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  • PANGAEA  (9,469)
  • American Institute of Physics (AIP)
  • 2015-2019  (9,469)
  • 2018  (9,469)
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  • 2015-2019  (9,469)
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  • 1
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-12-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 2
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-01-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 3
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-01-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 4
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-03-15
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 5
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-03-26
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 6
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-05-04
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 7
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-09-14
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 8
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-01-23
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 9
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-01-22
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 10
    Publication Date: 2018-01-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 11
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-01-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 12
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-01-23
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 13
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 14
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 15
    Publication Date: 2018-06-05
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 16
    Publication Date: 2018-07-13
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 17
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 18
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 19
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-24
    Repository Name: EPIC Alfred Wegener Institut
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  • 20
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-04-24
    Repository Name: EPIC Alfred Wegener Institut
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  • 21
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 22
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-11-28
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 23
    Publication Date: 2018-07-13
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 24
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 25
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 26
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 27
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , notRev
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  • 28
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-02-05
    Repository Name: EPIC Alfred Wegener Institut
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  • 29
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-24
    Repository Name: EPIC Alfred Wegener Institut
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  • 30
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-07-19
    Repository Name: EPIC Alfred Wegener Institut
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  • 31
    Publication Date: 2018-11-30
    Repository Name: EPIC Alfred Wegener Institut
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  • 32
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    PANGAEA
    In:  EPIC3Bremerhaven, PANGAEA
    Publication Date: 2018-11-28
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  • 33
  • 34
    Publication Date: 2020-02-12
    Description: The airborne laser scanning (ALS) datasets were acquired at the Arctic tundra site of Trail Valley Creek (TVC), Northwest Territories, Canada, which is underlain by continuous permafrost. Basic processing and filtering steps were applied to the ALS point cloud. Based on a classification into ground and vegetation points, a Digital Terrain Model (DTM) and rasters of mean and maximum vegetation heights are derived. Detailed metadata are included.
    Language: English
    Type: info:eu-repo/semantics/workingPaper
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  • 35
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    PANGAEA
    In:  Supplement to: Kremer, Anne; Stein, Ruediger; Fahl, Kirsten; Ji, Z; Yang, Z; Wiers, Steffen; Matthiessen, Jens; Forwick, Matthias; Löwemark, Ludvig; O'Regan, Matthew; Chen, Jiaming; Snowball, Ian (2018): Changes in sea ice cover and ice sheet extent at the Yermak Plateau during the last 160 ka - Reconstructions from biomarker records. Quaternary Science Reviews, 182, 93-108, https://doi.org/10.1016/j.quascirev.2017.12.016
    Publication Date: 2023-03-16
    Description: The Yermak Plateau is located north of Svalbard at the entrance to the Arctic Ocean, i.e. in an area highly sensitive to climate change. A multi proxy approach was carried out on Core PS92/039-2 to study glacial-interglacial environmental changes at the northern Barents Sea margin during the last 160 ka. The main emphasis was on the reconstruction of sea ice cover, based on the sea ice proxy IP25 and the related phytoplankton - sea ice index PIP25. Sea ice was present most of the time but showed significant temporal variability decisively affected by movements of the Svalbard Barents Sea Ice Sheet. For the first time, we prove the occurrence of seasonal sea ice at the eastern Yermak Plateau during glacial intervals, probably steered by a major northward advance of the ice sheet and the formation of a coastal polynya in front of it. Maximum accumulation of terrigenous organic carbon, IP25 and the phytoplankton biomarkers (brassicasterol, dinosterol, HBI III) can be correlated to distinct deglaciation events. More severe, but variable sea ice cover prevailed at the Yermak Plateau during interglacials. The general proximity to the sea ice margin is further indicated by biomarker (GDGT) - based sea surface temperatures below 2.5 °C.
    Keywords: Arctic Ocean; ARK-XXIX/1, TRANSSIZ; AWI_Paleo; KAL; Kasten corer; Paleoenvironmental Reconstructions from Marine Sediments @ AWI; Polarstern; PS92; PS92/039-2
    Type: Dataset
    Format: application/zip, 4 datasets
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  • 36
    Publication Date: 2023-03-16
    Description: Past sea ice conditions and open water phytoplankton production were reconstructed from a sediment core taken in Disko Bugt, West Greenland, using the sea ice biomarker IP~25~ and other specific phytoplankton biomarker (i.e., brassicasterol, dinosterol, HBI III) records. Our biomarker record indicates that Disko Bugt experienced a gradual expansion of seasonal sea ice during the last 2.2 kyr. Maximum sea ice extent was reached during the Little Ice Age around 0.2 kyr BP. Superimposed on this longer term trend, we find short-term oscillations in open water primary production and terrigenous input, which may be related to the Atlantic Multidecadal Oscillation and solar activity changes as potential climatic trigger mechanisms. A direct sample-to-sample multiproxy comparison of our new biomarker record with microfossil (i.e., benthic foraminifera, dinocysts, and diatoms) and other geochemical records (i.e., alkenone biomarkers) indicates that different proxies are influenced by the complex environmental system with pronounced seasonal changes and strong oceanographic gradients, e.g., freshwater inflow from the Greenland Ice Sheet. Differences in sea ice reconstructions may indicate that the IP~25~ record reflects only the relatively short sea ice season (spring), whereas other microfossil reconstructions may reflect a longer (spring–autumn) interval.
