30 years of synoptic observations from Neumayer Station with links to datasets

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.

(Table 1) UK research stations, refuges and major depots between 1944 and 2010

Globally, areas categorically known to be free of human visitation are rare, but still exist in Antarctica. Such areas may be among the most pristine locations remaining on Earth and, therefore, be valuable as baselines for future comparisons with localities impacted by human activities, and as sites preserved for scientific research using increasingly sophisticated future technologies. Nevertheless, unvisited areas are becoming increasingly rare as the human footprint expands in Antarctica. Therefore, an understanding of historical and contemporary levels of visitation at locations across Antarctica is essential to a) estimate likely cumulative environmental impact, b) identify regions that may have been impacted by non-native species introductions, and c) inform the future designation of protected areas under the Antarctic Treaty System. Currently, records of Antarctic tourist visits exist, but little detailed information is readily available on the spatial and temporal distribution of national governmental programme activities in Antarctica. Here we describe methods to fulfil this need. Using information within field reports and archive and science databases pertaining to the activities of the United Kingdom as an illustration, we describe the history and trends in its operational footprint in the Antarctic Peninsula since c. 1944. Based on this illustration, we suggest that these methodologies could be applied productively more generally.