Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

Bathymetry grids and maps of Mozambique Ridge compiled from cruises 64PE305, 64PE306, SO-87, SO-182, SO-183, ME-33/2, ME-63/1 bathymetry

Based on data from R.V. Pelagia, R.V. Sonne and R.V. Meteor multibeam sonar surveys, a high resolution bathymetry was generated for the Mozambique Ridge. The mapping area is divided into five sheets, one overview and four sub-sheets. The boundaries are (west/east/south/north): Sheet 1: 28°30' E/37°00' E/36°20' S/24°50' S; Sheet 2: 32°45' E/36°45' E/28°20' S/25°20' S; Sheet 3: 31°30' E/36°45' E/30°20' S/28°10' S; Sheet 4: 30°30' E/36°30' E/33°15' S/30°15' S; Sheet 5: 28°30' E/36°10' E/36°20' S/33°10' S. Each sheet was generated twice: one from swath sonar bathymetry only, the other one is completed with depths from ETOPO2 predicted bathymetry. Basic outcome of the investigation are Digital Terrain Models (DTM), one for each sheet with 0.05 arcmin (~91 meter) grid spacing and one for the entire area (sheet 1) with 0.1 arcmin grid spacing. The DTM's were utilized for contouring and generating maps. The grid formats are NetCDF (Network Common Data Form) and ASCII (ESRI ArcGIS exchange format). The Maps are formatted as jpg-images and as small sized PNG (Portable Network Graphics) preview images. The provided maps have a paper size of DIN A0 (1189 x 841 mm).

Physical properties and sedimentology on core GeoB1510-1

Detailed information about the sediment properties and microstructure can be provided through the analysis of digital ultrasonic P wave seismograms recorded automatically during full waveform core logging. The physical parameter which predominantly affects the elastic wave propagation in water-saturated sediments is the P wave attenuation coefficient. The related sedimentological parameter is the grain size distribution. A set of high-resolution ultrasonic transmission seismograms (ca. 50-500 kHz), which indicate downcore variations in the grain size by their signal shape and frequency content, are presented. Layers of coarse-grained foraminiferal ooze can be identified by highly attenuated P waves, whereas almost unattenuated waves are recorded in fine-grained areas of nannofossil ooze. Color-encoded pixel graphics of the seismograms and instantaneous frequencies present full waveform images of the lithology and attenuation. A modified spectral difference method is introduced to determine the attenuation coefficient and its power law a = kfn. Applied to synthetic seismograms derived using a "constant Q" model, even low attenuation coefficients can be quantified. A downcore analysis gives an attenuation log which ranges from ca. 700 dB/m at 400 kHz and a power of n = 1-2 in coarse-grained sands to few decibels per meter and n ? 0.5 in fine-grained clays. A least squares fit of a second degree polynomial describes the mutual relationship between the mean grain size and the attenuation coefficient. When it is used to predict the mean grain size, an almost perfect coincidence with the values derived from sedimentological measurements is achieved.

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.