OBJECTIVES: Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care. MATERIALS AND METHODS: myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology. RESULTS: Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface. DISCUSSION: The ability to search and explore myAURA's heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support. CONCLUSION: Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.

Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some basic requirements: node labels should not overlap, edges should not cross, edge lengths should be preserved, and the output should be compact. There are many algorithms for drawing trees, although very few take node labels or edge lengths into account, and none optimizes all requirements above. With this in mind, we propose a new scalable method for readable tree layouts. The algorithm guarantees that the layout has no edge crossings and no label overlaps, and optimizes one of the remaining aspects: desired edge lengths and compactness. We evaluate the performance of the new algorithm by comparison with related earlier approaches using several real-world datasets, ranging from a few thousand nodes to hundreds of thousands of nodes. Tree layout algorithms can be used to visualize large general graphs, by extracting a hierarchy of progressively larger trees. We illustrate this functionality by presenting several map-like visualizations generated by the new tree layout algorithm.

Epilepsy is a common chronic neurological disease. People with epilepsy (PWE) and their caregivers face several challenges related to their epilepsy management, including quality of care, care coordination, side effects, and stigma management. The sociotechnical issues of the information management contexts and challenges for epilepsy care may be mitigated through effective information management. We conducted 4 focus groups with 5 PWE and 7 caregivers to explore how they manage epilepsy-related information and the challenges they encountered. Primary issues include challenges of finding the right information, complexities of tracking and monitoring data, and limited information sharing. We provide a framework that encompasses three attributes - individual epilepsy symptoms and health conditions, information complexity, and circumstantial constraints. We suggest future design implications to mitigate these challenges and improve epilepsy information management and care coordination.

There are over three million people living with epilepsy in the U.S. People with epilepsy experience multiple daily challenges such as seizures, social isolation, social stigma, experience of physical and emotional symptoms, medication side effects, cognitive and memory deficits, care coordination difficulties, and risks of sudden unexpected death. In this work, we report findings collected from 3 focus groups of 11 people with epilepsy and caregivers and 10 follow-up questionnaires. We found that these participants feel that most people do not know how to deal with seizures. To improve others' abilities to respond safely and appropriately to someone having seizures, people with epilepsy and caregivers would like to share and educate the public about their epilepsy conditions, reduce common misconceptions about seizures and prevent associated stigma, and get first aid help from the public when needed. Considering social stigma, we propose design implications of future technologies for effective delivery of appropriate first aid care information to bystanders around individuals with epilepsy when they experience a seizure.

Along with significant opportunities, Massively Open Online Courses (MOOCs) provide major challenges to students (keeping track of course materials and effectively interacting with teachers and fellow students), teachers (managing thousands of students and supporting their learning progress), researchers (understanding how students interact with materials and each other), and MOOC platform developers (supporting effective course design and delivery in a scalable way). This article demonstrates the use of data analysis and visualization as a means to empower students, teachers, researchers, and platform developers by making large volumes of data easy to understand. First, we introduce the insight needs of different stakeholder groups. Second, we compare the wide variety of data provided by major MOOC platforms. Third, we present a novel framework that distinguishes visualizations by the type of questions they answer. We then review the state of the art MOOC visual analytics using a tabulation of stakeholder needs versus visual analytics workflow types. Finally, we present new data analysis and visualization workflows for statistical, geospatial, and topical insights. The workflows have been optimized and validated in the Information Visualization MOOC (IVMOOC) annually taught at Indiana University since 2013. All workflows, sample data, and visualizations are provided at http://cns.iu.edu/2016-MOOCVis.html .

Abstract We investigate the memorability of data represented in two different visualization designs. In contrast to recent studies that examine which types of visual information make visualizations memorable, we examine the effect of different visualizations on time and accuracy of recall of thedisplayed data, minutes and days after interaction with the visualizations. In particular, we describe the results of an evaluation comparing the memorability of two different visualizations of the same relational data: node‐link diagrams and map‐based visualization. We find significant differences in the accuracy of the tasks performed, and these differences persist days after the original exposure to the visualizations. Specifically, participants in the study recalled the data better when exposed to map‐based visualizations as opposed to node‐link diagrams. We discuss the scope of the study and its limitations, possible implications, and future directions.

Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.

The amount of data about us and our world is increasing rapidly, and the capability to analyze large data sets---so-called big data---becomes a key basis of competition, underpinning new waves of productivity growth and innovation. The big data phenomenon is fueled by cheap sensors and high-throughput simulation models, the increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet. It exists from social media to cell biology offering unparalleled opportunities to document the inner workings of many complex systems [1]. Research by MGI and McKinsey's Business Technology Office argues that there will be a shortage of talent necessary for organizations to take advantage of big data. "By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions" [2].

We visualize 113 years of bibliographic data from the American Physical Society. The 389,899 documents are laid out in a two dimensional time-topic reference system. The citations from 2005 papers are over laid as flow maps from each topic to the papers referenced by papers in the topic making intercitation patterns between topic areas visible. Paper locations of Nobel Prize predictions and winners are marked. Finally, though not possible to reproduce here, the visualization was rendered to, and is best viewed on, a 24" x 30" canvas at 300 dots per inch (DPI).

This paper presents the results of an analysis and visualization of 428,440 movies from the Internet Movie Database (IMDb) provided for the Graph Drawing 2005 contest. Simple statistics are presented as well as a tapestry of all movies with an overlay of the giant component of the co-actor network. Academy award winners are highlighted. Major insights are discussed.

This paper reports a bibliometric analysis of evolving co-author networks. Using 5,009 articles covering the years 1993 to 2005 from Transactions D. (Information Systems) of the Institute of Electronics Information and Communication Engineers (IEICE), we attempt to compare the network characteristics for each year, the co-author network characteristics for the entire time span, and the four major components of the entire data set. Finally, we analyze each of these in contrast to extant co-authorship network data and find that the pattern of co-authorship within Information Systems does not change significantly over this time period.

