Abstract Context Data visualizations and dashboards are essential in disseminating information to broad audiences. However, designing these instruments is complex due to the multitude of involved factors. Theoretical frameworks are therefore critical to guide the design and implementation of data visualizations. Objective This article presents the approach used to pinpoint key features that impact the creation of information visualizations, organize these features using meta‐modelling, and validate the final artefact through expert judgement. Method Meta‐modelling facilitates the distillation of characteristics and functionalities critical for the development of information systems. Utilizing this method, a dashboard meta‐model was constructed in preceding research, yielding a framework of abstract components and connections to delineate specific products. This meta‐model serves as a robust tool for the automated generation of information visualizations and dashboards. Nonetheless, to confirm its quality and efficacy, it underwent a thorough quality assessment and expert evaluation to identify and address potential shortcomings. Results The validation process resulted in positive ratings and constructive feedback to enhance the meta‐model's present iteration. Experts mainly highlighted the meta‐model's complexity and the clarity of certain entities, noting potential confusion for non‐experts. Conclusions While the meta‐model is confirmed to be a coherent and pertinent tool, it requires additional clarifications to simplify its complexity. Addressing this, graphical interfaces have been developed to enable users to interact with the dashboard meta‐model—and to create dashboard instances—through straightforward interactions.

Abstract The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine‐grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri‐variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.

MOTIVATION: The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources. RESULTS: We present a nucleosome pattern search method that converts levels of nucleosomal occupancy to a sequence-like format to which BWT searches can be applied. The method is embedded in a nucleosome map browser, 'Nucleosee', an interactive visual tool specifically designed to enhance BWT searches, giving them context and making them suitable for visual discourse analysis of the results. The proposed method is fast, flexible and sufficiently generic for the exploration of data in a broad and interactive way. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm and visual browser are available for testing at http://cpg3.der.usal.es/nucleosee. The source code and installation packages are also available at https://github.com/rodrigoSantamaria/nucleosee. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we introduce a new visualization tool for network-wide intrusion detection. It is based in multivariate anomaly detection with a combination between Principal Component Analysis (PCA) and a new variant called Group-wise PCA (GPCA). Combining these methodologies with the capabilities of interactive visualization, the resulting tool is a highly flexible and intuitive interface that allows the user to navigate through the enormous amount of data collected in the network, in order to find anomalous or unexpected behaviors. We use a real case study to illustrate the capability of the tool to unveil the complex mixture of information that can be found in network security/traffic data and identify and diagnose anomalies in it.

Motivation: AutoDock is a very popular software package for docking and virtual screening. However, currently it is hard work to visualize more than one result from the virtual screening at a time. To overcome this limitation we have designed JADOPPT, a tool for automatically preparing and processing multiple ligand-protein docked poses obtained from AutoDock. It allows the simultaneous visual assessment and comparison of multiple poses through clustering methods. Moreover, it permits the representation of reference ligands with known binding modes, binding site residues, highly scoring regions for the ligand, and the calculated binding energy of the best ranked results. Availability and Implementation: JADOPPT, supplementary material (Case Studies 1 and 2) and video tutorials are available at http://visualanalytics.land/cgarcia/JADOPPT.html. Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

The technology applied to educational contexts, and specially the learning platforms, provides students and teachers with a set of tools and spaces to carry out the learning processes. Information related to the participation and interaction of these stakeholders with their peers and with the platform is recorded. It would be useful to exploit this information in order to make decisions. However this is a complex activity mainly because of the huge quantity of information stored. This work presents a visual learning analytics system that makes possible the exploitation of that information. The system includes several tools that help to analyze users' interaction attending to different dimensions, such as: when interaction is carried out, which are more important contents for users, how they interact with others, etc. This system has been tested with the subject information recorded during five academic years. From this analysis it is possible to show that visual learning analytics may help to improve educational practices.

This proposes how to face up, from a technological point of view, the collection and storage of heterogeneous data coming from heterogeneous environments related to the Learning process in order to take advantage in tasks related to Learning Analytics. It introduces the problem with the heterogeneous data over the time and deals how to resolve this issue giving up a theoretical approach and a proof of concept.

Knowledge representation (KR) can be defined as a set of ontological commitments, provided with the capabilities of performing inference. The knowledge can be represented using an ontology, which provides a shared insight into a certain domain. The use of ontologies to represent knowledge also allows interoperation among knowledge-based systems. The process of building ontologies can be tedious and sometimes exhaustive. A possible solution in order to avoid this problem would be to reuse the ontologies previously created by others. This paper describes a case study of reusability using OWL-VisMod, a tool designed for developing ontological engineering based on visual conceptual modelling for OWL ontologies. A workflow performed with OWL-VisMod is described; including a decision-making process in order to decide whether or not it could be desirable to reuse an ontology, according to the requirements of a certain project.

