Biogeochemical data from terrestrial and aquatic ecosystems in a periglacial catchment, West Greenland

Global warming is expected to be most pronounced in the Arctic where permafrost thaw and release of old carbon may provide an important feedback mechanism to the climate system. To better understand and predict climate effects and feedbacks on the cycling of elements within and between ecosystems in northern latitude landscapes, a thorough understanding of the processes related to transport and cycling of elements is required. A fundamental requirement to reach a better process understanding is to have access to high-quality empirical data on chemical concentrations and biotic properties for a wide range of ecosystem domains and functional units (abiotic and biotic pools). The aim of this study is therefore to make one of the most extensive field data sets from a periglacial catchment readily available that can be used both to describe present-day periglacial processes and to improve predictions of the future. Here we present the sampling and analytical methods, field and laboratory equipment and the resulting biogeochemical data from a state-of-the-art whole-ecosystem investigation of the terrestrial and aquatic parts of a lake catchment in the Kangerlussuaq region, West Greenland. This data set allows for the calculation of whole-ecosystem mass balance budgets for a long list of elements, including carbon, nutrients and major and trace metals.

Inventory of SINOPS project data sets

During the SINOPS project, an optimal state of the art simulation of the marine silicon cycle is attempted employing a biogeochemical ocean general circulation model (BOGCM) through three particular time steps relevant for global (paleo-) climate. In order to tune the model optimally, results of the simulations are compared to a comprehensive data set of 'real' observations. SINOPS' scientific data management ensures that data structure becomes homogeneous throughout the project. Practical work routine comprises systematic progress from data acquisition, through preparation, processing, quality check and archiving, up to the presentation of data to the scientific community. Meta-information and analytical data are mapped by an n-dimensional catalogue in order to itemize the analytical value and to serve as an unambiguous identifier. In practice, data management is carried out by means of the online-accessible information system PANGAEA, which offers a tool set comprising a data warehouse, Graphical Information System (GIS), 2-D plot, cross-section plot, etc. and whose multidimensional data model promotes scientific data mining. Besides scientific and technical aspects, this alliance between scientific project team and data management crew serves to integrate the participants and allows them to gain mutual respect and appreciation.

R-ArcticNet, A Regional Hydrographic Data Network for the Pan-Arctic Region (ISO-image of CD-ROM)

We describe the contemporary hydrography of the pan-Arctic land area draining into the Arctic Ocean, northern Bering Sea, and Hudson Bay on the basis of observational records of river discharge and computed runoff. The Regional Arctic Hydrographic Network data set, R-ArcticNET, is presented, which is based on 3754 recording stations drawn from Russian, Canadian, European, and U.S. archives. R-ArcticNET represents the single largest data compendium of observed discharge in the Arctic. Approximately 73% of the nonglaciated area of the pan-Arctic is monitored by at least one river discharge gage giving a mean gage density of 168 gages per 106 km2. Average annual runoff is 212 mm yr?1 with approximately 60% of the river discharge occurring from April to July. Gridded runoff surfaces are generated for the gaged portion of the pan-Arctic region to investigate global change signals. Siberia and Alaska showed increases in winter runoff during the 1980s relative to the 1960s and 1970s during annual and seasonal periods. These changes are consistent with observations of change in the climatology of the region. Western Canada experienced decreased spring and summer runoff.

Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates - Gridded data product (NetCDF) - 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. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.

Real-time surface current data in the Ibiza Channel from January to October 2016

The data set consists of maps of total velocity of surface currents in the Ibiza Channel, derived from HF radar measurements.

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.