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  • 1
    Publication Date: 2024-05-26
    Keywords: A Palaeoreanalysis To Understand Decadal Climate Variability; DATE/TIME; Description; ELEVATION; File name; GlobCover; Identification; LATITUDE; LONGITUDE; PALAEO-RA; Station label; Temperature, air, monthly mean; Uniform resource locator/link to source data file
    Type: Dataset
    Format: text/tab-separated-values, 3365786 data points
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  • 2
    Publication Date: 2024-05-26
    Keywords: A Palaeoreanalysis To Understand Decadal Climate Variability; Binary Object; DATE/TIME; ELEVATION; GlobCover; Identification; LATITUDE; LONGITUDE; PALAEO-RA; Station label; Uniform resource locator/link to source data file; Variable
    Type: Dataset
    Format: text/tab-separated-values, 17586 data points
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  • 3
    Publication Date: 2024-05-26
    Description: Multibeam bathymetry raw data was recorded in the North Atlantic during cruise SO276 MerMet 17-6 that took place between 2020-06-22 and 2020-07-26. The data was collected using the ship's own Kongsberg EM 122. Sound velocity profiles (SVP) were applied on the data for calibration. Please see environmental data (zip file) and the cruise report for details. The bathymetric data acquisition was carried out within the IceAge project which aims to investigate the North Atlantic ecosystem. Working areas have been selected to offer a variability of environments (e.g., variability of water depth, and geological setting (shelf, mid-ocean ridge, deep basin)). The bathymetric data were used to plan ROV dives and sampling stations as well as background for habitat mapping studies.
    Keywords: Binary Object; Binary Object (File Size); Binary Object (Media Type); Comment; Data file recording distance; Data file recording duration; DATE/TIME; ELEVATION; Event label; File content; iAtlantic; IceAge; Icelandic marine Animals: Genetics and Ecology; Integrated Assessment of Atlantic Marine Ecosystems in Space and Time; KEM122; Kongsberg datagram raw file name; KONGSBERG EM122; LATITUDE; LONGITUDE; MerMet 17-6; Norwegian Sea, Arctic Ocean; Number of pings; Ship speed; SO276; SO276_0_Underway-1; Sonne_2; Start of data file, depth; Start of data file, heading; Start of data file recording, date/time; Start of data file recording, latitude; Start of data file recording, longitude; Stop of data file, depth; Stop of data file, heading; Stop of data file recording, date/time; Stop of data file recording, latitude; Stop of data file recording, longitude
    Type: Dataset
    Format: text/tab-separated-values, 4002 data points
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  • 4
    Publication Date: 2024-05-26
    Description: Cell division of the coccolithophore Emiliania huxleyi and other phytoplankton typically becomes entrained to diel light/dark cycles under laboratory conditions, with division occurring primarily during dark phases and production occurring during light phases. Under these conditions, the increase in a culture's cell and biomass concentrations deviates from an exponential function on time scales 〈 24 h. We here present a dataset of short-term changes in cell and biomass concentrations of fast dividing, dilute-batch cultures of E. huxleyi grown under a 16:8 h light/dark cycle. This dataset was used to derive linear models describing the diel course in the concentrations of cells, particulate organic carbon (POC) and particulate inorganic carbon (PIC) and for the calculation of daily means of cellular quotas and production rates in Kottmeier et al. (2020). We also present the given seawater carbonate chemistry as well as cellular quotas of particulate organic nitrogen (PON) and chlorophyll a (Chl. a), and the ratios of PIC:POC, POC:PON, POC:cell volume and Chl. a:POC in the course of the 24 h sampling period.
    Keywords: 1; Alkalinity, potentiometric; Alkalinity, total; Alkalinity, total, standard deviation; Calculated using CO2SYS; Carbon, inorganic, dissolved; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, relative concentration; Carbon, organic, particulate; Carbon, organic, particulate, per cell; Carbon, organic, particulate, relative concentration; Carbon, organic, particulate/Nitrogen, organic, particulate ratio; Carbon dioxide, partial pressure; Carbon dioxide, partial pressure, standard deviation; Cell concentration, relative; Chlorophyll a/particulate organic carbon ratio; Chlorophyll a per cell; Colorimetric autoanalysis; Consumption of carbon, inorganic, dissolved, standard deviation; Coulter counter, Beckman Coulter, Multisizer 3; Elemental analyzer, EuroVector, EA 3000; Exponential growth; Fluorometer, Turner Design, TD-700; Growth rate; Growth rate, standard deviation; Hand net; HN; Identification; Irradiance; Light/dark cycles; Light meter; LM; Particulate inorganic carbon/particulate organic carbon ratio; Particulate organic nitrogen per cell; pH; pH, standard deviation; Phase; Phased cell division; PIC production; POC production; Potentiometric; Registration number of species; Salinity; SALINO; Salinometer; SO136; SO136_006-A_HPN; Sonne; Species; Stage; Strain; TASQWA; Temperature, water; Temperature sensor; Time in hours; Treatment: light:dark cycle; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 1983 data points
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  • 5
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    PANGAEA
    In:  Scripps Institution of Oceanography, UC San Diego | Supplement to: SCRIPPS Institution of Oceanography (1962): PROA Expedition April-August 1962, list of core and dredge samples, R/V Spencer F. Baird. Scripps Institution of Oceanography, UC San Diego, unpublished, 64 pp, https://www.ngdc.noaa.gov/mgg/curator/data/spencer_f._baird/proa/15065003.pdf
    Publication Date: 2024-05-26
    Description: The cores and dredges described in this report were taken on the PROA Expedition in April 1962 until August 1962 by the Scripps Institution of Oceanography from the R/V Spencer F. Baird. A total of 180 cores and dredges were recovered and are available at Scripps for sampling and study.
    Keywords: Comment; Core; CORE; Deposit type; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Description; Dredge; DRG; Elevation of event; Event label; GC; Gravity corer; Identification; Latitude of event; Longitude of event; Method/Device of event; NOAA and MMS Marine Minerals Geochemical Database; NOAA-MMS; Pacific Ocean; PC; Photo/Video; Piston corer; Position; PROA; PROA-009D; PROA-011P; PROA-011PG; PROA-015D; PROA-029D; PROA-063PG; PROA-072D; PROA-079P; PROA-084P; PROA-099P; PROA-101P; PROA-102C1; PROA-102C2; PROA-103V; PROA-105G; PROA-108C; PROA-108P; PROA-108PG; PROA-112P; PROA-113P; PROA-113PG; PROA-113V; PROA-116P; PROA-123G; PROA-137G; PROA-139G; PROA-141G; PROA-147G; PROA-147V; PROA-148G; PROA-150G; PROA-151G; PROA-156G; PROA-157G; PROA-159G; PROA-160G; PROA-161G; PROA-162G; PROA-167G; PROA-168G; PROA-169G; PROA-175G; PROA-PC7; PV; Quantity of deposit; Sediment type; Size; Spencer F. Baird; Substrate type; Uniform resource locator/link to image; Visual description
    Type: Dataset
    Format: text/tab-separated-values, 467 data points
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  • 6
    Publication Date: 2024-05-26
    Description: Abstract
    Description: This collection contains 10500 computationally generated, randomised 2D microstructures, their geometrical and electrical properties, and the Matlab software package used to calculate these properties. The two-phase microstructures (mineral matrix, pore space) represent three different pore space types (microfracture networks, intergranular pore space, oomoldic pore space) and are organised into 35 ensembles - with common modelling parameters - of 100 individual microstructure realisations each. For all realisations, several geometrical properties (percolation, total porosity, connected porosity, isolated porosity, surface area, fractal dimension) and physical properties (formation factor from electrical resistivity, electrical tortuosity) are given. The collection also includes a Matlab-based finite element simulation package derived from the FEMALY library, which can be used to compute the properties of any given 2D raster microstructure.
    Keywords: petrophysics ; microstructure ; finite element method ; permeability and porosity ; statistical methods ; EARTH SCIENCE 〉 SOLID EARTH 〉 ROCKS/MINERALS/CRYSTALS 〉 SEDIMENTARY ROCKS 〉 SEDIMENTARY ROCK PHYSICAL/OPTICAL PROPERTIES ; EARTH SCIENCE 〉 SOLID EARTH 〉 ROCKS/MINERALS/CRYSTALS 〉 SEDIMENTARY ROCKS 〉 SEDIMENTARY ROCK PHYSICAL/OPTICAL PROPERTIES 〉 ELECTRICAL ; science 〉 natural science 〉 earth science 〉 geophysics
    Type: Collection , Collection
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  • 7
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    Instituto de Investigaciones Marinas y Costeras "José Benito Vives de Andréis" | Santa Marta, Colombia
    Publication Date: 2024-05-25
    Description: En el inhóspito, Agreste y poco conocido Pacífico colombiano, se destaca un lugar especial por haberse constituido en el transcurso de las últimas dos décadas en epicentro de la investigación en biodiversidad marina: Isla Gorgona. Su condición insular y de Parque Nacional Natural hacen de ella, aunque poco accesible, un escenario ideal para la observación contemplativa y minuciosa de las muchas expresiones que la naturaleza ha sabido reunir allí, tanto en tierra como en las aguas que la circundan. Es lugar de paso obligado para grandes cetáceos y aves migratorias, posee formaciones coralinas que albergan una característica diversidad de peces e invertebrados, además de playas, acantilados, fondos de arena de roca que propician la coexistencia de variadas y contrastantes comunidades bióticas que han cautivado la atención de biólogos y estudiantes, lo que le ha valido el calificativo de "isla ciencia". Este libro da a conocer sus atributos naturales.
    Description: Published
    Description: Refereed
    Keywords: Peces marinos ; ASFA_2015::C::Coral reefs ; ASFA_2015::CComunidades coralinas ; ASFA_2015::AArrecifes coralinos ; ASFA_2015::E::Ecology
    Repository Name: AquaDocs
    Type: Book/Monograph/Conference Proceedings
    Format: 160pp.
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  • 8
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    Naturalis Biodiversity Center
    In:  Blumea: Biodiversity, Evolution and Biogeography of Plants vol. 65 no. 3, pp. 179-187
    Publication Date: 2024-05-25
    Description: During the preparation of the accounts of Artabotrys (Annonaceae) and Magnolia (Magnoliaceae) for the Flora of Singapore, the types of all relevant names were evaluated. New lectotypes are designated for A. suaveolens and M. maingayi and a second-step lectotypification is performed for M. elegans. The citation of a lectotype locality is corrected for A. costatus and the citation of an isolectotype is improved for A. maingayi. We also clarify the previous use of the term ‘type’ to designate specimens that are in fact lectotypes for several names in Magnolia.
    Keywords: Plant Science ; Ecology ; Evolution ; Behavior and Systematics ; Annonaceae ; Artabotrys ; lectotypification ; Magnoliaceae ; nomenclature ; Singapore
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 9
    Publication Date: 2024-05-25
    Description: Palmitic acid (PA) is ubiquitous in the biosphere and its hydrogen isotopic composition (δ2HPA) was proposed as a potential paleoenvironmental proxy for salinity, with δ2HPA values increasing with salinity. In this study, we analyzed 40 surface sediment samples from Baffin Bay and the Labrador Sea to examine the isotopic composition of PA in relation to local environmental variables, including salinity. In contrast to expectations, our results show a negative relationship between the δ2HPA and sea-surface salinity, raising questions about its pertinence/usefulness as a salinity proxy. Instead, our results suggest that the relative abundance of distinct organisms that employ different metabolisms is key in determining the hydrogen isotopic fractionations in PA. Whereas we show that PA is mostly produced through photoautotrophic metabolisms by diatoms and dinoflagellates, varying contributions from heterotrophic metabolisms may obscure the stable isotope composition of PA. Surprisingly, we found no correlation between the stable carbon isotopic composition of the sedimentary organic matter (δ13Corg) and palmitic acid (δ13CPA), implying major differences in either the dominant organisms producing sedimentary PA or in carbon isotope fractionation during lipid biosynthesis. We also found that the presence of extended sea-ice cover leads to enriched carbon and hydrogen isotopic compositions in PA. These enriched values suggest heterotrophic biodegradation in the water column and/or in the sediment as well as an increase in grazing activities. We propose that sea-ice cover and surface water oxygenation modulate the relative impact of phototrophic and heterotrophic metabolisms, and therefore the isotopic composition of marine sedimentary PA.
    Keywords: Average chain length; Baffin Bay; BC; Bottom water salinity, annual mean; Bottom water temperature; Box corer; Carbon; Carbon, inorganic, total; Carbon, organic; Carbon isotopes; Carbon organic/Nitrogen, molar ratio; Carbon Preference Index; Davis Strait; DB3.02; DB3.08; DB3.10; DB3.14; DB3.31; DB3.32; DB3.34; DB3.35; DB6.02; DB6.04; DB6.05; DB6.07; DB6.08; DB6.09; DEPTH, sediment/rock; Dinoflagellate cyst, heterotrophic; Dinoflagellate cyst, per unit sediment mass; Dinoflagellate cyst, phototrophic; Event label; FB1.02; FB1.07; GeoB22315-3; GeoB22318-1; GeoB22319-1; GeoB22344-2; GeoB22350-2; GeoB22353-2; GeoB22356-2; GeoB22358-2; Grab; GRAB; HB2.04; Hexadecanoic acid, δ13C; Hexadecanoic acid, δ13C, standard deviation; Hudson Strait; Hydrogen isotopes; Labrador Sea; lipid biomarkers; Maria S. Merian; MSM45; MSM45_002-4; MSM45_009-3; MSM45_030-3; MSM45_401-4; MSM45_408-3; MSM45_417-3; MSM45_424-3; MSM45_430-3; MSM45-018-3; MSM45-024-3; MSM46; MSM46_11-5; MSM46_14-2; MSM46_25-1; MSM46_28-3; MSM66; MSM66/15-3; MSM66/18-1; MSM66/19-1; MSM66/44-2; MSM66/50-2; MSM66/53-2; MSM66/56-2; MSM66/58-2; MUC; MultiCorer; n-fatty acid C16, per unit mass total organic carbon; n-fatty acid C16, per unit sediment mass; n-fatty acid C16:1, per unit mass total organic carbon; n-fatty acid C16:1, per unit sediment mass; n-fatty acid C18, per unit mass total organic carbon; n-fatty acid C18, per unit sediment mass; n-fatty acid C18:1, per unit mass total organic carbon; n-fatty acid C18:1, per unit sediment mass; n-fatty acid C18:2, per unit mass total organic carbon; n-fatty acid C18:2, per unit sediment mass; Nitrate; Nitrogen; Oxygen, apparent utilization; Oxygen saturation; Paamiut; Paamiut2014; Palmitic acid; Palynomorpha, reworked per unit sediment mass; Phosphate; Pollen, per unit sediment mass; Primary production of carbon per area, yearly; Q7.03; Q7.04; Ratio; Saturated fatty acids, per unit mass total organic carbon; Saturated fatty acids, per unit sediment mass; Sea ice cover duration; Sea surface salinity, annual mean; Sea surface salinity, summer; Sea surface salinity, winter; Sea surface temperature, annual mean; Sea surface temperature, summer; Sea surface temperature, winter; Silicate; Site; Spores per unit sediment mass; U5.04; U5.10; U5.14; V4.03; δ13C, organic carbon; δ18O, water; δ Deuterium, palmitic acid; δ Deuterium, palmitic acid, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 2194 data points
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  • 10
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    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel
    Publication Date: 2024-05-25
    Description: Raw data acquired by position sensors on board RV MERIAN during expedition MSM123 were processed to receive a validated master track which can be used as reference of further expedition data. During MSM123 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and the GPS receivers Trimble SPS855 and SAAB R4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: 1 sec resolution; BELS; CT; DAM_Underway; DAM Underway Research Data; Maria S. Merian; MSM123; MSM123-track; Underway cruise track measurements
    Type: Dataset
    Format: application/zip, 121.8 MBytes
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  • 11
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    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel
    Publication Date: 2024-05-25
    Description: Raw data acquired by position sensors on board RV METEOR during expedition M198 were processed to receive a validated master track which can be used as reference of further expedition data. During M198 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and two C and C Technologies GPS receivers C-NAV3050 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: Calculated; Course; CT; DAM_Underway; DAM Underway Research Data; DATE/TIME; LATITUDE; LONGITUDE; M198; M198-track; Meteor (1986); MIDES; Speed; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 3742 data points
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  • 12
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    PANGAEA
    In:  Max Planck Institute for Marine Microbiology
    Publication Date: 2024-05-25
    Description: Raw data acquired by position sensors on board RV MERIAN during expedition MSM125 were processed to receive a validated master track which can be used as reference of further expedition data. During MSM125 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and the GPS receivers Trimble SPS855 and SAAB R4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: 1 sec resolution; CT; DAM_Underway; DAM Underway Research Data; EqTestGOC; Maria S. Merian; MSM125; MSM125-track; Underway cruise track measurements
    Type: Dataset
    Format: application/zip, 25.8 MBytes
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  • 13
    Publication Date: 2024-05-25
    Keywords: 20-mer oligonucleotide; Calculated; Colorado, U.S.A., North America; DATE/TIME; Depth, relative; ELEVATION; Elevation 2; Fluorescent dye, Cyanine 3; Hybridization marker; LATITUDE; LONGITUDE; Rilfe; Sample code/label; Sampling Well; Signal/noise ratio; WELL
    Type: Dataset
    Format: text/tab-separated-values, 8480 data points
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  • 14
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; GeoB16705-1; GeoB16705-10; GeoB16705-2; GeoB16705-3; GeoB16705-4; GeoB16705-5; GeoB16705-7; GeoB16705-8; GeoB16705-9; H1210P01; H1210P02; H1210P03; H1210P04; H1210P05; H1210P07; H1210P08; H1210P09; H1210P10; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; MARUM; MEMO; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 992 data points
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  • 15
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; GeoB16718-1; GeoB16718-10; GeoB16718-4; GeoB16718-5; GeoB16718-7; GeoB16718-8; H1211P01; H1211P04; H1211P05; H1211P07; H1211P08; H1211P10; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; MARUM; MEMO; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 658 data points
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  • 16
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; GeoB16733-1; GeoB16733-2; GeoB16733-3; GeoB16733-4; GeoB16733-5; GeoB16733-6; GeoB16733-7; GeoB16733-8; H1213P01; H1213P02; H1213P03; H1213P04; H1213P05; H1213P06; H1213P07; H1213P08; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; MARUM; MEMO; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 885 data points
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  • 17
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16760-1; GeoB16760-2; GeoB16760-3; H1216P01; H1216P02; H1216P03; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 346 data points
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  • 18
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16766-1; GeoB16766-2; GeoB16766-3; GeoB16766-4; H1217P01; H1217P02; H1217P03; H1217P04; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 440 data points
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  • 19
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16784-1; GeoB16784-10; GeoB16784-11; GeoB16784-2; GeoB16784-3; GeoB16784-4; GeoB16784-5; GeoB16784-6; GeoB16784-7; GeoB16784-8; GeoB16784-9; H1222P01; H1222P02; H1222P03; H1222P04; H1222P05; H1222P06; H1222P07; H1222P08; H1222P09; H1222P10; H1222P11; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 1190 data points
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  • 20
    Publication Date: 2024-05-25
    Description: The dataset presents the greenhouse gas production (CO2 and CH4) from sediment of a terrestrial permafrost outcrop (Byk14-A-1; 71.85175°N, 129.350883°E), the thermokarst lake Goltsovoye (PG2412 (TKL), 71.74515°N, 129.30217°E), the nearly-closed Polar Fox Lagoon (PG2411 (LAG1), 71.743056°N, 129.337778°E) and the semi-open Uomullyakh Lagoon (PG2410-1 (LAG1), 71.730833°N, 129.2725°E). We incubated the samples anaerobically at 4 °C under fresh (c=0 g/L), brackish (c=13g/L) and marine (36g/L) conditions for one year and measured carbon dioxide (CO2) and methane (CH4) concentrations regularly in a 250 µL subsample using gas chromatography with an Agilent GC 7890A equipped with an Agilent HP-PLOT Q column. Cumulative CO2 and CH4 concentrations and production rates per day are given over time for all samples with three replicates each per gram of dry weight and normalised to gram of soil organic carbon (SOC).
