With our increasing ability to capture or produce and to store large multivariate data, these data sets are increasing both in size and complexity. Many conventional techniques for visualizing multivariate data suffer from problems like cluttered displays since they are not designed to handle these amounts of entries. We present a novel method to overcome this problem by interactively selecting and displaying statistics derived from the data in a separate view. Changes in the display are visually tracked by animation and vector plotting for easy comparison of various measures applied to different subsets of the data.
{"title":"Visual data analysis using tracked statistical measures within parallel coordinate representations","authors":"Daniel Ericson, Handledare Jimmy Johansson","doi":"10.1109/CMV.2005.21","DOIUrl":"https://doi.org/10.1109/CMV.2005.21","url":null,"abstract":"With our increasing ability to capture or produce and to store large multivariate data, these data sets are increasing both in size and complexity. Many conventional techniques for visualizing multivariate data suffer from problems like cluttered displays since they are not designed to handle these amounts of entries. We present a novel method to overcome this problem by interactively selecting and displaying statistics derived from the data in a separate view. Changes in the display are visually tracked by animation and vector plotting for easy comparison of various measures applied to different subsets of the data.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128242941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is a common task when analyzing a large dataset (e.g., census database) to create some kind of overview of the original dataset, which is small enough to be easily manipulated, while remains the key characteristics of the data. Many aggregation techniques have been proposed to help users better understand the dataset and find desired information in it. However, the user can easily get lost after several aggregation operations, since there is rarely mechanism facilitating the user to remember what he or she has done in previous steps. In this paper, we present a prototype, namely MUSA, for multiple-step aggregation visualization. We aimed at designing a tool not only to help users obtain various levels of overviews to narrow their selections, but also to effectively visualize the aggregation processes to enhance the context awareness. We also conducted an informal user study to evaluate the tool.
{"title":"MUSA - a prototype for multiple-step aggregation visualization","authors":"Tao Ni","doi":"10.1109/CMV.2005.12","DOIUrl":"https://doi.org/10.1109/CMV.2005.12","url":null,"abstract":"It is a common task when analyzing a large dataset (e.g., census database) to create some kind of overview of the original dataset, which is small enough to be easily manipulated, while remains the key characteristics of the data. Many aggregation techniques have been proposed to help users better understand the dataset and find desired information in it. However, the user can easily get lost after several aggregation operations, since there is rarely mechanism facilitating the user to remember what he or she has done in previous steps. In this paper, we present a prototype, namely MUSA, for multiple-step aggregation visualization. We aimed at designing a tool not only to help users obtain various levels of overviews to narrow their selections, but also to effectively visualize the aggregation processes to enhance the context awareness. We also conducted an informal user study to evaluate the tool.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125484428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microarray time-course data relate to the recorded activity of thousands of genes, in parallel, over multiple discrete points in time during a biological process. Existing techniques that attempt to support the exploratory analysis of this data rely on static clustering views, interactive clustering views or coordinated clustering and graph views and are limited in that they fail to account for less dominant patterns in the data such as those that involve a subset of genes or a limited interval of the time-course. In this paper, we describe an alternative approach which avoids this limitation by using combined parallel views to present different complementary aspects of the data (i.e. timing, activity and change-in-activity). An example of how the views are combined to reveal significant patterns in the data (including those which cannot be found using clustering based techniques) is described and used to illustrate the benefits of combined parallel views to support exploratory-analysis of this type of data.
{"title":"Coordinated parallel views for the exploratory analysis of microarray time-course data","authors":"Paul Craig, Jessie Kennedy, Andrew Cumming","doi":"10.1109/CMV.2005.5","DOIUrl":"https://doi.org/10.1109/CMV.2005.5","url":null,"abstract":"Microarray time-course data relate to the recorded activity of thousands of genes, in parallel, over multiple discrete points in time during a biological process. Existing techniques that attempt to support the exploratory analysis of this data rely on static clustering views, interactive clustering views or coordinated clustering and graph views and are limited in that they fail to account for less dominant patterns in the data such as those that involve a subset of genes or a limited interval of the time-course. In this paper, we describe an alternative approach which avoids this limitation by using combined parallel views to present different complementary aspects of the data (i.e. timing, activity and change-in-activity). An example of how the views are combined to reveal significant patterns in the data (including those which cannot be found using clustering based techniques) is described and used to illustrate the benefits of combined parallel views to support exploratory-analysis of this type of data.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125577276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Complex simulations (in particular, those involving multiple coupled physics) cannot be understood solely using geometry-based visualizations. Such visualizations are necessary in interpreting results and gaining insights into kinematics, however they are insufficient when striving to understand why or how something happened, or when investigating a simulation's dynamic evolution. For multiphysics simulations (e.g. those including solid dynamics with thermal conduction, magnetohydrodynamics, and radiation hydrodynamics) complex interactions between physics and material properties take place within the code which must be investigated in other ways. Drawing on the extensive previous work in view coordination, brushing and linking techniques, and powerful visualization libraries, we have developed Prism, an application targeted for a specific analytic need at Sandia National Laboratories. This multiview scientific visualization tool tightly integrates geometric and phase space views of simulation data and material models. Working closely with analysts, we have developed this production tool to promote understanding of complex, multiphysics simulations. We discuss the current implementation of Prism, along with specific examples of results obtained by using the tool.
