Single non-normalized data of electron probe analyses of all glass shard samples from the Seward Peninsula and the Lipari obsidian reference standard

Permafrost degradation influences the morphology, biogeochemical cycling and hydrology of Arctic landscapes over a range of time scales. To reconstruct temporal patterns of early to late Holocene permafrost and thermokarst dynamics, site-specific palaeo-records are needed. Here we present a multi-proxy study of a 350-cm-long permafrost core from a drained lake basin on the northern Seward Peninsula, Alaska, revealing Lateglacial to Holocene thermokarst lake dynamics in a central location of Beringia. Use of radiocarbon dating, micropalaeontology (ostracods and testaceans), sedimentology (grain-size analyses, magnetic susceptibility, tephra analyses), geochemistry (total nitrogen and carbon, total organic carbon, d13Corg) and stable water isotopes (d18O, dD, d excess) of ground ice allowed the reconstruction of several distinct thermokarst lake phases. These include a pre-lacustrine environment at the base of the core characterized by the Devil Mountain Maar tephra (22 800±280 cal. a BP, Unit A), which has vertically subsided in places due to subsequent development of a deep thermokarst lake that initiated around 11 800 cal. a BP (Unit B). At about 9000 cal. a BP this lake transitioned from a stable depositional environment to a very dynamic lake system (Unit C) characterized by fluctuating lake levels, potentially intermediate wetland development, and expansion and erosion of shore deposits. Complete drainage of this lake occurred at 1060 cal. a BP, including post-drainage sediment freezing from the top down to 154 cm and gradual accumulation of terrestrial peat (Unit D), as well as uniform upward talik refreezing. This core-based reconstruction of multiple thermokarst lake generations since 11 800 cal. a BP improves our understanding of the temporal scales of thermokarst lake development from initiation to drainage, demonstrates complex landscape evolution in the ice-rich permafrost regions of Central Beringia during the Lateglacial and Holocene, and enhances our understanding of biogeochemical cycles in thermokarst-affected regions of the Arctic.

Compilation of organic carbon distribution and sedimentology in the surface sediments on the continental margin offshore southwestern Africa

In this study we demonstrate the relevance of lateral particle transport in nepheloid layers for organic carbon (OC) accumulation and burial across high-productive continental margins. We present geochemical data from surface sediments and suspended particles in the bottom nepheloid layer (BNL) from the most productive coastal upwelling area of the modern ocean, the Benguela upwelling system offshore southwest Africa. Interpretation of depositional patterns and comparison of downslope trends in OC content, organic matter composition, and 14C age between suspended particles and surface sediments indicate that lateral particle transport is the primary mechanism controlling supply and burial of OC. We propose that effective seaward particle transport primarily along the BNL is a key process that promotes and maintains local high sedimentation rates, ultimately causing high preservation of OC in a depocenter on the upper slope offshore Namibia. As lateral transport efficiently displaces areas of enhanced OC burial from maximum production at highly productive continental margins, vertical particle flux models do not sufficiently explain the relationship between primary production and shallow-marine OC burial. On geologic time scales, the widest distribution and strongest intensity of lateral particle transport is expected during periods of rapid sea-level change. At times in the geologic past, widespread downslope lateral transport of OC thus may have been a primary driver of enhanced OC burial at deeper continental slopes and abyssal basins.

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.