Abstract
AbstractSummaryUnderstanding input-output relationships within multivariate datasets is an ubiquitous task in the life and data sciences. For this, visual analysis is indispensable for providing expressive summaries and preparing decision-making. We present the visual analysis approach and softwareMooViE, which is designed to strike the balance between being tailored to the specific data semantic and while being broadly applicable.MooViEsupports the data exploration process for extracting important information from the data and captures the result in a fresh single-view visualization.MooViEis implemented in C++ to facilitate fast access and effective interaction with comprehensive multivariate datasets. We showcase the engine for various application fields, relevant to the life sciences.Availability and ImplementationThe source code is available under MIT license athttps://jugit.fz-juelich.de/IBG-1/ModSim/MooViEandhttps://github.com/JuBiotechMooViE, with detailed documentation and usage instructions (https://moovie.readthedocs.io), as well as zenodo-archived releases (https://doi.org/10.5281/zenodo.10997388). Platform independent Docker images are also available (jugit-registry.fz-juelich.de/ibg-1/modsim/moovie/moovie).ContactKatharina Nöhk.noeh@fz-juelich.de
Publisher
Cold Spring Harbor Laboratory