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.

In the geological context, the analysis of multidimensional data is a very common task and requires visualization techniques for exploration. General Line Coordinates (GLC) is a technique especially intended for lossless representation among the various techniques available for the visualization of multidimensional data. In this study, the application of GLC for pattern analysis and identification in mineral datasets is investigated. Furthermore, we develop a web-based tool to explore the possibility of employing GLC to leverage these approaches to derive visual discovery rules for pattern recognition.

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.

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.

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.