instructions on how to install Java:/en/download/help/index_installing.xmlOnce you have Java Runtime setup on your computer, you can proceed to install CiteSpace.4How to Install and Configure CiteSpaceCiteSpace is provided as a zip file for 64-bit and 32-bit computers. For Mac users, you need to download the 64-bit version.4.1Where Can I download CiteSpace from the Web?You can download the latest version of CiteSpace from the following website:/~cchen/citespace/download.htmlFigure 12.The download page of CiteSpace.After you download the zip file to your computer, unpack the zip file to a folder of your choice.Figure 13. CiteSpace is unpacked to the D drive on a computer.Now you can start CiteSpace by double clicking on the StartCiteSpace file.If you need to modify the amount memory allocated for CiteSpace (more precisely for Java Virtual Machine on which CiteSpace to be running), you can edit StartCiteSpace as a plain text file with any text editor.4.2What is the maximum number of records that I can handle with CiteSpace?This question needs to be answered at two levels: the number of records processed by CiteSpace and the number of nodes visualized, i.e. you can see and interact with them in CiteSpace.The first number is the total number of records in your downloaded dataset. CiteSpace reads through each record in your download files.The second number is determined by the selection criteria you specify and by the amount of memory, i.e. RAM, available on your computer. The more RAM you can make available for CiteSpace, the larger sized network you can visualize with a faster response rate.The speed of processing is also affected by a few computationally expensive algorithms such as Pathfinder network scaling and cluster labeling. Empirically, the best options for Pathfinder network scaling would be 50~500 nodes per slice. With faster computers or if you can wait for a bit longer, you can raise the number accordingly.The completion time of cluster labeling is related to the size of your dataset. If the entire timespan of your dataset is 100 years but you will only need to consider the most recent 10 years, it will be a good idea to carve out a much smaller dataset as long as it covers the 10 years of interest. It will reduce the processing time considerably.4.3How to configure the memory allocation for CiteSpace?The performance of CiteSpace is influenced by the amount of memory accessible to the Java Virtual Machine (JVM) on which CiteSpace is running. To analyze a large amount of records, you should consider allocating as much as memory for CiteSpace to use.You can modify the StartCiteSpace.cmd file to optimize the setting. More specifically, modify line 14 in the file. For example, -Xmx2g means that CiteSpace may get a maximum of 2GB of RAM to work with. Save the file after making any changes. And restart CiteSpace.Figure 14. Configure the memory for Java in line 14.4.4How to uninstall CiteSpaceYou can use the following steps to remove cached copies of CiteSpace from your computer.Figure 15. In a Command Prompt window, type javaws –viewer.When you see a list of cached copies of CiteSpace in the Java Cache Viewer, select the items that you want to remove and then click on the button with a red cross.Figure 16. Select a cached copy of CiteSpace and remove the item.4.5On Mac or Unix-based SystemsThe following example shows you the basic steps to get started with CiteSpace on a Mac. First, go