Salinization Model with links to source files

This computational model of irrigation agriculture is used to study the effects of salinization in Mesopotamia. Scholars have long suspected that central and southern Mesopotamia present environments which limited agricultural production over long-term periods. In regions such as central Mesopotamia, where salinization likely affected settlement in different periods but was more manageable than in more southern regions, fallowing regimes, natural and engineered leaching, and decisions made on when to crop were strategies applied in order to limit the effects of salinization. The model is used to assess the effectiveness of these coping strategies by incorporating projected climate, soil, and landscape conditions with agricultural practices. The simulation results not only demonstrate the effectiveness and limitations of techniques to inhibiting progressive salinization but can be compared with the archaeological record in order to determine if the results correspond to past events and help to interpret past settlement history.

Integrated topographic database for King George Island, South Shetland Islands, with links to shapefiles

SCAR KGIS (SCAR King George Island GIS Project) was an integrated topographic database for King George Island, South Shetland Islands, including the SCAR Feature Catalogue to semantically integrate the data sets. The project, operated by the University of Freiburg, was available at http://portal.uni-freiburg.de/AntSDI as "The Antarctic Spatial Data Infrastructure (AntSDI)". Operation ended in 2007. The remaining data files were archived in shape format (zipped) in projections as recommended by SCAR. The source data was provided by a variety of institutions which were not referenced in the original product.

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.