    Keywords: AWI_Paleo; Paleoenvironmental Reconstructions from Marine Sediments @ AWI
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  • 37
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    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Description: Using a microprofiler and electrochemical oxygen sensors after Revsbech (1989) in situ oxygen profiles measurements were conducted with a spatial resolution if 100 µm and a temporal resolution of 30 seconds at three stations in Potter Cove in austral summer 2015.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 38
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    PANGAEA
    In:  Supplement to: Braeckman, Ulrike; Janssen, Felix; Lavik, Gaute; Elvert, Marcus; Marchant, Hannah K; Buckner, Caroline; Bienhold, Christina; Wenzhöfer, Frank (2018): Carbon and nitrogen turnover in the Arctic deep sea: in situ benthic community response to diatom and coccolithophorid phytodetritus. Biogeosciences, 15(21), 6537-6557, https://doi.org/10.5194/bg-15-6537-2018
    Publication Date: 2023-03-16
    Description: This is a dataset from an in situ experiment at station S2 from the LTER monitoring site HAUSGARTEN, performed in June-July 2013 during Maria S Merian expedition MSM29. The in situ responses of Arctic deep-sea benthos to input of phytodetritus of a diatom (Thalassiosira sp.) as opposed to a coccolithophorid (Emiliania huxleyi) were investigated in incubation chambers of benthic landers. Using 13C and 15N labelled phytodetritus harvested from cultures of these species, we traced the fate of the respective phytodetritus into different parts of the food web (respiration, assimilation by bacteria and infauna 〉250 µm), in a short (4d) and long (14d) term experiment. The benthic landers were lowered to the sea floor, where they enclosed ~ 20cm of sediment and ~10 cm of overlying water. During respectively 4d and 14d, the temperature and concentrations of O2, DIC, 13C-DIC, NHx, NOx, 15N-NH4, 15N-NOx were measured. Upon recovery of the landers, the sediment was retrieved and subsampled in vertical horizons to measure pigment, TOC and TN, 13C-POC and 15N-PN concentrations, pore water concentrations of DIC, 13C-DIC, NHx, NOx, 15N-NH4 and 15N-NOx and the assimilation of 13C in bacterial fatty acids (iC15:0 and aiC15:0) and in fauna 〉 250 µm
    Keywords: Hausgarten; Long-term Investigation at AWI-Hausgarten off Svalbard
    Type: Dataset
    Format: application/zip, 10 datasets
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  • 39
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    PANGAEA
    In:  Supplement to: Izett, Robert W; Manning, Cara C M; Hamme, Roberta C; Tortell, Philippe Daniel (2018): Refined Estimates of Net Community Production in the Subarctic Northeast Pacific Derived From ΔO2/Ar Measurements With N2O-Based Corrections for Vertical Mixing. Global Biogeochemical Cycles, 32(3), 326-350, https://doi.org/10.1002/2017GB005792
    Publication Date: 2023-03-16
    Description: We present the first field application of a N2O-based approach to correct for vertical mixing in the estimation of net community production (NCP) from mixed layer O2 measurements. Using new ship-based observations of N2O and biological oxygen saturation anomalies (DeltaO2/Ar) from the Subarctic Northeast Pacific, we provide refined mixed layer NCP estimates across contrasting hydrographic regimes and a comprehensive assessment of the methodological considerations and limitations of the approach. Increased vertical mixing coefficients at the base of the mixed layer, derived using N2O measurements, corresponded with periods of heightened wind speed and coastal upwelling. Corrections were most significant in coastal regions where the vertical supply of low-O2 water can otherwise falsely imply net heterotrophy from negative DeltaO2/Ar measurements. After correcting for the mixing flux, all coastal stations showed autotrophic signatures, with maximum NCP exceeding 100 mmol O2 m-2 day-1 in the spring and summer. Vertical fluxes were lower in off-shelf waters, but often contributed more than 50 % to corrected NCP. At some oceanic stations, however, the co-occurrence of N2O minima and O2 maxima resulted in biased (over-estimated) N2O corrections. Evaluating vertical fluxes in these regions remains a challenge for ship-based studies. Nonetheless, our refined NCP estimates show better coherence with surface chlorophyll, temperature, and mixed layer depth than uncorrected values. Potential mixed layer N2O production introduces some uncertainty in the approach, but errors are likely to be small. Ultimately, this work provides rationale for the adoption of the N2O correction to refine NCP estimates, particularly in coastal waters. Data in this submission include the ancillary components required to replicate all calculations, and conclusions made in the main manuscript.
