Phytoplankton production, photosynthetic (P vs E) parameters, and particulate organic carbon and nitrogen within distinct water masses of eastern Australian coastal and shelf waters between 29 °S and 36 °S during spring 2010

The present dataset is part of an interdisciplinary project carried out on board the RV Southern Surveyor off New South Wales (Australia) from the 15th to the 31st October 2010. The main objective of the research voyage was to evaluate how the East Australian Current (EAC) affects the optical, chemical, physical, and biological water properties of the continental shelf and slope off the NSW coast.

Kiel fjord pCO2 datasets between 2012 (July) and 2015 (January) measured using a HydroC® pCO2 sensor

The HydroC® CO2 sensor was deployed from a pontoon at the waterfront of the GEOMAR west shore building into Kiel Fjord, Western Baltic Sea (Kiel, Germany; 54°19'48.78"N, 010° 8'59.44"E). Since the pontoon is floating the deployment depth of the sensor was constant at 1m. Data of three deployment intervals are published here: 1) July 2012 - December 2012 2) April 2013 - June 2013 3) November 2013 - January 2015 Data are processed and corrected, for documentation and graphical overview see further details.

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.