Diatom isotope data (d18Odiatom and d30Sidiatom) from IODP Site 303-U1304 (North Atlantic)

The Last Interglacial (LIG), corresponding to Marine Isotope Stage (MIS) 5e, provides a reference of interglacial climate variability in the absence of anthropogenic forcing. Using an expanded section of the LIG gained at Integrated Ocean Drilling Program Site U1304 in the Subarctic Atlantic, we demonstrate that the early MIS 5e was marked by oceanographic conditions conducive for high diatom production and accumulation. The appearance of diatom-dominated laminated oozes ~3 k.y. after the beginning of MIS 5e at ca. 125 ka coincides with a shift to higher d30Sidiat values together with the dominance of Thalassiothrix longissima, indicative of increased nutrient availability and silicic acid utilization in surface waters. Though the Subarctic Front provided the physical conditions for high diatom production and deposition, these processes alone are insufficient to explain the high rates of siliceous productivity and the formation of diatomaceous sediments. Instead, the additional presence of an increased nutrient pool provided by Subantarctic Mode Water played the decisive role in initiating and sustaining diatom production. The high diatom productivity and the occurrence of diatomaceous sediments in the late Quaternary challenge the current hypothesis of a silica-depleted North Atlantic during the LIG.

Oceanographic data, oxygen and methane concentrations, and C-CH4 isotope ratios measured at Jaco Scar

Methane (CH4) concentrations and CH4 stable carbon isotopic composition (d13CCH4) were investigated in the water column within Jaco Scar. It is one of several scars formed by massive slides resulting from the subduction of seamounts offshore Costa Rica, a process that can open up structural and stratigraphical pathways for migrating CH4. The release of large amounts of CH4 into the adjacent water column was discovered at the outcropping lowermost sedimentary sequence of the hanging wall in the northwest corner of Jaco Scar, where concentrations reached up to 1,500 nmol L-1. There CH4-rich fluids seeping from the sedimentary sequence stimulate both growth and activity of a dense chemosynthetic community. Additional point sources supplying CH4 at lower concentrations were identified in density layers above and below the main plume from light carbon isotope ratios. The injected CH4 is most likely a mixture of microbial and thermogenic CH4 as suggested by d13CCH4 values between -50 and -62 per mil Vienna Pee Dee Belemnite. This CH4 spreads along isopycnal surfaces throughout the whole area of the scar, and the concentrations decrease due to mixing with ocean water and microbial oxidation. The supply of CH4 appears to be persistent as repeatedly high CH4 concentrations were found within the scar over 6 years. The maximum CH4 concentration and average excess CH4 concentration at Jaco Scar indicate that CH4 seepage from scars might be as significant as seepage from other tectonic structures in the marine realm. Hence, taking into account the global abundance of scars, such structures might constitute a substantial, hitherto unconsidered contribution to natural CH4 sources at the seafloor.

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.