to the CiteSpace homepage in a browser such as Chrome and download the latest 64-bit version.Figure 17. On a Mac, go to the CiteSpace home page in a browser such as Chrome and download the latest 64-bit version. Once the download is completed, follow the option “Show in Finder.” It will take you to a list of files downloaded to your Mac. The most recent file should be the zip file for CiteSpace.Figure 18. Choose “Show in Finder.”Figure 19. The downloaded zip file is shown in your Finder.Double-click on the zip file to unzip the file to a folder in the current folder.Figure 20. The zip file is unzipped to a new folder on the list.Figure 21. The new folder contains CiteSpaceII.jar and a lib folder.The simplest way to get started with CiteSpace is to open the CiteSpaceII.jar by clicking on it while holding the “Control” key on Mac. Select Open from the pop-up menu.Figure 22. Click on the CiteSpaceII.jar while holding the “Control” key and select “Open.”Due to the Java security settings, you will see a dialog box with two options for Open or Cancel.Choose Open to proceed. It will not harm your computer.Figure 23. Choose “Open” from the dialog box to proceed.After you choose Open, CiteSpace is getting started on Mac. You will see its opening page asfollows. Choose “Agree” to continue.Figure 24. CiteSpace is now started on Mac.Figure 25. Screenshots of running the Demo project of CiteSpace on Mac.It is a good idea to get familiar with the basic functions of CiteSpace by going through the Demo project on terrorism, which is included in the zip file.If you want to configure various Java Virtual Machine parameters in more detail than what is shown in the above example, you may generate a bash file for your Mac as follows.The Mac equivalent of the StartCiteSpace.cmd would be a bash file, which should have a file extension of .sh and should be executable. Let’s name the file as StartCiteSpace.sh to be consistent.1.The content of the StartCiteSpace.sh file should have the following two lines:#!/bin/bashjava -Xms1g -Xmx4g -Xss5m -jar CiteSpaceIII.jar2.The following instruction turns the StartCiteSpace.sh file to an executable file:chmod +x StartCiteSpace.sh3.To invoke the executable file, simply type its name or double click on it.StartCiteSpace5Get Started with CiteSpace5.1Try it with a demonstrative datasetWhen you installed CiteSpace for the first time, a demonstrative dataset on terrorism research is setup for you to play with and get familiar with the major analytic functions in CiteSpace.If you have never used CiteSpace before, I strongly recommend you to start with this demo dataset.To launch CiteSpace, double click on the StartCiteSpace.cmd file. You will see a command prompt window first. This window will also display various information on the status and any errors.Figure 26. The command prompt window.You will see another window of “About CiteSpace” – it displays system information of your computer, including the Java version.To proceed, you need to click on the Agree button. CiteSpace may collect user driven events for research purposes.Figure 27. The “About CiteSpace” window. To proceed, click on the Agree button.Next, you will see the main user interface of CiteSpace.The user interface is divided into left and right halves. The left-hand side contains controls of projects (i.e. input datasets) and progress report windows. The right-hand side contains several panels for configuring