    Keywords: Anaerobic incubation; Arctic permafrost coasts; AWI Arctic Land Expedition; Bykovsky_2017_spring; Carbon dioxide, production, anaerobic, per mass soil organic carbon; Carbon dioxide, production, anaerobic, per soil dry mass; Carbon dioxide, production rate, per mass soil organic carbon; Carbon dioxide, production rate, per soil dry mass; carbon dioxide production; Core; Date; Day of experiment; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Event label; EXPO; Exposure; Goltsovoye Lake, Siberia, Russia; Laboratory experiment; Lena_Delta_Sobo-Byk_2014; Methane, production, anaerobic, per mass soil organic carbon; Methane, production, anaerobic, per soil dry mass; Methane, production rate, per mass soil organic carbon; Methane, production rate, per soil dry mass; methane production; Mobile drilling rig, Geotechnika, URB-4T; PETA-CARB; PG2410-1; PG2411-1; PG2412-1; PG-BYK14-1-A; Polar Fox Lagoon; Rapid Permafrost Thaw in a Warming Arctic and Impacts on the Soil Organic Carbon Pool; Replicate; RU-Land_2014_Lena_Sobo-Byk; RU-Land_2017_Lena_Bykovsky; Sample ID; Sediment type; thermokarst lagoon formation; thermokarst lakes; Treatment; Type of study; Uomullyakh Lagoon
    Type: Dataset
    Format: text/tab-separated-values, 18900 data points
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  • 21
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    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel
    Publication Date: 2024-05-25
    Description: Raw data acquired by position sensors on board RV METEOR during expedition M198 were processed to receive a validated master track which can be used as reference of further expedition data. During M198 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and two C and C Technologies GPS receivers C-NAV3050 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: 1 sec resolution; CT; DAM_Underway; DAM Underway Research Data; M198; M198-track; Meteor (1986); MIDES; Underway cruise track measurements
    Type: Dataset
    Format: application/zip, 47.1 MBytes
    Location Call Number Expected Availability
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  • 22
    facet.materialart.
    Unknown
    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel
    Publication Date: 2024-05-25
    Description: Raw data acquired by position sensors on board RV MERIAN during expedition MSM123 were processed to receive a validated master track which can be used as reference of further expedition data. During MSM123 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and the GPS receivers Trimble SPS855 and SAAB R4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: BELS; Calculated; Course; CT; DAM_Underway; DAM Underway Research Data; DATE/TIME; LATITUDE; LONGITUDE; Maria S. Merian; MSM123; MSM123-track; Speed; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 9432 data points
    Location Call Number Expected Availability
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  • 23
    facet.materialart.
    Unknown
    PANGAEA
    In:  Max Planck Institute for Marine Microbiology
    Publication Date: 2024-05-25
    Description: Raw data acquired by position sensors on board RV MERIAN during expedition MSM125 were processed to receive a validated master track which can be used as reference of further expedition data. During MSM125 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and the GPS receivers Trimble SPS855 and SAAB R4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: Calculated; Course; CT; DAM_Underway; DAM Underway Research Data; DATE/TIME; EqTestGOC; LATITUDE; LONGITUDE; Maria S. Merian; MSM125; MSM125-track; Speed; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 2032 data points
    Location Call Number Expected Availability
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  • 24
    Publication Date: 2024-05-25
    Keywords: Calculated; Colorado, U.S.A., North America; Date; DATE/TIME; Depth, relative; ELEVATION; Elevation 2; LATITUDE; LONGITUDE; Rilfe; Sample code/label; Sampling Well; Signal/noise ratio; WELL
    Type: Dataset
    Format: text/tab-separated-values, 2386 data points
    Location Call Number Expected Availability
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  • 25
    Publication Date: 2024-05-25
    Keywords: Acetate; Aluminium; Aluminium, standard deviation; Arsenic; Arsenic, standard deviation; Barium, standard deviation; Barium 2+; Boron; Boron, standard deviation; Bromide; Bromine; Bromine, standard deviation; Calcium; Calcium, standard deviation; Carbon, inorganic, total; Chloride; Chromium; Chromium, standard deviation; Cobalt; Cobalt, standard deviation; Colorado, U.S.A., North America; Conductivity, electrolytic; Damage rate, standard deviation; DATE/TIME; Depth, logging; Depth, relative; ELEVATION; Elevation 2; Iron; Iron, standard deviation; Iron 2+; LATITUDE; Lithium; Lithium, standard deviation; LONGITUDE; Magnesium; Magnesium, standard deviation; Manganese; Manganese, standard deviation; Molybdenum; Molybdenum, standard deviation; pH; Potassium; Rilfe; Rubidium; Rubidium, standard deviation; Sample code/label; Sampling Well; Selenium; Selenium, standard deviation; Silicate; Silicate, standard deviation; Sodium; Sodium, standard deviation; Strontium, standard deviation; Strontium 2+; Sulfate; Sulfide; Thiosulfate; Titanium; Titanium, standard deviation; Uranium; Uranium, standard deviation; Vanadium; Vanadium, standard deviation; Water level; WELL; Zinc; Zinc, standard deviation; δ18O; δ18O, standard deviation; δ34S; δ34S, standard deviation; δ Deuterium; δ Deuterium, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 58204 data points
    Location Call Number Expected Availability
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  • 26
    facet.materialart.
    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16752-2; GeoB16752-3; GeoB16752-4; GeoB16752-5; GeoB16752-6; GeoB16752-7; GeoB16752-8; H1215P02; H1215P03; H1215P04; H1215P05; H1215P06; H1215P07; H1215P08; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 765 data points
    Location Call Number Expected Availability
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  • 27
    facet.materialart.
    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16774-1; GeoB16774-10; GeoB16774-11; GeoB16774-2; GeoB16774-3; GeoB16774-4; GeoB16774-5; GeoB16774-6; GeoB16774-7; GeoB16774-8; GeoB16774-9; H1219P01; H1219P02; H1219P03; H1219P04; H1219P05; H1219P06; H1219P07; H1219P08; H1219P09; H1219P10; H1219P11; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 1214 data points
    Location Call Number Expected Availability
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  • 28
    facet.materialart.
    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16790-1; GeoB16790-2; GeoB16790-3; GeoB16790-4; GeoB16790-5; H1223P01; H1223P02; H1223P03; H1223P04; H1223P05; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 556 data points
    Location Call Number Expected Availability
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  • 29
    Publication Date: 2024-05-25
    Keywords: 2,6,10,14-Tetramethyl-7-(3-methylpent-4-enyl)pentadecane, per unit mass total organic carbon; 24-Methylcholesta-5,22E-dien-3beta-ol, per unit mass total organic carbon; 4alpha,23,24-Trimethyl-5alpha-cholest-22E-en-3beta-ol, per unit mass total organic carbon; AMD14; AMD14_101; AMD14_115; AMD14_200; Arctic Ocean; ARK-XXIX/2.1; ARK-XXVIII/4 ALEX2014; ARK-XXXI/4; Baffin Bay; BC; Box corer; Carbon, organic, total; CCGS Amundsen; Core; CORE; CORIBAR; Davis Strait; DB3.02; DB3.08; DB3.10; DB3.11; DB3.12; DB3.13; DB3.14; DB3.15; DB3.16; DB3.20; DB3.23; DB3.24; DB3.25; DB3.26; DB3.27; DB3.30; DB3.31; DB3.32; DB3.33; DB3.34; DB3.35; DB3.36; DB3.37; DB3.39; DB3.42; DB6.01; DB6.02; DB6.05; DB6.06; DB6.07; DB6.08; DB6.09; EGS-1; Event label; FB1.02; FB1.04; FB1.05; FB1.07; FB1.12; FRAM-2014/15_ice_drift; FRAM2014/15-08-06; FRAM2014/15-11-09; FRAM2014/15-13-11; FRAM2014/15-15-13; FRAM2014/15-15-14; FRAM2014/15-15-15; FRAM2014/15-15-16; FRAM2014/15-15-17; FRAM2014/15-15-18; G. O. Sars (2003); GC; GeoB17601-2; GeoB17602-1; GeoB17603-1; GeoB17604-1; GeoB17605-1; GeoB17606-1; GeoB17607-1; GeoB17608-1; GeoB17609-1; GeoB17609-3; GeoB17610-1; GeoB17611-2; GeoB17612-1; GeoB17613-1; GeoB17614-1; GeoB17615-1; GeoB17616-1; GeoB17617-1; GeoB17618-1; GeoB17619-1; GeoB17620-1; GeoB17621-1; GeoB17622-1; GeoB17623-1; GeoB19904-1; GeoB19905-2; GeoB19916-5; GeoB19920-4; GeoB19927-2; GeoB19931-2; GeoB19933-2; GeoB19940-3; GeoB19946-3; GeoB19948-2; GeoB19953-5; GeoB19959-3; GeoB19961-2; GeoB19963-2; GeoB19969-2; GeoB19973-3; GeoB22304-4; GeoB22305-2; GeoB22306-2; GeoB22315-3; GeoB22316-1; GeoB22317-1; GeoB22318-1; GeoB22319-1; GeoB22320-1; GeoB22321-1; GeoB22329-3; GeoB22331-2; GeoB22333-3; GeoB22334-1; GeoB22336-2; GeoB22344-2; GeoB22346-2; GeoB22348-2; GeoB22350-2; GeoB22351-2; GeoB22353-2; GeoB22356-2; GeoB22357-2; GeoB22358-2; GeoB22359-2; Giant box corer; GKG; Gravity corer; Greenland Sea; GS15-198-36; GS15-198-37; GS15-198-38; GS15-198-39; GS15-198-40; GS15-198-41; GS15-198-42; GS15-198-43; GS15-198-44; GS15-198-45; GS15-198-46; GS15-198-47; GS15-198-48; GS15-198-49; GS15-198-50; GS15-198-51; GS15-198-52; GS15-198-53; GS15-198-54; GS15-198-55; GS15-198-56; GS15-198-58; GS15-198-59; GS15-198-60; GS15-198-61; GS15-198-62; GS15-198-63; GS16-204-19; GS16-204-21; GS16-204-22; GS16-204-23; GS16-204-24; GS2015-198; GS2016-204; HB2.01; HB2.02; HB2.03; HB2.04; HB2.06; Heat-Flow probe; HF; Highly branched isoprenoids, diunsatured, per unit mass total organic carbon; Highly branched isoprenoids (E), triunsatured, per unit mass total organic carbon; Highly branched isoprenoids (Z), triunsatured, per unit mass total organic carbon; HUD2008/29; HUD2008/29_14; HUD2008/29_47; HUD2008/29_55; HUD2008/29_66; HUD2013/29; HUD2013/29_51; HUD2013/29_52; HUD2013/29_54; HUD2013/29_68; HUD2013/29_78; HUD2013/29_79; Hudson; Labrador Sea; LATITUDE; LONGITUDE; Maria S. Merian; MOOR; Mooring; MSM12/2; MSM12/2_642-2; MSM12/2_643-2; MSM12/2_645-3; MSM12/2_646-2; MSM12/2_647-1; MSM12/2_649-4; MSM12/2_650-2; MSM12/2_651-2; MSM12/2_653-3; MSM12/2_654-1; MSM12/2_656-2; MSM12/2-01-02; MSM12/2-02-02; MSM12/2-03-02; MSM12/2-04-02; MSM12/2-05-01; MSM12/2-06-03; MSM12/2-07-01; MSM12/2-08-02; MSM12/2-09-02; MSM12/2-10-01; MSM12/2-12-02; MSM30; MSM30_463-2; MSM30_466-1; MSM30_467-1; MSM30_469-1; MSM30_471-1; MSM30_472-1; MSM30_474-1; MSM30_476-1; MSM30_477-1; MSM30_477-3; MSM30_479-1; MSM30_480-2; MSM30_482-1; MSM30_483-1; MSM30_485-1; MSM30_486-1; MSM30_488-1; MSM30_490-2; MSM30_493-1; MSM30_499-1; MSM30_500-1; MSM30_501-1; MSM30_502-1; MSM30_503-1; MSM31; MSM31_550-5; MSM31_557-2; MSM31_561-2; MSM31_575-3; MSM31_585-4; MSM44; MSM44_330-1; MSM44_331-2; MSM44_342-5; MSM44_346-4; MSM44_353-2; MSM44_357-2; MSM44_359-2; MSM44_366-3; MSM44_372-3; MSM44_374-2; MSM44_379-5; MSM44_385-3; MSM44_387-2; MSM44_389-2; MSM44_395-2; MSM44_399-3; MSM46; MSM46_10-8; MSM46_12-5; MSM46_14-2; MSM46_16-6; MSM46_19-3; MSM46_20-3; MSM46_22-2; MSM46_25-1; MSM46_28-3; MSM46_3-5; MSM46_4-5; MSM46_5-8; MSM46_6-4; MSM46_7-10; MSM66; MSM66/05-2; MSM66/15-3; MSM66/16-1; MSM66/17-1; MSM66/18-1; MSM66/19-1; MSM66/20-1; MSM66/21-1; MSM66/29-3; MSM66/31-2; MSM66/33-3; MSM66/34-1; MSM66/36-2; MSM66/4-4; MSM66/44-2; MSM66/46-2; MSM66/48-2; MSM66/50-2; MSM66/51-2; MSM66/53-2; MSM66/56-2; MSM66/57-2; MSM66/58-2; MSM66/59-2; MSM66/6-2; MUC; MultiCorer; Multicorer with television; North Greenland Sea; Norwegian Sea; Paamiut; Paamiut2014; Polarstern; PS109; PS109_105-1; PS109_115-2; PS109_125-1; PS109_129-1; PS109_139-1; PS109_19-2; PS109_36-2; PS109_46-2; PS109_76-1; PS109_85-1; PS109_93-2; PS115/1; PS115/1_10-1; PS115/1_17-2; PS115/1_18-1; PS115/1_19-2; PS115/1_21-1; PS115/1_22-2; PS115/1_26-1; PS115/1_4-2; PS115/1_47-1; PS115/1_48-1; PS115/1_50-1; PS115/1_51-1; PS115/1_52-2; PS115/1_6-1; PS115/1_7-1; PS115/1_9-4; PS87; PS87/029-3; PS87/030-3; PS87/035-3; PS87/040-3; PS87/067-3; PS87/068-3; PS87/070-3; PS87/074-2; PS87/076-3; PS87/079-3; PS87/086-2; PS87/099-4; PS93/011-4; PS93/016-5; PS93/017-5; PS93/018-4; PS93/020-5; PS93/023-4; PS93/024-6; PS93/030-4; PS93/031-1; PS93/039-7; PS93/041-2; PS93/046-4; PS93.1; Q7.01; Q7.03; Q7.04; Q7.07; Sabvabba; South Atlantic Ocean; Station label; TVMUC; U5.04; U5.05; U5.08; U5.09; U5.10; U5.14; U5.15; V4.01; V4.02; V4.03; V4.04
    Type: Dataset
    Format: text/tab-separated-values, 2046 data points
    Location Call Number Expected Availability
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  • 30
    Publication Date: 2024-05-25
