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.

Field measurements of the atmosphere, ocean, sea ice and sub-ice platelet layer at Atka Bay in 2013

Ice shelves strongly interact with coastal Antarctic sea ice and the associated ecosystem by creating conditions favourable to the formation of a sub-ice platelet layer. The close investigation of this phenomenon and its seasonal evolution remain a challenge due to logistical constraints and a lack of suitable methodology. In this study, we characterize the seasonal cycle of Antarctic fast ice adjacent to the Ekström Ice Shelf in the eastern Weddell Sea. We used a thermistor chain with the additional ability to record the temperature response induced by cyclic heating of resistors embedded in the chain. Vertical sea-ice temperature and heating profiles obtained daily between November 2012 and February 2014 were analyzed to determine sea-ice and snow evolution, and to calculate the basal energy budget. The residual heat flux translated into an ice-volume fraction in the platelet layer of 0.18 ± 0.09, which we reproduced by a independent model simulation and agrees with earlier results. Manual drillings revealed an average annual platelet-layer thickness increase of at least 4m, and an annual maximum thickness of 10m beneath second-year sea ice. The oceanic contribution dominated the total sea-ice production during the study, effectively accounting for up to 70% of second-year sea-ice growth. In summer, an oceanic heat flux of 21 W/m**2 led to a partial thinning of the platelet layer. Our results further show that the active heating method, in contrast to the acoustic sounding approach, is well suited to derive the fast-ice mass balance in regions influenced by ocean/ice-shelf interaction, as it allows sub-diurnal monitoring of the platelet-layer thickness.

Reference list of 269 sources used for exploratory search

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.