the process with various parameters.In a nutshell, the process in CiteSpace takes an input dataset specified in the current project, constructs network models of bibliographic entities, and visualizes the networks for interactive exploration for trends and patterns identified from the dataset.The demo project contains a dataset on publications about terrorism research. These bibliographic records were retrieved from the Web of Science. See later sections on tips for how to construct your own dataset.5.1.1The Demo ProjectWe will start the process and explain how CiteSpace is designed to help you answer some of the key questions about a knowledge domain, i.e. a field of study, a research area, or a set of publications defined by the user.Press the green GO! button to start the process.Figure 28. The main user interface of CiteSpace.CiteSpace will read the data files in the current project (Demo) and report its progress in the two windows on the left-hand side of the user interface. When the modeling process is completed, you have three options to choose: Visualize, Save As GraphML, or Cancel.Visualize:This option will take you to the visualization window for further interactive exploration. Save As GraphML:This option will save the constructed network in a file in a common graph format. No visualization.Cancel:This option will not generate any interactive visualization nor save any files. It allowsyou to reconfigure the process and re-run the process.Figure 29. CiteSpace is ready to visualize the constructed network.If you click on the Visualize button, a new window will pop up. This is the Visualization Window. Initially you will see some movements on your screen with a black background. Once the movements are settled, the background color turns to white.Let’s focus on what the initial visualization tells us and then explore what else we can find by using additional functions.First, CiteSpace visualizes a merged network based on several networks corresponding to snapshots of consecutive years. In the Demo project example, the overall time span is from 1996 through 2003. The merged network characterizes the development of the field over time, showing the most important footprints of the related research activities. Each dot represents a node in the network. In the Demo case, the nodes are cited references. CiteSpace can generate networks of other types of entities. Here let’s focus on cited references only for now. Lines that connect nodes are co-citation links; again, CiteSpace can generate networks of other types of links. The colors of these lines are designed to show when a connection was made for the first time. Note that this is influenced by the scope and the depth of the given dataset.The color encoding makes it easy for us to tell which part of the network is old and which is new. If you see that some references are shown with labels, then you will know that these references are highly cited, suggesting that they are probably landmark papers in the field. A list on the left side of the window shows all the nodes appeared in the visualization. The list can be sorted by the frequency of citations, Betweenness centrality, or by year or references as text. You can alsochoose to show or hide a node on the list.Figure 30. The Visualization window.A control panel is shown on the right-hand side of the Visualization Window. You can change how node labels are displayed by a combination