Oxygen isotope stratigraphy Ojplus03 for the last 838,000 years

Global warming is real and has been with us for at least two decades. Questions arise regarding the response of the ocean to greenhouse forcing, including expectations for changes in ocean circulation, in uptake of excess carbon dioxide, and in upwelling activity. The large climate variations of the ice ages, within the last million years, offer the opportunity to study responses of the ocean to climate change. A histogram of sealevel positions for the last 700,000 years (based on a new d/sup 18/O stratigraphy here compiled) shows that the present is near the margin of the range of fluctuations, with only 6 percent of positions indicating a warmer climate. Thus, the future will be largely outside of experience with regard to fluctuations of the recent geologic past. The same is true for greenhouse forcing. Our inability to explain sudden climate change in the past, including the rapid rise of carbon dioxide during deglaciation, and differences in ocean productivity between glacial and interglacial conditions, demonstrates a lack of understanding that makes predictions suspect. This is the lesson from ice age studies.

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.