    Keywords: (9E)-2,6,10,14-Tetramethyl-7-(3-methylpent-4-enyliden)pentadeca-9-ene per unit sediment mass; (9Z)-2,6,10,14-Tetramethyl-7-(3-methylpent-4-enyliden)pentadeca-9-ene per unit sediment mass; 2,10,14-Trimethyl-6-enyl-7-(3-methylpent-1-enyl)pentadecene/2,6,10,14-Tetramethyl-7-(3-methylpent-4-enyl)pentadecane ratio; 2,10,14-Trimethyl-6-enyl-7-(3-methylpent-1-enyl)pentadecene per unit sediment mass; 2,6,10,14-Tetramethyl-7-(3-methylpent-4-enyl)pentadecane per unit sediment mass; 24-Methylcholesta-5,22E-dien-3beta-ol per unit sediment mass; 4alpha,23,24-Trimethyl-5alpha-cholest-22E-en-3beta-ol per unit sediment mass; AMD14; AMD14_101; AMD14_115; AMD14_200; Arctic Ocean; ARK-XXIX/2.1; ARK-XXVIII/4 ALEX2014; ARK-XXXI/4; Baffin Bay; BC; Box corer; CCGS Amundsen; Core; CORE; CORIBAR; Davis Strait; DB3.02; DB3.08; DB3.10; DB3.11; DB3.12; DB3.13; DB3.14; DB3.15; DB3.16; DB3.20; DB3.23; DB3.24; DB3.25; DB3.26; DB3.27; DB3.30; DB3.31; DB3.32; DB3.33; DB3.34; DB3.35; DB3.36; DB3.37; DB3.39; DB3.42; DB6.01; DB6.02; DB6.05; DB6.06; DB6.07; DB6.08; DB6.09; EGS-1; Event label; FB1.02; FB1.04; FB1.05; FB1.07; FB1.12; FRAM-2014/15_ice_drift; FRAM2014/15-08-06; FRAM2014/15-11-09; FRAM2014/15-13-11; FRAM2014/15-15-13; FRAM2014/15-15-14; FRAM2014/15-15-15; FRAM2014/15-15-16; FRAM2014/15-15-17; FRAM2014/15-15-18; G. O. Sars (2003); GC; GeoB17601-2; GeoB17602-1; GeoB17603-1; GeoB17604-1; GeoB17605-1; GeoB17606-1; GeoB17607-1; GeoB17608-1; GeoB17609-1; GeoB17609-3; GeoB17610-1; GeoB17611-2; GeoB17612-1; GeoB17613-1; GeoB17614-1; GeoB17615-1; GeoB17616-1; GeoB17617-1; GeoB17618-1; GeoB17619-1; GeoB17620-1; GeoB17621-1; GeoB17622-1; GeoB17623-1; GeoB19904-1; GeoB19905-2; GeoB19916-5; GeoB19920-4; GeoB19927-2; GeoB19931-2; GeoB19933-2; GeoB19940-3; GeoB19946-3; GeoB19948-2; GeoB19953-5; GeoB19959-3; GeoB19961-2; GeoB19963-2; GeoB19969-2; GeoB19973-3; GeoB22304-4; GeoB22305-2; GeoB22306-2; GeoB22315-3; GeoB22316-1; GeoB22317-1; GeoB22318-1; GeoB22319-1; GeoB22320-1; GeoB22321-1; GeoB22329-3; GeoB22331-2; GeoB22333-3; GeoB22334-1; GeoB22336-2; GeoB22344-2; GeoB22346-2; GeoB22348-2; GeoB22350-2; GeoB22351-2; GeoB22353-2; GeoB22356-2; GeoB22357-2; GeoB22358-2; GeoB22359-2; Giant box corer; GKG; Gravity corer; Greenland Sea; GS15-198-36; GS15-198-37; GS15-198-38; GS15-198-39; GS15-198-40; GS15-198-41; GS15-198-42; GS15-198-43; GS15-198-44; GS15-198-45; GS15-198-46; GS15-198-47; GS15-198-48; GS15-198-49; GS15-198-50; GS15-198-51; GS15-198-52; GS15-198-53; GS15-198-54; GS15-198-55; GS15-198-56; GS15-198-58; GS15-198-59; GS15-198-60; GS15-198-61; GS15-198-62; GS15-198-63; GS16-204-19; GS16-204-21; GS16-204-22; GS16-204-23; GS16-204-24; GS2015-198; GS2016-204; HB2.01; HB2.02; HB2.03; HB2.04; HB2.06; Heat-Flow probe; HF; HUD2008/29; HUD2008/29_14; HUD2008/29_47; HUD2008/29_55; HUD2008/29_66; HUD2013/29; HUD2013/29_51; HUD2013/29_52; HUD2013/29_54; HUD2013/29_68; HUD2013/29_78; HUD2013/29_79; Hudson; Labrador Sea; LATITUDE; LONGITUDE; Maria S. Merian; MOOR; Mooring; MSM12/2; MSM12/2_642-2; MSM12/2_643-2; MSM12/2_645-3; MSM12/2_646-2; MSM12/2_647-1; MSM12/2_649-4; MSM12/2_650-2; MSM12/2_651-2; MSM12/2_653-3; MSM12/2_654-1; MSM12/2_656-2; MSM12/2-01-02; MSM12/2-02-02; MSM12/2-03-02; MSM12/2-04-02; MSM12/2-05-01; MSM12/2-06-03; MSM12/2-07-01; MSM12/2-08-02; MSM12/2-09-02; MSM12/2-10-01; MSM12/2-12-02; MSM30; MSM30_463-2; MSM30_466-1; MSM30_467-1; MSM30_469-1; MSM30_471-1; MSM30_472-1; MSM30_474-1; MSM30_476-1; MSM30_477-1; MSM30_477-3; MSM30_479-1; MSM30_480-2; MSM30_482-1; MSM30_483-1; MSM30_485-1; MSM30_486-1; MSM30_488-1; MSM30_490-2; MSM30_493-1; MSM30_499-1; MSM30_500-1; MSM30_501-1; MSM30_502-1; MSM30_503-1; MSM31; MSM31_550-5; MSM31_557-2; MSM31_561-2; MSM31_575-3; MSM31_585-4; MSM44; MSM44_330-1; MSM44_331-2; MSM44_342-5; MSM44_346-4; MSM44_353-2; MSM44_357-2; MSM44_359-2; MSM44_366-3; MSM44_372-3; MSM44_374-2; MSM44_379-5; MSM44_385-3; MSM44_387-2; MSM44_389-2; MSM44_395-2; MSM44_399-3; MSM46; MSM46_10-8; MSM46_12-5; MSM46_14-2; MSM46_16-6; MSM46_19-3; MSM46_20-3; MSM46_22-2; MSM46_25-1; MSM46_28-3; MSM46_3-5; MSM46_4-5; MSM46_5-8; MSM46_6-4; MSM46_7-10; MSM66; MSM66/05-2; MSM66/15-3; MSM66/16-1; MSM66/17-1; MSM66/18-1; MSM66/19-1; MSM66/20-1; MSM66/21-1; MSM66/29-3; MSM66/31-2; MSM66/33-3; MSM66/34-1; MSM66/36-2; MSM66/4-4; MSM66/44-2; MSM66/46-2; MSM66/48-2; MSM66/50-2; MSM66/51-2; MSM66/53-2; MSM66/56-2; MSM66/57-2; MSM66/58-2; MSM66/59-2; MSM66/6-2; MUC; MultiCorer; Multicorer with television; North Greenland Sea; Norwegian Sea; Paamiut; Paamiut2014; Phytoplankton biomarker Brassicasterol IP25 index; Phytoplankton biomarker C25 HBI (Z) triene IP25 index; Phytoplankton biomarker Dinosterol IP25 index; Phytoplankton biomarker HBI TR25 index; Polarstern; PS109; PS109_105-1; PS109_115-2; PS109_125-1; PS109_129-1; PS109_139-1; PS109_19-2; PS109_36-2; PS109_46-2; PS109_76-1; PS109_85-1; PS109_93-2; PS115/1; PS115/1_10-1; PS115/1_17-2; PS115/1_18-1; PS115/1_19-2; PS115/1_21-1; PS115/1_22-2; PS115/1_26-1; PS115/1_4-2; PS115/1_47-1; PS115/1_48-1; PS115/1_50-1; PS115/1_51-1; PS115/1_52-2; PS115/1_6-1; PS115/1_7-1; PS115/1_9-4; PS87; PS87/029-3; PS87/030-3; PS87/035-3; PS87/040-3; PS87/067-3; PS87/068-3; PS87/070-3; PS87/074-2; PS87/076-3; PS87/079-3; PS87/086-2; PS87/099-4; PS93/011-4; PS93/016-5; PS93/017-5; PS93/018-4; PS93/020-5; PS93/023-4; PS93/024-6; PS93/030-4; PS93/031-1; PS93/039-7; PS93/041-2; PS93/046-4; PS93.1; Q7.01; Q7.03; Q7.04; Q7.07; Sabvabba; South Atlantic Ocean; Station label; TVMUC; U5.04; U5.05; U5.08; U5.09; U5.10; U5.14; U5.15; V4.01; V4.02; V4.03; V4.04
    Type: Dataset
    Format: text/tab-separated-values, 2993 data points
    Location Call Number Expected Availability
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  • 31
    Publication Date: 2024-05-25
    Keywords: Akademik Boris Petrov; AMD14; AMD14_101; AMD14_115; AMD14_200; ARA2B; ARA2B-11_BOX-01; ARA2B-15; ARA2B-16a_BOX-01; ARA2B-16B; ARA2B-18A; ARA2B-18B; ARA2B-1A; ARA2B-1B; ARA2B-2; ARA2B-3A; ARA2B-3B; ARA2B-8_BOX-01; ARA2B-9_BOX-01; ARA3B; ARA3B_01; ARA3B_08MUC-02; ARA3B_09MUC-02; ARA3B_10MUC-02; ARA3B_11MUC-02; ARA3B_12; ARA3B_13MUC-01; ARA3B_14MUC-01; ARA3B_15b; ARA3B_16MUC-01; ARA3B_18MUC-01; ARA3B_19MUC-02; ARA3B_26; ARA3B_27; ARA3B_28; ARA3B_29MUC-02; ARA3B_30MUC-01; ARA3B_38aMUC-01; ARA3B_41MUC-03; Araon; ARC/ASP13_Tyro-5; ARC/ASP13_YS163; ARC/ASP14_Tyro-8; ARC/ASP14_YS3.14; ARC/IGN15_SD60; ARC/IGN15_Tyro-100; ARC/IGN15_YS3.18; ARC/IGN15_YSD; ARC/IGN15_Z60; ARC-1; ARC-2; ARC-3; ARC-4; ARC-5; ARC-6; ARC-7; ARC-8; ArcticNet2005; ArcticNet2005_ARC-1; ArcticNet2005_ARC-2; ArcticNet2005_ARC-3; ArcticNet2005_ARC-4; ArcticNet2005_ARC-5; ArcticNet2005_ARC-6; ArcticNet2005_ARC-7; ArcticNet2005_ARC-8; Arctic Ocean; ARK-VIII/2; ARK-X/2; ARK-XI/1; ARK-XIV/1a; ARK-XIX/4a; ARK-XV/2; ARK-XVI/1; ARK-XVI/2; ARK-XVII/1; ARK-XVII/2; ARK-XVIII/1; ARK-XXIX/2.1; ARK-XXVI/3; ARK-XXVII/3; ARK-XXVIII/4 ALEX2014; ARK-XXXI/4; Baffin Bay; Barents_625; Barents_627; Barents_629; Barents_631; Barents_633; Barents_635; Barents_639; Barents_643; Barents_645; Barents_647; Barents_649; Barents_651; Barents_653; Barents_655; Barents_657; Barents_659; Barents_661; Barents_663; Barents_665; Barents_667; Barents_669; Barents_671; Barents_673; Barents_675; Barents_677; Barents_679; Barents_681; Barents_690; Barents_692; Barents_St02; Barents_St03; Barents_St04; Barents_St06; Barents_St07; Barents_St09; Barents_St11; Barents_St12; Barents_St13; Barents_St14; Barents_St15; Barents_St17; Barents_St18; Barents_St19; Barents_St20; Barents_St21; Barents_St22; Barents_St23; Barents_St24; Barents_St25; Barents_St26; Barents_St27; Barents_St29; Barents_St30; Barents_St31; Barents_St32; Barents_St34; Barents_St35; Barents_St36; Barents_St37; Barents_St38; Barents_St39; Barents_St40; Barents_St41; Barents_St43; Barents_St44; Barents_St45; Barents Sea; BC; Bering Sea; Box corer; BP00; BP00-02; BP00-04; BP00-05; BP00-07; BP00-08; BP00-09; BP00-13; BP00-14; BP00-15; BP00-16; BP00-17; BP00-22; BP00-23; BP00-26; BP00-27; BP00-28; BP00-29; BP00-30; BP00-31; BP00-35; BP00-36; BP00-38; BP01; BP01-38; BP01-43; BP01-64; BP01-67; BP01-73a; BP01-74; BP01-75; BP01-76; BP01-78; BP01-79; BP02; BP02-01B; BP02-02B; BP02-03/01; BP02-05/01; BUCKET; Bucket water sampling; CCGS Amundsen; Core; CORE; CORIBAR; CTD/Rosette; CTD-RO; Davis Strait; DB3.02; DB3.08; DB3.10; DB3.11; DB3.12; DB3.13; DB3.14; DB3.15; DB3.16; DB3.20; DB3.23; DB3.24; DB3.25; DB3.26; DB3.27; DB3.30; DB3.31; DB3.32; DB3.33; DB3.34; DB3.35; DB3.36; DB3.37; DB3.39; DB3.42; DB6.01; DB6.02; DB6.05; DB6.06; DB6.07; DB6.08; DB6.09; DEPTH, water; Dredge; DRG; East Greenland Sea; East Siberian Sea; EGS-1; Event label; FB1.02; FB1.04; FB1.05; FB1.07; FB1.12; FRAM-2014/15_ice_drift; FRAM2014/15-08-06; FRAM2014/15-11-09; FRAM2014/15-13-11; FRAM2014/15-15-13; FRAM2014/15-15-14; FRAM2014/15-15-15; FRAM2014/15-15-16; FRAM2014/15-15-17; FRAM2014/15-15-18; G. O. Sars (2003); GC; GeoB17601-2; GeoB17602-1; GeoB17603-1; GeoB17604-1; GeoB17605-1; GeoB17606-1; GeoB17607-1; GeoB17608-1; GeoB17609-1; GeoB17609-3; GeoB17610-1; GeoB17611-2; GeoB17612-1; GeoB17613-1; GeoB17614-1; GeoB17615-1; GeoB17616-1; GeoB17617-1; GeoB17618-1; GeoB17619-1; GeoB17620-1; GeoB17621-1; GeoB17622-1; GeoB17623-1; GeoB19904-1; GeoB19905-2; GeoB19916-5; GeoB19920-4; GeoB19927-2; GeoB19931-2; GeoB19933-2; GeoB19940-3; GeoB19946-3; GeoB19948-2; GeoB19953-5; GeoB19959-3; GeoB19961-2; GeoB19963-2; GeoB19969-2; GeoB19973-3; GeoB22304-4; GeoB22305-2; GeoB22306-2; GeoB22315-3; GeoB22316-1; GeoB22317-1; GeoB22318-1; GeoB22319-1; GeoB22320-1; GeoB22321-1; GeoB22329-3; GeoB22331-2; GeoB22333-3; GeoB22334-1; GeoB22336-2; GeoB22344-2; GeoB22346-2; GeoB22348-2; GeoB22350-2; GeoB22351-2; GeoB22353-2; GeoB22356-2; GeoB22357-2; GeoB22358-2; GeoB22359-2; Giant box corer; GKG; Gravity corer; Gravity corer (Kiel type); Greenland Sea; GS15-198-36; GS15-198-37; GS15-198-38; GS15-198-39; GS15-198-40; GS15-198-41; GS15-198-42; GS15-198-43; GS15-198-44; GS15-198-45; GS15-198-46; GS15-198-47; GS15-198-48; GS15-198-49; GS15-198-50; GS15-198-51; GS15-198-52; GS15-198-53; GS15-198-54; GS15-198-55; GS15-198-56; GS15-198-58; GS15-198-59; GS15-198-60; GS15-198-61; GS15-198-62; GS15-198-63; GS16-204-19; GS16-204-21; GS16-204-22; GS16-204-23; GS16-204-24; GS2015-198; GS2016-204; HB2.01; HB2.02; HB2.03; HB2.04; HB2.06; HE153; HE153/1239-2; HE153/1241-1; HE153/1251-2; HE153/1254-2; HE153/1255-2; HE153/1261-2; HE153/1262-2; HE153/1263-2; HE153/1265-2; HE153/1269-2; HE153/1270-2; HE153/1273-2; HE153/1286-2; HE153/1287-2; HE153/1288-2; HE153/1289-2; HE153/1290-2; Heincke; Helmer Hanssen; HG_I; HG_II; HG_IV; HG_IX; HH11; HH11-133GC; HH11-134BC; HH11-135GC; HH11-136BC; HH11-137BC; HH11-138GC; HH11-140BC; HH13-19; HH13-21; HH13-23E; HH13-25F; HH2011; HH2013; HUD2008/29; HUD2008/29_14; HUD2008/29_47; HUD2008/29_55; HUD2008/29_66; HUD2013/29; HUD2013/29_51; HUD2013/29_52; HUD2013/29_54; HUD2013/29_68; HUD2013/29_78; HUD2013/29_79; Hudson; Hudson Bay; Hurry Inlet, East Greenland; Iceland Sea; INOPEX; Investigator; James Clark Ross; JR142; JR142-GC10; JR142-GC11; JR142-GC12; JR142-GC13; JR142-GC14; JR142-GC15; JR142-GC17; JR142-GC19; JR142-GC20; JR142-GC21; JR142-GC22; JR142-GC23; JR142-GC4; JR142-GC5; JR142-GC6; JR142-GC7; JR142-GC8; JR142-GC9; JR20060728; JR20080823; JR211; JR211-04GC; JR211-10BC; JR211-12GC; JR211-13GC; JR211-15GC; JR211-26GC; JR211-28GC; JR211-33GC; KAL; Kapitan Dranitsyn; Kara Sea; Kasten corer; KD9523-8; KD9529-12; KD9533-11; KD9541-13; KD9548-13; KD9565-12; KD9568-8; KD9572-1; Kempe Fjord, East Greenland; Labrador Sea; Laptev Sea; LATITUDE; LONGITUDE; Maria S. Merian; MOOR; Mooring; MSM12/2; MSM12/2_642-2; MSM12/2_643-2; MSM12/2_645-3; MSM12/2_646-2; MSM12/2_647-1; MSM12/2_649-4; MSM12/2_650-2; MSM12/2_651-2; MSM12/2_653-3; MSM12/2_654-1; MSM12/2_656-2; MSM12/2-01-02; MSM12/2-02-02; MSM12/2-03-02; MSM12/2-04-02; MSM12/2-05-01; MSM12/2-06-03; MSM12/2-07-01; MSM12/2-08-02; MSM12/2-09-02; MSM12/2-10-01; MSM12/2-12-02; MSM30; MSM30_463-2; MSM30_466-1; MSM30_467-1; MSM30_469-1; MSM30_471-1; MSM30_472-1; MSM30_474-1; MSM30_476-1; MSM30_477-1; MSM30_477-3; MSM30_479-1; MSM30_480-2; MSM30_482-1; MSM30_483-1; MSM30_485-1; MSM30_486-1; MSM30_488-1; MSM30_490-2; MSM30_493-1; MSM30_499-1; MSM30_500-1; MSM30_501-1; MSM30_502-1; MSM30_503-1; MSM31; MSM31_550-5; MSM31_557-2; MSM31_561-2; MSM31_575-3; MSM31_585-4; MSM44; MSM44_330-1; MSM44_331-2; MSM44_342-5; MSM44_346-4; MSM44_353-2; MSM44_357-2; MSM44_359-2; MSM44_366-3; MSM44_372-3; MSM44_374-2; MSM44_379-5; MSM44_385-3; MSM44_387-2; MSM44_389-2; MSM44_395-2; MSM44_399-3; MSM46; MSM46_10-8; MSM46_12-5; MSM46_14-2; MSM46_16-6; MSM46_19-3; MSM46_20-3; MSM46_22-2; MSM46_25-1; MSM46_28-3; MSM46_3-5; MSM46_4-5; MSM46_5-8; MSM46_6-4; MSM46_7-10; MSM66; MSM66/05-2; MSM66/15-3; MSM66/16-1; MSM66/17-1; MSM66/18-1; MSM66/19-1; MSM66/20-1; MSM66/21-1; MSM66/29-3; MSM66/31-2; MSM66/33-3; MSM66/34-1; MSM66/36-2; MSM66/4-4; MSM66/44-2; MSM66/46-2; MSM66/48-2; MSM66/50-2; MSM66/51-2; MSM66/53-2; MSM66/56-2; MSM66/57-2; MSM66/58-2; MSM66/59-2; MSM66/6-2; MSN; MUC; MULT; MultiCorer; Multicorer with television; Multiple investigations; Multiple opening/closing net; Nitrate; North Greenland Sea; North Pacific Ocean; Northwestern Passages; Norwegian Sea; NOW-1; NOW-2; NOW-3; NOW-4; NOW-5; Number; OBS; OBS314; Ocean bottom seismometer; Paamiut; Paamiut2014; Phosphate; Polarstern; Primary production of carbon; PS109; PS109_105-1; PS109_115-2; PS109_125-1; PS109_129-1; PS109_139-1; PS109_19-2; PS109_36-2; PS109_46-2; PS109_76-1; PS109_85-1; PS109_93-2; PS19/040; PS19/045; PS19/078; PS19/080; PS19/082; PS19/102; PS19/116; PS19/119; PS19/126; PS19/132; PS19/134; PS19/136; PS19/143; PS19 EPOS II; PS2111-2; PS2113-1; PS2117-1; PS2119-2; PS2121-1; PS2131-1; PS2142-3; PS2144-3; PS2148-1; PS2149-1; PS2150-
    Type: Dataset
    Format: text/tab-separated-values, 15755 data points
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  • 32
    Publication Date: 2024-05-25
    Keywords: Conductivity, average; Depth, bottom/max; ELEVATION; Heat flow; LATITUDE; LONGITUDE; Method comment; Number; Number of conductivity measurements; Number of temperature data; Sample, optional label/labor no; Temperature gradient
    Type: Dataset
    Format: text/tab-separated-values, 1334 data points
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  • 33
    Publication Date: 2024-05-25
    Keywords: 24; Center for Marine Environmental Sciences; GC; GeoB16421-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 34
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Kopf, Achim J; Asshoff, Kira; Belke-Brea, M; Bergenthal, Markus; Bohrmann, Gerhard; Bräunig, Anja; Düßmann, Ralf; Feseker, Tomas; Fleischmann, Timo; Franke, Philipp; Geprägs, Patrizia; Hammerschmidt, Sebastian; Heesemann, Bernd; Herschelmann, Oliver; Hüpers, Andre; Ikari, Matt J; Kaszemeik, Kai; Kaul, Norbert; Kimura, Toshinori; Kitada, Kazuya; Klar, Steffen; Lange, Matthias; Madison, Melissa; Mai, Anh Hoang; Noorlander, Cornelis; Pape, Thomas; Rehage, Ralf; Reuter, Christian; Rosiak, Uwe; Saffer, Demian M; Schmidt, Werner; Seiter, Christian; Spiesecke, Ulli; Stachowski, Adrian; Ojima, Takanori; Tryon, Michael; Vahlenkamp, Maximilian; Wei, Jiayong; Wiemer, Gauvain; Wintersteller, Paul; Zarrouk, Marcel (2013): Report and preliminary results of R/V SONNE cruise SO222. MEMO: MeBo drilling and in situ Long-term Monitoring in the Nankai Trough accretionary complex, Japan. Leg A: Hong Kong, PR China, 09.06.2012 - Nagoya, Japan, 30.06.2012. Leg B: Nagoya, Japan, 04.07.2012 - Pusan, Korea, 18.07.2012. Berichte aus dem MARUM und dem Fachbereich Geowissenschaften der Universität Bremen, 297, 121 pp, urn:nbn:de:gbv:46-00103543-13
    Publication Date: 2024-05-25
    Description: Temperature gradients and in-situ thermal conductivity, measured with the Bremen heat flow probe. There are 21 channels, spaced 0.26 cm.