of a few threshold values through sliders. You can also change the size of a node by sliding the node size slider.To answer the typical questions we asked before, let’s use several functions in CiteSpace to obtain more specific information through clustering, labeling, and exploring.5.1.2ClusteringAlthough we can probably eyeball the visualized network and identify some prominent groupings, CiteSpace provides more precise ways to identify groupings, or clusters, using theclustering function.To start the clustering function, simply click on this icon .How do I know whether the clustering process is completed? You will see #clusters on the upper right corner of the canvas. In the Demo example, a total of 37 clusters of co-cited references are identified. Each cluster corresponds to an underlying theme, a topic, or a line of research.The signature of the network is shown on the upper left corner of the display. In particular, the modularity Q and the mean silhouette scores are two important metrics that tell us about the overall structural properties of the network. For example, the modularity Q of 0.7141 is relatively high, which means that the network is reasonably divided into loosely coupled clusters. The mean silhouette score of 0.5904 suggests that the homogeneity of these clusters on averageis not very high, but not very low either.Figure 32. The clustering process is completed. 37 clusters are identified (#clusters shown in the upper right corner).Modularity and silhouette scores are shown in the signature of the network on the left.Figure 33. Members of different clusters are shown in different colors.You can inspect various measures of each cluster in a summary table of all the clusters using: Clusters ►4. Summarization of Clusters. The Silhouette column shows the homogeneity of a cluster. The higher the silhouette score, the more consistent of the cluster members are, provided the clusters in comparison have similar sizes. If the cluster size is small, then a high homogeneity does not mean much. For example, cluster #9 has 7 members and a silhouette of 1.00, this is most likely due to the possibility that all 7 references are the citation references of the same underlying author. In other words, cluster #9 may reflect the citing behavior or preferences of a single paper, thus it is less representative.The average year of publication of a cluster indicates whether it is formed by generally recent papers or old papers. This is a simple and useful indicator.Figure 34. A summary table of clusters.5.1.3Generate Cluster LabelsTo characterize the nature of an identified cluster, CiteSpace can extract noun phrases from the titles (T in the following icon), keyword lists (K), or abstracts (A) of articles that cited the particular cluster.Let’s ask CiteSpace to choose noun phrases from titles (i.e. select the T icon). This process may take a while as CiteSpace needs to compute several selection metrics. Once the process is finished, the chosen labels will be displayed. By default, labels based on one of the three selection algorithms will be shown, namely, tf*idf. Our study has found that LLR usually gives the best result in terms of the uniqueness and coverage.Figure 35. Icons for performing Clustering and Labeling functions.Cluster labels are displayed once the process is completed. The clusters are numbered in the descending order of the cluster size, starting from the largest