We present the preliminary design and results of Qvis, a visual analytics tool for exploring quantum device performance data. Qv is helps uncover temporal and multivariate variations in noise properties of quantum dev...
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The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies o...
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The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive dataexploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.
The aim of this paper is to carry out a comprehensive review of 921 papers in the Scopus database on "building energy analysis using BIM" from 2005 to 2022. Using a systematic approach to the primary data in...
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As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain dataanalysis falls short for complex tasks, ...
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Salivary gland tumors require comprehensive data collection and analysis to support clinical decision-making, yet existing databases need more focus on specific tumor-related data and visualization tools. This absence...
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The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable dataexploration with ego- or exocentric views, have the user operate at d...
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The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable dataexploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this article, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants' spatial abilities. While egocentric room-scale exploration significantly reduced mental workload, exocentric exploration improved performance in some tasks. Combining navigation and manipulation made tasks easier by reducing workload, temporal demand, and physical effort.
Existing datavisualization design guidelines focus primarily on constructing grammatically-correct visualizations that faithfully convey the values and relationships in the underlying data. However, a designer may cr...
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ISBN:
(纸本)9798400703300
Existing datavisualization design guidelines focus primarily on constructing grammatically-correct visualizations that faithfully convey the values and relationships in the underlying data. However, a designer may create a grammatically-correct visualization that still leaves audiences susceptible to reasoning misleaders, e.g. by failing to normalize data or using unrepresentative samples. Reasoning misleaders are especially pernicious when presenting public policy data, where data-driven decisions can affect public health, safety, and economic development. Through textual analysis, a formative evaluation, and iterative design with 19 policy communicators, we construct an actionable visualization design framework, v-FRAMER, that effectively synthesizes ways of mitigating reasoning misleaders. We discuss important design considerations for frameworks like v-FRAMER, including using concrete examples to help designers understand reasoning misleaders, and using a hierarchical structure to support example-based accessing. We further describe v-FRAMER's congruence with current practice and how practitioners might integrate the framework into their existing workfows. Related materials available at: https://***/q3uta/.
This study capitalizes on cloud technology to revolutionize video processing, offering real-time object detection and labeling. Users can effortlessly upload videos via our user-friendly web portal, initiating cloud-b...
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Solar photovoltaic (Pv) modules are a popular source of clean energy, and effective monitoring and optimization of their performance requires the ability to explore and analyze the measurement data from their sensors....
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This article presents a comprehensive study on the utilization of data mining technologies and ontological approaches in the field of government procurement. It highlights the complexities involved in defining procure...
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