    Keywords: Center for Marine Environmental Sciences; gas hydrates; Japan; Kumano Basin; marine heat flow; MARUM; mud volcanism
    Type: Dataset
    Format: application/zip, 16 datasets
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  • 35
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Long, Philip E; Williams, Kenneth H; Davis, James A; Fox, Patricia M; Wilkins, Michael J; Yabusaki, Steven B; Fang, Yilin; Waichler, Scott R; Berman, Elena S F; Gupta, Manish; Chandler, Darrell P; Murray, Chris; Peacock, Aaron D; Giloteaux, Ludovic; Handley, Kim M; Lovley, Derek R; Banfield, Jillian F (2015): Bicarbonate impact on U(VI) bioreduction in a shallow alluvial aquifer. Geochimica et Cosmochimica Acta, 150, 106-124, https://doi.org/10.1016/j.gca.2014.11.013
    Publication Date: 2024-05-25
    Description: Field-scale biostimulation and desorption tracer experiments conducted in a uranium (U) contaminated, shallow alluvial aquifer have provided insight into the coupling of microbiology, biogeochemistry, and hydrogeology that control U mobility in the subsurface. Initial experiments successfully tested the concept that Fe-reducing bacteria such as Geobacter sp. could enzymatically reduce soluble U(VI) to insoluble U(IV) during in situ electron donor amendment (Anderson et al., 2003; Williams et al., 2011). In parallel, in situ desorption tracer tests using bicarbonate amendment demonstrated rate-limited U(VI) desorption (Fox et al., 2012). These results and prior laboratory studies underscored the importance of enzymatic U(VI)-reduction and suggested the ability to combine desorption and bioreduction of U(VI). Here we report the results of a new field experiment in which bicarbonate-promoted uranium desorption and acetate amendment were combined and compared to an acetate amendment-only experiment in the same experimental plot. Results confirm that bicarbonate amendment to alluvial aquifer sediments desorbs U(VI) and increases the abundance of Ca-uranyl-carbonato complexes. At the same time, the rate of acetate-promoted enzymatic U(VI) reduction was greater in the presence of added bicarbonate in spite of the increased dominance of Ca-uranyl-carbonato aqueous complexes. A model-simulated peak rate of U(VI) reduction was ~3.8 times higher during acetate-bicarbonate treatment than under acetate-only conditions. Lack of consistent differences in microbial community structure between acetate-bicarbonate and acetate-only treatments suggest that a significantly higher rate of U(VI) reduction in the bicarbonate-impacted sediment may be due to a higher intrinsic rate of microbial reduction induced by elevated concentrations of the bicarbonate oxyanion. The findings indicate that bicarbonate amendment may be useful in improving the engineered bioremediation of uranium in aquifers.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 36
    Publication Date: 2024-05-25
    Keywords: 28; Center for Marine Environmental Sciences; GC; GeoB16425-1; Gravity corer; MARUM; SO219A/2; Sonne
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    Format: unknown
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  • 37
    Publication Date: 2024-05-25
    Keywords: 29; Center for Marine Environmental Sciences; GC; GeoB16426-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 38
    Publication Date: 2024-05-25
    Keywords: 27; Center for Marine Environmental Sciences; GC; GeoB16423-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 39
    Publication Date: 2024-05-25
    Keywords: 30; Center for Marine Environmental Sciences; GC; GeoB16427-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 40
    Publication Date: 2024-05-25
    Keywords: 33; Center for Marine Environmental Sciences; GC; GeoB16431-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 41
    Publication Date: 2024-05-25
    Keywords: 32; Center for Marine Environmental Sciences; GC; GeoB16429-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 42
    Publication Date: 2024-05-25
    Keywords: 34; Center for Marine Environmental Sciences; GC; GeoB16433-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 43
    Publication Date: 2024-05-25
    Keywords: 35; Center for Marine Environmental Sciences; GC; GeoB16435-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 44
    Publication Date: 2024-05-25
    Keywords: 36; Center for Marine Environmental Sciences; GC; GeoB16437-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 45
    Publication Date: 2024-05-25
    Keywords: 37; Center for Marine Environmental Sciences; GeoB16437-2; MARUM; MUC; MultiCorer; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 46
    Publication Date: 2024-05-25
    Keywords: 39; Center for Marine Environmental Sciences; GC; GeoB16439-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
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  • 47
    Publication Date: 2024-05-25
    Keywords: 41; Center for Marine Environmental Sciences; GC; GeoB16442-1; Gravity corer; MARUM; SO219A/2; Sonne
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    Format: unknown
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  • 48
    Publication Date: 2024-05-25
    Keywords: 43; Center for Marine Environmental Sciences; GC; GeoB16444-1; Gravity corer; MARUM; SO219A/2; Sonne
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    Format: unknown
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  • 49
    Publication Date: 2024-05-25
    Keywords: 46; Center for Marine Environmental Sciences; GC; GeoB16447-1; Gravity corer; MARUM; SO219A/2; Sonne
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  • 50
    Publication Date: 2024-05-25
    Description: Batyhmetry data based on the KONGSBERG Multibeam system EM120 was recorded during the RV SONNE cruise SO219A (Yokohama-Yokohama). The cruise took place between 08.03.2012 and 06.04.2012 in the Northwestern Pacific (Japan Trench) and was divided into two legs. The main research goal of the cruise was the investigation of the sedimentary fingerprint of the region caused by the 2011 Tohoku-Oki earthquake. Therefore the Remotely Operated Vehicle (ROV) QUEST (MARUM) was used to investigate fault zones and normal fault systems, which might co-seismically moved during the earthquake. Additionally slumps, seeps, and other structures , which might be related to the fault systems and therefore to the earth quake, were further explored using the ship-mounded hydroacoustic equipment (Multibeam and Parasound). CI Citation: Paul Wintersteller (seafloor-imaging@marum.de) as responsible party for bathymetry raw data ingest and approval. Description of the data source: During the RV SONNE cruise SO219A the KONGSBERG EM120 multibeam echosounder with a nominal sounding frequency of 12 kHz was utilized. Up to 191 individual beams are formed for each ping. The seafloor is detected using amplitude and phase information for each beam sounding. The beams could covered a swath width of 150° across track, however due to deficient data quality the maximum swath width during the cruise was only up to 120°/130°. For further information consult https://www.km.kongsberg.com/. Responsible person during this cruise / PI: Christian dos Santos Ferreira (cferreira@marum.de) Chief Scientist: Prof. Dr. Gerold Wefer (gwefer@marum.de) CR: https://elib.suub.uni-bremen.de/edocs/00103574-1.pdf CSR: https://www2.bsh.de/aktdat/dod/fahrtergebnis/2012/20120100.htm, https://www2.bsh.de/aktdat/dod/fahrtergebnis/2012/20120101.htm A special thanks goes to the following watchkepper during SO219A: Sato, Takeshi, Kanamatsu, Toshiya ; Macron, Yann; Römer, Miriam; Podszun, Lina
    Keywords: batyhmetry; Center for Marine Environmental Sciences; CT; EM120; File format; File name; File size; hydroacoustic; Japan; Japan Trench; MARUM; ROV; Sedimentology; SO219A; SO219A/1; SO219A/1-track; Sonne; Underway cruise track measurements; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 2720 data points
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  • 51
    Publication Date: 2024-05-25
    Description: Bathymetry data based on multibeam echosounder EM120 was conducted during R/V SONNE cruise SO222 between 09.06.2012 and 30.06.2012 (Leg A) and 04.07.2012 and 18.07.2012 (Leg B) in the Nankai Trough off Japan. The main objective of the cruise was MeBo drilling and long-term monitoring of active mud volcanoes in the northern Kumano Basin, which carry gas hydrates and are overlying an area of high strain. Hydroacoustic work on board aimed at a high spatial coverage of the Kumano Basin and its mud volcanoes. Furthermore, bathymetric mapping revealed additional features in the study area, of which 5 previously unknown mud volcanoes were identified. Additionally, coring with gravity corers and the MeBO drill rig as well as the deployment of CORK observation instruments and an ROV helped to study the activity and products of mud volcanoes in the Kumano Basin. CI Citation: Paul Wintersteller (seafloor-imaging@marum.de) as responsible party for bathymetry raw data ingest and approval. Description of the data source: During the SO222 cruise, the hull-mounted KONGSBERG EM120 multibeam ecosounder (MBES) was utilized for bathymetric mapping as it is able to perform in water depths up to 11,000 m. Two transducer arrays transmit acoustic signals of 11.25 to 12.6 kHz in successive emission-reception cycles. 191 overlapping beams in a 2° (TX) by 2° (RX) footprint form each acoustic ping, resulting in a beam width of maximum 150° across track and 2° along track. For further information on the system, consult https://www.km.kongsberg.com/. The depth of the water column is estimated through the two-way-travel time, beam angle and ray bending due to refraction in the water column by sound speed variations. Combining lateral and center beams secures measurement accuracy practically independent of the beam-pointing angle. During the cruise SO222, the MBES and PARASOUND sub-bottom profiler were continuously acquiring data of about 1500 km track lengths. Two sound velocity profiles (SVP) were calculated based on SEABIRD CTD measurements and applied to the MBES, whereas no SVP was available for the southern Kumano Basin due to strong currents. Responsible person during this cruise / PI: Paul Wintersteller (pwintersteller@marum.de) Chief Scientist: Achim Kopf (a.kopf@marum.de) CR: http://elib.suub.uni-bremen.de/edocs/00103543-1.pdf CSR: https://www2.bsh.de/aktdat/dod/fahrtergebnis/2012/20120280.htm
    Keywords: Bathymetry; Center for Marine Environmental Sciences; CT; EM120; File format; File name; File size; hydroacoustics; Japan; Kumano Basin; MARUM; MeBo; MEMO; Mud volcanoes; Nankai Trough; SO222; SO222-track; Sonne; Underway cruise track measurements; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 640 data points
    Location Call Number Expected Availability
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  • 52
    Publication Date: 2024-05-25
    Keywords: 47; Center for Marine Environmental Sciences; GC; GeoB16449-1; Gravity corer; MARUM; SO219A/2; Sonne
    Type: Dataset
    Format: unknown
    Location Call Number Expected Availability
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  • 53
    Publication Date: 2024-05-25
    Description: Batyhmetry data based on the KONGSBERG Multibeam system EM120 was recorded during the RV SONNE cruise SO219A (Yokohama-Yokohama). The cruise took place between 08.03.2012 and 06.04.2012 in the Northwestern Pacific (Japan Trench) and was divided into two legs. The main research goal of the cruise was the investigation of the sedimentary fingerprint of the region caused by the 2011 Tohoku-Oki earthquake. Therefore the Remotely Operated Vehicle (ROV) QUEST (MARUM) was used to investigate fault zones and normal fault systems, which might co-seismically moved during the earthquake. Additionally slumps, seeps, and other structures , which might be related to the fault systems and therefore to the earth quake, were further explored using the ship-mounded hydroacoustic equipment (Multibeam and Parasound). CI Citation: Paul Wintersteller (seafloor-imaging@marum.de) as responsible party for bathymetry raw data ingest and approval. Description of the data source: During the RV SONNE cruise SO219A the KONGSBERG EM120 multibeam echosounder with a nominal sounding frequency of 12 kHz was utilized. Up to 191 individual beams are formed for each ping while the seafloor is detected using amplitude and phase information for each beam sounding. The beams could have covered a swath width of 150° across track, however due to deficient data quality the maximum swath width during the cruise was only up to 120°/130°. For further information consult https://www.km.kongsberg.com/. Responsible person during this cruise / PI: Christian dos Santos Ferreira (cferreira@marum.de) Description of data processing: The open source software MB-System suite (Caress, D.W., and D.N. Chayes, MB-System Version 5.5, open source software distributed from the MBARI and L-DEO web sites, 2000-2012.) was utilized for this purpose. Roll corrections have been applied to the SO219A bathymetry data using a rollbias of -0.11. Using Mbeditviz, artefacts were cleaned manually. NetCDF (GMT) grids of the edited data as well as statistics were created with mbgrid. The published bathymetric grid of the cruise SO219A has a resolution of 100 m. No total propagated uncertainty (TPU) has been calculated to gather vertical or horizontal accuracy. A higher resolution is, at least partly, achievable. The grid extended with _num represents a raster dataset with the statistical number of beams/depths taken into account to create the depth of the cell. The extended _sd -grid contains the standard deviation for each cell. All grids produced are retrievable through the PANGAEA database (www.pangaea.de). Postprocessing and products were conducted by the Seafloor-Imaging & Mapping group of MARUM/FB5, responsible person: Paul Wintersteller (seafloor-imaging@marum.de). Projection: Geographic Coordinate System / WGS84 Chief Scientist: Prof. Dr. Gerold Wefer (gwefer@marum.de) CR: https://elib.suub.uni-bremen.de/edocs/00103574-1.pdf CSR: https://www2.bsh.de/aktdat/dod/fahrtergebnis/2012/20120100.htm, https://www2.bsh.de/aktdat/dod/fahrtergebnis/2012/20120101.htm Raw data: beim Upload der grids: Link zu den Rohdaten (wenn schon vorhanden) A special thanks goes to the following watchkepper during SO219A: Sato, Takeshi, Kanamatsu, Toshiya
    Keywords: Bathymetry; Center for Marine Environmental Sciences; CT; EM120; File format; File name; File size; hydroacoustic; Japan; Japan Trench; MARUM; ROV; Sedimentology; SO219A; SO219A/1; SO219A/1-track; Sonne; Underway cruise track measurements; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 12 data points
    Location Call Number Expected Availability
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  • 54
    Publication Date: 2024-05-25
    Description: Bathymetry data based on multibeam echosounder (MBES) KONGSBERG EM120 was conducted during R/V SONNE cruise SO222 between 09.06.2012 and 30.06.2012 (Leg A) and 04.07.2012 and 18.07.2012 (Leg B) in the Nankai Trough off Japan. The main objective of the cruise was MeBo drilling and long-term monitoring of active mud volcanoes in the northern Kumano Basin, which carry gas hydrates and are overlying an area of high strain. Hydroacoustic work on board aimed at a high spatial coverage of the Kumano Basin and its mud volcanoes. Furthermore, bathymetric mapping revealed additional features in the study area, of which five previously unknown mud volcanoes were identified. Additionally, coring with gravity corers and the MARUM-MeBo70 drill rig as well as the deployment of CORK observation instruments and the remotely operated vehicle (ROV) MARUM-QUEST helped to study the activity and products of mud volcanoes in the Kumano Basin. CI Citation: Paul Wintersteller (seafloor-imaging@marum.de) as responsible party for bathymetry raw data ingest and approval. Description of the data source: During the SO222 cruise, the hull-mounted KONGSBERG EM120 multibeam ecosounder (MBES) was utilized for bathymetric mapping as it is able to perform in water depths up to 11,000 m. Two transducer arrays transmit acoustic signals of 11.25 to 12.6 kHz in successive emission-reception cycles. 191 overlapping beams in a 2° (TX) by 2° (RX) footprint form each acoustic ping, resulting in a beam width of maximum 150° across track and 2° along track. For further information on the system, consult https://www.km.kongsberg.com/. The depth of the water column is estimated through the two-way-travel time, beam angle and ray bending due to refraction in the water column by sound speed variations. Combining lateral and center beams secures measurement accuracy practically independent of the beam-pointing angle. During the cruise SO222, the MBES and PARASOUND sub-bottom profiler were continuously acquiring data of about 1500 km track lengths. Two sound velocity profiles (SVP) were calculated based on SEABIRD CTD measurements and applied to the MBES, whereas no SVP was available for the southern Kumano Basin due to strong currents. Responsible person during this cruise / PI: Paul Wintersteller (pwintersteller@marum.de) Description of data processing: Postprocessing and products were conducted by the Seafloor-Imaging & Mapping group of MARUM/FB5, responsible person: Paul Wintersteller (seafloor-imaging@marum.de). The open source software MB-System suite (Caress, D.W., and D.N. Chayes, MB-System Version 5.5, open source software distributed from the MBARI and L-DEO web sites, 2000-2012.) was utilized for this purpose. CTD measurements were carried out to conduct sound velocity profiles (SVP), which were applied during the acquisition of the hydroacoustic data. A roll correction was applied to the SO222data. Tide, pitch and heave corrections were not applied. Using Mbeditviz, artefacts were cleaned manually. NetCDF (GMT) grids of the edited data as well as statistics were created with mbgrid. The published bathymetric grids of the cruise SO222 have a resolution of 30 m. No total propagated uncertainty (TPU) has been calculated to gather vertical or horizontal accuracy. A higher resolution is, at least partly, achievable. The grid extended with _num represents a raster dataset with the statistical number of beams/depths taken into account to create the depth of the cell. The extended _sd -grid contains the standard deviation for each cell. All grids produced are retrievable through the PANGAEA database (www.pangaea.de). Projection: Geographic Coordinate System / WGS84 Chief Scientist: Achim Kopf (a.kopf@marum.de) CR: http://elib.suub.uni-bremen.de/edocs/00103543-1.pdf CSR: https://www2.bsh.de/aktdat/dod/fahrtergebnis/2012/20120280.htm Raw data: https://doi.pangaea.de/10.1594/PANGAEA.901244
    Keywords: Bathymetry; Caribbean; Center for Marine Environmental Sciences; Cold water corals; CT; EM120; File format; File name; File size; Florida Strait; hydroacoustics; Japan; MARUM; MEMO; MSM20/4; SO222; SO222-track; Sonne; Underway cruise track measurements; Uniform resource locator/link to file; WACOM
    Type: Dataset
    Format: text/tab-separated-values, 16 data points
    Location Call Number Expected Availability
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  • 55
    Publication Date: 2024-05-25
    Description: Shallow sediment cores were collected with gravity cores from sites in the Japan Trench during cruise SO219A with R/V SONNE. In order to determine ex-situ concentrations of methane dissolved in pore water, sediment samples were taken from the sediment cores with cut-off syringes and transferred into glass vials. Methane concentrations in the headspace gas were used to calculate concentrations of methane dissolved in pore water (uncorrected for sediment porosity and Bunsen coefficient).
