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Educational Data Virtual Lab (EDVL) is an open-source platform for data exploration and analysis that combines the power of a coding environment, the convenience of an interactive visualization engine, and the infrastructure needed to handle the complete data lifecycle. Based on the building blocks of the FIWARE European platform and Apache Zeppelin, this tool allows domain experts to become acquainted with data science methods using the data available within their own organization, ensuring that the skills they acquire are relevant to their field and driven by their own professional goals. We used EDVL in a pilot study in which we carried out a focus group within a multinational company to gain insight into potential users' perceptions of EDVL, both from the educational and operational points of view. The results of our evaluation suggest that EDVL holds a great potential to train the workforce in data science skills and to enable collaboration among professionals with different levels of expertise.

This article describes the motivation, design, and evaluation of the VisVisual toolkit to engage students in learning essential visualization concepts, algorithms, and techniques. The toolkit includes four independent components: 1) VolumeVisual, 2) FlowVisual, 3) GraphVisual, and 4) TreeVisual, covering scalar and vector data visualization in scientific visualization and graph and tree layouts in information visualization. Complementary to the toolkit design is resource development, aiming to help instructors integrate VisVisual into their curriculum.

With the widespread advent of visualization techniques to convey complex data, visualization literacy (VL) is growing in importance. Two noteworthy facets of literacy are user understanding and the discovery of visual patterns with the help of graphical representations. The research literature on VL provides useful guidance and opportunities for further studies in this field. This introduction summarizes and presents research on VL that examines how well users understand basic and advanced data representations. To the best of our knowledge, this is the first tutorial article on interactive VL. We describe evaluation categories of existing relevant research into unique subject groups that facilitate and inform comparisons of literacy literature and provide a starting point for interested readers. In addition, the introduction also provides an overview of the various evaluation techniques used in this field of research and their challenging nature. Our introduction provides researchers with unexplored directions that may lead to future work. This starting point serves as a valuable resource for beginners interested in the topic of VL.

To fully leverage the benefits of augmented and mixed reality (AR/MR) in supporting users, it is crucial to establish a consistent and well-defined situated visualization (SV) model. SV encompasses visualizations that adapt based on context, considering the relevant visualizations within their physical display environment. Recognizing the potential of SV in various domains such as collaborative tasks, situational awareness, decision-making, assistance, training, and maintenance, AR/MR is well-suited to facilitate these scenarios by providing additional data and context-driven visualization techniques. While some perspectives on the SV model have been proposed, such as space, time, place, activity, and community, a comprehensive and up-to-date systematization of the entire SV model is yet to be established. Therefore, there is a pressing need for a more comprehensive and updated description of the SV model within the AR/MR framework to foster research discussions. [ABSTRACT FROM AUTHOR]

The ability to recognize misleading data visualizations is a key aspect of visualization literacy. In this article, we argue that learning to successfully identify a deceptive graphic requires strategies that deliberately force learners to take an active role in the visualization process. We describe a series of experiments where three groups of learners were shown various deceptive graphics and asked to answer a series of questions. Three different interventions were analyzed to compare the educational effectiveness of the strategies used to engage learners into the process of identifying deceptive visualizations. Our results suggest that the ability to identify deceptive visualizations must be explicitly taught as a core element of visualization literacy. Although both traditional and self-learning approaches are beneficial, the more active the intervention, the higher its educational effectiveness. [ABSTRACT FROM AUTHOR]

Data visualization is hard to master because of the inherent complexities that characterize the challenge of facilitating understanding. Competence with data visualization is gaining in recognition as an essential capability and thus fostering the necessary skills is paramount to prepare students for their future professional activity in this field; yet, it is a challenge for educators to design programs that cover all facets. This article presents a framework that profiles the range of different capability "ingredients" which form the recipe of expertise in data visualization, from the point of view of an experienced practitioner. [ABSTRACT FROM AUTHOR]

We share our experiences teaching university students about clustering algorithms using EduClust, an online visualization we developed. EduClust supports professors in preparing teaching material and students in visually and interactively exploring cluster steps and the effects of changing clustering parameters. We used EduClust for two years in our computer science lectures on clustering algorithms and share our experience integrating the online application in a data science curriculum. We also point to opportunities for future development. [ABSTRACT FROM AUTHOR]

