PurposeQualitative data that reflects patients' experiences are the foundation of any patient-reported outcome measure (PROM) development and validation study;however, there is limited understanding of the type of...
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PurposeQualitative data that reflects patients' experiences are the foundation of any patient-reported outcome measure (PROM) development and validation study;however, there is limited understanding of the type of data visualization techniques that facilitate communication of this data. The goal of this targeted literature review was to investigate data visualization methods that have been used in published PROM development and validation literature to report qualitative *** literature search in OVID via MEDLINE was conducted among the top 10 non-disease-specific journals publishing PROM qualitative development and validation studies. Studies that reported qualitative methods to develop/validate a PROM and included data visualization in the form of tables or figures were included. Article characteristics and data visualization types were *** total of 185 articles were included in data extraction. Most articles (n = 109, 59.1%) included figures (n = 172, average 2 relevant figures per article) in the form of hierarchy/flowcharts (n = 124, 72.1%) and bar charts (n = 29, 16.9%). Information reported in figures included depiction of conceptual frameworks (n = 112, 65.1%) and concept frequency (n = 40, 24.4%). Most articles (n = 152, 81.7%) included tables (n = 307, average 2 relevant tables per article). Information reported in tables included concept frequency (n = 133, 43.3%) and cognitive debriefing and revisions (n = 91, 29.6%).Conclusiondata visualization techniques used to report qualitative results in the identified PROM qualitative development and validation studies were heterogeneous, and many studies did not utilize any data visualization techniques. This study will inform the development of guidance for using data visualizations to report qualitative PROM research.
In order to achieve real-time monitoring of employment positions and evaluate industry development potential, a method for building a B/S based employment position data visualization monitoring platform was explored. ...
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data visualization can be a challenging task in our everyday lives, especially when dealing with large and complex datasets. Multiple researchers have dedicated their efforts to this topic and have put forth various s...
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data visualizations are increasingly used by news outlets on social media to communicate insights to a broad audience. However, little is known about how readers interact with and respond to data visualizations in the...
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Results of meta-analyses are of interest not only to researchers but often to policy-makers and other decision-makers (e.g., in education and medicine), and visualizations play an important role in communicating data ...
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Results of meta-analyses are of interest not only to researchers but often to policy-makers and other decision-makers (e.g., in education and medicine), and visualizations play an important role in communicating data and statistical evidence to the broader public. Therefore, the potential audience of meta-analytic visualizations is broad. However, the most common meta-analytic visualization – the forest plot – uses non-optimal design principles that do not align with data visualization best practices and relies on statistical knowledge and conventions not likely to be familiar to a broad audience. Previously, the Meta-Analytic Rain Cloud (MARC) plot has been shown to be an effective alternative to a forest plot when communicating the results of a small meta-analysis to education practitioners. However, the original MARC plot design was not well-suited for meta-analyses with large numbers of effect sizes as is common across the social sciences. This paper presents an extension of the MARC plot, intended for effective communication of moderate to large meta-analyses (k = 10, 20, 50, 100 studies). We discuss the design principles of the MARC plot, grounded in the data visualization and cognitive science literature. We then present the methods and results of a randomized survey experiment to evaluate the revised MARC plot in comparison to the original MARC plot, the forest plot, and a bar plot. We find that the revised MARC plot is more effective for communicating moderate to large meta-analyses to non-research audiences, offering a 0.30, 0.34, and 1.07 standard deviation improvement in chart users’ scores compared to the original MARC plot, forest plot, and bar plot, respectively.
Natural Language to visualization (NL2VIS) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from large-scale data. Recent advancements in ...
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Integrating geosemantically annotated, crawled data into virtual reality (VR) poses several challenges, particularly in presenting and interacting with information. Unlike desktop visualizations that dominate a screen...
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ISBN:
(数字)9798331514846
ISBN:
(纸本)9798331525637
Integrating geosemantically annotated, crawled data into virtual reality (VR) poses several challenges, particularly in presenting and interacting with information. Unlike desktop visualizations that dominate a screen, VR visualizations must coexist with the immersive environment, occupying limited space without disrupting natural surroundings. Representing relationships between spatially distributed entities, such as those derived from news articles and social media, adds complexity, requiring innovative approaches to highlight connections effectively. Furthermore, user interaction with these visualizations must leverage free movement and sensory feedback while addressing the constraints of a smaller physical space and avoiding motion sickness. This paper introduces GeoCROW, an immersive geospatial data visualization and interaction platform. We describe our VR prototype designed to meet these challenges, including a user study and stakeholder workshop feedback. Our results contribute to the visualization of geospatial data, and we discuss the future iterations of the platform to improve it.
Research on cognitive biases and heuristics has become increasingly popular in the visualization literature in recent years. Researchers have studied the effects of biases on visualization interpretation and subsequen...
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As the Square Kilometre Array (SKA) radio telescopes approach full operation, they are set to generate hundreds of petabytes of data annually, offering unprecedented resolution and detail in astrophysical observations...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
As the Square Kilometre Array (SKA) radio telescopes approach full operation, they are set to generate hundreds of petabytes of data annually, offering unprecedented resolution and detail in astrophysical observations. This massive influx of data presents significant challenges in terms of storage, processing, analysis and visualization. Scientific visualization becomes essential in this context, enabling researchers to interpret complex, high-dimensional datasets and extract valuable insights. This paper presents the latest developments of VisIVO Visual Analytics, an interactive visualization tool that is evolving to meet the needs of the SKA and similar large-scale astrophysical projects such as the James Webb Space Telescope and the Atacama Large Millimeter/submillimeter Array (ALMA). Originally designed as a desktop application, VisIVO is transitioning to a client-server architecture, allowing it to efficiently run remote visualization pipelines on remote servers. This shift significantly enhances the tool’s ability to handle large scale datasets, facilitating real-time, interactive analysis of complex data. This work also explores how VisIVO Visual Analytics can distribute computational workloads across multiple nodes in high-performance computing (HPC) clusters for parallel visualization pipelines, enabling advanced and interactive techniques to derive meaningful insights from the vast volumes of data generated by next-generation observatories.
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