cluster #0, the second largest #1, and so on.Figure 36. Cluster labels are generated and displayed.To make it easier to see which clusters are the largest, you can choose to change the font size of the labels from the uniformed to proportional:Display ►Label Font Size ►Cluster: Uniformed/ProportionalThis is a toggle function. That means there are two states. Your selection will switch back and forth between the two states, i.e. either using a uniformed font size or proportional.Figure 37. Set the cluster labels’ font size proportional to their size.Figure 38. Cluster labels’ font sizes are proportional to the size of a cluster. The largest cluster is #0 on biologicalterrorism.5.1.4Where are the major areas of research based on the input dataset?This is one of the primary questions that CiteSpace is designed to answer. To answer this question, we will focus on the big picture of the collection of publications represented by your dataset. Let’s make a few adjustments with the sliders in the control panel on the right so that the information of our interest will be shown clearly and information that is less relevant to this question right now will be temporarily hidden from the view.1.Node SizeAt this level, we don’t really need to see the size of a node, although it provides rich information about the history of a node. Use the slider under Article Labeling ►Node Size ►[Slide to 0] (marked by the pointer #1 in the following figure).2.Cluster Label SizeThe font size of the cluster labels are controlled by a slider with two controls: one control the threshold for showing or hiding a label based on the size of the cluster (i.e. to make sure large-enough clusters are always labeled), and the other control the font size of the cluster labels (marked by the pointer #2 in the screenshot).3.Transparency of LinksDetailed links would be useful later, but we can ignore them for now using the transparency slider to set all the links’ transparency to the lowest level, i.e. invisible. In hindsight, a more accurate term would be completely transparent.After making these minor adjustments, it will be straightforward to answer the question: Where are the major areas of research? Evidently, the largest area (cluster #0 with the largest number of member references) is biological terrorism. The second largest is posttraumatic stress (cluster #1), i.e. PTSD. The third one is ocular injury (cluster #2). The fourth one is blast (cluster #3). And there are a few smaller clusters. So now we have a general idea what constituted terrorism research during the period of 1996 and 2003. You can repeat the process on a current dataset to get an up-to-date big picture.。
Scopus(Elsevier出版社)讲解
天津大学 Tianjin University
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John Wiley
天津大学 Tianjin University
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Springer
SpringerLink是施普林格出版社和它的合作公司推出的 科学、技术和医学（STM）方面的在线信息资源。服务范 围涵盖行为科学、生命科学、商业与经济、化学和材料科 学、计算机科学、地球和环境科学、工程学、人文社会科 学和法律、数学、医学、物理和天文学研究领域，提供 2777种学术期刊。期刊最早可回溯到1997年。
天津大学 Tianjin University
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9000多种同行评审期刊，非全文刊几乎囊括了世界所有顶 级学术期刊的索引和文摘部分，并且期刊数量不断增加中； 每篇文章提供HTML 和PDF格式，并提供全球通用的 Citation 信息，方便读者下载和引用
天津大学 Tianjin University
天津大学 Tianjin University
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• Elsevier科学出版公司近几年已经与Pergamon、North Holland、Excerpt Medica 等著名出版社合并，将其出 版的1100多种期刊全部数字化，通过网络向用户提供服务。
天津大学 Tianjin University