This paper describes a data management system that serves leaders and members of scholarly units (e.g., research groups, laboratories or centers) to organize, store, and access information relevant to their work and to the operation of their unit. In particular, we discuss the representation of major data types such as people, projects, teaching, publications, presentations, calls & events, datasets, software, hardware, and funding as well as their interlink ages. Based on this representation, different analyses and visualizations become possible. Each visualization is presented in detail and its usage and relevance for the management and presentation of a unit's operation is discussed. We conclude with an outlook and discussion.

This paper presents a novel approach (1) to define, analyze, and map a scientific discipline and (2) to compare and map the expertise of single authors based on personal bibliography files, e.g., bibtex or EndNote files. Section one motivates this research and relates it to existing work. Section two explains the general procedure from data harvesting, parsing, cleaning via the analysis and mapping of the data to the interpretation of results. To illustrate the new approach, we asked major experts on 'network science' to share their bibtex or EndNote files with us. Using this data set we exemplify data harvesting, data cleaning and integration; give simple statistics, analysis and visualization results such as a map of the network science discipline based on co-author networks and a map of the content coverage and overlay of contributing experts. We conclude with a discussion of challenges and opportunities as well as planned future work

What is the structure of the research reported on melanoma? How has it evolved over the last 40 years? Which parts of this research field are correlated with the study of genes and proteins? Are there sudden increases in the number of occurrences of certain gene or protein names, reflecting a surge of interest? How are genes, protein and papers interconnected via co-occurrence patterns? This paper aims to provide answers to these questions by analyzing a data set consisting of papers from Medline, genes from the Entrez Gene database, and proteins from UniProt. Word burst detection and co-occurrence analyses were both performed. The spatial layout algorithm VxOrd was applied to create the very first map that shows papers, genes, and proteins and their co-occurrence relationships. The results were validated by five domain experts leading to a number of interesting facts pertaining to structure and dynamics of the melanoma research field.

Course Web sites are fast becoming standard features of college courses. Some students expect all their courses to have such sites, just like they expect them to have syllabi. Course Web sites help professors communicate with students and students communicate with each other. They might summarize lectures, present assignments, serve as repositories of examples developed by students as well as the professor, and provide links to additional related information on the Web.This panel will discuss various aspects of course Web sites, focusing on their development, maintenance, and use. We will share experiences by presenting examples, highlighting successes and failures, and describing our hopes and concerns for the future. The panel will welcome comments from those in the audience with similar and even contradictory experiences. Our intention is to broaden participants' thinking on the implementation and use of course Web sites and spawn insights that might lead to more effective use of this important course component.

Abstract This article reports research on analyzing and visualizing the impact of governmental funding on the amount and citation counts of research publications. For the first time, grant and publication data appear interlinked in one map. We start with an overview of related work and a discussion of available techniques. A concrete example—grant and publication data from Behavioral and Social Science Research, one of four extramural research programs at the National Institute on Aging (NIA)—is analyzed and visualized using the VxInsight® visualization tool. The analysis also illustrates current existing problems related to the quality and existence of data, data analysis, and processing. The article concludes with a list of recommendations on how to improve the quality of grant‐publication maps and a discussion of research challenges for indicator‐assisted evaluation and funding of research.

This paper reports work in progress on the analysis and visualization of the spatial and temporal distribution of user interaction data collected in three-dimensional (3D) virtual worlds. Two tools are introduced. The "WorldMapper" reads in a so-called propdump file and creates a 2D clickable map showing the layout of the world as well as interaction possibilities such as teleports and clickable web links. The second tool visualizes user interaction data such as navigation, chatting, and Web access activity overlaid on the world map. Resulting visualizations are meant to support social navigation, design evaluation and optimization, and the study of virtual communities. Both tools are demonstrated on a 19-person information treasure hunt for information in a 3D virtual world. The paper concludes with a discussion and an outlook.

The paper reports work in progress on the analysis and visualization of chat log data collected in 3-dimensional (3-D) virtual worlds. In particular, it presents an analysis and visualization of six log files containing chat utterances from 32 people that attended a demonstration of different learning environments in a 3-D virtual world. We start with an introduction and a review of research in social visualization. Subsequently, we introduce the 3D online browser technology used and present the analysis and discussion of chat log data. The paper concludes with a discussion and an outlook.

This paper argues for the creation of a software repository for research and education in information visualization (IV). It starts with an introduction and overview of IV online resources and software repositories. Next, we introduce the code repository that we have created and demonstrate how it was used in the IV course which one of the authors teaches. Sample implementations by students are presented as well. We conclude with a discussion of ways to grow and maintain the software repository and invite contributions.

Information visualization can improve the access and manipulation of digital data if it conforms to human expectations, and supports the particular interface, task, data, and user. Detailed usability studies are required to prove this assumption. The paper reports a usability study of the data analysis algorithm applied in the LVis, Digital Library Visualizer that spatially visualizes search results derived from user queries of digital library collections. In particular, it presents a comparison of free sorting results for image data done by 20 human subjects and the data analysis result derived via Latent Semantic Analysis from the textual image descriptions. The results of the study suggest that people, if asked to categorize with no constraints, will use features that are often derivable from textual metadata. Human clustering strategies and resulting demands for image browsing systems are discussed and future work is outlined.

LVis (Library Visualizer) is a joint project aimed at the 2D and 3D visualization of search results derived from user queries of digital library collections. This paper gives an overview of the intent of the project, the data mining techniques applied, the visualization metaphors used and the usability studies that have been planned and undertaken to date.