    Keywords: 27; Center for Marine Environmental Sciences; DEPTH, sediment/rock; GC; GeoB16423-1; Gravity corer; MARUM; Methane, porewater; SO219A/2; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
    Location Call Number Expected Availability
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  • 56
    Publication Date: 2024-05-25
    Description: The Deep-sea Sponge Microbiome Project is a large-scale study, integrating 16S amplicon sequencing data of seawater, sediment, and sponges, with a large set of ecological and physical metadata. The present dataset includes NCBI-accession numbers, sample collection details, and diverse measurements, adding up to 50 entries for each of the 1546 covered samples.
    Keywords: Accession number, genetics; Ada Rebikoff; Agassiz Trawl; AGT; Alkalinity, total; Anchor dredge; Angeles Alvarino; ANT-XXXI/2 FROSN; Arctic Ocean; Area/locality; ARK-XXVII/2; ARK-XXX/3; ARK-XXXI/2; Azores2018; Bay of Biscay; BC; BEAM; Beam trawl; Bleiksdjupet; Bottle, Niskin; Bottom trawl; Box corer; BT; Campaign; Carbon, inorganic, particulate; Carbon, organic, dissolved; Carbon, organic, particulate; Carbon dioxide, total; Celtic Voyager; Class; Conductivity; CTD; CTD/Rosette; CTD1; CTD10; CTD11; CTD12; CTD13; CTD14; CTD15; CTD2; CTD3; CTD4; CTD5; CTD6; CTD7; CTD8; CTD9; CTD-RO; CV13012; CV13012_A; DATE/TIME; Deep-sea; Deep-sea Sponge Grounds Ecosystems of the North Atlantic; Density, sigma, in situ; DEPTH, water; derived from MODIS remote sensing data; Distance; Dive_041; Dive_042; Dive_043; Dive_044; Dive_045; Dive_046; DIVER; DR10; DR15; DR4; DR7; DR9; Dredge, chain bag; Dredge, rock; Dredge, triangle; DRG_A; DRG_C; DRG_R; Duse Bay; Event label; extracted from GLODAPv2.2020; extracted from the World Ocean Atlas 2018 (WOA18); Family; G. O. Sars (2003); Gear; Genus; Geological feature; Grab; GRAB; GS16A-202; GS2016109A; GS2016109A-01-CTD-01; GS2016109A-06-ROV-01; GS2016109A-09-BC-01; GS2016109A-10-BC-02; GS2016109A-14-CTD-02; GS2016109A-16-CTD-04; GS2016109A-18-CTD-06; GS2016109A-21-BC-05; GS2016109A-24-CTD-07; GS2016109A-26-CTD-09; GS2016109A-27-CTD-10; GS2016109A-28-CTD-11; GS2016109A-32-ROV-05; GS2016109A-33-AGT-01; GS2017110; GS2017110-02-ROV-02; GS2017110-03-CTD-01; GS2017110-04-CTD-02; GS2017110-05-ROV-03; GS2017110-06-ROV-04; GS2017110-08-ROV-05; GS2017110-09-ROV-6; GS2017110-15-CTD-05; GS2017110-16-ROV8; GS2017110-19-ROV10; GS2017110-22-BC-02; GS2017110-23-ROV12; GS2017110-26-CTD-08; GS2017110-28-CTD-10; GS2017110-30-CTD-12; GS2017110-34-ROV-15; GS2017110-40-ROV-18; GS2017110-41-ROV-19; GS2017110-42-CTD-16; GS2017110-44-BC-1; GS2017110-45-BC-2; GS2017110-46-BC-3; GS2017110-47-BC-4; GS2017110-50-CTD-19; GS2017110-54-CTD-20; GS2017110-57-AGT-01; GS2017110-59-CTD-21; GS2017110-60-BC-5; GS2017110-61-BC-6; GS2017110-62-BC-7; GS2017110-63-ROV-24; GS2017110-67-CTD-23; GS2017110-68-ROV-25; GS2017110-71-BC-8; GS2017110-72-BC-9; GS2017110-73-BC-10; GS2017110-74-ROV-26; GS2018108; GS2018108-01-ROV-01; GS2018108-02-CTD-01; GS2018108-03-ROV-02; GS2018108-04-ROV-03; GS2018108-05-CTD-02; GS2018108-07-ROV-05; GS2018108-08-ROV-06; GS2018108-12-CTD-03; GS2018108-13-CTD-04; GS2018108-14-CTD-05; GS2018108-17-AGT-01; GS2018108-19-ROV-12; GS2018108-22-CTD-07; GS2018108-23-ROV-15; GS2018108-25-ROV-17; GS2018108-29-CTD-09; GS2018108-30-CTD-10; GS2018108-31-CTD-11; GS2018108-34-ROV-22; GS2018108-37-CTD-12; GS2018108-39-ROV-26; GS2018108-43-ROV-30; GS2018108-44-ROV-31; GS2018108-46-ROV-33; GS2018108-48-CTD-13; GS2018108-55-CTD-14; GS2018108-58-ROV-43; GS2018108-62-CTD-15; GS2018108-63-ROV-47; GS2018108-64-ROV-48; GS2018108-66-CTD-16; GS2018108-70-ROV-50; GS2018108-77-CTD-24; GS2018108-78-ROV-52; GS2018108-79-ROV-53; Gulf of Bothnia, Baltic sea; H045_A; Hans Brattström; HB2016952; HB2016952_2; HB2016952_5; HB2016952_6; HB2016952_7; HB2016952_8; HB27102017_A; HB27102017_B; HB27102017_C; HB27102017a; HB27102017b; HUD16/19_010; HUD16/19_012; HUD16/19_013; HUD16/19_018; HUD16/19_020; HUD16/19_383; HUD16/19_387; HUD16/19_391; HUD16/19_392; HUD16/19_395; HUD2016019; Hudson; Identification; James Clark Ross; JR17003A; JR17003A_12; JR17003A_19; JR17003A_42; JR17003A_44; JR17003A_46-1; KB2017610; KB2017610_CTD7; KB2017610_KB-28; KB2017610_KB-32; KB2017610_KB-60; KB2017610_KB-61; KB2017610_ROV9; Korsfjord; Kristine Bonnevie; LATITUDE; LONGITUDE; LULA0718_Dive1; LULA0718_Dive2; LULA0718_Dive3; Malangsgrunnen; Maria S. Merian; Martha L. Black; meta-analysis; microbes; MLB2017001; MLB2017001_004; MLB2017001_005; MLB2017001_006; MLB2017001_015; MLB2017001_017; MLB2017001_020; MOOR; Mooring; MSM86; MSM86_006; MSM86_008; MSM86_009; MSM86_010; MSM86_012; MSM86_013; MSM86_015; MSM86_016; MSM86_019; MSM86_021; MSM86_022; MSM86_027; MSM86_028; MSM86_031; MSM86_032; MSM86_034; MSM86_035; MSM86_036; MSM86_038; MSM86_040; MSM86_041; MSM86_052; MSM86_054; MSM86_061; MSM86_062; MSM86_063; MSM86_067; MSM86_080; MSM86_081; MSM86_083; MSM86_086; MSM86_088; MSM86_090; MSM86_091; MSM86_094; MSM86_101; MSM86_106; Multicorer with television; NIS; Nitrate; Nitrogen, total dissolved; Nitrogen/Phosphorus ratio; North Greenland Sea; ocean; Ocean; Order; OT; OTNMoor_275; Otter trawl; Oxygen, apparent utilization; Oxygen, dissolved; Oxygen saturation; PAA2014007; PAA2014007_003; PAA2014007_056; PAA2014007_068; PAA2014007_070; PAA2014007_078; PAA2014007_079; PAA2014007_088; PAA2014007_110; PAA2014007_120; PAA2014007_123; PAA2014007_124; PAA2014007_125; PAA2014007_131; PAA2014007_133; PAA2014007_136; Paamiut; pH; Phosphate; Phylum; Polarstern; Pori Bac NewZ; Pressure, water; Prince Gustav Channel; Profile; Project; PS101; PS101/088-1; PS101/092-1; PS101/093-1; PS101/094-1; PS101/123-1; PS101/154-1; PS101/155-1; PS101/170-1; PS101/172-1; PS101/193-1; PS101/194-1; PS101/196-1; PS101/197-1; PS101/198-1; PS101/200-1; PS101/208-1; PS101/216-1; PS107; PS107_2-1; PS107_33-1; PS107_47-1; PS107_6-3; PS80; PS80/176-9; PS80/192-1; PS96; PS96/006-1; PS96/009-3; PS96/009-4; Realm; Remote operated platform for oceanography; Remote operated vehicle; ROPOS; ROPOS 2028; ROPOS 2029; ROPOS 2030; ROPOS 2034; ROV; Salinity; Sample type; Sampling by diver; Schultz Bank; Scotia; Scotia_0915S; Scotia_0915S_A; Scotia_0915S_B; Scotia_0915S_C; Scotia_0915S_D; Sea surface chlorophyll a; seawater; sediment; Silicate; Silicon/Phosphorus ratio; SO254; SO254_10-1; SO254_1-1; SO254_14-1; SO254_18-1; SO254_2-1; SO254_22-1; SO254_23-1; SO254_33-1; SO254_34-1; SO254_36-1; SO254_69-1; SO254_76-1; SO254_77-1; SO254_78-1; SO254_79-1; SO254_8-1; SO254_81-1; SO254_84-1; SO254_85-1; SO254_diver; Sognefjord; Sonne_2; South Atlantic Ocean; South Pacific Ocean; Species; sponge; SponGES; SponGES_0617; SPONGES_0617_04-DR4; SPONGES_0617_06-BT2; SPONGES_0617_07-CTD1; SPONGES_0617_09-DR5; SPONGES_0617_10-DR6; SPONGES_0617_12-CTD2; SPONGES_0617_13-CTD3; SPONGES_0617_15-DR7; SPONGES_0617_18-CTD4; SPONGES_0617_19-CTD5; SPONGES_0617_20-BT3; SPONGES_0617_23-DR9; SPONGES_0617_24-CTD6; SPONGES_0617_26-BT4; SPONGES_0617_27-CTD7; SPONGES_0617_28-DR10; SPONGES_0617_29-CTD8; SPONGES_0617_37-DR11; SPONGES_0617_38-DR12; SPONGES_0617_40-CTD9; SPONGES_0617_41-BT5; SPONGES_0617_42-CTD10; SPONGES_0617_43-BC1; SPONGES_0617_45-BC2; SPONGES_0617_46-CTD11; SPONGES_0617_47-BT6; SPONGES_0617_48-DR14; SPONGES_0617_49-CTD12; SPONGES_0617_50-BT7; SPONGES_0617_52-BT9; SPONGES_0617_53-BC3M1; SPONGES_0617_54-BT10; SPONGES_0617_55-CTD13; SPONGES_0617_56-BT11; SPONGES_0617_57-BT12; SPONGES_0617_58-CTD14; SPONGES_0617_59-BC4M1; SPONGES_0617_60-DR15; SPONGES_0617_61-CTD15; SPONGES_0617_63-DR16; Station label; Stjernsund; SUB; Submersible; Sula reef; TAD; Television-Grab; Temperature, water; Tromsoflaket East; Tromsøflaket; TVG; TVMUC; Uniform resource locator/link to reference; Vesteris; Water bodies; Weddell Sea; Zone
    Type: Dataset
    Format: text/tab-separated-values, 54242 data points
    Location Call Number Expected Availability
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  • 57
    Publication Date: 2024-05-25
    Description: This dataset contains occurrence records (i.e., species name, latitude, longitude, depth (where available), and metadata) for six species of the demosponge genus Geodia Lamarck, 1815, belonging to the Geodiidae family: Geodia atlantica (Stephens, 1915); Geodia barretti Bowerbank, 1858; Geodia macandrewii Bowerbank, 1858; Geodia phlegraei (Sollas, 1880); Geodia hentscheli Cárdenas et al. 2010; and Geodia parva Hansen, 1885. The records fall in the North Atlantic and Arctic Oceans, and are used/described in the linked article by Roberts et al. (2021). Note that the dataset provided has not been subjected to any of the filtering stages applied in that manuscript for the purposes of various novel biogeographical analyses (i.e., it is original and complete), and the taxonomic identifications have been rigorously checked (as described therein). Definitions of institution abbreviations used in the 'Museum Specimen / Picture Information' column of the dataset spreadsheet have been provided in an accompanying table (see Comment field below). Where records are derived from earlier literature sources, full references for citations given in the 'Campaign / Source' column (and further general information on many of the records) may be found in the articles by Cárdenas et al. (2010; 2013) and Cárdenas & Rapp (2015). An earlier version of this dataset may be accessed at the DRYAD repository: Cárdenas P, Rapp HT, Klitgaard AB, Best M, Thollesson M, Tendal OS (2013), Data from: Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region, Dryad, Dataset, doi:10.5061/dryad.td8sb
    Keywords: 87PA0028; 87PA0067; 87PA0078; 92PA0160002; 92PA0160005; 92PA0160014; 92PA0160028; 92PA0160050; 92PA0160052; 94PA0090001; 94PA0090002; 94PA0090009; 94PA0090010; 94PA0090019; 94PA0090020; 94PA0090026; 94PA0090039; 94PA0090041; 94PA0090043; 94PA0090045; 94PA0090049; 94PA0090062; Agassiz Trawl; AGT; Arctic Ocean; ARK-VII/2; ARK-XXII/1a; Barents Sea; BEAM; Beam trawl; BIODEEP2007_Dredge2; BIODEEP2007_ROV10; BIODEEP2007_ROV9; BIOFAR_St117; BIOFAR_St119; BIOFAR_St120; BIOFAR_St122; BIOFAR_St234; BIOFAR_St279; BIOFAR_St287; BIOFAR_St297; BIOFAR_St298; BIOFAR_St375; BIOFAR_St379; BIOFAR_St389; BIOFAR_St43; BIOFAR_St451; BIOFAR_St452; BIOFAR_St486; BIOFAR_St487; BIOFAR_St498; BIOFAR_St526; BIOFAR_St530; BIOFAR_St531; BIOFAR_St535; BIOFAR_St540; BIOFAR_St550; BIOFAR_St69; BIOFAR_St734; BIOFAR_St756; BIOFAR_St89; BIOFAR_St901; BIOICE_St2022; BIOICE_St2023; BIOICE_St2218; BIOICE_St2292; BIOICE_St2293; BIOICE_St2368; BIOICE_St2374; BIOICE_St2499; BIOICE_St2501; BIOICE_St2516; BIOICE_St2518; BIOICE_St2700; BIOICE_St2728; BIOICE_St2747; BIOICE_St2756; BIOICE_St2768; BIOICE_St2769; BIOICE_St2923; BIOICE_St2926; BIOICE_St2928; BIOICE_St3227; BIOICE_St3659; BIOICE_St3661; BIOSKAG2006_St20; BIOSYS2006_DR182; BIOSYS2006_VG20-1; Blacker1957_11; Blacker1957_130; Blacker1957_131; Blacker1957_14; Blacker1957_16; Blacker1957_164; Blacker1957_165; Blacker1957_168; Blacker1957_20; Blacker1957_21; Blacker1957_22; Blacker1957_24; Blacker1957_25; Blacker1957_27; Blacker1957_28; Blacker1957_33; Blacker1957_35; Blacker1957_36; Blacker1957_44; Blacker1957_45; Blacker1957_46; Blacker1957_53; Blacker1957_55; Blacker1957_56; Blacker1957_60; Blacker1957_61; Blacker1957_62; Blacker1957_68; Blacker1957_75; Blacker1957_8; Blacker1957_80; Blacker1957_81; Blacker1957_84; Blacker1957_9; Blacker1957_94; BMT19; Boury-Esnaultetal1994_CP62; Boury-Esnaultetal1994_CP63; Boury-Esnaultetal1994_CP92; Boury-Esnaultetal1994_CP98; Bowerbank1872a_Vikna; Bowerbank1872aPlateXI_Vikna; Brattholmen_St230407; Breitfuss1930_St1237; Breitfuss1930_St1347; Breitfuss1930_St1385; Burton1934_St548; Burton1959_EIceland; Burton1959_SEIceland; Campaign; CD80_St178; CD80_St18; CD80_St91; CE13008; CE13008_ROV32; CE2008-11_M11GHaul22; CE2008-11_M11GHaul23; Celtic Explorer; Celtic Sea; CENTOBBiogasII_DS33; CGB2011_11c-16-DR01; CGB2011_11c-19-ROV05; CGB2011_11c-30-DR05; CGB2011_11c-31-DR06; Comment; CorSeaCan_B12_CG_ACH_P01_20100809; CorSeaCan_B13_MOI-ACH-P06; CV13012_51; Dana_St6001; Davis Strait; Deep-sea Sponge Grounds Ecosystems of the North Atlantic; Depth, bottom/max; Depth, top/min; DEPTH, water; Dyrelivihavet2008_SandsfjordRogaland; E17044_SP17E44001; EBS; EcosystemBarentsSea2007_St2562; Epibenthic sledge; Event label; FRVScotia2012_S12_469; FRVScotia2012_S12/469; FRVScotia2012_S12-469; G. O. Sars (2003); Giant box corer; GKG; Greenland Sea; GS06/112; GS112_BMT19; GS14; GS14-AGT03; GS14-AGT07; GS14-DR02; GS14-DR09; GS14-DR12; H2DEEP2008_ROV5; HakonMosby_St237; HakonMosby_St242; HakonMosby_St245; HakonMosby_St86072701; HakonMosby_St93060602; HakonMosby_St93060612; HakonMosby_St93060613; HakonMosby_St93061106; Hentschel1929_St40; Hentschel1929_St41; Hentschel1929_St42; Howelletal2010_WSC11; Howelletal2010_WSCE10B; Howelletal2010_WSCE3; Howelletal2010_WSCE4; HUD2007-025_DiveR1059; HUD2010-029; HUD2010-029_R1335; HUD2010-029_R1336-07; HUD2010-029_R1339-10; HUD2010-029_R1340-12; HUD2010-029_R1340-4; HUD2010-029_R1341-18; HUD2013/29; HUD2013-029_DS1-I; Hudson; Iceland Sea; Identification; IngolfExpdt_St1; IngolfExpdt_St125; IngolfExpdt_St21; IngolfExpdt_St78; IngolfExpdt_St90; IngolfExpdt_St92; JAGO; Kara Sea; Kingstonetal1979_LabradorCoast; Koltun1964_St1; Koltun1964_St10; Koltun1964_St11; Koltun1964_St26; Koltun1964_St46; Koltun1964_St7; Koltun1964_St8; Koltun1964_St9; Koltun1966_NofFranzJosephLand; Koltun1966_NofKaraSea; Koltun1966_NWofLaptevSea; Labrador Sea; Langenuen_SteinnesetSt31; Laptev Sea; LATITUDE; LONGITUDE; Lundbeck1909_Angmagsalik; Lynch_St1971; Lynch_St1972; Lynch_St1973; Lynch_St721008; Lysefjord_Uksen; M85/3; M85/3_1123; M85/3_1132; M85/3_1136; M85/3_1219; M85/3_1223; MA0200057_St90; MagnusHeinason_St150990; MAR310_St1; Mareano_StR228-12; Mareano_StR262VL282; Mareano_StR828; Mareano_StR863; Mareano2009_StR469VL491; Mareano2011_StR729VL756; Mareano2011_StR731VL759; Mareano2011_StR744VL772; Mareano2011_StR758VL786; MAR-Eco2004_St50-373; MAR-Eco2004_St70_385; MAR-Eco2004_St70-385; MAR-Eco2004_St72-386; MedSeaCan_B7_MG_PO2_20090523; MedSeaCan_B7_PA_ACH_P02_20090519; Meteor (1986); More2005_St46; MULT; Multiple investigations; NEREIDA0609_BC89; NEREIDA0710_BC237; Nereida2009-2010_BC04; Nereida2009-2010_DR04-001; Nereida2009-2010_DR07-025; Nereida2009-2010_DR10; Nereida2009-2010_DR12; Nereida2009-2010_DR18; Nereida2009-2010_DR19; Nereida2009-2010_DR20; Nereida2009-2010_DR22; Nereida2009-2010_DR23; Nereida2009-2010_DR24; Nereida2009-2010_DR3; Nereida2009-2010_DR32; Nereida2009-2010_DR38; Nereida2009-2010_DR4; Nereida2009-2010_DR6; Nereida2009-2010_DR64; Nereida2009-2010_DR66; Nereida2009-2010_DR7; Nereida2009-2010_DR70; Nereida2009-2010_DR70_BOTTOM; Nereida2009-2010_DR74; Nereida2009-2010_DR74_BOTTOM; North Greenland Sea; North Sea; Norwegian Sea; PA2010-009_Set075; PA2010-009_Set104; PA2010-009_Set105; PA2010-009_Set108; PA2010-009_Set109; PA2010-009_Set111; PA2010-009_Set113; PA2010-009_Set114; PA2010-009_Set115; PA2010-009_Set116; PA2010-009_Set126; PA2010-009_Set141; PA2010-009_Set155; PA2010-009_Set156; PA2010-009_Set157; PA2010-009_Set159; PA2010-009_Set160; PA2010-009_Set161; PA2010-009_Set162; PA2010-009_Set163; PA2010-009_Set164; PA2010-009_Set167; PA2010-009_Set168; PAA2011007; PAA2011007_127_39; PAA2011007_225_114; PAA2011007_255_126; PAA2011007_262_128; PAA2011007_533_23; PAA2011007_634_139; PAA2013008; PAA2013008_157_44; PAA2013008_169_46; PAA2013008_174_47; PAA2013008_176_48; PAA2013008_177_50; PAA2013008_302_141; PAA2013008_305_142; PAA2013008_31_10; PAA2014007; PAA2014007_278_125; PAA2014007_286_127; PAA2014007_321_136; PAA2014007_514_152; PAA2015007; PAA2015007_126_32; PAA2015007_289_60; PAA2015007_299_62; PAA2015007_303_64; Paamiut; Polarstern; PS17; PS17/223; PS70; PS70/002-2; PS70/006-1; PS70/014-4; PS70/015-1; PS70/016-1; PS70/027-1; PS70/040-4; RVMichaelSars_St102; RVMichaelSars_St76; RVMichaelSars_St85; S10176_SP10176001; S11073_SP11073001; S11471_SP11471001; S12135_SP12135001; S12444_SP12444001; S12446_SP12446001; S12447; S15A13; S16185_SP16185001; S16379_SP16379003; S16A03_SP16A03017; S16A03_SP16A03029; S16A03_SP16A03039; S16A03_SP16A03041; S18A02; S18A03; Scotland Sea; ShinkaiMaru_St004; ShinkaiMaru_St109; ShinkaiMaru_St110; ShinkaiMaru_St15; ShinkaiMaru_St18; ShinkaiMaru_St1976; ShinkaiMaru_St21; ShinkaiMaru_St26; ShinkaiMaru_St29; ShinkaiMaru_St3; ShinkaiMaru_St32; ShinkaiMaru_St43; ShinkaiMaru_St50; ShinkaiMaru_St63; ShinkaiMaru_St70; ShinkaiMaru_St79; ShinkaiMaru_St9; ShinkaiMaru1987_St104; ShinkaiMaru1987_St67; Skagerrak; South Atlantic Ocean; Species; SponGES; St89SI0240086; Station label; Submersible JAGO; SwedishArcticExp1871_St37; T0406066; T8903301; T8905093; T8905125; T8905127; T8905185; T9405259; T9405264; T9405276; T9405305; T9405315; T9405317; T9406031; T9406032; T9406036; T9406066; ThalassaZ_Z407; ThalassaZ_Z408; Traena Deep; Trollholmflua; Tromso_Haugbernes; Western Basin; WH_St569; WH47566; WH47572; ZoolPolarExp1900_St30
    Type: Dataset
    Format: text/tab-separated-values, 2307 data points
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  • 58
    Publication Date: 2024-05-25
    Description: Shallow sediment cores were collected with gravity cores from sites in the Japan Trench during cruise SO219A with R/V SONNE. In order to determine ex-situ concentrations of methane dissolved in pore water, sediment samples were taken from the sediment cores with cut-off syringes and transferred into glass vials. Methane concentrations in the headspace gas were used to calculate concentrations of methane dissolved in pore water (uncorrected for sediment porosity and Bunsen coefficient).
