Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present SpinelVA, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. SpinelVA allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, SpinelVA integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions.

General Line Coordinates (GLC) are a relatively new set of line-based representations for visualizing multidimensional data with the distinctive characteristics of being reversible and lossless. Given these characteristics, the GLC have a high potential for exploratory multidimensional data analysis, however only partial implementations of some of the GLC techniques are available for the visualization community. In this paper, we present the GLC-Frame, an online exploration tool that supports a dual view and allows users to upload their own dataset and interactively explore the different GLC representations without writing code. We also present the GLC-Vis Library, an open-source data visualization library supporting GLC along with traditional interactions. Finally, we provide a set of usage examples showing how the different techniques behave in both the occlusion and the cluster identification problem. In addition, we present the interactions on GLC representations using the cars dataset. Both the GLC-Frame and the GLC-Vis Library provide an exploration space that will allow the visualization community to use these new techniques and evaluate their potential.

El Fierro intrusive body is one of the bodies that compose the La Jovita-Las Aguilas mafic-ultramafic belt, located in the Sierra Grande de San Luis, Argentina. The units of this belt carry a base metal sulfide (BMS) mineralization and platinum group minerals (PGM). The macroscopic description of mafic and ultramafic rocks, as is usually done by the mining exploration companies, leads to an imprecise modal classification of the rocks. In this study, we develop a random forest-based prediction model, which uses geochemical parameters to classify mafic and ultramafic rocks intercepted by drill cores. This model showed an accuracy of between 86% and 94%, and an f1_score of 96%. Random forest classification is a widely adopted Machine Learning approach to construct predictive models across various research domains. However, as models become more complex, their interpretation can be considerably difficult. To interpret the model results, we use both global and local perspectives, incorporating the SHAP (SHapley Additive exPlanations) method. The SHAP technique allows us to analyze individual samples using force plots, and provides a measure of the importance of each geochemical input attribute in the model output. As a result of analyzing the contribution of each input feature to the model, the three variables with the highest contributions were identified in the following order: Al2O3, MgO, and Sr.

Nowadays, the explosive growth of data generated from numerous sources results in ever-increasing volumes of data that are, therefore, difficult to understand, explore and analyze in order to extract information from them. The contribution of visualization to the exploration and understanding of these large datasets is very significant. Various application domains often require different visual representations, although several share the same intermediate steps, transformations, and/or manipulations of such data. These shared aspects lead to the requirement for a consistent and extensible visual analytics model across all application domains. In this context, we introduce the Unified Visual Analytics Model (UVAM), which combines the Unified Visualization Model (UVM) with the specific features of visual analytics. The UVAM is a state model represented as a flow between the many states that the data passes through throughout the process. It includes the user interactions with the data and its intermediate representations and how the user controls the transformations and, subsequently, modifications of the visualizations. This paper illustrates how the model includes various visual analytics processes and describes in detail a UVAM case study.

Technological advances in recent years have promoted the development of virtual reality systems that have a wide variety of hardware and software characteristics, providing varying degrees of immersion. Immersion is an objective property of the virtual reality system that depends on both its hardware and software characteristics. Virtual reality systems are currently attempting to improve immersion as much as possible. However, there is no metric to measure the level of immersion of a virtual reality system based on its characteristics. To date, the influence of these hardware and software variables on immersion has only been considered individually or in small groups. The way these system variables simultaneously affect immersion has not been analyzed either. In this paper, we propose immersion metrics for virtual reality systems based on their hardware and software variables, as well as the development process that led to their formulation. From the conducted experiment and the obtained data, we followed a methodology to generate immersion models based on the variables of the system. The immersion metrics presented in this work offer a useful tool in the area of virtual reality and immersive technologies, not only to measure the immersion of any virtual reality system but also to analyze the relationship and importance of the variables of these systems.