In an effort to develop visualization analysis and design skills in master's level information visualization students, the authors use a well-known analytical usability evaluation method, heuristic evaluation, with different sets of heuristics to teach students to analyze visualization applications. The proposed approach, used for three consecutive years, has successfully stimulated critical analysis and discussion sessions as well as helped raise students' awareness concerning the benefit of using systematic analysis methods and the strategies and guidelines that should be used to design visualization applications. [ABSTRACT FROM AUTHOR]

Geophysical experts aimed to establish a method to identify contamination by landfill leakage without chemically analyzing subsoil samples, which is time-consuming and expensive. To that end, researchers developed a software package that let the experts create 3D visualizations of geophysical data acquired around the landfill and apply statistical analysis to detect anomalous values. The data used, electrical resistivity, are typically sparse. So, the application employs kriging to interpolate the data and provide a volumetric representation of the subsoil resistivity. To avoid invalid conclusions, the visualization also represents uncertainty. The application enabled the experts to better understand the phenomenon and to develop and validate their method. Their evaluation of the application indicated that it helped them throughout the method's development and significantly eased their workload. [ABSTRACT FROM AUTHOR]

The past 10 years have seen profound changes in visualization algorithms, techniques, methodologies, and applications. These changes are forcing alterations to visualization courses. Unfortunately, outdated course content recommendations, together with profound changes in the underlying technology and methodology, are producing an unstable ground for educators at a time when visual representations are becoming increasingly important. To address this issue, educators held meetings or workshops at Siggraph 2011 and 2012 and a panel and workshop at Eurographics 2012. This article presents the insights gathered at these events. [ABSTRACT FROM AUTHOR]

Abstract: Color mapping is an important technique used in visualization to build visual representations of data and information. With output devices such as computer displays providing a large number of colors, developers sometimes tend to build their visualization to be visually appealing, while forgetting the main goal of clear depiction of the underlying data. Visualization researchers have profited from findings in adjoining areas such as human vision and psychophysics which, combined with their own experience, enabled them to establish guidelines that might help practitioners to select appropriate color scales and adjust the associated color maps, for particular applications. This survey presents an overview on the subject of color scales by focusing on important guidelines, experimental research work and tools proposed to help non-expert users. [Copyright &y& Elsevier]

Augmented Reality (AR) is a key technology in the transition to Industry 4.0 and smart manufacturing, gaining reputation in a wide range of industrial fields One promising application is in another field of Industry 4.0, the data monitoring, where AR can be used to visualize and interact with complex and big data in real time, potentially improving the efficiency and accuracy of decision-making In this work, we propose a Pervasive AR tool for data monitoring, developed in collaboration with industry partners. An web application of the same data monitoring function is also created for comparison purposes A Human-Centered Design (HCD) methodology was used to identify the needs and requirements of industrial analysts, which led to the development of these systems Preliminary user studies, with 17 participants having distinct levels of expertise in industry, data analysis and computer science, were conducted to collect opinions and suggestions for improvements, as well as, quantitative data regarding the technologies considered A succeeding user study was then prepared, in which 12 participants used two conditions (C1 — Hands-free Pervasive AR tool for HMDs and C2 — Web tool for tablet devices) to fulfill a set of data monitoring tasks in an industry assembly line The result of these studies confirm the potential of Pervasive AR for data monitoring, as it engages users, promote environmental awareness, contextualizes data and allows fast self-localization. [Display omitted] • Two user studies with more than 10 participants each, industrial settings tasks. • Understand which method (Web or AR) stands out according to the task characteristic. • The findings reveal that certain methods were more effective for types of task. • Display methods significantly influenced participants across analysis. • These studies confirm the potential of Pervasive AR for data monitoring. [ABSTRACT FROM AUTHOR]

Abstract: Polygonal meshes are used in several application areas to model different objects and structures. Depending on the application, mesh models sometimes have to be processed to, for instance, reduce their complexity (mesh simplification). Such operations introduce differences regarding the original mesh, whose evaluation is of paramount importance when choosing the processing methods to be applied for a particular purpose. Although some mesh analysis and comparison tools are described in the literature, little attention has been given to the way mesh features and mesh comparison results can be visualized. Moreover, particular functionalities have to be made available to enable systematic use and proper data analysis and exploration. PolyMeCo—a tool for polygonal mesh analysis and comparison—was designed and developed taking the above objectives into account. It enhances the way users analyze features and compare meshes by providing an integrated environment where various mesh quality measures and several visualization options are available and can be used in a coordinated way, thus leading to greater insight into the visualized data. [Copyright &y& Elsevier]