    Keywords: 47; Center for Marine Environmental Sciences; DEPTH, sediment/rock; GC; GeoB16449-1; Gravity corer; MARUM; Methane, porewater; SO219A/2; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 5 data points
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  • 59
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    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16794-1; GeoB16794-2; GeoB16794-3; GeoB16794-4; GeoB16794-5; GeoB16794-7; H1225P01; H1225P02; H1225P03; H1225P04; H1225P05; H1225P07; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 638 data points
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  • 60
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    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; gas hydrates; GeoB16778-1; H1220P02; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 113 data points
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  • 61
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    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; GeoB16724-1; GeoB16724-2; GeoB16724-4; GeoB16724-5; GeoB16724-6; GeoB16724-7; GeoB16724-8; H1212P01; H1212P02; H1212P04; H1212P05; H1212P06; H1212P07; H1212P08; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; MARUM; MEMO; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 763 data points
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  • 62
    Publication Date: 2024-05-25
    Keywords: Airborne Radar; ANT_2005/06; Antarctica; ANTSYO; EMR_transect_2005-2006; ice thickness; Ice thickness; Ice Thickness Radar (EMR); LATITUDE; LONGITUDE; POLAR 2
    Type: Dataset
    Format: text/tab-separated-values, 51644 data points
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  • 63
    Publication Date: 2024-05-25
    Description: Collection data of the marine sponge specimens studied; three species: Geodia barretti, Stryphnus fortis and Weberella bursa. Collection data includes locality, depth, salinity, bottom temperature, gear, date, cruise and station# and a collection number for every specimen (DFO Canada collections). NCBI SRA# of the 16S gene amplicon sequencing of their microbiome. Numbers of the UPLC-HRMS runs (four different modes for every specimen: HILIC+, HILIC-, RP+ and RP-). The raw UPLC-HRMS datasets are deposited in the repository metabolights (www.ebi.ac.uk/metabolights/MTBLS1388). QC runs are Quality Control runs for the UPLC-HRMS experiment (also deposited in metabolights).
    Keywords: Accession number, genetics; Code; Cruise/expedition; DATE/TIME; Deep-sea; Deep-sea Sponge Grounds Ecosystems of the North Atlantic; DEPTH, water; Event label; Gear; Geodia barretti; Identification; LATITUDE; LONGITUDE; marine sponges; metabolomics; microbiome; PAA2011007; PAA2011007_Gb10; PAA2011007_Gb15; PAA2011007_Gb20; PAA2011007_Gb21; PAA2011007_Gb4; PAA2011007_Gb6; PAA2011007_Sf11; PAA2011007_Sf14; PAA2011007_Sf4; PAA2011007_Sf7; PAA2011007_Wb10; PAA2011007_Wb12; PAA2011007_Wb14; PAA2011007_Wb15; PAA2011007_Wb2; PAA2011007_Wb5; PAA2011007_Wb9; PAA2013008; PAA2013008_Gb1; PAA2013008_Gb12; PAA2013008_Gb14; PAA2013008_Gb18; PAA2013008_Gb19; PAA2013008_Gb2; PAA2013008_Gb8; PAA2013008_Gb9; PAA2013008_Sf10; PAA2013008_Sf13; PAA2013008_Sf2; PAA2013008_Sf6; PAA2013008_Wb1; PAA2013008_Wb11; PAA2013008_Wb16; PAA2013008_Wb17; PAA2013008_Wb4; PAA2013008_Wb8; PAA2014007; PAA2014007_Gb13; PAA2014007_Gb3; PAA2014007_Gb5; PAA2014007_Sf1; PAA2014007_Sf12; PAA2014007_Sf15; PAA2014007_Sf3; PAA2014007_Sf5; PAA2014007_Wb13; PAA2014007_Wb3; PAA2014007_Wb6; PAA2014007_Wb7; PAA2015007; PAA2015007_Gb11; PAA2015007_Gb17; PAA2015007_Gb7; PAA2015007_Sf8; PAA2015007_Sf9; Paamiut; Salinity; Set; Species; SponGES; Stryphnus fortis; Temperature, water; Weberella bursa; Year of observation
    Type: Dataset
    Format: text/tab-separated-values, 739 data points
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  • 64
    Publication Date: 2024-05-25
    Description: Shallow sediment cores were collected with gravity cores from sites in the Japan Trench during cruise SO219A with R/V SONNE. In order to determine ex-situ concentrations of methane dissolved in pore water, sediment samples were taken from the sediment cores with cut-off syringes and transferred into glass vials. Methane concentrations in the headspace gas were used to calculate concentrations of methane dissolved in pore water (uncorrected for sediment porosity and Bunsen coefficient).
    Keywords: 28; Center for Marine Environmental Sciences; DEPTH, sediment/rock; GC; GeoB16425-1; Gravity corer; MARUM; Methane, porewater; SO219A/2; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
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  • 65
    Publication Date: 2024-05-25
    Description: Shallow sediment cores were collected with gravity cores from sites in the Japan Trench during cruise SO219A with R/V SONNE. In order to determine ex-situ concentrations of methane dissolved in pore water, sediment samples were taken from the sediment cores with cut-off syringes and transferred into glass vials. Methane concentrations in the headspace gas were used to calculate concentrations of methane dissolved in pore water (uncorrected for sediment porosity and Bunsen coefficient).
    Keywords: 24; Center for Marine Environmental Sciences; DEPTH, sediment/rock; GC; GeoB16421-1; Gravity corer; MARUM; Methane, porewater; SO219A/2; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 7 data points
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  • 66
    Publication Date: 2024-05-25
    Description: Shallow sediment cores were collected with gravity cores from sites in the Japan Trench during cruise SO219A with R/V SONNE. In order to determine ex-situ concentrations of methane dissolved in pore water, sediment samples were taken from the sediment cores with cut-off syringes and transferred into glass vials. Methane concentrations in the headspace gas were used to calculate concentrations of methane dissolved in pore water (uncorrected for sediment porosity and Bunsen coefficient).
    Keywords: 30; Center for Marine Environmental Sciences; DEPTH, sediment/rock; GC; GeoB16427-1; Gravity corer; MARUM; Methane, porewater; SO219A/2; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 6 data points
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  • 67
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    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16745-1; GeoB16745-2; GeoB16745-3; GeoB16745-4; GeoB16745-5; GeoB16745-6; GeoB16745-7; GeoB16745-8; GeoB16745-9; H1214P01; H1214P02; H1214P03; H1214P04; H1214P05; H1214P06; H1214P07; H1214P08; H1214P09; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 1017 data points
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  • 68
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    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; gas hydrates; GeoB16792-1; H1224P01; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 114 data points
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  • 69
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    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16769-1; GeoB16769-2; GeoB16769-3; GeoB16769-4; H1218P01; H1218P02; H1218P03; H1218P04; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 437 data points
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  • 70
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    Unknown
    PANGAEA
    Publication Date: 2024-05-25
    Keywords: Calculated; Center for Marine Environmental Sciences; Conductivity, thermal; Depth, relative; DEPTH, sediment/rock; Event label; gas hydrates; GeoB16778-2; GeoB16778-3; H1221P01; H1221P02; Heat flow probe; Heat-Flow probe; HF; Integrated thermal resistance; Japan; Kumano Basin; marine heat flow; MARUM; MEMO; mud volcanism; Optional event label; Sample code/label; SO222; Sonne; Temperature, in rock/sediment
    Type: Dataset
    Format: text/tab-separated-values, 235 data points
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  • 71
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    Exon Publications | Wilms Tumor
    Publication Date: 2024-05-24
    Description: The most important prognostic factors for Wilms tumor (WT) patients seem to be stage, histological subtype, and 1p/16q loss of heterozygosity (LOH) in chemotherapy-naive WTs. Over the last decade, age at diagnosis also was suggested to be an important risk factor for WT recurrence in Children’s Oncology Group (COG), United Kingdom (UK), and International Society of Pediatric Oncology (SIOP) studies. Several studies have analyzed age as a prognostic factor; these studies revealed age 〈2 years as a favorable prognostic factor, while age 〉4 years has been described as an adverse prognostic factor. In adults (〉18 years of age), WT represents less than 1% of all diagnosed renal tumors; therefore, diagnosis of WT in adults is often unexpected and poorly recognized, thereby inducing treatment delay with subsequent adverse outcome. One explanation for the higher risk of recurrence with increasing patient age is the higher frequency of anaplasia at higher age. Other suggested reasons are delay in diagnosis, advanced tumor stage at presentation, and intrinsically different biological behaviors. Whether age is really an independent risk factor, and whether age is a stronger prognostic factor than stage, histology, and LOH 1p/16q, needs to be further explored. This may provide some insight into whether older patients need to be treated more intensively, as is already advised for adult WT patients.
    Description: Published
    Keywords: MJR
    Language: English
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  • 72
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    Exon Publications | Wilms Tumor
    Publication Date: 2024-05-24
    Description: Wilms’ tumour (WT) is the most common paediatric renal tumour, which can present as a single nodule, as multifocal unilateral lesions or as bilateral tumours. Typically, WT comprises three histological components namely blastemal, epithelial and stromal. The proportion and the degree of maturation of these components vary significantly, making the histological appearance of each tumour unique. Classical triphasic WT rarely presents diagnostic difficulty for pathologists, but when only one component is present, especially in a small biopsy specimen, the differential diagnosis may include renal cell carcinoma, metanephric adenoma and hyperplastic nephrogenic rest for epithelial elements and clear cell sarcoma of the kidney, mesoblastic nephroma and synovial sarcoma for stromal elements. Pure blastemal-type WT may be difficult to distinguish from other embryonal ‘small round blue cell tumours’, including neuroblastoma, primitive neuroectodermal tumour/Ewing sarcoma, desmoplastic small round cell tumour and lymphoma. All the three components, though usually blastema, can become anaplastic, leading to the diagnosis of either focal or diffuse anaplasia. WT with diffuse anaplasia and WT with blastemal predominance (after preoperative che¬motherapy) are regarded as high-risk tumours and require more aggressive treatment. Careful assessment of the tumour and the normal kidney is critical for accurate subtyping and staging of WT, which is the basis for post-operative treatment. In addition, the identification and correct interpretation of nephrogenic rests may affect prognosis and management. Histological distinction between WT and nephrogenic rest is not always possible based on morphology alone, and implementation of new molecular genetic tools may aid in this regard. Other molecular genetic signatures of WT, such as P53 mutation and MYCN dysregulation, may provide future additional prognostic and therapeutic information.
    Description: Published
    Keywords: nephrogenic rest ; Wilms’ tumour ; MJR
    Language: English
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  • 73
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    Université de Carthage. Faculté des Sciences de Bizerte. Laboratoire de Biosurveillance de l’Environnement
    Publication Date: 2024-05-24
    Description: Le présent travail porte sur l’étude systématique et écologique du benthos dans les principaux étages bathymétriques du golfe de Tunis par le biais de plusieurs approches, dans le but d’établir l’état écologique du milieu. Il s’agit des paramètres de biodiversité, des faciès bionomiques, des interactions biotiques et abiotiques et des indices biotiques basés sur les groupes trophiques et les groupes écologiques. Cette étude a permis dans un premier temps d’enrichir la liste des invertébrés benthiques inventoriés dans le golfe de Tunis. Il s’agit de 4 espèces de Bryozoaires nouvelles pour la science (Trematooecia ligulata Ayari et Taylor, 2008, Herentia baptooecium sp. nov., Herentia orthosa sp. nov. et Cellepora sinusa sp. nov) et de 70 espèces nouvelles pour la Tunisie dont 24 Polychètes, 24 Bryozoaires, 7 Cnidaires et 5 Amphipodes. De plus, la description des faciès bionomiques a permis de préciser les limites des étages, mais également de tirer des renseignements scientifiques intéressants. Ainsi, au centre du golfe, l’étage bathyal supérieur commence à -120 m, après une légère déclivité du plateau continental à environ -100 m. La zone qui s’étend du nord de Ras El Fartas vers le large en passant par l’ouest de l’île de Zembra, est occupée par un herbier de Posidonies à -21 m et suivie d’un détritique côtier caractérisant la limite supérieure du circalittoral puis d’un détritique du large dont les caractéristiques témoignent de la régression du circalittoral. Au niveau de la troisième zone et en face de Sidi Daoued et de Ras El Ahmar, l’herbier de Posidonies se situe entre -22 et -38 m de profondeur. Ensuite, des faciès de sédiments meubles et de maërl en bon état se succèdent. A environ -67 m de profondeur, apparaît le rebord du plateau continental, suivi à -130 m, par l’étage bathyal supérieur vaseux. Au niveau de la quatrième zone, en face de Ras Gammarth, des tâches de Posidonies et de Cymodocées sont suivies d’un détritique côtier à -79 m, puis de la déclivité du plateau continental marquant le passage du circalittoral inférieur à l’étage bathyal supérieur caractérisé jusqu’ à -137 m par une vase appauvrie. Au niveau de la zone ouest, nous enregistrons un envasement en face de la lagune de Ghar El Melh et un faciès de maërl en face de Sidi Ali El Mekki. L’étude de la distribution des Polychètes en fonction des paramètres environnementaux considérés a permis quant à elle de déterminer les préférendums écologiques des espèces principales. Elle a montré, entre autres, que les Polychètes ne sont pas distribués dans le golfe de Tunis en fonction d’un seul gradient abiotique et que la profondeur joue ici un rôle primordial. L’approche écologique a été basée sur l’utilisation de plusieurs indices biotiques, et les résultats obtenus montrent que l’indice BENTIX est le plus fiable pour le cas du golfe de Tunis. L’utilisation conjointe des principaux indices a montré que l’état écologique est satisfaisant au large de Sidi Ali El Mekki, en face de Cap Farina, autour de l’île de Zembra, au nord du Cap Bon et en face de Ras El Ahmar et que le reste de la zone est en légère perturbation ou en changement vers un état de déséquilibre
    Description: This present work is about a systematic and an ecological study of the benthos within the main bathymetric levels of Tunis gulf using many approaches with aims to establish its ecological state. These are the biodiversity parameters, the bionomic features, biotic and abiotic interactions and the biotic indices based on the trophic groups and the ecological groups. Thanks to this present study, the species list of the macrobenthic invertebrates increases. Altogether 4 species of Bryozoa are newly described (Trematooecia ligulata Ayari et Taylor, 2008, Herentia baptooecium sp. nov., Herentia orthosa sp. nov. et Cellepora sinusa sp. nov) and 70 species are found here for the first time in Tunisia coast: 24 Polychaeta, 24 Bryozoans, 7 Cnidaria and 5 Amphipoda. In addition, description of the bionomic features, allowed to limit the bathymetric levels and also, provided much essential scientific informations. Thus, in the middle of the gulf, the upper bathyal level begins from -120 m after a slight declivity of the continental plateau at about -100 m. The area which extends from the northern of Ras Fartas to the offshore going by the west of Zembra Island is occupied by Posidonia meadows at -21 m and followed by a coastal detritic which characterizes the upper limit of the Circalittoral, after that an offshore detritic characterize the regression of the Circalittoral. At the third sector and in front of Sidi Daoued and Ras El Ahmar, The Posidonia meadows begin from -22 to -38 m. After that, soft bottoms and maerl in good state follow each other. The plateau continental edge is at about -67 m, and then the muddy Superior Bathyal comes at -130 m. Within the fourth area in front of Ras Gammarth, some Posidonia and Cymodocea are followed by a costal detritic at -79 m then by the continental plateau declivity witch indicates passing from Inferior Circalittoral to the Superior Bathyal characterized until -137 m by an impoverished muddy bottom. The west sector is characterized by a muddy bottom in front of the Ghar El Melh Lagoon and a maerl in front of Sidi Ali El Mekki. Study of Polychaeta distribution according to considered environmental parameters allowed us to establish the ecological preferendum of some principal species. It shows that Polychaeta were not distributed within Tunis Gulf according to only one abiotic gradient however the depth is a primordial factor. The ecological approach is based on the use of many biotic index, results obtained showed that the BENTIX index is the most adequate in the case of the Gulf of Tunis. According to the different index used simultaneously the ecological state is satisfactory offshore Sidi Ali El Mekki, in front of Cap Farina, around Zembra isle, at the north of Cap Bon, in front of Ras El Ahmar and that the rest of the area is slightly perturbed or on change toward an imbalance state.
