In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase ‘Rho’ metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package – using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy, and also available at the Python package index.
In this paper, we present a novel approach for local exceptionality detection on time series data. This method provides the ability to discover interpretable patterns in the data, which can be used to understand and predict the progression of a time series. This being an exploratory approach, the results can be used to generate hypotheses about the relationships between the variables describing a specific process and its dynamics. We detail our approach in a concrete instantiation and exemplary implementation, specifically in the field of teamwork research. Using a real-world dataset of team interactions we include results from an example data analytics application of our proposed approach, showcase novel analysis options, and discuss possible implications of the results from the perspective of teamwork research.
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