Experimental science has experienced a paradigm shift towards collecting massive volumes of data to develop and test sophisticated scientific hypotheses that are difficult to prove on a smaller scale. Often breakthroughs in science are enabled through the development of new scientific equipment and/or new deployments in inhospitable environments where human curation of the collection devices is limited. Both new equipment and remote deployments pose new challenges for data analysis, including Big Data management, patterns at multiple spatio-temporal (ST) resolutions, multidimensional and multiset data organization, variable signal-to-noise ratio along the measurement range, and complex repeatability phenomena such as batch effects.

MuSTARD is led by computational specialists with expertise on four different domains: precision and molecular medicine, climate change, volcano-seismic activity, and urban mobility models and smart cities. These application domains represent top challenges in our society, with research breakthroughs most often published in flagship journals. They also share several characteristics: they require some form of statistical support to address scientific questions, they need powerful visualizations for Big Data mining, and they share a complex ST structure at different resolution scales. Notably, these three characteristics have been addressed separately in computational science, but never in a single, unified framework. Our goal is to provide such a framework that can be used in these application domains (and potentially many others).