    Description: PhD
    Keywords: macro-phytobenthos ; macro-zoobenthos ; Bryozoaires ; Polychètes ; systématique ; bionomie ; facteurs abiotiques ; indices biotiques ; état écologique
    Repository Name: AquaDocs
    Type: Thesis/Dissertation
    Format: 429 pp.
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  • 74
    Publication Date: 2024-05-24
    Description: Marine waste, including aquatic by-products, poses a significant environmental challenge and garners increasing attention for its potential valorization. The development of cost-effective, environmentally friendly, and circular technologies for transforming marine biomass into value-added products is crucial for the successful implementation of sustainable aquatic industries. This involves focusing on strategies that simultaneously reduce waste and energy demand. This document presents the research andbiotechnological innovations carried out under the ARIBiotech project, which aims to create new biological products from marine waste. In a circular economy perspective, this initiative seeks to turn sources of pollution into sustainable opportunities, contributing to the preservation of marine ecosystems while fostering innovative solutions. Exploiting marine biomass and valorizing sea by-products, whether by using them directly or extracting biopolymers, appears to be a promising solution for a more sustainable use of marine resources, leading to increased economic benefits. However, the realization of such developments is hindered by the lack of appropriate regulatory frameworks to enable the use of waste and by-products, ensuring product safety, quality, and acceptability. This white paper showcases a diverse range of bioproducts (Crab waste hydrolyzate, chitin, chitosan, collagen, gelatin, cellulose aerogels, shell powder, and bioactive extracts) derived from the application of biotechnologies on various marine waste and co-products,highlighting their potential to support sustainable development. This document aims to encourage policymakers to support the creation of alliances and innovations in blue biotechnology and enable the general public to benefit from advances in creating bioproducts from marine waste.
    Description: Published
    Description: Refereed
    Keywords: Marine waste ; Bioproducts ; Biotechnological innovations ; Circular economy ; Sustainable development ; Bio-technologie bleue ; Valorisation déchets marins
    Repository Name: AquaDocs
    Type: Book/Monograph/Conference Proceedings
    Format: 26 pp.
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  • 75
    Publication Date: 2024-05-24
    Description: A new subgenus Peculiaripalpus subgen. nov. with a new species Partnunia (Peculiaripalpus) longlingensis sp. nov. which belongs to Partnunia Piersig, 1896 is described and illustrated. Partnunia represents a newly record genus of Protziinae Koenike, 1909 for Chinese fauna. The diagnosis of Partnunia is modified according to the new species. An updated key is provided for the subfamilies, genera and subgenera of Hydryphantidae.
    Keywords: water mites; new taxa; scanning electron microscope
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 76
    Publication Date: 2024-05-24
    Description: Although Pliocene temperature and pCO2 are similar to those predicted in the IPPC RCP4.5 scenario, the distribution of coral reefs in the center of maximum coral diversity, the Coral Triangle, during this period has not been explored. We discovered a significantly lower occurrence of reefs during the Pliocene, which we refer to as the Pliocene Reef Gap, but this decrease was not associated with a drop in coral genus richness. While some of the multiple local causes that drove this decline, such as sea level rise, are analogs to drivers of Anthropocene reef decline, neither warming nor increasing pCO2 are among them.
    Keywords: Pliocene · Neogene · Coral triangle · ; Paleontology · Anthropocene · Coral reef decline
    Repository Name: National Museum of Natural History, Netherlands
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  • 77
    Publication Date: 2024-05-24
    Description: A new tetramic acid glycoside, aurantoside L (1), was isolated from the sponge Siliquariaspongia japonica collected at Tsushima Is., Nagasaki Prefecture, Japan. The structure of aurantoside L (1) composed of a tetramic acid bearing a chlorinated polyene system and a trisaccharide part was elucidated using spectral analysis. Aurantoside L (1) showed anti-parasitic activity against L. amazonensis with an IC50 value of 0.74 μM.
    Keywords: aurantosides ; Siliquariaspongia japonica ; marine sponge ; nuclear magnetic resonance ; mass ; spectrometry ; anti-leishmanial activity ; marine natural products
    Repository Name: National Museum of Natural History, Netherlands
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  • 78
    Publication Date: 2024-05-24
    Description: Traditional morphological methods for species identification are highly time consuming, especially for small organisms, such as Foraminifera, a group of shell-building microbial eukaryotes. To analyze large amounts of samples more efficiently, species identification methods have extended to molecular tools in the last few decades. Although a wide range of phyla have good markers available, for Foraminifera only one hypervariable marker from the ribosomal region (18S) is widely used. Recently a new mitochondrial marker cytochrome oxidase subunit 1 (COI) has been sequenced. Here we investigate whether this marker has a higher potential for species identification compared to the ribosomal marker. We explore the genetic variability of both the 18S and COI markers in 22 benthic foraminiferal morphospecies (orders Miliolida and Rotaliida). Using single-cell DNA, the genetic variability within specimens (intra) and between specimens (inter) of each species was assessed using next-generation sequencing. Amplification success rate was twice as high for COI (151/200 specimens) than for 18S (73/200 specimens). The COI marker showed greatly decreased intra- and inter-specimen variability compared to 18S in six out of seven selected species. The 18S phylogenetic reconstruction fails to adequately cluster multiple species together in contrast to COI. Additionally, the COI marker helped recognize misclassified specimens difficult to morphologically identify to the species level. Integrative taxonomy, combining morphological and molecular characteristics, provides a robust picture of the foraminiferal species diversity. Finally, we suggest the use of a set of sequences (two or more) to describe species showing intra-genomic variability additionally to using multiple markers. Our findings highlight the potential of the newly discovered mitochondrial marker for molecular species identification and metabarcoding purposes.
    Keywords: protist ; high-throughput sequencing ; metabarcoding ; intra-genomic variation ; benthic foraminifera
    Repository Name: National Museum of Natural History, Netherlands
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  • 79
    Publication Date: 2024-05-24
    Description: Foraminifera are a species-rich phylum of rhizarian protists that are highly abundant in many marine environments and play a major role in global carbon cycling. Species recognition in Foraminifera is mainly based on morphological characters and nuclear 18S ribosomal RNA barcoding. The 18S rRNA contains variable sequence regions that allow for the identification of most foraminiferal species. Still, some species show limited variability, while others contain high levels of intragenomic polymorphisms, thereby complicating species identification. The use of additional, easily obtainable molecular markers other than 18S rRNA will enable more detailed investigation of evolutionary history, population genetics and speciation in Foraminifera. Here we present the first mitochondrial cytochrome c oxidase subunit 1 (COI) gene sequences (“barcodes”) of Foraminifera. We applied shotgun sequencing to single foraminiferal specimens, assembled COI, and developed primers that allow amplification of COI in a wide range of foraminiferal species. We obtained COI sequences of 49 specimens from 17 species from the orders Rotaliida and Miliolida. Phylogenetic analysis showed that the COI tree is largely congruent with previously published 18S rRNA phylogenies. Furthermore, species delimitation with ASAP and ABGD algorithms showed that foraminiferal species can be identified based on COI barcodes.
    Repository Name: National Museum of Natural History, Netherlands
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  • 80
    Publication Date: 2024-05-24
    Description: Mitochondria originated from an ancient bacterial endosymbiont that underwent reductive evolution by gene loss and endosymbiont gene transfer to the nuclear genome. The diversity of mitochondrial genomes published to date has revealed that gene loss and transfer processes are ongoing in many lineages. Most well-studied eukaryotic lineages are represented in mitochondrial genome databases, except for the superphylum Retaria—the lineage comprising Foraminifera and Radiolaria. Using singlecell approaches, we determined two complete mitochondrial genomes of Foraminifera and two nearly complete mitochondrial genomes of radiolarians. We report the complete coding content of an additional 14 foram species. We show that foraminiferan and radiolarian mitochondrial genomes contain a nearly fully overlapping but reduced mitochondrial gene complement compared to other sequenced rhizarians. In contrast to animals and fungi, many protists encode a diverse set of proteins on their mitochondrial genomes, including several ribosomal genes; however, some aerobic eukaryotic lineages (euglenids, myzozoans, and chlamydomonas-like algae) have reduced mitochondrial gene content and lack all ribosomal genes. Similar to these reduced outliers, we show that retarian mitochondrial genomes lack ribosomal protein and tRNA genes, contain truncated and divergent small and large rRNA genes, and contain only 14 or 15 proteincoding genes, including nad1, -3, -4, -4L, -5, and -7, cob, cox1, -2, and -3, and atp1, -6, and -9, with forams and radiolarians additionally carrying nad2 and nad6, respectively. In radiolarian mitogenomes, a noncanonical genetic code was identified in which all three stop codons encode amino acids. Collectively, these results add to our understanding of mitochondrial genome evolution and fill in one of the last major gaps in mitochondrial sequence databases.
    Keywords: Foraminifera ; mitochondrial evolution ; mitochondrial genome ; Radiolaria ; Retaria ; Rhizaria
    Repository Name: National Museum of Natural History, Netherlands
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  • 81
    Publication Date: 2024-05-24
    Description: Monitoring community composition of Foraminifera (single-celled marine protists) pro-vides valuable insights into environmental conditions in marine ecosystems. Despitethe efficiency of environmental DNA (eDNA) and bulk-sample DNA (bulk-DNA) me-tabarcoding to assess the presence of multiple taxa, this has not been straightforwardfor Foraminifera partially due to the high genetic variability in widely used ribosomalmarkers. Here, we test the correctness in retrieving foraminiferal communities by me-tabarcoding of mock communities, bulk-DNA from coral reef sediment samples, andeDNA from their associated ethanol preservative using the recently sequenced cy-tochrome c oxidase subunit 1 (COI) marker. To assess the detection success, we com-pared our results with large benthic foraminiferal communities previously reportedfrom the same sampling sites. Results from our mock communities demonstrate thatall species were detected in two mock communities and all but one in the remainingfour. Technical replicates were highly similar in number of reads for each assigned ASVin both the mock communities and bulk-DNA samples. Bulk-DNA showed a signifi-cantly higher species richness than their associated eDNA samples, and also detectedadditional species to what was already reported at the specific sites. Our study con-firms that metabarcoding using the foraminiferal COI marker adequately retrieves thediversity and community composition of both the mock communities and the bulk-DNA samples. With its decreased variability compared with the commonly used nu-clear 18 S rRNA, the COI marker renders bulk-DNA metabarcoding a powerful tool toassess foraminiferal community composition under the condition that the referencedatabase is adequate to the target taxa.
    Keywords: bulk-sample ; DNA ; community composition ; coral reef ; environmental DNA ; foraminifera ; metabarcoding
    Repository Name: National Museum of Natural History, Netherlands
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  • 82
    Publication Date: 2024-05-24
    Description: Ecological regime shifts in the marine realm have been recorded from a variety of systems and locations around the world. Coral reefs have been especially affected, with their benthic habitat changing from a dominance of stony corals to a dominance of other organisms such as fleshy algae. To detect changes in the benthic habitat of coral reefs, simple tools applicable on a global scale are necessary for future monitoring programs. Hence, the aim of this research is to explore the hypothesis that shifts in assemblages of large benthic foraminifera (LBF) can detect early signs of degradation in the reef benthic habitat. To do so, data on living assemblages of LBF collected between 1997 and 2018 at 12 islands in the Spermonde Archipelago (South Sulawesi, Indonesia) were analyzed. Foraminiferal specimens were morphologically identified to the species level and statistical analyses performed to assess changes in their assemblage composition. A clear temporal shift was observed. Typical foraminiferal assemblages in a coral-dominated (e.g., Amphistegina lobifera, Calcarina spengleri, Heterostegina depressa) and fleshy algaedominated (e.g., Neorotalia gaimardi, C. mayori) reef habitats were identified and significantly linked to the substrate type. Other species (e.g., Elphidium spp., Peneroplis planatus and Sphaerogypsina globulus) seem to reflect a spatial and temporal gradient of anthropogenic pollution from local inhabited islands and ongoing urban development on the mainland. Hence communities of LBF consistently follow gradual shifts in environmental conditions. Additionally to foraminiferal assemblages being an indicator for actual reef condition, closely monitoring LBF may provide early information on reef degradation, in time to take action against identified stressors (e.g., eutrophication or intensive fishing) at local and regional scales. The circumtropical distribution of LBF is such that they can be included worldwide in reef monitoring programs, conditional to calibration to the regional species pool.
    Keywords: Temporal dynamics ; Bioindicator ; Early detection ; Coral reef ; Spermonde Archipelago ; Indonesia
    Repository Name: National Museum of Natural History, Netherlands
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  • 83
    Publication Date: 2024-05-24
    Description: Foraminifera are a species-rich phylum of rhizarian protists that are highly abundant in most marine environments. Molecular methods such as metabarcoding have revealed a high, yet undescribed diversity of Foraminifera. However, so far only one molecular marker, the 18S ribosomal RNA, was available for metabarcoding studies on Foraminifera. Primers that allow amplification of foraminiferal mitochondrial cytochrome oxidase I (COI) and identification of Foraminifera species were recently published. Here we test the performance of these primers for the amplification of whole foraminiferal communities, and compare their performance to that of the highly degenerate LerayXT primers, which amplify the same COI region in a wide range of eukaryotes. We applied metabarcoding to 48 samples taken along three transects spanning a North Sea beach in the Netherlands from dunes to the low tide level, and analysed both sediment samples and meiofauna samples, which contained taxa between 42 mm and 1 mm in body size obtained by decantation from sand samples. We used single-cell metabarcoding (Girard et al., 2022) to generate a COI reference library containing 32 species of Foraminifera, and used this to taxonomically annotate our community metabarcoding data. Our analyses show that the highly degenerate LerayXT primers do not amplify Foraminifera, while the Foraminifera primers are highly Foraminifera- specific, with about 90% of reads assigned to Foraminifera and amplifying taxa from all major groups, i.e., monothalamids, Globothalamea, and Tubothalamea. We identified 176 Foraminifera ASVs and found a change in Foraminifera community composition along the beach transects from high tide to low tide level, and a dominance of single-chambered monothalamid Foraminifera. Our results highlight that COI metabarcoding can be a powerful tool for assessing Foraminiferal communities.
    Keywords: Foraminifera ; Metabarcoding ; Beach ; Community composition ; Intertidal ; Molecular ; biodiversity
    Repository Name: National Museum of Natural History, Netherlands
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  • 84
    Publication Date: 2024-05-24
    Description: In the marine realm, microorganisms are responsible for the bulk of primary production, thereby sustaining marine life across all trophic levels. Longhurst provinces have distinct microbial fingerprints; however, little is known about how microbial diversity and primary productivity change at finer spatial scales. Here, we sampled the Atlantic Ocean from south to north (~50°S–50°N), every ~0.5° latitude. We conducted measurements of primary productivity, chlorophyll-a and relative abundance of 16S and 18S rRNA genes, alongside analyses of the physicochemical and hydrographic environment. We analysed the diversity of autotrophs, mixotrophs and heterotrophs, and noted distinct patterns among these guilds across provinces with high and low chlorophyll-a conditions. Eukaryotic autotrophs and prokaryotic heterotrophs showed a shared inter-province diversity pattern, distinct from the diversity pattern shared by mixotrophs, cyanobacteria and eukaryotic heterotrophs. Additionally, we calculated samplewise productivity-specific length scales, the potential horizontal displacement of microbial communities by surface currents to an intrinsic biological rate (here, specific primary productivity). This scale provides key context for our trophically disaggregated diversity analysis that we could relate to underlying oceanographic features. We integrate this element to provide more nuanced insights into the mosaic-like nature of microbial provincialism, linking diversity patterns to oceanographic transport through primary production.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
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  • 85
    Publication Date: 2024-05-24
    Description: This paper demonstrates how historical research is a valuable tool for identifying past geological, geomorphological and climatic hazards and therefore critical for mitigating and reducing future risk. The authors describe the potential of a scientific field that straddles that of the geologist, geographer, historian and archivist. Historical records include a range of materials and sources of information, which can be very diverse; from written documents to cartographies, and from drawings to marble tombstones. They are all useful and convey important data, on the date of the event, the size of the phenomena, sometimes on ground effects, damage or magnitude. The authors discuss how to conduct historical research by providing a list of locations and how important historical documents can be found. Works that mention geological phenomena are listed, starting with the first occasional descriptions by individuals in letters, up to very specific publications in individual fields of interest. With this introduction, the editors of the Special Issue wish to draw attention to the importance of historical documentation, which is too often ignored or considered of low priority by the scientific community, but can contain key information on events, their impacts and social and cultural adaptations.
