(Table 2, Page 252) Average chemical composition of 11 manganese nodules and crust from the Pacific Ocean

The ability of the hydrated oxides of manganese and iron to adsorb ions from solution (scavenging) is considered in relation to some problems in marine geology, chemistry, and biology. In the ferruginous sediments of the Pacific Ocean, iron oxides are accompanied by titanium, cobalt, and zirconium in amounts proportional to the iron content. Similarly, copper and nickel are linearly related to the manganese content. These observations are explained on the basis of scavenging. An electrochemical theory for the formation of manganese nodules is presented. Marine sediments are classified on the basis of the geosphere in which the solid phases originate. The distribution of certain ionic species in sea water between the solid and aqueous phases is considered on the basis of scavenging and co-ordination compound theory. The concentration of minor elements by members of the marine biosphere is explained either by the direct uptake of the element or by the uptake of iron or manganese oxides with the accompanying scavenged element.

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.