{"title":"Prism: a multi-view visualization tool for multi-physics simulation","authors":"D. Rogers, C. Garasi","doi":"10.1109/CMV.2005.15","DOIUrl":"https://doi.org/10.1109/CMV.2005.15","url":null,"abstract":"Complex simulations (in particular, those involving multiple coupled physics) cannot be understood solely using geometry-based visualizations. Such visualizations are necessary in interpreting results and gaining insights into kinematics, however they are insufficient when striving to understand why or how something happened, or when investigating a simulation's dynamic evolution. For multiphysics simulations (e.g. those including solid dynamics with thermal conduction, magnetohydrodynamics, and radiation hydrodynamics) complex interactions between physics and material properties take place within the code which must be investigated in other ways. Drawing on the extensive previous work in view coordination, brushing and linking techniques, and powerful visualization libraries, we have developed Prism, an application targeted for a specific analytic need at Sandia National Laboratories. This multiview scientific visualization tool tightly integrates geometric and phase space views of simulation data and material models. Working closely with analysts, we have developed this production tool to promote understanding of complex, multiphysics simulations. We discuss the current implementation of Prism, along with specific examples of results obtained by using the tool.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133639006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Matkovič, J. Juric, Z. Konyha, J. Krasser, H. Hauser
The paper describes a method developed for interactive data visualization and exploration with applications in the automotive industry. The input data set contains a large number of function graphs. Each of the graphs is characterized by a set of basic attributes. The technique that is used for visualization includes two linked views: a map view (or attribute space view), where all function graphs are represented as a point or an icon on the map, and a linked function graph view. The map view provides additional visualization possibilities and allows user interaction. Additional features like brushing in both views, graph management, and related issues like interpolation of the graphs are described.
{"title":"Interactive visual analysis of multi-parameter families of function graphs","authors":"K. Matkovič, J. Juric, Z. Konyha, J. Krasser, H. Hauser","doi":"10.1109/CMV.2005.10","DOIUrl":"https://doi.org/10.1109/CMV.2005.10","url":null,"abstract":"The paper describes a method developed for interactive data visualization and exploration with applications in the automotive industry. The input data set contains a large number of function graphs. Each of the graphs is characterized by a set of basic attributes. The technique that is used for visualization includes two linked views: a map view (or attribute space view), where all function graphs are represented as a point or an icon on the map, and a linked function graph view. The map view provides additional visualization possibilities and allows user interaction. Additional features like brushing in both views, graph management, and related issues like interpolation of the graphs are described.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122877867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Convertino, C. Ganoe, W. A. Schafer, B. Yost, John Millar Carroll
In this paper we investigate strategies to support knowledge sharing in distributed, synchronous collaboration. Our goal is to propose, justify, and assess a multiple view approach to support common ground in geo-collaboration within multi-role teams. We argue that a collaborative workspace, which includes multiple role-specific views coordinated with a team view, affords a clear separation between role-specific and shared data, enables the team to filter out role-specific details and share strategic knowledge, and allows serendipitous learning about knowledge and expertise within the team. We discuss some key issues that need to be addressed when designing multiple views as a collaborative visualization. We illustrate the design features of a geo-collaborative prototype that address these issues in the context of two collaborative scenarios. We finally describe a laboratory method for investigating how multi-role teams establish common ground while the amount of prior shared knowledge and the type of visualization are experimentally manipulated.