MOTIVATION: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data. RESULTS: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. AVAILABILITY AND IMPLEMENTATION: BicOverlapper 2.0 is available at: http://vis.usal.es/bicoverlapper2

Recent research has confirmed the impact of electronic media usage on student outcomes. However, little is known about the impact of measurable reading access to a forum and other resources. We have developed a tool to study this aspect through visual analysis. Results from this study showed that not only participation on electronic discussion for a, such as reading or posting, but also the reading access to the resource, can alert about the student's performance. This came about after proving the existence of a strong influence relationship between the frequency of reading access to discussion for a and the frequency of access to posting for a, as well as between the frequency of reading access to resources and the frequency of access to posting for a in the outcome of the students. However, we have found that the union of the variables above, i.e., frequency of posts in for a, reading of discussion for a and reading of resources can be visually analyzed, thus obtaining a better visualization of the patterns of students' performance.

The literature in educational research has established that monitoring student learning is a crucial component of high quality education. When instructors and students are not in a face-to-face traditional environment, new forms of student monitoring must be explored. The effective use of CMS requires that instructors been provided with appropriate means of diagnosing problems so that they can take immediate actions to prevent or overcome those situations. The aim of this research is to support the comprehension of the evolution of the semantic content within eLearning environments through uncovering by means of visual representations. Therefore, we have carried out the mining of an eLearning database and represented the more relevant results by depicting them using a visualization based in the tag cloud visual representation. In this paper, we introduce a visual representation, which integrate a wave-graph and a bar-graph into a tag cloud representation to understand the evolution of words.

Software maintenance is a complex process that requires the understanding and comprehension of software project details. It involves the understanding of the evolution of the software project, hundreds of software components and the relationships among software items in the form of inheritance, interface implementation, coupling and cohesion. Consequently, the aim of evolutionary visual software analytics is to support software project managers and developers during software maintenance. It takes into account the mining of evolutionary data, the subsequent analysis of the results produced by the mining process for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper proposes an evolutionary visual software analytics tool for the exploration and comparison of project structural, interface implementation and class hierarchy data, and the correlation of structural data with metrics, as well as socio-technical relationships. Its main contribution is a tool that automatically retrieves evolutionary software facts and represent them using a scalable visualization design.

Despite the development of contents specifically for LMS, and in spite of the various features they provide, the knowledge of the actual use made by individual participants is emerging as a unavoidable necessity, so as to ensure proper operation and effective use of LMS. Moreover, the use of highly interactive visual tools can provide the benefit of understanding the educational process recorded in the database associated with the current platforms. However, there exists the problem of the integration, or not, with the LMS and therefore the transparent retrieval information and usage of these tools for the students as well as tutors.

SUMMARY: Treevolution is a tool for the representation and exploration of phylogenetic trees that facilitates visual analysis. There are several useful tools to visualize phylogenetic trees, but their level of interaction is usually low, especially in the case of radial representations. Highly interactive visualizations can improve the exploration and understanding of phylogenetic trees. Treevolution implements strategies to interact with phylogenetic trees in order to allow a more thorough analysis by users. AVAILABILITY: Treevolution is available at http://vis.usal.es/treevolution. Additional figures, a user's guide, a video demo and some examples are available at the same site. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The use of highly interactive visual tools can provide the benefit of understanding the educational process recorded in the database associated with the current e-learning systems. This is the path followed by ViMoodle, a visual analytical tool whose main function is to support the Moodle e-learning platform.

UNLABELLED: BicOverlapper is a tool to visualize biclusters from gene-expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. AVAILABILITY: The BicOverlapper software and supplementary material are available at http://vis.usal.es/bicoverlapper

BACKGROUND: Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results. RESULTS: We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapper CONCLUSION: The main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually.

MOTIVATION: Although natural products represent a reservoir of molecular diversity, the process of isolating and identifying active compounds is a bottleneck in drug discovery programs. The rapid isolation and identification of the bioactive component(s) of natural product mixtures during the bioassay-guided fractionation have become crucial factors in the competition with chemical compound libraries and combinatorial synthetic efforts. In this respect, the use of spectral databases in identification processes is indispensable. RESULTS: We have developed a database containing (13)C spectral information of over 6000 natural compounds, which allows for fast identifications of known compounds present in the crude extracts and provides insight into the structural elucidation of unknown compounds. AVAILABILITY: http://c13.usal.es

In this work we present a hierarchical method to evaluate the configuration space of a mobile robot working in large environments. It is based on convolution of discrete functions representing the workspace and the robot to obtain a function that represents the C-space. An important key in the work is that the representation of discrete functions is made by hierarchical data structures, so the information is highly compact, and high dimensions or/and resolutions workspaces can be considered. It is important to highlight that the hierarchical data structures no only have been used for the representation, but for the evaluation. So, intermediate arrays to represent the discrete information are not used in the proposed method. Also, as the method is hierarchical, calculations at high resolutions are performed only in those regions where it is necessary. This makes the method suitable when the robot moves in large environments where other methods will fail due to the memory needs.

