Visual analysis of multidimensional data requires effective ways to reduce data dimensionality to encode them visually. Multidimensional projections (MDP) figure among the most important visualization techniques in this context, transforming multidimensional data into scatter plots where patterns reflect some notion of similarity in the data. However, MDP come with distortions that make visual patterns not trustworthy. Moreover, the patterns present in scatter plots might not be enough to allow an understanding of multidimensional data, motivating the development of layout enrichment methodologies that operate with MDP. This survey attempts to cover the main aspects of MDP as a visualization and visual analytic tool, providing detailed analysis and taxonomies taht organize MDP techniques according to their main properties and traits. The survey also approaches the different types of distortions that can result from MDP mappings while overviewing existing mechanisms to quantitatively evaluate such distortions. A qualitative analysis of the impact of distortions on the different analytic tasks is also presented, providing guidelines for users to choose a proper MDP for an intended. Finally, layout enrichment schemes to debunk MDP distortions and/or reveal relevant information not directly inferable from the scatter plot are reviewed and discussed in the light of new taxonomies.

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In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-ofthe-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

Multidimensional scaling is a must-have tool for visual data miners, projecting multidimensional data onto a two-dimensional plane. However, what we see is not necessarily what we think about. In many cases, end-users do not take care of scaling the projection space with respect to the multidimensional space. Anyway, when using non-linear mappings, scaling is not even possible. Yet, without scaling geometrical structures which might appear do not make more sense than considering a random map. Without scaling, we shall not make inference from the display back to the multidimensional space. No clusters, no trends, no outliers, there is nothing to infer without first quantifying the mapping quality. Several methods to qualify mappings have been devised. Here, we propose CheckViz, a new method belonging to the framework of Verity Visualization. We define a two-dimensional perceptually uniform colour coding which allows visualizing tears and false neighbourhoods, the two elementary and complementary types of geometrical mapping distortions, straight onto the map at the location where they occur. As examples shall demonstrate, this visualization method is essential to help users make sense out of the mappings and to prevent them from over interpretations. It could be applied to check other mappings as well.