{"title":"A multiple view approach to support common ground in distributed and synchronous geo-collaboration","authors":"G. Convertino, C. Ganoe, W. A. Schafer, B. Yost, John Millar Carroll","doi":"10.1109/CMV.2005.2","DOIUrl":"https://doi.org/10.1109/CMV.2005.2","url":null,"abstract":"In this paper we investigate strategies to support knowledge sharing in distributed, synchronous collaboration. Our goal is to propose, justify, and assess a multiple view approach to support common ground in geo-collaboration within multi-role teams. We argue that a collaborative workspace, which includes multiple role-specific views coordinated with a team view, affords a clear separation between role-specific and shared data, enables the team to filter out role-specific details and share strategic knowledge, and allows serendipitous learning about knowledge and expertise within the team. We discuss some key issues that need to be addressed when designing multiple views as a collaborative visualization. We illustrate the design features of a geo-collaborative prototype that address these issues in the context of two collaborative scenarios. We finally describe a laboratory method for investigating how multi-role teams establish common ground while the amount of prior shared knowledge and the type of visualization are experimentally manipulated.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128619698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we integrate radviz and parallel coordinates, two methods able to handle multidimensional datasets, exploiting their contrasting characteristics. From on side radviz offers good direct data manipulation (i.e., brushing) techniques and low cluttering but it fails in providing visualization of quantitative information; conversely, parallel coordinates clearly shows the values of data attributes and their ranges but suffers from high cluttering also on small datasets and presents tedious manipulation techniques. We developed a prototype, called SpringView, that allows for simultaneously viewing both radviz and parallel coordinates and implements several useful techniques to manipulate the data, both interactively and, more interestingly, automatically. We challenged our approach against two well know multidimensional datasets, proving its effectiveness.
{"title":"SpringView: cooperation of radviz and parallel coordinates for view optimization and clutter reduction","authors":"E. Bertini, L. Dell'Aquila, G. Santucci","doi":"10.1109/CMV.2005.17","DOIUrl":"https://doi.org/10.1109/CMV.2005.17","url":null,"abstract":"In this paper we integrate radviz and parallel coordinates, two methods able to handle multidimensional datasets, exploiting their contrasting characteristics. From on side radviz offers good direct data manipulation (i.e., brushing) techniques and low cluttering but it fails in providing visualization of quantitative information; conversely, parallel coordinates clearly shows the values of data attributes and their ranges but suffers from high cluttering also on small datasets and presents tedious manipulation techniques. We developed a prototype, called SpringView, that allows for simultaneously viewing both radviz and parallel coordinates and implements several useful techniques to manipulate the data, both interactively and, more interestingly, automatically. We challenged our approach against two well know multidimensional datasets, proving its effectiveness.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"66 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present linked 2D/3D texture advection for the interactive exploration of 3D flow. 3D texture advection facilitates a dense representation of the 3D structure of unsteady flow but is subject to problems of occlusion and clutter. Therefore, it is difficult for the user to explore features in occluded regions. We overcome the occlusion problem by adopting an additional 2D representation on several parallel slices through the data set. By linking these two views, our approach allows the user to gain unrestricted access to all spatial areas of the data set and, at the same time, retain a view on the 3D nature of the flow. Furthermore, the 2D view is used to visualize an additional attribute of the data set by color coding, such as vortex strength, temperature, or velocity magnitude. The 2D view lets the user explore flow features by selecting interesting values in this attribute space. A brushing and linking mechanism provides immediate feedback by highlighting selected data values in both the 2D and 3D representations. Finally, we discuss a GPU implementation of our visualization approach that is the technical basis for interactive exploration and real-time visualization without the need for preprocessing.
{"title":"Interactive exploration of unsteady 3D flow with linked 2D/3D texture advection","authors":"T. Schafhitzel, D. Weiskopf, T. Ertl","doi":"10.1109/CMV.2005.9","DOIUrl":"https://doi.org/10.1109/CMV.2005.9","url":null,"abstract":"In this paper, we present linked 2D/3D texture advection for the interactive exploration of 3D flow. 3D texture advection facilitates a dense representation of the 3D structure of unsteady flow but is subject to problems of occlusion and clutter. Therefore, it is difficult for the user to explore features in occluded regions. We overcome the occlusion problem by adopting an additional 2D representation on several parallel slices through the data set. By linking these two views, our approach allows the user to gain unrestricted access to all spatial areas of the data set and, at the same time, retain a view on the 3D nature of the flow. Furthermore, the 2D view is used to visualize an additional attribute of the data set by color coding, such as vortex strength, temperature, or velocity magnitude. The 2D view lets the user explore flow features by selecting interesting values in this attribute space. A brushing and linking mechanism provides immediate feedback by highlighting selected data values in both the 2D and 3D representations. Finally, we discuss a GPU implementation of our visualization approach that is the technical basis for interactive exploration and real-time visualization without the need for preprocessing.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122518697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the text document visualization community, statistical analysis tools (e.g., principal component analysis and multidimensional scaling) and neurocomputation models (e.g., self-organizing feature maps) have been widely used for dimensionality reduction. Often the resulting dimensionality is set to two, as this facilitates plotting the results. The validity and effectiveness of these approaches largely depend on the specific data sets used and semantics of the targeted applications. To date, there has been little evaluation to assess and compare dimensionality reduction methods and dimensionality reduction processes, either numerically or empirically. The focus of this paper is to propose a mechanism for comparing and evaluating the effectiveness of dimensionality reduction techniques in the visual exploration of text document archives. We use multivariate visualization techniques and interactive visual exploration to study three problems: (a) Which dimensionality reduction technique best preserves the interrelationships within a set of text documents; (b) What is the sensitivity of the results to the number of output dimensions; (c) Can we automatically remove redundant or unimportant words from the vector extracted from the documents while still preserving the majority of information, and thus make dimensionality reduction more efficient. To study each problem, we generate supplemental dimensions based on several dimensionality reduction algorithms and parameters controlling these algorithms. We then visually analyze and explore the characteristics of the reduced dimensional spaces as implemented within a linked, multiview multidimensional visual exploration tool, XmdvTool. We compare the derived dimensions to features known to be present in the original data. Quantitative measures are also used in identifying the quality of results using different numbers of output dimensions.
