(Table 3) Concentration of Au, Pd, and Pt in the interstitial waters of ODP Leg 125 samples

Palladium, platinum, and gold were analyzed for 20 interstitial water samples from Leg 125. No Pd or Pt was detected in fluids from serpentinite muds from Conical Seamount in the Mariana forearc, indicating that low-temperature seawater-peridotite interaction does not mobilize these elements into the serpentinizing fluids to levels above 0.10 parts per billion (ppb) in solution. However, Au may be mobilized in high pH solutions. In contrast, fluids from vitric-rich clays on the flanks of the Torishima Seamount in the Izu-Bonin forearc have Pd values of between 4.0 and 11.8 nmol/L, Pt values between 2.3 and 5.0 nmol/L and Au values between 126.9 and 1116.9 pmol/L. The precious metals are mobilized, and possibly adsorbed onto clay mineral surfaces, during diagenesis and burial of the volcanic-rich clays. Desorption during squeezing of the sediments may produce the enhanced precious metal concentrations in the analyzed fluids. The metals are mobilized in the fluids probably as neutral hydroxide, bisulfide, and ammonia complexes. Pt/Pd ratios are between 0.42 and 2.33, which is much lower than many of the potential sources for Pt and Pd but is consistent with the greater solubility of Pd compared with Pt in most natural low-temperature fluids.

Distribution of Au and Pd in basalts and diabases in DSDP/ODP Hole 504B

Au contents have been determined in 77 samples of basalts and sheeted diabase dikes. Pd has been evaluated in 39 of the samples. The mean amount of Au is 3 parts per billion (ppb), fluctuating from 0.4 to 10 ppb. Au contents appear to be independent in type and intensity of alteration as well as with depth sub-bottom, although in the lower part of Hole 504B, 1900-2000 mbsf, Au contents are markedly decreased (mean: 1.1 ppb) and show a distinct correlation with a decrease in Zn contents. Pd contents vary from 2 to 360 ppb (mean: 37 ppb) Pd is higher in basalts (53.7 ppb) and lower in diabase dikes (30 ppb), especially in moderately or strongly altered ones (12.5 ppb).

Distribution of sponge spicule types, Goniothecium odontella and Diploneis cf. bomboides in selected spicule clusters from ODP Leg 112 holes (Table 1)

Clusters of sponge spicules found in Quaternary deep-water sediments at Sites 685 and 688 off Peru represent single individuals of small sponges or fragments of larger sponges. The spicule assemblages constituting these clusters probably represent a few demosponge species of the subclass Tetractinomorpha and order Astrophorida, because triaenes and microscleric euasters, as well as abundant monaxons, are present. As proved by incorporated Neogene diatoms, these spicule clusters are allochthonous. The sponge individuals probably inhabited deeper neritic environments during late Neogene time.

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.