    Type: Dataset
    Format: application/zip, 20 datasets
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  • 40
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    PANGAEA
    In:  Supplement to: Kruse, Stefan; Epp, Laura Saskia; Wieczorek, Mareike; Pestryakova, Luidmila A; Stoof-Leichsenring, Kathleen Rosmarie; Herzschuh, Ulrike (2018): High gene flow and complex treeline dynamics of Larix Mill. stands on the Taymyr Peninsula (north-central Siberia) revealed by nuclear microsatellites. Tree Genetics & Genomes, 14(2), https://doi.org/10.1007/s11295-018-1235-3
    Publication Date: 2023-03-16
    Description: Arctic treelines are facing a strong temperature increase as a result of recent global warming, causing possible changes in forest extent, which will alter vegetation-climate feedbacks. However, the mode and strength of the response is rather unclear, as potential changes are happening in areas that are very remote and difficult to access, and empirical data are still largely lacking. Here, we assessed the current population structure and genetic differentiation of Larix Mill. tree stands within the northernmost latitudinal treeline reaching ~72° N in the southern lowlands of the Taymyr Peninsula (~100° E). We sampled 743 individuals belonging to different height classes (seedlings, saplings, trees) at eleven locations along a gradient from 'single tree' tundra over 'forest line' to 'dense forest' stands and conducted investigations applying eight highly polymorphic nuclear microsatellites. Results suggest a high diversity within subpopulations (HE=0.826-0.893), coupled, however, with heterozygote deficits in all subpopulations, but pronounced in 'forest line' stands. Overall, genetic differentiation of subpopulations is low (FST=0.005), indicating a region-wide high gene flow, although 'forest line' stands harbour few rare and private alleles, likely indicating greater local reproduction. 'Single tree' stands, located beyond the northern forest line, are currently not involved in treeline expansion, but show signs of a long-term refuge, namely asexual reproduction and change of growth-form from erect to creeping growth, possibly having persisted for thousands of years. The lack of differentiation between the subpopulations points to a sufficiently high dispersal potential, and thus a rapid northward migration of the Siberian arctic treeline under recent global warming seems potentially unconstrained, but observations show it to be unexpectedly slow.
    Keywords: AWI_Envi; Polar Terrestrial Environmental Systems @ AWI
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  • 41
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    PANGAEA
    In:  Supplement to: Beamish, Alison Leslie; Coops, Nicholas; Chabrillat, Sabine; Heim, Birgit (2017): A phenological approach to spectral differentiation of low-arctic tundra vegetation communities, North Slope, Alaska. Remote Sensing, 9(12), 1200, https://doi.org/10.3390/rs9111200
    Publication Date: 2023-03-16
    Description: Ground-based spectroscopy measurements acquired systematically within the Toolik Vegetation Grid in the 2015 and 2016 growing seasons and within the Imnavait Vegetation Grid in the 2016 growing season. Data were collected in 68 distinct 1 x 1 m long-term monitoring plots representing five distinct vegetation communities. Spectral measurements were acquired two times throughout the season in 2015 representing peak and late season and three times in 2016 representing early, peak and late season. Data were acquired using a GER 1500 field spectrometer (350-1050 nm; 512 bands, spectral resolution 3 nm, spectral sampling 1.5 nm, and 8! field of view). Spectra were collected under clear weather conditions at the highest solar zenith angle between 10:00 and 14:00 local time. Data were collected at nadir approximately 1 m off the ground resulting in a Ground Instantaneous Field of View (GIFOV) of approximately 15 cm in diameter. Nine point measurements of upwelling radiance (Lup) were collected in 1 x 1 m plots representative of the five vegetation communities and averaged to characterize the spectral variability and to reduce noise. Downwelling radiance (Ldown) was measured as the reflectance from a white Spectralon© plate. Surface reflectance (R) was processed as Lup/Ldown x 100 (0-100%). Reflectance spectra were preprocessed with a Savitzky-Golay smoothing filter (n = 11) and subset to 400-985 nm to remove sensor noise at the edges of the radiometer detector.
