Seeing things the same way: perspectives and lessons learned from research-design collaborations

Information visualizations are increasingly being developed by informatics researchers to communicate health information to lay audiences. For high-quality results, it is advisable to collaborate with creative professionals such as graphic designers, illustrators, or user interface/user experience designers. However, such collaborations are often a novel experience for both parties, each of which may be unfamiliar with the needs and processes of the other. We have coalesced our experiences from both the research and design perspectives to offer practical guidance in hopes of promoting the success of future collaborations. We offer suggestions for determining design needs, communicating with design professionals, and carrying out the design process. We assert that successful collaborations are predicated on careful and intentional planning at the outset of a project, a thorough understanding of each party's scope expertise, clear communication, and ample time for the design process to unfold.

EvoLaps 2: Advanced phylogeographic visualization

EvoLaps is a user-friendly web application designed to visualize the spatial and temporal spread of pathogens. It takes an annotated tree as entry, such as a maximum clade credibility tree obtained through continuous phylogeographic inference. By following a 'Top-Down' reading of a tree recursively, transitions (latitude/longitude changes from a node to its children) are represented on a cartographic background using graphical paths. The complete set of paths forms the phylogeographic scenario. EvoLaps offers several features to analyze complex scenarios: (1) enhanced path display using multiple graphical variables with time-dependent gradients, (2) cross-highlighting and selection capabilities between the phylogeographic scenario and the phylogenetic tree, (3) production of specific spatio-temporal scales and synthetic views through dynamic and iterative clustering of localities into spatial clusters, (4) animation of the phylogeographic scenario using tree brushing, which can be done manually or automatically, gradually over time or at specific time intervals, and for the entire tree or a specific clade, and (5) an evolving library of additional tools. EvoLaps is freely available for use at evolaps.org.

Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever-growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta-survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

Assigning trend-based conservation status despite high uncertainty

Estimates of temporal trends in species' occupancy are essential for conservation policy and planning, but limitations to the data and models often result in very high trend uncertainty. A critical source of uncertainty that degrades scientific credibility is that caused by disagreement among studies or models. Modelers are aware of this uncertainty but usually only partially estimate it and communicate it to decision makers. At the same time, there is growing awareness that full disclosure of uncertainty is critical for effective translation of science into policies and plans. But what are the most effective approaches to estimating uncertainty and communicating uncertainty to decision makers? We explored how alternative approaches to estimating and communicating uncertainty of species trends could affect decisions concerning conservation status of freshwater fishes. We used ensemble models to propagate trend uncertainty within and among models and communicated this uncertainty with categorical distributions of trend direction and magnitude. All approaches were designed to fit an established decision-making system used to assign species conservation status by the New Zealand government. Our results showed how approaches that failed to fully disclose uncertainty, while simplifying the information presented, could hamper species conservation or lead to ineffective decisions. We recommend an approach that was recently used effectively to communicate trend uncertainty to a panel responsible for setting the conservation status of New Zealand's freshwater fishes.

The HRA Organ Gallery Affords Immersive Superpowers for Building and Exploring the Human Reference Atlas with Virtual Reality

The Human Reference Atlas (HRA, https://humanatlas.io ) funded by the NIH Human Biomolecular Atlas Program (HuBMAP, https://commonfund.nih.gov/hubmap ) and other projects engages 17 international consortia to create a spatial reference of the healthy adult human body at single-cell resolution. The specimen, biological structure, and spatial data that define the HRA are disparate in nature and benefit from a visually explicit method of data integration. Virtual reality (VR) offers unique means to enable users to explore complex data structures in a threedimensional (3D) immersive environment. On a 2D desktop application, the 3D spatiality and real-world size of the 3D reference organs of the atlas is hard to understand. If viewed in VR, the spatiality of the organs and tissue blocks mapped to the HRA can be explored in their true size and in a way that goes beyond traditional 2D user interfaces. Added 2D and 3D visualizations can then provide data-rich context. In this paper, we present the HRA Organ Gallery, a VR application to explore the atlas in an integrated VR environment. Presently, the HRA Organ Gallery features 55 3D reference organs,1,203 mapped tissue blocks from 292 demographically diverse donors and 15 providers that link to 5,000+ datasets; it also features prototype visualizations of cell type distributions and 3D protein structures. We outline our plans to support two biological use cases: on-ramping novice and expert users to HuBMAP data available via the Data Portal ( https://portal.hubmapconsortium.org ), and quality assurance/quality control (QA/QC) for HRA data providers . Code and onboarding materials are available at https://github.com/cns-iu/ccf-organ-vr-gallery#readme .

Advancing Nursing Research Through Interactive Data Visualization With R Shiny

Scientific data visualization is a critical aspect of fully understanding data patterns and trends. To date, the majority of data visualizations in nursing research - as with other biomedical fields - have been static. The availability of electronic scientific journal articles (which are quickly becoming the norm) has created new opportunities for dynamic and interactive data visualization which carry added cognitive benefits and support the ability to understand data more fully. Therefore, here we highlight the benefits of R, an open-source programming language, for scientific data visualization, with a specific focus on creating dynamic, interactive figures using the R shiny package. For R users, we have included a tutorial with example code to create three increasingly complex shiny applications. For individuals more interested in understanding the potential of R shiny as an innovative tool to interact with research data, we have included links to online versions of the examples that do not require any programming or R experience. We believe that widespread adoption of dynamic and interactive scientific data visualization will further support nurse scientists' higher-level mission of advancing our understanding of health and wellness of individuals and communities.

