At surface behaviour at location on spot of southern elephant seals from Marion Island in 2004 with links to datasets

A novel classification system was applied to the sea level anomaly (SLA) environment around Marion Island. We classified the SLA seascape into habitat types and calculated percentage of habitat use of ten juvenile southern elephant seals (SES). Movements were compared to SLA and SLA slope values indicative of ocean eddy features. This classification provides a measure of habitat change due to seasonal fluctuations in SLA. Some of the seals made two migrations in different seasons, each of similar duration and proportions of potential foraging behaviour. The seals in this study did not use any intense eddy features, but their behaviours varied with SLA class. Potential foraging behaviour was positively influenced by negative SLA values (i.e. areas of below average sea surface height). Searching behaviour during the winter was more likely at eddy edges where high SLA slope values correlated with low SLA values. Though the seals did not forage within newly spawned eddies, they did forage near the sub-Antarctic front. Plankton and other biological resources transported by eddies formed at the subtropical convergence zone are evidently concentrated in this region and enhance the food chain there, forming a foraging ground for juvenile SES from Marion Island.

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.

GLOBEC-Germany - Integrated Datasets

The international Global Ocean Ecosystem Dynamics (GLOBEC) programme was initiated in 1991 by the Scientific Committee on Oceanic Research (SCOR) and the Intergovernmental Oceanographic Commission (IOC) of the UNESCO. It was a core project of the International Geosphere-Biosphere Project (IGBP) with its research topics aiming at understanding how global change impacts abundance, diversity and productivity of marine populations (Barange & Harris 2003). GLOBEC-Germany was the national German contribution to this core project focussing on the Baltic Sea and North Sea, to which Germany has adjoining coastlines. The two seas exhibit a gradient from marine (North Sea) to almost fresh water conditions (outer ends of the Baltic Sea). Main topic of the project was the investigation of interactions between zooplankton and fish under the influence of physical processes (Alheit 2004). Main sampling areas were located in the southern North Sea and German Bight, as well as in the Bornholm Basin in the Baltic Sea (Tamm et al. 2007).