    Keywords: AWI_Envi; MULT; Multiple investigations; Polar Terrestrial Environmental Systems @ AWI; ToolikL_plot; Toolik Lake, Alaska
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 42
    Publication Date: 2023-03-16
    Description: Our data, as part of the OISO (Ocean Indien Service d'Observation) campaign, contributes to a better understanding of the physical and biological factors controlling N2 fixation in the Southern Indian Ocean and the French Southern and Antarctic lands during Austral summer January and February 2017. We measured N2 and C fixation as well as NH4+ and NO3- assimilation in 3-6 replicates per station. Additionally, we measured diagnostic pigment concentrations to evaluate phtosynthetic community composition. For pigment analysis 4L water was filtered through 25mm Whatman GF/F filters (pressure drop 〈10kPa). Samples were stored at -80°C until analysis. Pigments were analysed using High Performance Liquid Chromatography (HPLC). Pigment concentration were calculated according to Kilias et al (2013, doi:10.1111/jpy.12109). N2 fixation experiments were carried out in three to six replicates for each station. Incubations were done in pre-acid washed polycarbonate bottles on deck with ambient light conditions. All polycarbonate incubation bottles were rinsed with deionized water, and seawater prior to incubation. We used the combination of the bubble approach (Montoya et al., 1996) and the dissolution method (Mohr et al., 2010, doi:10.1371/journal.pone.0012583) proposed by Klawonn et al. (2015, doi:10.3389/fmicb.2015.00769). Bottles were filled up to capacity to avoid air contamination. Incubations were initialized by adding a 10 ml 15-15N gas bubble. Bottles were gently rocked for 15 minutes. Finally, the remaining bubble was removed to avoid equilibration between gas and aqueous phase. after 24 hours a water subsample was taken to a 12 ml exetainer and preserved with 100 µl HgCl2 solution for later determination of exact 15N-15N concentration. Natural 15N2 was determined using Membrane Inlet Mass Spectrometry (MIMS; GAM200, IPI) for each station. Analysis of 15N2 incorporated was carried out by the Isotopic Laboratory at the UC Davis, California campus. We used stable isotope tracers (15N) to measure dissolved inorganic nitrogen (DIN) assimilation rates. Experiments were initiated by adding a known concentration of 0.05 of K15NO3 and 15NH4Cl for oligotrophic waters of the IO and 0.625 µmol L-1 for HNLC regions in the ACC and PF (Knap et al., 1994, Waite et al., 2007, doi:10.1016/j.dsr2.2006.12.010) to one litre polycarbonate bottles. For C assimilation experiments, we added 20 µmol L-1 of NaH13CO3 to one of each of N2 fixation, NH4+ and NO3- assimilation experiment bottles. For incubation, we followed the same procedure as for N2 fixation experiments. Findings reveal that N2 fixation occurs throughout the whole sampling area up to 55°S latitude. In addition, variations of N2 fiaxation rates between replicates were relatively high indicating a great heterogeneity of the French Southern and Antarctic waters. References: Montoya 1996: Montoya, Joseph P., et al. "A Simple, High-Precision, High-Sensitivity Tracer Assay for N (inf2) Fixation." Applied and environmental microbiology 62.3 (1996): 986-993. Knap et al 1994: Knap, A., Michaels, A., Close, A., Ducklow, H. & Dickson, A. 1994. Protocols for the Joint Global Ocean Flux Study (JGOFS) Core Measurements, JGOFS, Reprint of the IOC Manuals and Guides No. 29. UNESCO, 19, 1.
    Type: Dataset
    Format: application/zip, 4 datasets
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  • 43
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    PANGAEA
    In:  Supplement to: van der Jagt, Helga; Friese, Carmen A; Stuut, Jan-Berend W; Fischer, Gerhard; Iversen, Morten Hvitfeldt (2018): The ballasting effect of Saharan dust deposition on aggregate dynamics and carbon export: Aggregation, settling, and scavenging potential of marine snow. Limnology and Oceanography, 63(3), 1386-1394, https://doi.org/10.1002/lno.10779
    Publication Date: 2023-03-16
    Description: Lithogenic material such as Saharan dust can be incorporated into organic aggregates and act as ballast, potentially enhancing the marine carbon export via increased sinking velocities of aggregates. We studied the ballasting effects of Saharan dust on the aggregate dynamics in the upwelling region off Cape Blanc (Mauritania). Aggregate formation from a natural plankton community exposed to Saharan dust deposition resulted in higher abundance of aggregates with higher sinking velocities compared to aggregate formation with low dust. This higher aggregate abundance and sinking velocities potentially increased the carbon export 10-fold when the aggregates were ballasted by Saharan dust. After aggregate formation in the surface waters, subsequent sinking through suspended Saharan dust minerals had no influence on aggregate sizes, abundance, and sinking velocities. We found that aggregates formed in the surface ocean off Mauritania were already heavily ballasted with lithogenic material and could therefore not scavenge any additional minerals during their descent. This suggests that carbon export to the deep ocean in regions with high dust deposition is strongly controlled by dust input to the surface ocean while suspended dust particles in deeper water layers do not significantly interact with sinking aggregates.