The HRA Organ Gallery affords immersive superpowers for building and exploring the Human Reference Atlas with virtual reality

The Human Reference Atlas (HRA, https://humanatlas.io) funded by the NIH Human Biomolecular Atlas Program (HuBMAP, https://commonfund.nih.gov/hubmap) and other projects engages 17 international consortia to create a spatial reference of the healthy adult human body at single-cell resolution. The specimen, biological structure, and spatial data that define the HRA are disparate in nature and benefit from a visually explicit method of data integration. Virtual reality (VR) offers unique means to enable users to explore complex data structures in a three-dimensional (3D) immersive environment. On a 2D desktop application, the 3D spatiality and real-world size of the 3D reference organs of the atlas is hard to understand. If viewed in VR, the spatiality of the organs and tissue blocks mapped to the HRA can be explored in their true size and in a way that goes beyond traditional 2D user interfaces. Added 2D and 3D visualizations can then provide data-rich context. In this paper, we present the HRA Organ Gallery, a VR application to explore the atlas in an integrated VR environment. Presently, the HRA Organ Gallery features 55 3D reference organs, 1,203 mapped tissue blocks from 292 demographically diverse donors and 15 providers that link to 6,000+ datasets; it also features prototype visualizations of cell type distributions and 3D protein structures. We outline our plans to support two biological use cases: on-ramping novice and expert users to HuBMAP data available via the Data Portal (https://portal.hubmapconsortium.org), and quality assurance/quality control (QA/QC) for HRA data providers. Code and onboarding materials are available at https://github.com/cns-iu/hra-organ-gallery-in-vr.

CiteSpace-Based Visualization Analysis of Forensic Research in China from 2010 to 2019

OBJECTIVES

To analyze the research status of forensic medicine in China from 2010 to 2019, obtain the development trend of forensic medicine and explore the hotspots and research frontiers.

METHODS

The forensic medical academic papers published on China National Knowledge Infrastructure (CNKI) database from 2010 to 2019 were collected. CiteSpace 5.7.R1, an information visualization analysis software, was used to analyze publication organizations, authors, keywords, and other elements.

RESULTS

The majority of the research institutions were universities, provincial and ministerial scientific research and forensic institutions. Forensic pathology was still an important branch of forensic medicine and a popular research direction. The "polymorphism" and "Y chromosome" had been the research hotspots in recent years. "Medical damage" and "standard" were the most novel studies.

CONCLUSIONS

In order to provide scientific basis and research direction for forensic research, this paper analyzes the cooperation network, research hotspots and research innovation in forensic research.

NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems

Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time "NeuroVis," real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.

Impacts of Visualizations on Decoy Effects

The decoy effect is a well-known, intriguing decision-making bias that is often exploited by marketing practitioners to steer consumers towards a desired purchase outcome. It demonstrates that an inclusion of an alternative in the choice set can alter one’s preference among the other choices. Although this decoy effect has been universally observed in the real world and also studied by many economists and psychologists, little is known about how to mitigate the decoy effect and help consumers make informed decisions. In this study, we conducted two experiments: a quantitative experiment with crowdsourcing and a qualitative interview study—first, the crowdsourcing experiment to see if visual interfaces can help alleviate this cognitive bias. Four types of visualizations, one-sided bar chart, two-sided bar charts, scatterplots, and parallel-coordinate plots, were evaluated with four different types of scenarios. The results demonstrated that the two types of bar charts were effective in decreasing the decoy effect. Second, we conducted a semi-structured interview to gain a deeper understanding of the decision-making strategies while making a choice. We believe that the results have an implication on showing how visualizations can have an impact on the decision-making process in our everyday life.

Fractional-Order Sensing and Control: Embedding the Nonlinear Dynamics of Robot Manipulators into the Multidimensional Scaling Method

This paper studies the use of multidimensional scaling (MDS) to assess the performance of fractional-order variable structure controllers (VSCs). The test bed consisted of a revolute planar robotic manipulator. The fractional derivatives required by the VSC can be obtained either by adopting numerical real-time signal processing or by using adequate sensors exhibiting fractional dynamics. Integer (fractional) VCS and fractional (integer) sliding mode combinations with different design parameters were tested. Two performance indices based in the time and frequency domains were adopted to compare the system states. The MDS generated the loci of objects corresponding to the tested cases, and the patterns were interpreted as signatures of the system behavior. Numerical experiments illustrated the feasibility and effectiveness of the approach for assessing and visualizing VSC systems.

An evaluation study of biclusters visualization techniques of gene expression data

Biclustering is a non-supervised data mining technique used to analyze gene expression data, it consists to classify subgroups of genes that have similar behavior under subgroups of conditions. The classified genes can have independent behavior under other subgroups of conditions. Discovering such co-expressed genes, called biclusters, can be helpful to find specific biological features such as gene interactions under different circumstances. Compared to clustering, biclustering has two main characteristics: bi-dimensionality which means grouping both genes and conditions simultaneously and overlapping which means allowing genes to be in more than one bicluster at the same time. Biclustering algorithms, which continue to be developed at a constant pace, give as output a large number of overlapping biclusters. Visualizing groups of biclusters is still a non-trivial task due to their overlapping. In this paper, we present the most interesting techniques to visualize groups of biclusters and evaluate them.

