Oligocene strontium isotope stratigraphy of Indian Ocean sediments

Strontium isotope (87Sr/86Sr) ages have been established for Oligocene samples of Leg 119 Site 744, Leg 120 Sites 747 and 748, and Leg 121 Sites 756 and 757. Ages were determined using the strontium isotope age equation of Miller et al. (1988) and preliminary correlations have been made with available nannofossil biostratigraphy. The strontium isotope ages calculated here augment biostratigraphy, which for the Oligocene is characterized by long biozones, and provide additional detail where the paleomagnetic record is not clear (Sites 756 and 757). Results from the lower latitude Ninetyeast Ridge sites where standard calcareous nannofossil datums are present are compared to those of the higher latitude Kerguelen Plateau sites in order to examine biostratigraphic events across latitude in the Indian Ocean. The 87Sr/86Sr determined ages are used here as a tool for correlation.

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.