    Keywords: Center for Marine Environmental Sciences; MARUM; SeaPump; Seasonal and regional food web interactions with the biological pump
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 44
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    In:  Supplement to: Beamish, Alison Leslie; Coops, Nicholas; Hermosilla, T; Chabrillat, Sabine; Heim, Birgit (2018): Monitoring pigment-driven vegetation changes in a low-Arctic tundra ecosystem using digital cameras. Ecosphere, 9(2), e02123, https://doi.org/10.1002/ecs2.2123
    Publication Date: 2023-03-16
    Description: Ground-based spectroscopy measurements acquired systematically within the Toolik Vegetation Grid in the 2016 growing season. All data were collected in a subset of 1 x 1 m long-term monitoring plots representing three distinct vegetation communities three times representing early, peak and late season. Spectral data were acquired using a GER 1500 field spectrometer (350-1050 nm; 512 bands, spectral resolution 3 nm, spectral sampling 1.5 nm, and 8! field of view). Spectra were collected under clear weather conditions at the highest solar zenith angle between 10:00 and 14:00 local time. Data were collected at nadir approximately 1 m off the ground resulting in a Ground Instantaneous Field of View (GIFOV) of approximately 15 cm in diameter. Nine point measurements of upwelling radiance (Lup) were collected in each plot and averaged to characterize the spectral variability and to reduce noise. Downwelling radiance (Ldown) was measured as the reflectance from a white Spectralon© plate. Surface reflectance (R) was processed as Lup/Ldown x 100 (0-100%). Reflectance spectra were preprocessed with a Savitzky-Golay smoothing filter (n = 11) and subset to 400-985 nm to remove sensor noise at the edges of the radiometer detector. Digital camera data were acquired using a consumer-grade camera (Panasonic DM3 LMX, Japan) approximately 1 m off the ground with a white frame for registration of off nadir images. For detailed definitions of the RGB indices see metadata.docx. Leaves and stems of the dominant vascular species in a subset of the sampled plots were collected at early, peak, and late season for chlorophyll and carotenoid analysis.Samples were placed in porous tea bags and preserved in a silica gel desiccant in an opaque container for up to 3 months until pigment extraction (Esteban et al. 2009, doi:10.1007/s11120-009-9468-5). Each sample was homogenized by grinding with a mortar and pestle. Approximately 1.00 mg (+/- 0.05 mg) of homogenized sample was placed into a vial with 2 ml of dimethylformamide (DMF). Vials were then wrapped in aluminum foil to eliminate any degradation of pigments due to UV light and stored in a fridge (4C) for 24 hrs. Samples were measured into a cuvette prior to spectrophotometric analysis. Bulk pigments concentrations were then estimated using a spectrophotometer measuring absorption at 646.8, 663.8 and 480 nm (Porra et al. 1989, doi:10.1016/S0005-2728(89)80347-0) . Absorbance (A) values at specific wavelengths were transformed into µg/mg concentrations of chlorophyll a, Chla, chlorophyll b, Chlb, total chlorophyll, Chl, carotenoids, Car (for equations see metadata.docx). Pigment concentration was calculated as the average concentration of the dominant species in each plot. mean_"pigment" represents the mean of all biomass from each vegetation community and sd_"pigment" represents the standard deviation of each vegetation community.
    Keywords: AWI_Envi; MULT; Multiple investigations; Polar Terrestrial Environmental Systems @ AWI; ToolikL_plot; Toolik Lake, Alaska
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 45
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    In:  Supplement to: Park, Eunmi; Hefter, Jens; Fischer, Gerhard; Mollenhauer, Gesine (2018): TEX86 in sinking particles in three eastern Atlantic upwelling regimes. Organic Geochemistry, 124, 151-163, https://doi.org/10.1016/j.orggeochem.2018.07.015
    Publication Date: 2023-03-16
    Description: Seasonal variations in fluxes of isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs) and the estimated temperatures based on TEX86 are examined in sinking particles collected using moored sediment traps in the eastern Atlantic upwelling regions. In the equatorial Guinea Basin, GDGT fluxes show a correlation with opal fluxes, implying that GDGTs are mainly transported via aggregation with diatoms. The flux-weighted TEXH86 temperatures derived from particles collected both at 853 m and 3,921 m depth correspond to the water temperature (24.1 °C) of ca. 50 m depth, where nitrate concentration starts to increase, potentially as a consequence of nitrification by Thaumarchaeota. This suggests that nutrient concentrations may affect the depth habitat of Thaumarchaeota, and it determines at which water depth the TEXH86 temperature is recorded. In the coastal upwelling off Namibia, TEXH86 temperatures are similar to satellite-derived sea surface temperature (SST) during the warm season, but the record derived from the trap is delayed relative to the SST by approximately 26 days. Warm biases, however, occur during the cold season, similar to what has previously been observed in the filamentous upwelling region off Cape Blanc. In both coastal upwelling regions, oxygen minimum zones (OMZs) are a common feature, and higher TEX86 values have been found within the OMZs in the water column off Cape Blanc and elsewhere. Thus, contributions from GDGTs produced in OMZs might explain the warmer temperature estimates during the cold season in both regions. We thus conclude that in the eastern Atlantic upwelling system, TEXH86 temperature estimates are influenced by non-thermal factors such as nutrient depth distributions and GDGTs produced in the OMZ. In paleoenvironmental records of TEX86, non-thermal signals have to be considered on regional scales.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 46