Physically walking in Virtual Reality (VR) creates a truly compelling user experience. Many navigation techniques for VR have been presented in the literature. The room-scale technique allows a natural and intuitive navigation through physically walking in the virtual environment, but it is limited to the available physical space. The dimensions of the virtual space can be extended by applying translation gains, i.e., a mapping of physical movements to virtual ones. Previous works have studied the threshold at which users detect the spatial manipulation. However, little is known about the user experience and usability beyond this threshold. This paper presents a user study with 110 participants that explores the effect of using translation gains beyond the detection threshold on cybersickness and presence. The objective of this paper is to assess whether translations gains higher than the ones used in redirection techniques can be used for walking in a bigger virtual environment than the tracking area without influencing user comfort and experience. Results showed no difference in presence scores and minimal cybersickness symptoms when using no gain and a 1.5 × gain, but started to be of concern with a 2 × gain. This contribution supports the use of translation gains and the development of novel applications that allow the exploration of bigger virtual environments, thus improving presence and user experience.

Technological advances in recent years have promoted the development of virtual reality systems that have a wide variety of hardware and software characteristics, providing varying degrees of immersion. Immersion is an objective property of the virtual reality system that depends on both its hardware and software characteristics. Virtual reality systems are currently attempting to improve immersion as much as possible. However, there is no metric to measure the level of immersion of a virtual reality system based on its characteristics. To date, the influence of these hardware and software variables on immersion has only been considered individually or in small groups. The way these system variables simultaneously affect immersion has not been analyzed either. In this paper, we propose immersion metrics for virtual reality systems based on their hardware and software variables, as well as the development process that led to their formulation. From the conducted experiment and the obtained data, we followed a methodology to find immersion models based on the variables of the system. The immersion metrics presented in this work offer a useful tool in the area of virtual reality and immersive technologies, not only to measure the immersion of any virtual reality system but also to analyze the relationship and importance of the variables of these systems.

Data visualization aims to explore and analyze data quickly, interactively, and intuitively using visual representations. Faced with the constant growth of data in terms of volume and diversity, visualization techniques must confront the challenge of dealing with increasingly large datasets in terms of representation, interaction, and performance. Therefore, these techniques must be able to effectively convey the characteristics of the information space and inspire discovery.In this article, we present VISUEL, a web dynamic dashboard for data visualization. VISUEL supports multiple coordinated views, integrating visualization techniques such as scatter plots, parallel coordinates, and box plots, and interactive schematic maps to represent information enriched with spatial references.VISUEL is fully interactive, supporting traditional interactions like filtering, selection, brushing and linking, and zooming, among others. It also allows the user to configure the visual representation of their data, by selecting the color and shape of the representations.We illustrate the usefulness of this tool using real-life data related to the wine industry in Argentina. Important aspects of our case study are discovered through the construction and analysis of multiple views.

Multidimensional data visualization is one of the primary foundations supporting data analysis used for understanding the hidden relationships between items and dimensions of complex data. The line-based visualization techniques are a fundamental class of multidimensional visualization techniques and cover an important set of methods that are relevant to the visual exploratory analysis. Recently, General Line Coordinates (GLCs) were introduced. These are losslessly line-based visualization techniques for multidimensional data. Particular cases of GLCs are the non-paired GLCs, which generalize the radial and parallel coordinates and have proved to be highly suitable for visualizing multidimensional data. In this context, we conduct a systematic paper review of the 2D non-paired GLC (2D-NP-GLC) visualization techniques present in the literature. We organize the 2D-NP-GLC contributions in a unified reference framework in which both the representations and the associated interactions are considered. Focusing jointly on these two criteria, we provide a useful common space for the design and development of 2D-NP-GLC techniques. Besides, this framework integrates the 2D-NP-GLC contributions and helps to identify under-explored areas that may be candidates for further research.