Can scientists use simple infographics to convince? Effects of the "flatten the curve" charts on perceptions of and behavioral intentions toward social distancing measures during the COVID-19 pandemic

Infographics of modest complexity are commonly used to convey knowledge to non-experts. However, little is known regarding how the use of infographics may convince the public and lead to massive behavioral changes in response to an acute cause. In March 2020, scientists and journalists revamped a scholarly published graph into the "flatten the curve" (FTC) mantra that defined the United States' initial response to the COVID-19 pandemic. This study examined how Americans' awareness of the flatten the curve charts relates to their perceived effectiveness of social distancing measures, perceived controllability of the pandemic, and behavioral intentions toward social distancing measures. Implications on visual communication of science are discussed.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

Feasibility and acceptability of using information visualizations to improve HIV-related communication in a limited-resource setting: a short report

Infographics (visualizations that present information) can assist clinicians to offer health information to patients with low health literacy in an accessible format. In response, we developed an infographic intervention to enhance clinical, HIV-related communication. This study reports on its feasibility and acceptability at a clinical setting in the Dominican Republic. We conducted in-depth interviews with physicians who administered the intervention and patients who received it. We conducted audio-recorded interviews in Spanish using semi-structured interview guides. Recordings were professionally transcribed verbatim then analyzed using descriptive content analysis. Physician transcripts were deductively coded according to constructs of Bowen et al.'s feasibility framework and patient transcripts were inductively coded. Three physicians and 26 patients participated. Feasibility constructs endorsed by physicians indicated that infographics were easy to use, improved teaching, and could easily be incorporated into their workflow. Coding of patient transcripts identified four categories that indicated the intervention was acceptable and useful, offered feedback regarding effective clinical communication, and recommended improvements to infographics. Taken together, these data indicate our intervention was a feasible and acceptable way to provide clinical, HIV-related information and provide important recommendations for future visualization design as well as effective clinical communication with similar patient populations.

A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling

Small multiples are miniature representations of visual information used generically across many domains. Handling large numbers of small multiples imposes challenges on many analytic tasks like inspection, comparison, navigation, or annotation. To address these challenges, we developed a framework and implemented a library called PILlNG.JS for designing interactive piling interfaces. Based on the piling metaphor, such interfaces afford flexible organization, exploration, and comparison of large numbers of small multiples by interactively aggregating visual objects into piles. Based on a systematic analysis of previous work, we present a structured design space to guide the design of visual piling interfaces. To enable designers to efficiently build their own visual piling interfaces, PILlNG.JS provides a declarative interface to avoid having to write low-level code and implements common aspects of the design space. An accompanying GUI additionally supports the dynamic configuration of the piling interface. We demonstrate the expressiveness of PILlNG.JS with examples from machine learning, immunofluorescence microscopy, genomics, and public health.

Critical Review of Visual Models for Police Use of Force Decision-Making

Recent calls for widespread police reform include re-examination of existing training and practice surrounding the use of force (UOF, e.g., verbal and non-verbal communication, physical tactics, firearms). Visual models representing police UOF decision-making are used for both police training and public communication. However, most models have not been empirically developed or assessed in either the applied police or vision science literatures, representing significant gaps in knowledge. The purpose of the current review is to provide a novel, relevant, and practical analysis of the visual components of three common police UOF decision-making model types (circular, cyclical, staircase). We begin with a critical evaluation of the visual features specific to each model type (i.e., shape), followed by critical reviews of common visual features, including colour, implied motion, text, and clarity. The insights provided by the current work afford scientists from visual disciplines a unique opportunity to contribute meaningfully to the improvement of existing police UOF practices, with the goal of promoting public and occupational safety. To this end, we conclude with evidence-based recommendations for designing visual models that effectively promote training of police and communication of police UOF decision-making to the public.

[Visual analysis of knowledge map of network Meta-analysis in traditional Chinese medicine based on CiteSpace]

Network Meta-analysis has been widely applied in the field of traditional Chinese medicine(TCM) due to its unique advantages. This study aimed to conduct a visual analysis on the state of the application network Meta-analysis in the field of traditional Chinese medicine. Databases of CNKI and Web of Science were retrieved to identify the qualified literatures and then screen out their titles and abstracts. Institutions, authors, cited references, and keywords were analyzed using the information visualization analysis software CiteSpace. Finally, 79 English and 186 Chinese articles were included. The results indicated that the literatures were mainly published in Chinese, and the number of articles was increased rapidly since 2015. Cooperation between institutions and authors were mainly concentrated inside the institutions. The most important four institutions were four universities who attached more importance to evidence-based medical education and academic exploration. The keywords beside the method of network Meta-analysis could be summarized into three types: the main interventions in traditional Chinese medicine(Chinese herbal injection, herb medicine, acupuncture, etc.), disease types(cancer, circulatory system disease, bone joint disease, urinary system disease, etc.) and the outcome of interests(efficacy, safety, symptom, survive, mortality, etc.), which reflected the current research hotspots to certain extent. In addition, the most cited articles were methodology articles, including the introduction of methodology and the guides of application software, suggesting that the exploration of methodological articles will be extremely concerned.

