Global distributions of diazotrophs abundance and biomass - Depth integrated values computed from a collection of source datasets - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs abundance and biomass, computed from a collection of source data sets.

Biomass of large protists from the Infrakingdom Rhizaria in the global ocean

The present data set is a worldwide compilation from 11 oceanographic expeditions during which an underwater vision profiler (UVP) was deployed in situ to determine the vertical distribution (biomass) of 4 taxonomic groups of plankton larger than 600 µm, belonging to the Infrakingdom Rhizaria, including Collodaria, Acantharia, Phaeodaria and other Rhizaria. Vertical distributions are binned in four layers: 0-100, 0-200, 100-500 and 0-500 m.

Global distributions of diazotrophs nitrogen fixation rates - Depth integrated values computed from a collection of source datasets - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs nitrogen fixation rates, computed from a collection of source data sets.

Registry of all stations from the Tara Oceans Expedition (2009-2013)

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set is a registry of all stations conducted during the Tara Oceans Expedition (2009-2013). The registry provides details about the scientific interest of each station, including (1) the geographic context, (2) legal context, (3) the environmental features that were targeted, and (4) a detailed account of devices deployed during the station. Uniform resource locators (URLs) offer direct links to the corresponding (1) physical oceanographic context reports, (2) list of samples collected during the station, (3) environmental data published at PANGAEA, and (4) nucleotides data published at the European Nucleotides Archive (EBI-ENA).

Annotated record of the detailed examination of Mn deposits from DSDP Hole 2-9A

The Atlantic Advisory Panel proposed that Site 9 should be drilled on the northeastern flank of the Bermuda Rise (lat. 32° 37' N., long. 59° 10' W.), which is about 100 miles west of the Sohm Abyssal Plain. The bottom of this region consists of low linear ridges that are roughly parallel and oriented in a northwest-southeasterly direction. Scattered seamounts, some of which have peaks 2000 fathoms (3660 meters) below sea level, arise from the otherwise featureless sea floor between the ridges. The primary purpose in drilling Site 9 was to examine a sedimentary column where seismic reflectors were largely absent and to determine the age of sediments overlying acoustical basement in the examination of sea floor spreading.

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.