Plio-Pleistocene diatom biostratigraphy from ODP Leg 177, Atlantic sector of the Southern Ocean

Seven sites were drilled during Ocean Drilling Program Leg 177 in the Atlantic sector of the Southern Ocean (SO) on a transect over the Antarctic Circumpolar Current from the Subantarctic to the Antarctic Zone. At four sites sediments were recovered with a Pliocene/Pleistocene sediment package of up to 580 m allowing the refinement of previous diatom zonation concepts. Samples were analyzed on stratigraphic distribution and abundance of diatom species. A refined diatom biozonation tied to the geomagnetic polarity record is proposed. For the middle and late Pleistocene two zonations applicable to the northern and southern area of the SO were constructed, considering different latitudinal distributions of biostratigraphic diatom marker species. The southern zonation for the Pleistocene relies on the occurrence of species of the genus Rouxia, R. leventerae and R. constricta n. sp. as well as on a revised last occurrence datum (LOD) of Actinocyclus ingens (0.38 Ma, late marine isotope stage (MIS) 11). The use of these new stratigraphic marker species refines the temporal resolution for biostratigraphic age assignment to up to 0.1 Myr. In particular the LOD of R. leventerae as an indicator for the MIS 6/5 boundary (Termination II) will improve future dating of carbonate-free Antarctic sediments. These new data were obtained from sediments of Sites 1093 and 1094 (Antarctic Zone). The northern zonation for the middle and late Pleistocene time interval is based on the Pleistocene abundance pattern of Hemidiscus karstenii which was already proposed by previous investigations (e.g. Gersonde and Barcena, 1998). One new species (R. constricta) and two new combinations (Fragilariopsis clementia, Fragilariopsis reinholdii) are proposed in this study.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.