Polysaccharide Multilayer Films in Sensors for Detecting Prostate Tumor Cells Based on Hyaluronan-CD44 Interactions

The increasing need for point-of-care diagnosis has sparked the development of label-free sensing platforms, some of which are based on impedance measurements with biological cells. Here, interdigitated electrodes were functionalized with layer-by-layer (LbL) films of hyaluronan (HA) and chitosan (CHI) to detect prostatic tumor cells (PC3 line). The deposition of LbL films was confirmed with atomic force microscopy and polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS), which featured the vibrational modes of the HA top layer capable of interacting specifically with glycoprotein CD44 receptors overexpressed in tumor cells. Though the CHI/HA LbL films cannot be considered as a traditional biosensor due to their limited selectivity, it was possible to distinguish prostate tumor cells in the range from 50 to 600 cells/µL in in vitro experiments with impedance spectroscopy. This was achieved by treating the impedance data with information visualization methods, which confirmed the distinguishing ability of the films by observing the absence of false positives in a series of control experiments. The CD44–HA interactions may, therefore, be exploited in clinical analyses and point-of-care diagnostics for cancer, particularly if computational methods are used to process the data.

Promoting Latino Self-Management Through Use of Information Visualizations: A Case Study in New York City

Social determinants of health, including low health literacy and limited English proficiency, and other factors place Latinos at high risk for health disparities related to chronic diseases. Such diseases require self-management skills as well as medical management. Well-designed visualizations are suitable for visualizing data related to self-management because they can help narrow the comprehension gap between individuals with low and high levels of health literacy by leveraging existing visual analysis skills while reducing the demand on literacy and numeracy competencies. Use of information visualizations also supports a common visual representation across languages to address limited English proficiency. This report illustrates the use of information visualizations for communication related to self-management through research-based case examples and summarizes key lessons from studies with Latinos in New York City.

Scholar Plot: Design and Evaluation of an Information Interface for Faculty Research Performance

The ability to objectively assess academic performance is critical to rewarding academic merit, charting academic policy, and promoting science. Quintessential to performing these functions is first the ability to collect valid and current data through increasingly automated online interfaces. Moreover, it is crucial to remove disciplinary and other biases from these data, presenting them in ways that support insightful analysis at various levels. Existing systems are lacking in some of these respects. Here we present Scholar Plot (SP), an interface that harvests bibliographic and research funding data from online sources. SP addresses systematic biases in the collected data through nominal and normalized metrics. Eventually, SP combines synergistically these metrics in a plot form for expert appraisal, and an iconic form for broader consumption. SP's plot and iconic forms are scalable, representing equally well individual scholars and their academic units, thus contributing to consistent ranking practices across the university organizational structure. In order to appreciate the design principles underlying SP, in particular the informativeness of nominal vs. normalized metrics, we also present the results of an evaluation survey taken by senior faculty (n = 28) with significant promotion and tenure assessment experience.

Towards Adaptive Information Visualization - A Study of Information Visualization Aids and the Role of User Cognitive Style

Information Visualization systems have traditionally followed a one-size-fits-all model, whereby the same visualization is shown to each user, without taking into consideration an individual user's preferences, abilities, or context. By contrast, given the considerable cognitive effort involved in using Information Visualizations, this paper investigates the effect of an individual user's cognitive style on Information Visualization performance. In addition, this paper studies several interactive “visualization aids” (i.e., interactive overlays that can aid in visualization comprehension), as well as the effect of cognitive style on aid choices and preferences. The results from a user study show that cognitive style plays a significant role when performing tasks with Information Visualizations in general, and that there are clear differences in terms of individual aid choices and preferences. These findings also provide motivation for the development of adaptive and personalized Information Visualization systems that could better assist users according to their individual cognitive style.

RadialPheno: A tool for near-surface phenology analysis through radial layouts

PREMISE

Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations.

METHODS AND RESULTS

We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool.

CONCLUSIONS

RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long-term leafing phenology data associated with near-surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground-based direct visual observations and camera-derived phenology observations.

Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments

In the information age, the ability to read and construct data visualizations becomes as important as the ability to read and write text. However, while standard definitions and theoretical frameworks to teach and assess textual, mathematical, and visual literacy exist, current data visualization literacy (DVL) definitions and frameworks are not comprehensive enough to guide the design of DVL teaching and assessment. This paper introduces a data visualization literacy framework (DVL-FW) that was specifically developed to define, teach, and assess DVL. The holistic DVL-FW promotes both the reading and construction of data visualizations, a pairing analogous to that of both reading and writing in textual literacy and understanding and applying in mathematical literacy. Specifically, the DVL-FW defines a hierarchical typology of core concepts and details the process steps that are required to extract insights from data. Advancing the state of the art, the DVL-FW interlinks theoretical and procedural knowledge and showcases how both can be combined to design curricula and assessment measures for DVL. Earlier versions of the DVL-FW have been used to teach DVL to more than 8,500 residential and online students, and results from this effort have helped revise and validate the DVL-FW presented here.

Research progress on international studies on applied ecology based on Web of Science

Applied ecology is one of the most important scientific and technological tools for natural resources management and environment protection. Under the current situation of natural resource shortage, serious environmental pollution, and ecosystem degradation, understanding the theoretical basis, research methods and research hotspots of international studies on applied ecology is of great significance for consolidating the knowledge base, indicating the research direction, defining the strategic position of China's studies on applied ecology. Based on the principle of bibliometrics and information visualization software (CiteSpace and Carrot²), we took two document datasets as research objects which were searched from Web of Science based on different search strategies in the field of international studies on applied ecology from 1980 to 2018. We analyzed the spatial and temporal distribution of the literature, the core research forces, the evolution and frontier trend of research hotspots from three dimensions, i.e. research carrier, research strength, and research content. Our results could reveal its evolutionary trajectory, research status and development trend and provide reference for future studies on applied ecology in China.