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    In:  Supplement to: Pauli, Nora-Charlotte; Paiva, Filipa; Briski, Elizabeta (2018): Are Ponto-Caspian species able to cross salinity barriers? A case study of the gammarid Pontogammarus maeoticus. Ecology and Evolution, 8(19), 9817-9826, https://doi.org/10.1002/ece3.4461
    Publication Date: 2023-03-16
    Description: Recently, Ponto-Caspian species (i.e. area of Azov, Black and Caspian Seas) have invaded brackish and freshwater habitats of the North and Baltic Seas and the Laurentian Great Lakes in much higher numbers than expected based on shipping frequency and environmental conditions among these regions. Therefore, it has been hypothesized that Ponto-Caspian species may have inherent advantages over other species in colonizing new habitats, or that they are of freshwater origin. To test these hypotheses, we conducted artificial selection experiment on Ponto-Caspian amphipod Pontogammarus maeoticus collected from 10 PSU to evaluate adaptation capacity of this species to different salinities. Our results indicated that selection to lower salinity than that of population's ambient salinity is possible. Though, generation time in lower salinity conditions took slightly longer. On the contrary, selection to higher salinity was unsuccessful. Taking into account the results from this and previous studies and the geological history of the Ponto-Caspian region, we suggest that majority of the Ponto-Caspian relict fauna might be of freshwater origin and lack necessary genetic background for adaptation to fully marine conditions. Further selection studies using more species and populations, as well as molecular techniques, should be conducted to confirm this hypothesis on a broader scale. Consequently, if Ponto-Caspian relict species are of freshwater origin, the perception that they are better colonizers than species from other regions might be inclined by the fact that areas with biggest introduction frequency of nonindigenous species (i.e., shipping ports) are environmentally variable habitats which often include freshwater conditions.
    Keywords: AWI_BioOce; Biological Oceanography @ AWI
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 47
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    In:  Supplement to: Hörner, Tanja; Stein, Ruediger; Fahl, Kirsten (2018): Paleo-sea ice distribution and polynya variability on the Kara sea shelf during the last 12 ka. arktos - The Journal of Arctic Geosciences, https://doi.org/10.1007/s41063-018-0040-4
    Publication Date: 2023-03-16
    Description: The Kara Sea is an important area for paleo-climatic research since sea ice and brine formation take place on its shelf-two processes inducing supra-regional climatic implications and thereby connecting regional environmental variability with global climatic conditions. To gain information about past sea ice coverage and variations, three sediment cores distributed in the southern and central parts of the marginal Sea were investigated. By applying the sea ice biomarker IP25 and the PIP25 index [phytoplankton biomarker (dinosterol)-IP25 index] post-glacial sea ice variability could be detected in the central Kara Sea (Core BP00-36/4), with most intense sea ice cover between 12.4 and 11.8 ka coinciding with the Younger Dryas (12.9-11.6 ka), and reduced sea ice cover between 10 and 8 ka during the Holocene Thermal Maximum. During the last ~7 ka, increasing sea ice indicators might indicate a Holocene cooling trend, probably induced by declining summer insolation. Furthermore, temporal changes in the fast ice?polynya distribution in the southern Kara Sea were detected: expanding fast ice during the late Holocene and a cyclic short-term Holocene climate variability documented by abrupt changes in the sea ice coverage at the BP00-07/7 core site. Core BP99-04/7 from the Yenisei estuary recorded consistently seasonal sea ice cover since ~9.3 ka, apart from five short phases of fast ice expansion to the core site. The strong influence of river run-off as well as estuary processes might prevent the detection of (short-term) climatic signals at this study site.
    Keywords: AWI_Paleo; Paleoenvironmental Reconstructions from Marine Sediments @ AWI
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 48
    Publication Date: 2023-03-14
    Description: Ocean acidification causes an accumulation of CO2 in marine organisms and leads to shifts in acid-base parameters. Acid-base regulation in gill breathers involves a net increase of internal bicarbonate levels through transmembrane ion exchange with the surrounding water. Successful maintenance of body fluid pH depends on the functional capacity of ion-exchange mechanisms and associated energy budget. For a detailed understanding of the dependence of acid-base regulation on water parameters, we investigated the physiological responses of the shore crab Carcinus maenas to 4 weeks of ocean acidification [OA, P(CO2)w = 1800 µatm], at variable water bicarbonate levels, paralleled by changes in water pH. Cardiovascular performance was determined together with extra-(pHe) and intracellular pH (pHi), oxygen consumption, haemolymph CO2 parameters, and ion composition. High water P(CO2) caused haemolymph P(CO2) to rise, but pHe and pHi remained constant due to increased haemolymph and cellular [HCO3-]. This process was effective even under reduced seawater pH and bicarbonate concentrations. While extracellular cation concentrations increased throughout, anion levels remained constant or decreased. Despite similar levels of haemolymph pH and ion concentrations under OA, metabolic rates, and haemolymph flow were significantly depressed by 40 and 30%, respectively, when OA was combined with reduced seawater [HCO3-] and pH. Our findings suggest an influence of water bicarbonate levels on metabolic rates as well as on correlations between blood flow and pHe. This previously unknown phenomenon should direct attention to pathways of acid-base regulation and their potential feedback on whole-animal energy demand, in relation with changing seawater carbonate parameters.