HESPERIA is an information system for helping in the collaborative research of a multidisciplinary group in the paleoclimatology field. While there exist international databases and information systems that provide long-term storage of georeferenced data, they cannot be used during the research stage, since they are limited to published data. The system is designed to manage a collaborative project, keeping track of the shared seabed samples, providing visual analysis and giving the possibility of using partial results. The basic technical structure corresponds to a three-tiered client/server architecture that assures scalability and eases long-term maintenance requirements

Currently, the Modern Analog Technique (MAT) is one of the most used techniques in paleo-oceanography and it is applied for the quantitative reconstruction of environmental conditions of the past. Through the calculation of distances between modern and paleo data, patterns (analogs) are found, and after an interactive analysis, paleoenvironmental features are reconstructed. PaleoAnalogs is a powerful and flexible tool, developed with Java technology, making it a multiplatform tool to be executed in any operating system; the tool can automatically take on the appearance and behavior of whatever operating system it happens to be running under; it is an interactive tool that permits 3D plots helping the analysis of three variables; it includes 8 different types of distance measures; it is designed to carry out the reconstructions using different distance coefficients and for helping in the comparison between different results; and it provides a wizard for making associations (equivalent taxa, additions) of taxonomic categories between the modern data and the fossil data.

Ocean dynamics modeling is essential for predicting the impact of climatic change on human activities. The need of large time series (e.g. paleoclimatic data) has been exposed as one of the challenges to embark on decadal climate predictability. The goal of this study was to reconstruct the surface water dynamics in the South China Sea during the last 130 kyears. In order to do that it was necessary to assemble integrated comprehensive datasets obtained from a gravity core recovered in the Sunda Slope using different techniques such as quantitative analyses from coccolithophores, stable isotopes and biomarkers. Thanks to data mining, through a variety of data analysis tools, from Silicon Graphics' Mineset, such as decision trees and clustering we have established the variations in the water column stratification (relative position of nutricline/thermocline). This study is a good example of combining data mining and paleo-oceanography to explain some general paleodynamics, including short-time events, showing the potential to monitor and predict in the context of decadal time-series.

In order to monitor and make predictions, palaeoceanographers need to manipulate, integrate and analyse paleo time-series that are obtained from a number of independent techniques and instruments which, moreover, are usually produced by different researches and/or laboratories. The result is a collection of datasets with unequal age precision. This situation worsens since, at present, there is not an assembly of integrated tools that permits the palaeontologists to perform a study without having to use applications of different natures (spreadsheets, image editors, time-series analysers, etc.) which are of general purpose and, usually, expensive and platform-dependent. Hence, the goal of this project was to develop a platform-independent full featured tool that could simplify the palaeocenographers work, being used during the whole process: from the beginning, merging and correlating different datasets, continuing with interactive analyses, to the end, plotting the results. PaleoPlot is a general tool which could be used for any kind of time series, although it is optimised for the use in the stratigraphy area. Finally, it must be said that PaleoPlot is currently been used as an educational tool on palaeontology courses at the University of Salamanca.

This paper presents a practical method to evaluate the full set of robot configurations that produce collisions between the environment and the cylindrical manipulator. The method is based on a sound mathematical formalism so the feasibility of the results is guaranteed. The application of the formalism is performed by means of choosing a suitable coordinate functions, in the workspace, as well as in the configuration space. It leads to the fact that the obstacle representation in the configuration space could be seen as a convolution of two functions that describe the cylindrical robot and the obstacles respectively. Additionally, the computational load is independent of the shape and number of obstacles and of the robot shape. The algorithm used is the fast Fourier transform. Since it eases the parallel implementation of the resulting algorithms and reduces significantly the computational load, so the computational time is highly optimized.

A general method to obtain the obstacle representation in the robot configuration space is presented. The method is based on a solid mathematical formalism that has as a key element a mathematical expression to evaluate the C-obstacle representation. The use of the proposed method and the choice of a suitable coordinate system, in the workspace as well as in the configuration space, lead to the fact that the obstacle representation in the configuration space could be seen as a convolution of two functions that describe the robot and the obstacles, respectively. The computational load can then be reduced drastically if convolution properties are applied. The method is directly applicable to mobile and articulated robots without any kind of adaptation or restriction of the shape of the elements that constitute the robot and the shape of the obstacles. Hence, its utilization is quite suitable to motion planning and control problems.