{"title":"Exploration of dimensionality reduction for text visualization","authors":"Shiping Huang, M. Ward, E. Rundensteiner","doi":"10.1109/CMV.2005.8","DOIUrl":"https://doi.org/10.1109/CMV.2005.8","url":null,"abstract":"In the text document visualization community, statistical analysis tools (e.g., principal component analysis and multidimensional scaling) and neurocomputation models (e.g., self-organizing feature maps) have been widely used for dimensionality reduction. Often the resulting dimensionality is set to two, as this facilitates plotting the results. The validity and effectiveness of these approaches largely depend on the specific data sets used and semantics of the targeted applications. To date, there has been little evaluation to assess and compare dimensionality reduction methods and dimensionality reduction processes, either numerically or empirically. The focus of this paper is to propose a mechanism for comparing and evaluating the effectiveness of dimensionality reduction techniques in the visual exploration of text document archives. We use multivariate visualization techniques and interactive visual exploration to study three problems: (a) Which dimensionality reduction technique best preserves the interrelationships within a set of text documents; (b) What is the sensitivity of the results to the number of output dimensions; (c) Can we automatically remove redundant or unimportant words from the vector extracted from the documents while still preserving the majority of information, and thus make dimensionality reduction more efficient. To study each problem, we generate supplemental dimensions based on several dimensionality reduction algorithms and parameters controlling these algorithms. We then visually analyze and explore the characteristics of the reduced dimensional spaces as implemented within a linked, multiview multidimensional visual exploration tool, XmdvTool. We compare the derived dimensions to features known to be present in the original data. Quantitative measures are also used in identifying the quality of results using different numbers of output dimensions.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123590026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Chastine, Ying Zhu, J. Brooks, G. Owen, R. Harrison, I. Weber
Molecular modeling has been a long-standing research area for biologists. However, the existing molecular modeling software lacks strong support for collaborative research. In this paper, we describe our effort to develop a collaborative multiview virtual environment for molecular visualization and modeling. In our virtual environment, the users are able to visualize large molecular structures in real-time, create their own view, or share their view with others in the system. The system allows for individual or coordinated collaborative manipulation of the virtual molecular model. Our virtual environment is integrated with a molecular dynamics simulator, and therefore our system is not merely a visualization tool, but an environment where biologists can collaboratively construct their models and test their hypotheses.
{"title":"A collaborative multi-view virtual environment for molecular visualization and modeling","authors":"J. Chastine, Ying Zhu, J. Brooks, G. Owen, R. Harrison, I. Weber","doi":"10.1109/CMV.2005.1","DOIUrl":"https://doi.org/10.1109/CMV.2005.1","url":null,"abstract":"Molecular modeling has been a long-standing research area for biologists. However, the existing molecular modeling software lacks strong support for collaborative research. In this paper, we describe our effort to develop a collaborative multiview virtual environment for molecular visualization and modeling. In our virtual environment, the users are able to visualize large molecular structures in real-time, create their own view, or share their view with others in the system. The system allows for individual or coordinated collaborative manipulation of the virtual molecular model. Our virtual environment is integrated with a molecular dynamics simulator, and therefore our system is not merely a visualization tool, but an environment where biologists can collaboratively construct their models and test their hypotheses.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122609449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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