The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.
CO2 is the strongest anthropogenic forcing agent for climate change since pre-industrial times. Like other greenhouse gases, CO2 absorbs terrestrial surface radiation and causes emission from the atmosphere to space. As the surface is generally warmer than the atmosphere, the total long-wave emission to space is commonly less than the surface emission. However, this does not hold true for the high elevated areas of central Antarctica. For this region, the emission to space is higher than the surface emission; and the greenhouse effect of CO2 is around zero or even negative, which has not been discussed so far. We investigated this in detail and show that for central Antarctica an increase in CO2 concentration leads to an increased long-wave energy loss to space, which cools the Earth-atmosphere system. These findings for central Antarctica are in contrast to the general warming effect of increasing CO2.
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