Reading is a complex task that can provide valuable information about our perceptual and cognitive processes. To understand how people read, researchers have embraced the use of eye-tracking techniques. Recent research work studies the eye movements during reading of short sentences, and however, the extension of these findings to natural reading has not been yet studied in depth. The visual analysis of eye movement data has become an emerging field providing important means to support statistical analysis and hypothesis building. In this work, we focus on the visual analysis of the natural reading of a particular type of text, the micro-stories, which are short-length texts that condense a large amount of information. We present a novel visualization technique for analyzing eye movement data during the reading of micro-stories. In the design of the proposed technique, we consider all the characteristics defined for a typical reading experiment, integrating all of them into a single view. We also provide associated interactions to facilitate exploration. Our novel technique allows the analysis of eye movements during micro-story reading helping the experts to explore relationships among characteristics and to discover hidden relations that help to understand the cognitive process involved.

While information is growing exponentially, datasets are getting bigger and bigger containing valuable information that can expand human knowledge. To extract meaningful information from these dense datasets, the need for effective graphical representations that take advantage of the human's visual perception capabilities is revealed. The visualization of this kind of data is a complex task. These big datasets are in general inherently multidimensional (n-D), facing the challenge of finding suitable mappings from the n-D space to a 2D or 3D space. Even though multiple visualization methods have been developed for n-D data, many of them do not allow the complete restoration of the data from its reduced representation and/or do not represent the complete n-D dataset. The General Lines Coordinates (GLC) are reversible visual representations that preserve n-D information for knowledge discovery. In this paper, we present the npGLC-Vis Library, a data visualization library supporting Non-Paired General Line Coordinates (npGLC) with associated traditional interactions like brushing, zooming, and panning. npGLC-Vis is a collection of visualization methods, designed for experimenting with npGLC techniques in the development of visualization applications. We present the library design and implementation, exemplifying it through the representation of different datasets.

Mobile phones offer an excellent low-cost alternative for Virtual Reality. However, the hardware constraints of these devices restrict the displayable visual complexity of graphics. Image-based rendering techniques arise as an alternative to solve this problem, but usually, the support of collisions and irregular surfaces (i.e., any surface that is not flat or even) represents a challenge. In this work, we present a technique suitable for both virtual and real-world environments that handle collisions and irregular surfaces for an image-based rendering technique in low-cost virtual reality. We also conducted a user evaluation for finding the distance between images that presents a realistic and natural experience by maximizing the perceived virtual presence and minimizing the cybersickness effects. The results prove the benefits of our technique for both virtual and real-world environments.

Mobile phones offer an excellent low-cost alternative for Virtual Reality. However, the hardware constraints of these devices restrict the displayable visual complexity of graphics. Image-Based Rendering techniques arise as an alternative to solve this problem, but usually, generating the stereoscopic effect to improve depth perception presents a challenging problem. In this work, we present an Image-Based Rendering technique for low-cost virtual reality that incorporates stereoscopy to improve depth perception. We also conducted a user evaluation to analyze the stereoscopic effect of the technique, especially considering the effect on depth perception, presence, and navigation. The results prove the benefits of our technique for both virtual and real-world environments.

The spinel group minerals provide useful information regarding the geological environment in which the host rocks were formed, constituting excellent petrogenetic indicators, and guides in the search for mineral deposits of economic interest. In this article, we present the Spinel Web, a web application to visualize the chemical composition of spinel group minerals. Spinel Web integrates most of the diagrams commonly used for analyzing the chemical characteristics of the spinel group minerals. It incorporates parallel coordinates and a 3D representation of the spinel prisms. It also provides coordinated views and appropriate interactions for users to interact with their datasets. Spinel Web also supports semi-automatic categorization of the geological environment of formation through a standard Web browser.

Motion capture (MoCap) data as time series provide a rich source of input for human movement analysis; however, their multidimensional nature makes them difficult to process and compare. In this paper, we propose a visual analysis technique that allows the comparison of MoCap data obtained from karate katas. These consist of a series of predefined movements that are executed independently by several subjects at different times and speeds. For the comparative analysis, the proposed solution presents a visual comparison of the misalignment between a set of time series, based on dynamic time warping. We propose an overview of the misalignment between the data corresponding to n different subjects. A detailed view focusing on the comparison between two of them can be obtain on demand. The proposed solution comes from a combination of signal processing and data visualization techniques. A web application implementing this proposal completes the contribution of this work.