Multidimensional Charts

This work presents a new visual representation of multidimensional data and compares its usefulness in terms of effectiveness and efficiency with tabular representations. This idea makes two important contributions. First, it shows the feasibility of representing multidimensional data in 2D and 3D charts that are understandable by humans. Second, it builds on the theory of cognitive fit by testing the appropriateness of graphical representations to convey information of complex problems. In particular, it shows multidimensional data representation that has not been tested, perhaps due to the lack of a suitable graphical representation. The charts proposed are better representations of information stored in data warehouses than those provided by data cubes. The proposed format can be used to represent fuzzy variables, and are suitable for implementation in dashboards

High throughput powder diffraction: II Applications of clustering methods and multivariate data analysis

In high throughput crystallography is possible to accumulate over 1000 powder diffraction patterns on a series of related compounds, often polymorphs. We present a method that can analyse such data, automatically sort the patterns into related clusters or classes, characterise each cluster and identify any unusual samples containing, for example, unknown or unexpected polymorphs. Mixtures may be analysed quantitatively if a database of pure phases is available. A key component of the method is a set of visualisation tools based on dendrograms, cluster analysis, pie charts, principal component based score plots and metric multidimensional scaling. Applications are presented to pharmaceutical data, and inorganic compounds. The procedures have been incorporated into the PolySNAP commercial computer software

External validity of randomized controlled trials on Alzheimer's disease: the biases of frailty and biological aging

To date, the external validity of randomized controlled trials (RCTs) on Alzheimer's disease (AD) has been assessed only considering monodimensional variables. Nevertheless, looking at isolated and single characteristics cannot guarantee a sufficient level of appreciation of the AD patients' complexity. The only way to understand whether the two worlds (i.e., research and clinics) deal with the same type of patients is to adopt multidimensional approaches more holistically reflecting the biological age of the individual. In the present study, we compared measures of frailty/biological aging [assessed by a Frailty Index (FI)] of a sample of patients with AD resulted eligible and subsequently included in phase III RCTs compared to patients referring to the same clinical service, but not considered for inclusion. The "RCT sample" and the "real world sample" were found to be statistically similar for all the considered sociodemographic and clinical variables. Nevertheless, the "real world sample" was found to be significantly frailer compared to the "RCT sample," as indicated by higher FI scores [0.28 (SD 0.1) vs. 0.17 (SD 0.1);p < 0.001, respectively]. Moreover, when assessing the relationship between FI and age, we found that the correlation was almost null in the "RCT sample" (Spearman'sr = 0.01;p = 0.98), while it was statistically significant in the "real world sample" (r = 0.49;p = 0.02). The application of too rigid designs may result in the poor representativeness of RCT samples. It may even imply the study of a condition biologically different from that observed in the "real world." The adoption of multidimensional measures capable to capture the individual's biological age may facilitate evaluating the external validity of clinical studies, implicitly improving the interpretation of the results and their translation in the clinical arena

MetricsVis: A Visual Analytics Tool for Evaluating Multidimensional Data

Visualization for multidimensional data is a popular topic and many methods have been created to visualize this type of data. We developed a visual analytics tool to visualize multidimensional data for two distinct fields: resource allocation in law enforcement departments and phenotype traits of sorghum crops. For law enforcement departments, we designed a visualization tool to measure and compare police officer’s experience in different types of crimes. Our tool supports the analysis of the amount of experience each officer has in each crime category. Meanwhile, the field crop modeling project requires the visualization of the measured value of multiple traits of each sorghum category. In general, our visualization tool is now able to represent these multidimensional data in multiple graphs and charts, with a rich interaction set of selecting, grouping, and filtering. MetricsVis has been expanded this summer with the addition of 6 new graphs, the ability to use the sorghum crops dataset, and more data manipulation features. By being able to explore the data through several graphs and charts at the same time, this allows the user to easily query the data or find peculiarities in the data that they would have otherwise missed. We describe several case studies to validate the importance of our tool in analyzing the data in both projects. In the future, we would like to expand our tool for other similar datasets