Biogeochemical measurements of sediments collected at station MSM15/1_492-PUC6 (reference site 3) in the Black Sea during the MSM15/1 cruise in 2010

Biogeochemical measurements in sediment cores collected with the submersible JAGO (pusch cores) and a TV-MUC in the Black Sea during MSM15/1, Northwest Crimea (HYPOX Project), at water depths between 152-156 m. A series of microbial mats were sampled on the hypoxic region of the Crimean Shelf. Concentrations of organic carbon (Corg) and nitrogen (N) were measured on finely powdered, freeze-dried subsamples of sediment using a using a Fisons NA-1500 elemental analyzer. For organic carbon determination samples were pre-treated with 12.5% HCl to remove carbonates. Chlorophyll a (chl a), phaeopigments (PHAEO) and chloroplastic pigment equivalents (CPE) was measured according to Schubert et al., (2005) and total hydrolyzable amino acids (THAA) and single amino acid: ASP, GLU, SER, HIS, GLY, THR, ARG, ALA, TYR, MET, VAL, PHE, ILE, LEU, LYS following Dauwe et al., 1998.

Reference list of 269 sources used for exploratory search

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.