    Description: Published
    Description: 1777
    Description: JCR Journal
    Keywords: geological and geo-hydrological processes ; historical research; old documents ; land-use planning ; natursal hazard ; risk mitigation ; Europe
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 86
    Publication Date: 2024-05-24
    Language: English
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  • 87
    Publication Date: 2024-05-24
    Description: Palaeoclimate proxy records (such as time series derived from ice cores or stalagmites) from the same or nearby location would be expected to represent similar climate variation. This is called replication of proxy records but is often difficult to achieve, because either the proxies are not reflecting the paleoclimate variation, external factors overprint the climate signal in the proxy record, or chronological uncertainties cause a serious mismatch between the individual records. In order to minimize the later issue and take the chronological uncertainties into account, we combine a Monte Carlo based approach (COPRA) with an ensemble based windowed cross-correlation analysis. This allows the investigation of potential replication of proxy records from a statistical perspective. We demonstrate this approach by comparing two stalagmite δ18O records from Heshang cave and Sanbao cave, both strongly influenced by the East Asian Summer Monsoon and covering the period between 9000 yr BP and 500 yrBP. We find that both proxy records reproduce well, although not perfectly. Main issues are differences between the records caused by unresolved geochemical processes influencing the U-series system and possibly kinetic fractionation in the oxygen isotope system. Overall, the proposed approach can provide a means to extract a correction function which reduces the uncertainties in the dating procedure. This method is a precursory step towards composite reconstructions that are based on multiple, replicating, time series.
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  • 88
    Publication Date: 2024-05-24
    Description: Data and information are central to policy processes, as they frame the policy problem, the design and the implementation of policy, and evaluation of policy impacts. Better data and information infrastructure is expected to lead to better policies and outcomes, for example, by enabling transparent decision making and enhancing capacity and accountability. However, the collection, selection, representation, framing and application of data are not merely technical and apolitical procedures, but are dependent on the interests represented in the policy processes they aim to inform. Social scientists have pointed to the “politics of numbers” and their effects on forests and trees and on the people relying on them, as well as on those involved in their measurements. We use the case of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) international initiative and focus on the central aspect of understanding drivers of deforestation and measures of REDD+ performance to unpack the politics of policy processes. Data and information are socially constructed, and their interpretations are shaped by the contexts in which they emerge. Dominant beliefs in the transformative power of new data and technologies cannot explain why, often, new information does not translate into policy change and action to halt deforestation. Technological advances in making new and ever larger amounts of data available for analysis are a necessary yet insufficient condition for changing the business as usual in deforestation. Through openness, reflexivity and the tackling of silences in data and information related to the global political economy of deforestation the scientific community can make a key contribution to more equitable policy change.
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  • 89
    Publication Date: 2024-05-24
    Description: A promising way to speedup coupled reactive transport simulations is offered by the use of statistical surrogates instead of the "full-physics" geochemical submodels, which usually represent the computational bottleneck of such simulations. Data-driven surrogates are simplified statistical models obtained by fitting on an ensemble of precalculated full-physics simulations, capturing their behaviour while being fast to compute. Our previous work demonstrates the achievable speedups on a benchmark scenario [1]. However, more complex models require exponentially larger computational resources for surrogate fitting and tuning. For application in coupled reactive transport, it is required that the geochemical surrogates honour the charge and mass balance of chemical elements and species across the fluid and mineral phases, a feature which is not guaranteed by regressors. We evaluate the balance equations themselves as criteria for accuracy of surrogates. This allows the use of simple but fast surrogates in the parameter space regions where they are most accurate. The correctness of balance, evaluated at runtime during coupled simulations, can discriminate whether a surrogate response can be accepted or a costly full-physics geochemical simulation is needed. We present the performance evaluation of different surrogate models on reactive transport examples of increasing complexity, with geochemistry both at local thermodynamic equilibrium and kinetically controlled.
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  • 90
    Publication Date: 2024-05-24
    Description: The Mufushan area, which has abundant rare-metal pegmatites within and around the Mufushan Granite Complex, has become a major target for Ta-Nb-(Li-Be) exploration in South China. The age and origin of the pegmatites and associated rare-metal mineralization are still under debate. Here, we report the in situ U-Pb ages and geochemical characteristics of granites and pegmatites in the Guanyuan and Duanfengshan districts, which are located in the central and northern parts of the Mufushan Complex. Combined zircon, apatite and monazite U-(Th)-Pb dating revealed that biotite, two-mica, and muscovite granites from the Guanyuan and Duanfengshan districts were emplaced at 143–139 Ma, which overlaps with the U-Pb ages of columbite-group minerals (CGM) from different internal zones of the Duanfengshan pegmatites (142–140 Ma). Whole-rock major and trace element compositions and Sr-Nd-Hf isotope data reveal that the granites and pegmatites experienced continuous evolution from biotite, two-mica, and muscovite granites to pegmatite and that the magma originated from the partial melting of mica schists that are abundant in the Mufushan area. Temporal, chemical and mineralogical evidence indicates a genetic link between muscovite granite and Ta-Nb pegmatites. The textures and chemical compositions of CGM from different pegmatites exhibit features typical of magmatic CGM, indicating that fractional crystallization was the driving force that promoted Ta-Nb enrichment. The increasing alumina satu- ration index [ASI: molar Al/(Ca–1.67P + Na + K)] of pegmatitic melt due to albite crystallization may have been the main factor controlling CGM deposition, explaining why major Ta-Nb mineralization is bound to albite pegmatites. The Duanfengshan and other rare-metal pegmatites in the Mufushan area are derivatives of the most evolved granitic facies (i.e., muscovite granite) of the Mufushan Complex. The Duanfengshan and Renli pegmatite fields indicate that the Early Cretaceous (~140 Ma) may have been an important, underappreciated epoch for the formation of pegmatite-related rare-metal resources in the Mufushan area and beyond in South China.
    Language: English
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  • 91
    Publication Date: 2024-05-24
    Type: info:eu-repo/semantics/lecture
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  • 92
    Publication Date: 2024-05-24
    Description: The St1 Deep Heat Project was started in 2014 by the Finnish energy companies St1 and Fortum. This site is at Fortum's district heating plant at the Aalto University campus, west of Helsinki. The project began with the drilling of a cored 2015 m pilot hole, which encountered a few meters of alluvium over the expected crystalline basement. Follow-on ~6000 m deviated injection-andproduction wells were completed in 2018 and 2020. These wells were extensively logged, and the deep wells were stimulated after completion. In Oct 2018 the ~2500 m to ~5000 m vertical portion of the injection well was profiled with the GFZ German Geoscience Center 17 level, 10 m spaced Sercel borehole geophone array (a Vertical Seismic Profile - VSP). Near-surface shots at 4 offset and 1 near-well shot-points were used as sources. These data were analyzed and compared to (a) drilling data, (b) logging data, (c) surface geology, and (d) a short run of Seismic While Drilling (SWD) data recorded in the pilot hole using hammer-drill signals from the production hole. The VSP data establishes that a seismic velocity reversal - from a P-velocity of ~6.5 to ~6.1 km/s - extends from ~3000 m down at least to the bottom VSP position at 5000 m and is also seen in the well logs. Aside from several shallower structures, the most significant reflection feature found in these data is a ~400 m thick horizon that intersects the ~6000 m wells at ~5000 m. This horizon includes internal reflections that appear to correlate with a drilling-encountered and loginterpreted fracture zone. Owing to complex surrounding velocity structure, this feature's lateral continuation and ultimate attitude have been difficult to resolve. In one interpretation it appears as a 45 ENE dipping extension of a shallower reflector seen in the SWD data. The productionwell's trajectory was based on this interpretation - which drilling seems to confirm. Its consequences for the EGS project will be tested with a circulation campaign over the next months.
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  • 93
    Publication Date: 2024-05-24
    Description: Geomechanics play an important role in any underground activity, such as carbon dioxide (CO2) and hydrogen (H2) geo-storage, owing to the considerable hazards linked to the injection and withdrawal of fluids into and from the subsurface. In order to quantify these risks, knowledge of full stress tensor is required. Yet, most of our stress information in the Australian target basins for geo-storage is limited to the stress orientations, while stress magnitude data is sparse. 3D geomechanical modelling has proved to be an invaluable tool for prediction of full stress tensor. Nevertheless, a model requires some stress magnitude data in order to tune the model to be representative of real stress state. In situations where stress magnitude data is lacking, this means that the model is susceptible to significant uncertainties. Herein, we present a novel strategy for stress modelling, which involves the utilisation of indirect data such as borehole breakouts, drilling-induced fractures, seismic activity records, and formation integrity tests to calibrate a 3D geomechanical model. We employ the northern Bowen Basin, an onshore basin in Queensland, Australia, as a case study for a comprehensive 3D geomechanical modelling approach. We assess all the indirect information in the model’s volume to narrow down the model predictions and find the most reliable stress state. This innovative approach is an important step forward in stress modelling of Australian basins, where lack of stress magnitudes is a great challenge for geomechanical assessment of geo-storage.
    Language: English
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  • 94
    Publication Date: 2024-05-24
    Description: The ionosphere is an ionized part of the upper atmosphere, where the number of free electrons is large enough to affect the propagation of radio signals, including those of the GNSS systems. The knowledge of electron density values in the ionosphere is crucial for both industrial and scientific applications. Here, we develop a novel empirical model of electron density in the topside ionosphere using the radio occultation profiles collected by the CHAMP, GRACE, and COSMIC missions. We assume a linear decay of scale height with altitude and model four parameters, namely the F2-peak density and height (NmF2 and hmF2) and the slope and intercept of the linear scale height decay (dHs/dh and HO). The resulting model (NET) is based on feedforward neural networks. The model inputs include the the geographic and geomagnetic position, the solar flux and geomagnetic indices. The resulting density reconstructions are validated on more than a hundred million in-situ measurements from CHAMP, CNOFS and Swarm satellites, as well as on the GRACE/KBR data, and the developed NET model is compared to several topside options of the International Reference Ionosphere (IRI) model. The NET model yields highly accurate reconstructions of the topside ionosphere and gives unbiased predictions for different locations, seasons, and solar activity conditions.
    Language: English
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  • 95
    Publication Date: 2024-05-24
    Description: Continental subduction is the major cause of regional heterogeneities in the lithospheric mantle and contrasting types of magmatism and mineralization in post-collisional settings. We illustrate the relation between the nature of the subducted crust and the character of magmatism for the Late Miocene New Guinea Orogen that formed by the collision of the Australian continental margin with an island arc. The bipartite nature of the subducted Australian plate margin, with Precambrian crust in the west and Phanerozoic accreted arcs in the east, is reflected in the contrasting magmatism along the strike of the New Guinea Orogen. The chemical signature of the subducted crust is particularly prominent in small-volume Late Miocene–Quaternary ultrapotassic rocks of New Guinea. In the west, ultrapotassic lavas have low εNd values (−12.6 to −20.9), indicating the recycling of ancient continental material. Conversely, high εNd values of +3.5 to +4.5 are found in ultrapotassic lavas from eastern New Guinea. This suggests recycling of juvenile continental material, similar to the orthogneisses exposed in the Late Miocene ultrahigh-pressure metamorphic complex of the D'Entrecasteaux Islands. By comparison with ultrapotassic rocks from other orogenic belts, we show that crustal recycling is responsible for regionally contrasting redox conditions in the lithospheric mantle, which may explain why porphyry-type deposits are important in some regions but absent in others.
    Language: English
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  • 96
    Publication Date: 2024-05-24
    Description: Teleseismic back-projection imaging has emerged as a powerful tool for understanding the rupture propagation of large earthquakes. However, its application often suffers from artifacts related to the receiver array geometry. We developed a teleseismic back-projection technique that can accommodate data from multiple arrays. Combined processing of P and pP waveforms may further improve the resolution. The method is suitable for defining arrays ad-hoc to achieve a good azimuthal distribution for most earthquakes. We present a catalog of short-period rupture histories (0.5-2.0 Hz) for all earthquakes from 2010 to 2022 with Mw {greater than or equal to} 7.5 and depth less than 200 km (56 events). The method provides automatic estimates of rupture length, directivity, speed, and aspect ratio, a proxy for rupture complexity. We obtained short-period rupture length scaling relations that are in good agreement with previously published relations based on estimates of total slip. Rupture speeds were consistently in the sub-Rayleigh regime for thrust and normal earthquakes, whereas a tenth of strike-slip events propagated at supershear speeds. Many rupture histories exhibited complex behaviors, e.g., rupture on conjugate faults, bilateral propagation, and dynamic triggering by a P wave. For megathrust earthquakes, ruptures encircling asperities were frequently observed, with down-dip, up-dip, and balanced patterns. Although there is a preference for short-period emissions to emanate from central and down-dip parts of the megathrust, emissions up-dip of the main asperity are more frequent than suggested by earlier results. The data are presented as follows (and described in detail in the associated README): SUPPORTING DATA SET S1 (2024-001_Vera-et-al_Supporting-Data-S1.zip) This Data Set (S1) consists of *.bp files containing (1) short-period earthquake rupture patterns, (2) energy radiated maps, and (3) source time functions derived from back-projections (0.5-2.0 Hz). The Data Set S1 includes 56 folders, representing 56 processed earthquakes between 2010 and 2022 with a moment magnitude (Mw) greater than or equal to 7.5 and a depth less than 200 km. These folders are labeled in the format YYYYMMDDhhmm_EVENT_NAME_REGION (UTC) in *.bp format. SUPPORTING DATA SET S2 (2024-001_Vera-et-al_Supporting-Data-S2.csv) This Data Set (S2) comprises a *.csv file containing earthquake source information used in the back-projection and the resulting ruptur...
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  • 97
    Publication Date: 2024-05-24
    Description: Landslides are a major type of natural hazard that cause significant human and economic losses in mountainous regions worldwide. Optical and synthetic aperture radar (SAR) satellite data are increasingly being used to support landslide investigation due to their multi-spectral and textural characteristics, multi-temporal revisit rates, and large area coverage. Understanding landslide occurrence, kinematics and correlation to external triggering factors is essential for landslide hazard assessment. Landslides are usually triggered by rainfall and thus, are often covered by clouds, which limits the use of optical images only. Exploiting SAR data, and their cloud penetration and all weather measurement capability, provides more precise temporal characterization of landslide kinematics and its occurrence. However, except for a few research studies, the full potential of SAR data for operational landslide analysis are not fully exploited yet. This is a very demanding task, considering the availability of a vast amount of Sentinel-1 data that have been globally available since October 2014.In this presentation we summarise all the achievements that were made within the framework of MultiSat4SLOWS project (Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service), financed within the Helmholtz Imaging 2020 call. The project aims on developing a multi-sensor approach for detection and analysis of the landslide occurrence time and its spatial extent using freely available SAR data from Sentinel-1. Within this project, we generated a reference database based on Sentinel-1 and -2 data for training, testing and validation of deep learning algorithms. The reference database contains various landslide examples that occurred worldwide and include pre- and post-event polarimetric, coherence and backscatter features. Also, we investigated the applicability of SAR/InSAR time-series data for landslide time detection. Finally, we introduce a prototype of a Visual Analytics platform for rapid landslide analysis of spatial and temporal ground deformation patterns and correlation with external triggering factors.
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  • 98
    Publication Date: 2024-05-24
    Description: In geosciences, machine learning (ML) has become essential for solving complex problems, such as predicting natural disasters or analysing the impact of extreme temperatures on mortality rates. However, the integration of ML into geoscience scenarios faces significant challenges, especially in explaining the influence of hyperparameters (HP) on model performance and model behaviour in specific scenarios. The Explainable Artificial Intelligence (XAI) system ClarifAI developed at GFZ addresses these challenges by combining XAI concepts with interactive visualisation. ClarifAI currently provides users with two interactive XAI methods: HyperParameter Explorer (HPExplorer) and Hypothetical Scenario Explorer (HSExplorer). HPExplorer allows interactive exploration of the HP space by computing an interactive tour through stable regions of the HP space. We define a stable region in HP space as a subspace of HP space in which ML models show similar model performance. We also employ HP importance analysis to deepen the understanding of the impact of separate HPs on model performance.The Hypothetical Scenarios Explorer (HSExplorer) helps users explore model behaviour by allowing them to test how changes in input data affect the model's response. In our presentation, we will demonstrate how HSExplorer helps users understand the impact of individual HPs on model performance. As ClarifAI is an important research area in our lab, we are interested in discussing relevant XAI challenges with the XAI community in ESSI. Our goal is to create a comprehensive set of tools that explain the mechanics of ML models and allow practitioners to apply ML to a wide range of geoscience applications.
    Language: English
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  • 99
    Publication Date: 2024-05-24
    Description: Subduction zones are critical sites for recycling of Li and B into the mantle. The way of redistribution of Li and B and their isotopes in subduction settings is debated, and there is a lack of detailed studies on Li and B partitioning between minerals of different types of eclogites and the host rocks of the eclogites. We present Li and B concentration data of minerals and Li and B whole-rock isotope data for low-T and high-T eclogites and their phengite schist host rocks from the Changning–Menglian suture zone, SW China. Omphacite controls the Li budget in both the low-T and high-T eclogites. Low-T eclogites have Li and δ7Li values (8.4–27.0 ppm, – 5.5 to + 3.2 ‰) similar to the phengite schists (8.7–27.0 ppm, – 3.8 to + 3.0 ‰), suggesting that Li was added to low-T eclogites from the phengite schists. In contrast, high-T eclogites have much lower δ7Li values (– 13.2 to – 5.8 ‰) than the phengite schists, reflecting prograde loss of Li or exchange with wall rocks characterized by low δ7Li values. Phengite and retrograde amphibole/muscovite are the major B hosts for low-T and high-T eclogites, respectively. The budgets and isotopic compositions of B in eclogites are affected by the infiltration of fluids derived from phengite schists, as indicated by eclogite δ11B values (– 15.1 to – 8.1 ‰) overlapping with the values of the phengite schists (– 22.8 to – 9.5 ‰). Lithium and B in eclogites are hosted in different mineral phases that may have formed at different stages of metamorphism, implying that the contents and isotopic compositions of Li and B may become decoupled during subduction-related fluid-mediated redistribution. We suggest a mineralogical control on the redistribution of Li and B in eclogites during subduction and the exchange of Li and B with the immediate wall rocks. The observed contrasting Li and B isotopic signatures in eclogites are likely caused by a fluid-mediated exchange with different types of wall rocks during both prograde metamorphism and exhumation.
    Language: English
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  • 100
    Publication Date: 2024-05-24
    Description: Large-scale physics-based groundwater flow models are indispensable tools to contribute to an understanding required to manage our present and future water resources. For instance, large finite-element-based models can encompass up to 108 finite elements and require high performance computing environments to run. To effectively use such models for prediction and scenario analysis, proper calibrations against observation data are required. For calibration, two major challenges arise: 1) the high amount of time and computational resources required for the numerous model runs, and, 2) the high complexity of parameter optimization for heterogeneous domains that translates into multiple potentially suitable calibrations. Here, we provide a solution by integrating data science methods for meta modeling with Bayesian optimization and visual analytics into calibration workflows for physics-based groundwater flow models. With the open source simulator OpenGeoSys we developed a virtual aquifer (VA) model that serves as ground truth. From this we derived groundwater flow models with perturbed parameters for calibration. In our calibration method, we use machine-learning algorithms (e.g. Gaussian process regression) to build fast meta (surrogate) models of the physics-based groundwater flow model in order to explore the calibration parameter space in a step-wise interactive Bayesian optimization routine. In this routine, visual analytic tools (e.g. GCex) provide insights into the calibration progress and parameter sensitivities, which allow the modeler to analyze potential solutions and/or adjusting further steps in the optimization routine. In this way we are able to effectively combine the modelers expert knowledge with an intelligent parameter optimization strategy that allows the calibration of large and complex groundwater flow models with a minimum of computational resources.
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