Use of data processing for rapid detection of the prostate-specific antigen biomarker using immunomagnetic sandwich-type sensors

Diagnosis of cancer using electroanalytical methods can be achieved at low cost and in rapid assays, but this may require the combination with data treatment for determining biomarkers in real samples. In this paper, we report an immunomagnetic nanoparticle-based microfluidic sensor (INμ-SPCE) for the amperometric detection of the prostate-specific antigen (PSA) biomarker, the data of which were treated with information visualization methods. The INμ-SPCE consists of eight working electrodes, reference and counter electrodes. On the working electrodes, magnetic nanoparticles with secondary antibodies with the enzyme horseradish peroxidase were immobilized for the indirect detection of PSA in a sandwich-type procedure. Under optimal conditions, the immunosensor could operate within a wide range from 12.5 to 1111 fg·L^-1, with a low detection limit of 0.062 fg·L^-1. Multidimensional projections combined with feature selection allowed for the distinction of cell lysates with different levels of PSA, in agreement with results from the traditional enzyme-linked immunosorbent assay. The approaches for immunoassays and data processing are generic, and therefore the strategies described here may provide a simple platform for clinical diagnosis of cancers and other types of diseases.

Data-Driven Visual Performance Analysis in Soccer: An Exploratory Prototype

In soccer, understanding of collective tactical behavior has become an integral part in sports analysis at elite levels. Evolution of technology allows collection of increasingly larger and more specific data sets related to sport activities in cost-effective and accessible manner. All this information is minutely scrutinized by thousands of analysts around the globe in search of answers that can in the long-term help increase the performance of individuals or teams in their respective competitions. As the volume of data increases in size, so does the complexity of the problem and the need for suitable tools that leverage the cognitive load involved in the investigation. It is proven that visualization and computer-vision techniques, correctly applied to the context of a problem, help data analysts focus on the relevant information at each stage of the process, and generally lead to a better understanding of the facts that lie behind the data. In the current study, we presented a software prototype capable of assisting researchers and performance analysts in their duty of studying group collective behavior in soccer games and trainings. We used geospatial data acquired from a professional match to demonstrate its capabilities in two different case studies. Furthermore, we successfully proved the efficiency of the different visualization techniques implemented in the prototype and demonstrated how visual analysis can effectively improve some of the basic tasks employed by sports experts on their daily work, complementing more traditional approaches.

The Power of Shape: How Shape of Node-Link Diagrams Impacts Aesthetic Appreciation and Triggers Interest

Positive effects of aesthetically appreciated designs have long been studied and confirmed since the 19th century: such designs are more enjoyable, they are more forgivable for glitches and can increase users' performance. In the field of information visualization, studies of aesthetics are still a niche approach. In the current study, we aim to specifically understand which parameters in a visualization of node-link diagrams make them aesthetically pleasing-an important extension to already existing research on usability and readability aspects. We investigated how the shape of the outline of such diagrams influences the aesthetic judgments on two of the most important dimensions of aesthetic appeal: beauty and interest. We employed different outlines to node-link diagrams and compared them with uniformly filled shapes, varying two important variables typically impacting aesthetics: complexity and curvature. This was done for a short (100 ms) and ad libitum presentation time. Diagrams with curvier outlines were perceived as more beautiful, while diagrams with more complex outlines were considered to be more interesting. These dependencies already exist for presaccadic perception (100 ms) and are slightly stronger for unlimited presentation time. We also found that curvature is a predictor for beauty only for unlimited presentation time. Aesthetic appeal was very similar for diagrams and pure shapes, so many results from fundamental research on aesthetics can potentially be transferred to the community of network visualization, assisting to improve visualizations also in aesthetic regards.

An Information-Theoretic Framework for Evaluating Edge Bundling Visualization

Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented.

Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes

Background

The eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development challenges and tasks, benchmark datasets, and software for processing and evaluation. In 2012, it ran as a scientific workshop with the aim of establishing an evaluation lab, and since 2013, this annual workshop has been supplemented with 3 or more preceding labs each year. An evaluation lab is an activity where the participating individuals or teams’ goal is to solve the same problem, typically using the same dataset in a given time frame. The overall purpose of this initiative is to support patients, their next of kin, clinical staff, health scientists, and health care policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. In the CLEF eHealth 2013 to 2017 installations, the aim was to address patient-centric text processing. From 2015, the scope was also extended to aid both patients’ understanding and clinicians’ authoring of various types of medical content. CLEF eHealth 2017 introduced a new pilot task on technology-assisted reviews (TARs) in empirical medicine in order to support health scientists and health care policymakers’ information access.

Objectives

This original research paper reports on the outcomes of the first 6 installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analyzing the scholarly influence by reviewing CLEF eHealth papers and their citations.

Methods

A review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions were conducted. Citation content analysis was used for the publications and their citations collected from Google Scholar.

Results

As many as 718 teams registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. A total of 184 papers using CLEF eHealth data generated 1299 citations, yielding a total scholarly citation influence of almost 963,000 citations for the 741 coauthors, and included authors from 33 countries across the world. Eight tasks produced statistically significant improvements (2, 3, and 3 times with P<.001, P=.009, and P=.04, respectively) in processing quality by at least 1 out of the top 3 methods.

