Analysis of elements at DSDP Holes 70-506 and 70-509B

Sediments in the area of the Galapagos hydrothermal mounds are divided into two major categories. The first group, pelagic sediments, are nannofossil oozes with varying amounts of siliceous microfossils. The second group are hydrothermal sediments consisting of manganese-oxide crust fragments and green nontronitic clay granules. Hydrothermal sediments occur only in the upper half to two-thirds of the cores and are interbedded and mixed with pelagic sediments. Petrologic evidence indicates that hydrothermal nontronite forms as both a primary precipitate and as a replacement mineral of pre-existing pelagic sediment and hydrothermal manganese-oxide crust fragments. In addition, physical evidence supports chemical equations indicating that the pelagic sediments are being dissolved by hydrothermal solutions. The formation of hydrothermal nontronite is not merely confined to the surface of mounds, but also occurs at depth within their immediate area; hydrothermal nontronite is very likely forming today. Geologically speaking, the mounds and their hydrothermal sediments form almost instantaneously. The Galapagos mounds area is a unique one in the ocean basins, where pelagic sediments can be diagenetically transformed, dissolved, and replaced, possibly within a matter of years.

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.