天津大学 Tianjin University
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WorldSciNet
WorldSciNet电子期刊为新加坡世界科技出版公司 （World Scientific Publishing简称WSN）出版的数据库。 WorldSciNet包括117种高质量科技期刊的电子全文，最 早回溯至1973年。
scopus的使用 -回复
scopus的使用-回复Scopus是一个全球知名的文献检索数据库，含有广泛的学术资源，被广泛应用于学术界的科研工作中。
本文将详细介绍Scopus的使用方法，以及如何利用Scopus进行文献检索和分析。
第一步：注册和访问Scopus要使用Scopus，首先需要注册一个账号。
我们可以通过在Scopus官方网站上进行注册来获得一个个人账号。
注册后，在登录页面输入用户名和密码进行登录，即可访问Scopus的各项功能和资源。
第二步：了解Scopus的功能和特点在使用Scopus之前，为了更好地掌握其功能和特点，我们需要了解一些基本知识。
Scopus是一个包含全球学术文献的综合性数据库，其中包含了几乎所有科学领域的文献资源。
与其他数据库相比，Scopus具有以下几个特点：1. 广泛的覆盖范围：Scopus包含了来自全球各个学术领域的期刊文章、会议论文、图书、专利等资源。
这意味着我们可以在Scopus上找到几乎所有学科领域的文献资料。
2. 强大的检索功能：Scopus提供了高级的文献检索功能，我们可以通过关键词、作者、机构、期刊等多种方式进行检索。
此外，Scopus还支持高级检索，如按引用文献、时间范围、文献类型等条件进行筛选。
3. 文献评价指标：Scopus可以提供各种文献评价指标，如被引用次数、影响因子、h指数等。
这些指标可以帮助研究人员评估文献的影响力和重要性。
4. 共享和合作：Scopus还提供了一系列社交功能，我们可以将自己的研究成果上传并与他人分享，也可以与其他研究人员进行合作。
这些功能有助于促进学术交流和合作。
第三步：文献检索Scopus的文献检索功能非常强大，我们可以根据自己的需要进行高级检索。
以下是一些常用的检索技巧：1. 使用关键词：我们可以根据研究主题选择适当的关键词，通过在Scopus 的搜索框中输入关键词，进行简单检索。
2. 高级检索：在高级检索界面中，我们可以使用逻辑运算符（如AND、OR、NOT）和括号来构建复杂的检索式，以精确地定位所需文献。
DVB-S2和DVB-S的区别
DVB-S2和DVB-S究竟有什么区别摘要DVB-S.2作为新一代数字卫星广播标准即将出台，草案已正式发布，新标准在提升原有信道传输容量的同时，还将大大拓展业务范围，得到了广电、电信、计算机等领域的广泛关注。
在与以往标准相比较的基础上，本文阐述了新标准技术上的主要优势，并简要介绍了标准的研发背景、目前的进展及未来应用前景。
一数字卫星广播标准的发展沿革与DVB-S.2数字卫星广播标准发展始于1990年代初，应用较多的制式主要有两种，即欧洲的DVB-S 标准和美国GI公司开发的Digicipher标准，两种方式互不兼容，其差别主要在于数字信号的传输方式即信道编码，而信源编码部分都采用了MPEG-2。
从欧洲电信标准协会（ETSI）的ETS 300 421算起，DVB-S作为当今广播电视领域的主流卫星传输标准，问世已逾十年，在世界范围内得到广泛应用。
1995年中央电视台通过卫星播出数字压缩加扰电视节目时，我国尚未公布将DVB-S作为试行标准，当时采用了美国GI的Digicipher系统，随着近年来国内模拟卫星传输方式淡出市场，国内各上星频道普遍采用了DVB-S技术。
十年的使用期同时意味着DVB-S的核心技术与当今相关领域的前沿技术水平渐行渐远，因此，基于当前硬件支持能力和编码算法的最新成果，开发更适应当前乃至未来中长期业务发展需求的技术标准就成为当务之急，DVB-S.2也因此呼之欲出。
DVB-S.2由JTC（联合技术委员会）制定，JTC最初于1990年由EBU（欧广联）、ETSI 联合组建，负责制定广播电视及相关领域的技术标准，1995年该组织吸纳CENELEC（欧洲电工标准化委员会）加入，后者负责广播电视接收机方面的标准化工作。
DVB-S.2的制定采用ETSI的“两步式” 程序，2004年6月，公开发布DVB-S.2草案（即Draft ETSI EN 302 307 V1.1.1），目前进入公开的意见征询阶段（2004.6.2～2004.10.1）。
scopus的使用 -回复
scopus的使用-回复在科研领域，学术论文的发表和引用是评价学者学术影响力的重要指标。
然而，寻找并获取最新的学术文献信息却是一项耗时且繁琐的任务。
为了更方便地进行学术文献检索和引用分析，许多研究人员使用Scopus这一全球最大的引文数据库。
本文将详细介绍Scopus的使用方法，帮助读者更好地利用该工具加强自己的研究成果。
一、Scopus的概述Scopus是由Elsevier公司开发的学术引文数据库，涵盖了广泛的学科领域，包括自然科学、社会科学、医学和工程技术等。
其收录内容包括期刊论文、会议论文、书籍和专利等，文章数量庞大且持续更新，可帮助研究人员进行文献检索、引用分析和学术评估。
二、Scopus的注册和登录1. 访问Scopus官方网站，并点击注册按钮。
2. 填写个人信息，包括姓名、邮箱地址和所属机构等。
3. 验证邮箱，按照邮件中的指示完成验证流程。
4. 登录Scopus账号，探索更多功能和服务。
三、Scopus的文献检索1. 进入Scopus主页，点击搜索框。
2. 输入关键词或专题，根据需要选择高级检索或分类检索。
3. 根据时间范围、语言、作者和期刊等进行筛选。
4. 点击搜索按钮，浏览结果并选择合适的学术文献。
四、Scopus的引用分析1. 利用Scopus的引文搜索功能，查找特定文章的引用情况。
2. 输入文章的DOI或标题进行检索，获得该文章的引用列表。
3. 点击引用图表，查看被引用频次、时间趋势和引用来源等信息。
4. 利用这些信息，进行学术评估和研究前沿的发现。
五、Scopus的个人账号管理1. 点击账号设置，进行个人信息、密码和通知设置等操作。
2. 添加或删除关注的作者或期刊，获取其最新的研究成果。
3. 设置搜索警报，及时收到相关领域的最新文献。
4. 在个人主页上查看个人论文和引用的统计数据，实时了解自己的学术影响力。
六、Scopus的应用场景1. 学术论文写作与引用分析。
2. 研究课题的文献综述和前沿探索。
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摘要+参考文献页面（ 摘要+参考文献页面（续）
查看相关文献
摘要+参考文献页面（ 摘要+参考文献页面（续）
参考文献数量显示
该文献被引191次 次 该文献被引
检索历史
对检索式进行组配检索 清除检索史
#1 AND #2
设置检索提示
网络检索
通过Scirus科技检索引擎 科技检索引擎 通过 进行网络检索
在特定的时段中，哪些文献是我研究领域中较为热门的？发展趋势又如何？如何评价特 定时段的研究课题
– 可针对某篇具体的文章按年度进行引文分析
实时统计的引文信息
如何查看引文概览
引文概览
Citation Overview 分析结果页面
被引文献的题录信息 特定文献历年被引次数 特定年份所有文献被引次数 各文献特定年份范围被引次数小计， 各文献特定年份范围被引次数小计， 并可打开浏览完整的书目信息 各文献所有年份被引次数总计
文摘和参考文献
文摘回溯到1966年，目前拥有2700万条记录，每年新增110万条； 文摘回溯到1966年 目前拥有2700万条记录，每年新增110万条； 1966 2700万条记录 110万条 2005年初 年初， 10年的记录附有参考文献 年的记录附有参考文献， 千万参考文献（1996年以来 年以来） 2005年初，近10年的记录附有参考文献，共2亿4千万参考文献（1996年以来） 通过Scirus可检索 亿科学网页（包括一千三百万专利信息） 可检索2亿科学网页 包括一千三百万专利信息） 通过 可检索 亿科学网页（