Conclusions

These substantial participation numbers, large citation counts, and significant performance improvements encourage continuing to develop these technologies to address patient needs. Consequently, data and tools have been opened for future research and development, and the CLEF eHealth initiative continues to run new challenges.

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where). We conclude by highlighting research directions and open research problems. This survey helps researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.

Information Visualization and Feature Selection Methods Applied to Detect Gliadin in Gluten-Containing Foodstuff with a Microfluidic Electronic Tongue

The fast growth of celiac disease diagnosis has sparked the production of gluten-free food and the search for reliable methods to detect gluten in foodstuff. In this paper, we report on a microfluidic electronic tongue (e-tongue) capable of detecting trace amounts of gliadin, a protein of gluten, down to 0.005 mg kg^-1 in ethanol solutions, and distinguishing between gluten-free and gluten-containing foodstuff. In some cases, it is even possible to determine whether gluten-free foodstuff has been contaminated with gliadin. That was made possible with an e-tongue comprising four sensing units, three of which made of layer-by-layer (LbL) films of semiconducting polymers deposited onto gold interdigitated electrodes placed inside microchannels. Impedance spectroscopy was employed as the principle of detection, and the electrical capacitance data collected with the e-tongue were treated with information visualization techniques with feature selection for optimizing performance. The sensing units are disposable to avoid cross-contamination as gliadin adsorbs irreversibly onto the LbL films according to polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS) analysis. Small amounts of material are required to produce the nanostructured films, however, and the e-tongue methodology is promising for low-cost, reliable detection of gliadin and other gluten constituents in foodstuff.

A Review of Visual Representations of Physiologic Data

Background

Physiological data is derived from electrodes attached directly to patients. Modern patient monitors are capable of sampling data at frequencies in the range of several million bits every hour. Hence the potential for cognitive threat arising from information overload and diminished situational awareness becomes increasingly relevant. A systematic review was conducted to identify novel visual representations of physiologic data that address cognitive, analytic, and monitoring requirements in critical care environments.

Objective

The aims of this review were to identify knowledge pertaining to (1) support for conveying event information via tri-event parameters; (2) identification of the use of visual variables across all physiologic representations; (3) aspects of effective design principles and methodology; (4) frequency of expert consultations; (5) support for user engagement and identifying heuristics for future developments.

Methods

A review was completed of papers published as of August 2016. Titles were first collected and analyzed using an inclusion criteria. Abstracts resulting from the first pass were then analyzed to produce a final set of full papers. Each full paper was passed through a data extraction form eliciting data for comparative analysis.

Results

In total, 39 full papers met all criteria and were selected for full review. Results revealed great diversity in visual representations of physiological data. Visual representations spanned 4 groups including tabular, graph-based, object-based, and metaphoric displays. The metaphoric display was the most popular (n=19), followed by waveform displays typical to the single-sensor-single-indicator paradigm (n=18), and finally object displays (n=9) that utilized spatiotemporal elements to highlight changes in physiologic status. Results obtained from experiments and evaluations suggest specifics related to the optimal use of visual variables, such as color, shape, size, and texture have not been fully understood. Relationships between outcomes and the users’ involvement in the design process also require further investigation. A very limited subset of visual representations (n=3) support interactive functionality for basic analysis, while only one display allows the user to perform analysis including more than one patient.

Conclusions

Results from the review suggest positive outcomes when visual representations extend beyond the typical waveform displays; however, there remain numerous challenges. In particular, the challenge of extensibility limits their applicability to certain subsets or locations, challenge of interoperability limits its expressiveness beyond physiologic data, and finally the challenge of instantaneity limits the extent of interactive user engagement.

How Therapists Use Visualizations of Upper Limb Movement Information From Stroke Patients: A Qualitative Study With Simulated Information

Background

Stroke is a leading cause of disability worldwide, with upper limb deficits affecting an estimated 30% to 60% of survivors. The effectiveness of upper limb rehabilitation relies on numerous factors, particularly patient compliance to home programs and exercises set by therapists. However, therapists lack objective information about their patients’ adherence to rehabilitation exercises as well as other uses of the affected arm and hand in everyday life outside the clinic. We developed a system that consists of wearable sensor technology to monitor a patient’s arm movement and a Web-based dashboard to visualize this information for therapists.

Objective

The aim of our study was to evaluate how therapists use upper limb movement information visualized on a dashboard to support the rehabilitation process.

Methods

An interactive dashboard prototype with simulated movement information was created and evaluated through a user-centered design process with therapists (N=8) at a rehabilitation clinic. Data were collected through observations of therapists interacting with an interactive dashboard prototype, think-aloud data, and interviews. Data were analyzed qualitatively through thematic analysis.

Results

Therapists use visualizations of upper limb information in the following ways: (1) to obtain objective data of patients’ activity levels, exercise, and neglect outside the clinic, (2) to engage patients in the rehabilitation process through education, motivation, and discussion of experiences with activities of daily living, and (3) to engage with other clinicians and researchers based on objective data. A major limitation is the lack of contextual data, which is needed by therapists to discern how movement data visualized on the dashboard relate to activities of daily living.

Conclusions

Upper limb information captured through wearable devices provides novel insights for therapists and helps to engage patients and other clinicians in therapy. Consideration needs to be given to the collection and visualization of contextual information to provide meaningful insights into patient engagement in activities of daily living. These findings open the door for further work to develop a fully functioning system and to trial it with patients and clinicians during therapy.

