Data not only shapes our everyday lives; it has also changed the way we gain scientific knowledge. The recognition of patterns and structures in large amounts of data is perhaps the most prominent aspect of this change. Yet in the technological field, “big” data is not always available. Due to cost factors or the impossibility of measuring the desired amount of data, data acquisition often gets stuck in a bottleneck. This is especially the case when approaching new phenomena.
In order to not only be able to use the descriptive nature of big data but also to fully make use of its information content, as well as the predictive potential of small data sets, targeted experimental data collection and data analysis in connection with data-driven modelling and simulation must be considered as an overall process. To this end, the Centre for Advanced Analytics and Predictive Sciences (CAAPS) brings together the university's data-collection and data-processing disciplines in a multidisciplinary centre with the goal of enabling innovation in model and method development.
To combat the prevailing modularity of these research areas and to closely interlink data acquisition and analysis, the centre promotes the symbiosis of experimental methods, such as measurement, sensor technology, and imaging, with non-experimental methods, such as statistics, modelling, and simulation. The application of these methods to non-invasive medical diagnostics and materials research requires the interdisciplinary integration of high resolution data collection and simulation-based data processing for innovation. The fundamental methodological orientation of the centre promises synergies between applications in the fields of medicine and materials science, as well as in the quantitative life sciences, which are a recent addition to the university.
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