    Keywords: Bicarbonate; Eggs, hatched; EXP; Experiment; Incubation duration; pH; Pressure, air; Salinity; Spiekeroog_Island; Temperature, water; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 288 data points
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  • 49
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    In:  Institut für Meereskunde, Universität Hamburg
    Publication Date: 2023-03-10
    Keywords: cruise 02+03; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Event label; KJACK0107; KJACK2001_101; KJACK2001_102; KJACK2001_103; KJACK2001_104; KJACK2001_105; KJACK2001_106; KJACK2001_107; KJACK2001_108; KJACK2001_109; KJACK2001_110; KJACK2001_111; KJACK2001_112; KJACK2001_113; KJACK2001_114; KJACK2001_115; KJACK2001_116; KJACK2001_117; KJACK2001_118; KJACK2001_119; KJACK2001_120; KJACK2001_121; KJACK2001_122; KJACK2001_123; KJACK2001_124; KJACK2001_125; KJACK2001_126; KJACK2001_127; KJACK2001_128; KJACK2001_129; KJACK2001_130; KJACK2001_131; KJACK2001_132; KJACK2001_133; KJACK2001_134; KJACK2001_135; KJACK2001_136; KJACK2001_137; KJACK2001_138; KJACK2001_139; KJACK2001_140; KJACK2001_141; KJACK2001_142; KJACK2001_143; KJACK2001_144; KJACK2001_145; KJACK2001_146; KJACK2001_147; KJACK2001_148; KJACK2001_149; KJACK2001_150; KJACK2001_151; KJACK2001_152; KJACK2001_153; KJACK2001_154; KJACK2001_155; KJACK2001_156; KJACK2001_157; KJACK2001_158; KJACK2001_159; KJACK2001_160; KJACK2001_161; KJACK2001_162; KJACK2001_163; KJACK2001_164; KJACK2001_165; KJACK2001_166; KJACK2001_167; KJACK2001_169; KJACK2001_170; KJACK2001_171; KJACK2001_172; KJACK2001_173; KJACK2001_174; KJACK2001_175; KJACK2001_176; KJACK2001_177; KJACK2001_178; KJACK2001_179; KJACK2001_180; KJACK2001_181; KJACK2001_182; KJACK2001_183; KJACK2001_184; KJACK2001_185; KJACK2001_186; KJACK2001_187; KJACK2001_188; KJACK2001_189; KJACK2001_190; KJACK2001_191; KJACK2001_192; KJACK2001_193; KJACK2001_194; KJACK2001_195; KJACK2001_196; KJACK2001_197; KJACK2001_198; KJACK2001_199; KJACK2001_200; KJACK2001_201; KJACK2001_202; KJACK2001_203; KJACK2001_204; KJACK2001_205; KJACK2001_206; KJACK2001_207; KJACK2001_208; KJACK2001_209; KJACK2001_210; KJACK2001_211; KJACK2001_212; KJACK2001_213; KJACK2001_214; KJACK2001_215; KJACK2001_216; KJACK2001_217; KJACK2001_218; KJACK2001_219; KJACK2001_220; KJACK2001_221; KJACK2001_222; KJACK2001_223; KJACK2001_224; KJACK2001_225; KJACK2001_226; KJACK2001_227; KJACK2001_228; KJACK2001_229; KJACK2001_230; KJACK2001_231; KJACK2001_232; KJACK2001_233; KJACK2001_234; KJACK2001_235; KJACK2001_236; KJACK2001_237; KJACK2001_238; KJACK2001_239; KJACK2001_240; KJACK2001_241; KJACK2001_242; KJACK2001_243; KJACK2001_244; KJACK2001_245; KJACK2001_246; KJACK2001_247; KJACK2001_248; KJACK2001_249; KJACK2001_250; KJACK2001_251; KJACK2001_252; KJACK2001_253; KJACK2001_254; KJACK2001_255; KJACK2001_256; KJACK2001_257; KJACK2001_258; KJACK2001_259; KJACK2001_260; KJACK2001_261; KJACK2001_262; KJACK2001_263; KJACK2001_264; KJACK2001_265; KJACK2001_266; KJACK2001_267; KJACK2001_268; KJACK2001_269; KJACK2001_270; KJACK2001_271; Kommandor Jack; LATITUDE; LONGITUDE; Oxygen; Oxygen, dissolved; Pressure, water; Recalculated from ml/l by using (ml/l)*44.66; Salinity; South Atlantic Ocean; Temperature, water; UniHH_CTD
    Type: Dataset
    Format: text/tab-separated-values, 436810 data points
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  • 50
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    In:  Institut für Meereskunde, Universität Hamburg
    Publication Date: 2023-03-10
