(Table B2) Relative abundances of diatom species used in the Canoco analysis at sediment surface samples from the Chinese inshore waters

Canonical correspondence analysis has been used to analyze and to visualize the relationships between the main species and selected environmental variables in a study of diatoms from surface sediment samples in Chinese inshore waters. The result shows that the diatom distribution in Chinese inshore waters is closely correlated with the environmental variables and that the measured environmental variables account for the major changes of the diatom composition. Winter sea-surface temperature (WST), winter sea-surface salinity (WSS), water depth and summer sea-surface salinity (SSS) play an important role for the diatom distribution. Among the environmental factors, winter sea-surface temperature is the most important, controlling the distribution of diatoms in the surface sediments in Chinese inshore waters, and therefore, it may be potentially reconstructed in palaeoceanographic studies. Three diatom assemblages are distinguished, representing environments with different hydrological characteristics. The temperate-water diatom assemblage may be used as an indicator of the coastal circulation system of Bohai Sea and Yellow Sea. While the warm-temperate water diatom assemblage is closely related to Shanghai-Zhejiang-Fujian coastal currents and Northern Bay coastal currents of South China Sea. The deep water diatom assemblage is a response to that the waters are less controlled by coastal currents, but are more influenced by open sea currents, such as Kuroshio.

Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

Magnetic susceptibility and particle size for the Shilou red clay section on the eastern Chinese Loess Plateau

The Chinese Loess Plateau red clay sequences display a continuous alternation of sedimentary cycles that represent recurrent climatic fluctuations from 2.58 Ma to the Miocene. Deciphering such a record can provide us with vital information on global and Asian climatic variations. Lack of fossils and failure of absolute dating methods made magnetostratigraphy a leading method to build age models for the red clay sequences. Here we test the magnetostratigraphic age model against cyclostratigraphy. For this purpose we investigate the climate cyclicity recorded in magnetic susceptibility and sedimentary grain size in a red clay section previously dated 11Myr old with magnetostratigraphy alone. Magnetostratigraphy dating based on only visual correlation could potentially lead to erroneous age model. In this study the correlation is executed through the iteration procedure until it is supported by cyclostratigraphy; i.e., Milankovitch cycles are resolved in the best possible manner. Our new age model provides an age of 5.2Ma for the Shilou profile. Based on the new age model, wavelet analysis reveals the well-preserved 400 kyr and possible 100 kyr eccentricity cycles on the eastern Chinese Loess Plateau. Further, paleomonsoon evolution during 2.58-5.2Ma is reconstructed and divided into three intervals (2.58-3.6Ma, 3.6-4.5Ma, and 4.5-5.2Ma). The upper part, the youngest stage, is characterized by a relatively intensified summer monsoon, the middle stage reflects an intensification of the winter monsoon and aridification in Asia, and the earliest stage indicates that summer and winter monsoon cycles may have rapidly altered. The use of cyclostratigraphy along withmagnetostratigraphy gives us an effectivemethod of dating red clay sequences, and our results imply that many presently published age models for the red clay deposits should be perhaps re-evaluated.

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.