Feast for the Eyes: An Introduction to Data Visualization

Data visualization is defined as the use of data presented in a graphical or pictorial manner. While data visualization is not a new concept, the ease with which anyone can create a data-drive chart, image, or visual has encouraged its growth. The increase of free sources of data and need for user-created content on social media has also led to a rise in data visualization's popularity. This column will explore what data visualization is and how it is currently being used. It will also discuss the benefits, potential problems, and uses in libraries. A brief list of visualization guides is included.

Engaging Patients With Advance Directives Using an Information Visualization Approach

Despite the benefits of advance directives (AD) to patients and care providers, they are often not completed due to lack of patient awareness. The purpose of the current article is to advocate for creation and use of an innovative information visualization (infovisual) as a health communication tool aimed at improving AD dissemination and engagement. The infovisual would promote AD awareness by encouraging patients to learn about their options and inspire contemplation and conversation regarding their end-of-life (EOL) journey. An infovisual may be able to communicate insights that are often communicated in words, but are much more powerfully communicated by example. Furthermore, an infovisual could facilitate vivid understanding of options and inspire the beginning of often difficult conversations among care providers, patients, and loved ones. It may also save clinicians time, as care providers may be able to spend less time explaining details of EOL care options. Use of an infovisual could assist in ensuring a well-planned EOL journey.

Controlled Film Architectures to Detect a Biomarker for Pancreatic Cancer Using Impedance Spectroscopy

The need for analytical devices for detecting cancer at early stages has motivated research into nanomaterials where synergy is sought to achieve high sensitivity and selectivity in low-cost biosensors. In this study, we developed a film architecture combining self-assembled monolayer (SAM) and layer-by-layer (LbL) films of polysaccharide chitosan and the protein concanavalin A, on which a layer of anti-CA19-9 antibody was adsorbed. Using impedance spectroscopy with this biosensor, we were capable of detecting low concentrations of the antigen CA19-9, an important biomarker for pancreatic cancer. The limit of detection of 0.69U/mL reached is sufficient for detecting pancreatic cancer at very early stages. The selectivity of the biosensor was inferred from a series of control experiments with samples of cell lines that were tested positive (HT29) and negative (SW620) for the biomarker CA19-9, in addition to the lack of changes in the capacitance value for other analytes and antigen that are not related to this type of cancer. The high sensitivity and selectivity are ascribed to the very specific antigen-antibody interaction, which was confirmed with PM-IRRAS and atomic force microscopy. Also significant is that used information visualization methods to show that different cell lines and commercial samples containing distinct concentrations of CA19-9 and other analytes can be easily distinguished from each other. These computational methods are generic and may be used in optimization procedures to tailor biosensors for specific purposes, as we demonstrated here by comparing the performance of two film architectures in which the concentration of chitosan was varied.

Supramolecular Control in Nanostructured Film Architectures for Detecting Breast Cancer

The need for early detection of various diseases, including breast cancer, has motivated research into nanomaterials that can be assembled in organized films which serve as biosensors. Owing to the variety of possible materials and film architectures, procedures are required to design optimized biosensors. In this study, we combine surface-specific methods to monitor the assembly of antibodies on nanostructured films with two distinct architectures. In the first, a layer of the antibody type mouse anti-HER2 (clone tab250) was immobilized on a self-assembled monolayer (SAM) of 11-mercaptoundecanoic acid modified with N-hydroxysuccinimide (NHS) and 1-ethyl-3-(3-(dimethylamino)propyl)carbodiimide (EDC). In the second approach, a SAM of cysteamine was coated with a biotin/spreptavidin bilayer on which a layer of biotinylated antibody type MSx2HUp185/her biotin was adsorbed. The rougher, less passivating coating with cysteamine determined from cyclic voltammetry and scanning electron microscopy led to biosensors that are more sensitive to detect the breast cancer ERBB2 (HER2) biomarker in impedance spectroscopy measurements. This higher distinguishing ability of the cysteamine-containing film architecture was proven with information visualization methods to treat the impedance data. Polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS) confirmed that biosensing resulted from the antibody-ERBB2 antigen affinity.

A Visual Analytics Approach to Understanding Care Process Variation and Conformance

With greater pressures of providing high-quality care at lower cost due to a changing financial and policy environment, the ability to understand variations in care delivery and associated outcomes and act upon this understanding is of critical importance. Building on prior work in visualizing health-care event sequences and in collaboration with our clinical partner, we describe our process in developing a multiple, coordinated visualization system that helps identify and analyze care processes and their conformance to existing care guidelines. We demonstrate our system using data of 5,784 pediatric emergency department visits over a 13-month period for which asthma was the primary diagnosis.

GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs.

Business intelligence for the radiologist: making your data work for you

Although it remains absent from most programs today, business intelligence (BI) has become an integral part of modern radiology practice management. BI facilitates the transition away from lack of understanding about a system and the data it produces toward incrementally more sophisticated comprehension of what has happened, could happen, and should happen. The individual components that make up BI are common across industries and include data extraction and transformation, process analysis and improvement, outcomes measures, performance assessment, graphical dashboarding, alerting, workflow analysis, and scenario modeling. As in other fields, these components can be directly applied in radiology to improve workflow, throughput, safety, efficacy, outcomes, and patient satisfaction. When approaching the subject of BI in radiology, it is important to know what data are available in your various electronic medical records, as well as where and how they are stored. In addition, it is critical to verify that the data actually represent what you think they do. Finally, it is critical for success to identify the features and limitations of the BI tools you choose to use and to plan your practice modifications on the basis of collected data. It is equally important to remember that BI plays a critical role in continuous process improvement; whichever BI tools you choose should be flexible to grow and evolve with your practice.

Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error

When making an inference or comparison with uncertain, noisy, or incomplete data, measurement error and confidence intervals can be as important for judgment as the actual mean values of different groups. These often misunderstood statistical quantities are frequently represented by bar charts with error bars. This paper investigates drawbacks with this standard encoding, and considers a set of alternatives designed to more effectively communicate the implications of mean and error data to a general audience, drawing from lessons learned from the use of visual statistics in the information visualization community. We present a series of crowd-sourced experiments that confirm that the encoding of mean and error significantly changes how viewers make decisions about uncertain data. Careful consideration of design tradeoffs in the visual presentation of data results in human reasoning that is more consistently aligned with statistical inferences. We suggest the use of gradient plots (which use transparency to encode uncertainty) and violin plots (which use width) as better alternatives for inferential tasks than bar charts with error bars.

Visual Interpretation with Three-Dimensional Annotations (VITA): three-dimensional image interpretation tool for radiological reporting

This paper introduces a software framework called Visual Interpretation with Three-Dimensional Annotations (VITA) that is able to automatically generate three-dimensional (3D) visual summaries based on radiological annotations made during routine exam reporting. VITA summaries are in the form of rotating 3D volumes where radiological annotations are highlighted to place important clinical observations into a 3D context. The rendered volume is produced as a Digital Imaging and Communications in Medicine (DICOM) object and is automatically added to the study for archival in Picture Archiving and Communication System (PACS). In addition, a video summary (e.g., MPEG4) can be generated for sharing with patients and for situations where DICOM viewers are not readily available to referring physicians. The current version of VITA is compatible with ClearCanvas; however, VITA can work with any PACS workstation that has a structured annotation implementation (e.g., Extendible Markup Language, Health Level 7, Annotation and Image Markup) and is able to seamlessly integrate into the existing reporting workflow. In a survey with referring physicians, the vast majority strongly agreed that 3D visual summaries improve the communication of the radiologists' reports and aid communication with patients.

Perception of average value in multiclass scatterplots

The visual system can make highly efficient aggregate judgements about a set of objects, with speed roughly independent of the number of objects considered. While there is a rich literature on these mechanisms and their ramifications for visual summarization tasks, this prior work rarely considers more complex tasks requiring multiple judgements over long periods of time, and has not considered certain critical aggregation types, such as the localization of the mean value of a set of points. In this paper, we explore these questions using a common visualization task as a case study: relative mean value judgements within multi-class scatterplots. We describe how the perception literature provides a set of expected constraints on the task, and evaluate these predictions with a large-scale perceptual study with crowd-sourced participants. Judgements are no harder when each set contains more points, redundant and conflicting encodings, as well as additional sets, do not strongly affect performance, and judgements are harder when using less salient encodings. These results have concrete ramifications for the design of scatterplots.

Different Strokes for Different Folks: Visual Presentation Design between Disciplines

We present an ethnographic study of design differences in visual presentations between academic disciplines. Characterizing design conventions between users and data domains is an important step in developing hypotheses, tools, and design guidelines for information visualization. In this paper, disciplines are compared at a coarse scale between four groups of fields: social, natural, and formal sciences; and the humanities. Two commonplace presentation types were analyzed: electronic slideshows and whiteboard "chalk talks". We found design differences in slideshows using two methods - coding and comparing manually-selected features, like charts and diagrams, and an image-based analysis using PCA called eigenslides. In whiteboard talks with controlled topics, we observed design behaviors, including using representations and formalisms from a participant's own discipline, that suggest authors might benefit from novel assistive tools for designing presentations. Based on these findings, we discuss opportunities for visualization ethnography and human-centered authoring tools for visual information.

Building on faults: How to represent controversies with digital methods

In a previous article in this journal, I introduced Bruno Latour's cartography of controversies and I discussed half of it, namely how to observe techno-scientific controversies. In this article I will concentrate on the remaining half: how to represent the complexity of social debates in a legible form. In my previous paper, we learnt how to explore the richness of collective existence through Actor-Network Theory. In this one, I will discuss how to render such complexity through an original visualization device: the controversy-website. Capitalizing on the potential of digital technologies, the controversy-website has been developed as a multilayered toolkit to trace and aggregate information on public debates.

Visualization of parameter space for image analysis

Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step--initialization of sampling--and the last step--visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler--a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.

A Case-based Retrieval System using Natural Language Processing and Population-based Visualization

Electronic medical records capture large quantities of patient data generated as a result of routine care. Secondary use of this data for clinical research could provide new insights into the evolution of diseases and help assess the effectiveness of available interventions. Unfortunately, the unstructured nature of clinical data hinders a user's ability to understand this data: tools are needed to structure, model, and visualize the data to elucidate patterns in a patient population. We present a case-based retrieval framework that incorporates an extraction tool to identify concepts from clinical reports, a disease model to capture necessary context for interpreting extracted concepts, and a model-driven visualization to facilitate querying and interpretation of the results. We describe how the model is used to group, filter, and retrieve similar cases. We present an application of the framework that aids users in exploring a population of intracranial aneurysm patients.

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies -such as efficient data management- supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.