我的提示
系统根据您设置的检索式定期给您 发送最新被Scopus收录的相关文献 收录的相关文献； 发送最新被 收录的相关文献
查看最新结果
您设置一篇文章，如果该文章被别的文章引用，系 您设置一篇文章，如果该文章被别的文章引用， 统则自动给您发送提示信息。 统则自动给您发送提示信息。
检索提示
检索保存和设置检索提示
53%没有被同类数据库收录 53%没有被同类数据库收录
检 索
Scopus 检索
检索规则 基本检索、 基本检索、高级检索和作者检索 精练检索结果（ Results） 精练检索结果（Refine Results） 检索结果显示（排序、页面） 检索结果显示（排序、页面） 检索结果处理 检索式的编辑、 检索式的编辑、组配和保存
浏览已存列表
在“My List”及“Saved List”中进行参考文献管 及 中进行参考文献管 理
创建书目
运用QuikBib创建书目 创建书目 运用
参考文献
随时追踪研究课题的 最新文献
个性化服务
检索保存 检索提示 引文提示
注册
个人注册界面
个性化设置ຫໍສະໝຸດ 个人配置文件管理已存检索 电子邮件提示 管理已存列表 个人信息设定 更改登录密码
Scopus 使用指南及演示
康晓伶 爱思唯尔(Elsevier) (Elsevier)公司北京代表处 爱思唯尔(Elsevier)公司北京代表处
培训议程
Scopus 简介 Scopus 特征
内容/检索与浏览/全文链接/ 内容/检索与浏览/全文链接/个性化服务
支持服务 答疑
Scopus简介 Scopus简介
专利检索
专利信息来自 USPTO/EPO/WIPO/JPO
您也可以按照学科领域 来浏览期刊
浏览功能
按学科浏览
期刊检索
按期刊字顺浏览
期刊浏览结果
获取文献全文
全文链接
－View at Publisher 标准全文链接 View
通过交叉引用功能 (CrossRef)； 通过交叉引用功能 (CrossRef)； 对于未被CrossRef覆盖的800种期刊，Scopus建立了自己的知识链接库 CrossRef覆盖的800种期刊 建立了自己的知识链接库； 对于未被CrossRef覆盖的800种期刊，Scopus建立了自己的知识链接库； 无论您是否订购期刊，链接均可显示； 无论您是否订购期刊，链接均可显示； 不保证用户都有权限访问全文； 不保证用户都有权限访问全文
19%
12%
AUS CHIN INDIA JAPAN
27% 29% 13%
other
Scopus收录的中国期刊 收录的中国期刊
Scopus收录350种中国期刊，其中287种未被SCI收录 Scopus收录350种中国期刊，其中287种未被SCI收录 收录350种中国期刊 287种未被SCI 573,856篇来自中国的文章 573,856篇来自中国的文章 收录中国作者发表数量最多的160种期刊 收录中国作者发表数量最多的160种期刊 160
从基本检索入手
精炼检索结果
检索记录数量显示 检索结果 分布 编辑、保存、 排除以下范围 设置检索提示
限于以下范围
精练检索结果
精练检索结果（ 精练检索结果（续）
精练检索结果
迅速精炼检索结果
精练检索结果
检索记录排序标准
检索结果页面
摘要+ 摘要+参考文献页面
被引次数
文献标题 作者 作者机构
位置算符
W/n 两词相隔不超过n词，词序不定； 如：pain W/15 morphine; 两词相隔不超过n 词序不定； 两词相隔不超过n 词序一定； screening； PRE/n 两词相隔不超过n词，词序一定；如：neonatal PRE/3 screening；
词组检索
精确匹配； “oyster toadfish” 精确匹配； toadfish； oyster toadfish ＝ oyster AND toadfish；
Scopus内容 Scopus内容
收录数量
14200种期刊,包括12850种同行评审刊，750种会议录，600种商业出版物， 14200种期刊,包括12850种同行评审刊，750种会议录，600种商业出版物，535 种期刊 12850种同行评审刊 种会议录 种商业出版物 份开放存取（OA）期刊； 份开放存取（OA）期刊； 4000个出版商 个出版商； 4000个出版商；
摘要和参考文献页面的定制链接
标准链接和个性化链接
与化学事实数据库的链接
用户反馈: – 当进行文献检索时，很难找到相关的事实信息，如化合物 或反应 – 当进行事实数据检索时，又很难找到相关的原文献 现在Scopus和Beilstein（Elsevier子公司MDL）整合在一起 现有 500,000 记录
标准的全文链接
一次点击，即可从结果显示页面链接到全文
个性化全文链接
看全文
非全文链接
－馆藏书目Library Catalogue/OPAC 馆藏书目Library 馆藏书目
－文献传递、馆际互借或按篇付费（PPV） 文献传递、馆际互借或按篇付费（PPV） 亦可不显示相关链接（即链接显示功能可以启动也可以关闭） 亦可不显示相关链接（即链接显示功能可以启动也可以关闭）
为何取名Scopus？－取名于一种名叫 为何取名Scopus？－取名于一种名叫Phylloscopus Collybita Scopus？－取名于一种名叫 的鸟，据说这种鸟导航功能非常强大。 的鸟，据说这种鸟导航功能非常强大。 Scopus?－爱思唯尔（Elsevier）公司新近推出的具有 什么是 Scopus?－爱思唯尔（Elsevier）公司新近推出的具有 强大功能的多学科文摘索引型数据库。 强大功能的多学科文摘索引型数据库。
检索规则
逻辑算符
逻辑与： 逻辑与：and 逻辑或： 逻辑或：or 逻辑非：and not 逻辑非：
通配算符
？替代单个字母， 如：wom?n 可检出 woman, women; analy?e 可检出 analyse and analyze 替代单个字母， 替代任意个字母， pharmacology等 * 替代任意个字母，如：pharmac* 可检出 pharmacy, pharmacology等； sul*ur 可检出 sulfur, sulphur haemoglobin. h*emoglobin 可检出 hemoglobin and haemoglobin
设置检索提示
引文提示
设置引文提示
设置引文提示
2006年最新推出 年最新推出
引文追踪 Citation Tracker
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引文追踪
用户可自主进行引文分析
– 对特定的作者所发表的论文进行分析
从1996年至今，某作品历年来被引用多少次? 哪年被引用得最多？又是谁引用了该作品
– 对特定的学科领域内的课题有全面的了解
单数检索可同时检出复数形式
criterion 可检出 criteria 和 criterion.
基本检索
“lung cancer” smok*
高级检索
(TITLE("lung cancer") AND TITLE-ABS-KEY(stress)) AND DOCTYPE(article) AND PUBYEAR AFT 1999
－Full Text 个性化定制全文链接
图书馆可以选择只显示已定购期刊的全文链接； 图书馆可以选择只显示已定购期刊的全文链接； 直接让用户了解是否有权限访问全文； 直接让用户了解是否有权限访问全文； 可以通过LinkfinderPlus和SFX实现这种功能 可以通过LinkfinderPlus和SFX实现这种功能； LinkfinderPlus 实现这种功能
检索特征总结
一站式检索，简单易用节省时间, 一站式检索，简单易用节省时间,获取全面的有针对性的研究结果
数量众多期刊的整合 与网络资源（Scirus）、专利资源（Patents） ）、专利资源 与网络资源（Scirus）、专利资源（Patents）以及馆藏资源的整合 取代小型数据库,减少资源重复。增加全文电子资源的使用量 取代小型数据库,减少资源重复。