    Keywords: Chlorophyll a; Conductivity; CTD, Seabird; CTD/Rosette; CTD-R; CTD-RO; DATE/TIME; DEPTH, water; Elevation of event; Event label; LATITUDE; LONGITUDE; Pressure, water; Salinity; South Atlantic Ocean; Temperature, water; Transmission of light; UniHH_CTD; VA176; VA176_00-3; VA176_01-2; VA176_01-3; VA176_01-4; VA176_01-5; VA176_02; VA176_02-1; VA176_02-2; VA176_02-3; VA176_02-4; VA176_02-5; VA176_03; VA176_03-1; VA176_03-2; VA176_03-3; VA176_03-4; VA176_03-5; VA176_04; VA176_04-1; VA176_04-2; VA176_04-3; VA176_04-4; VA176_04-5; VA176_05; VA176_05-1; VA176_05-2; VA176_05-3; VA176_05-4; VA176_05-5; VA176_06; VA176_07; VA176_08; VA176_08-1; VA176_08-2; VA176_08-3; VA176_08-4; VA176_08-5; VA176_09; VA176_10; VA176_11; VA176_12; VA176_13; VA176_14; VA176_15; VA176_15-1; VA176_15-2; VA176_15-3; VA176_15-4; VA176_15-5; VA176_16; VA176_17; VA176_18; VA176_19; VA176_20; VA176_21; VA176_22; VA176_23; VA176_24; VA176_25; VA176_26; VA176_26-1; VA176_26-2; VA176_26-3; VA176_26-4; VA176_26-5; VA176_27; VA176_28; VA176_29; VA176_30; VA176_30-1; VA176_30-2; VA176_30-3; VA176_30-4; VA176_30-5; VA176_30-6; VA176_31; VA176_32; VA176_33; VA176_34; VA176_35; VA176_36; VA176_36-1; VA176_37; VA176_38; VA176_39; VA176_40; VA176_41; VA176_42; VA176_43; Valdivia (1961)
    Type: Dataset
    Format: text/tab-separated-values, 371658 data points
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  • 51
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    In:  Institut für Meereskunde, Universität Hamburg
    Publication Date: 2023-03-10
    Keywords: Conductivity; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Event label; Greenland Sea; Latitude of event; Longitude of event; POS290; POS290_336; POS290_337; POS290_338; POS290_339; POS290_341; POS290_342; POS290_343; POS290_344; POS290_345; POS290_346; POS290_347; POS290_355; POS290_356; POS290_358; POS290_359; POS290_360; POS290_361; POS290_362; POS290_363; POS290_364; POS290_365; POS290_366; POS290_367; POS290_368; POS290_369; POS290_370; POS290_371; POS290_376; POS290_378; POS290_379; POS290_380; POS290_381; POS290_385; POS290_386; POS290_387; POS290_388; POS290_389; POS290_390; POS290_392; POS290_394; POS290_395; POS290_396; POS290_397; POS290_398; POS290_399; POS290_400; POS290_401; POS290_402; POS290_403; POS290_404; POS290_405; POS290_406; POS290_407; POS290_408; POS290_409; POS290_410; POS290_411; POS290_412; POS290_413; POS290_414; POS290_415; POS290_416; POS290_417; POS290_418; POS290_419; POS290_420; POS290_421; POS290_422; POS290_423; POS290_424; POS290_425; POS290_426; POS290_427; POS290_428; POS290_429; POS290_430; Poseidon; Pressure, water; Salinity; South Atlantic Ocean; Temperature, water; Transmission of light; UniHH_CTD
    Type: Dataset
    Format: text/tab-separated-values, 688315 data points
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  • 52
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    In:  Institut für Meereskunde, Universität Hamburg
    Publication Date: 2023-03-10
    Keywords: Conductivity; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Event label; KONTROL 85; Latitude of event; Longitude of event; Pressure, water; Salinity; South Atlantic Ocean; Temperature, water; UniHH_CTD; VA35; VA35_01; VA35_02; VA35_03; VA35_04; VA35_05; VA35_06; VA35_07; VA35_08; VA35_09; VA35_10; VA35_11; VA35_12; VA35_13; VA35_14; VA35_15; VA35_16; VA35_17; VA35_18; VA35_19; VA35_20; VA35_21; VA35_22; VA35_23; VA35_24; VA35_25; VA35_26; VA35_27; VA35_28; VA35_29; VA35_30; VA35_31; VA35_32; VA35_33; VA35_34; VA35_35; VA35_36; VA35_37; VA35_38; VA35_39; VA35_40; Valdivia (1961)
    Type: Dataset
    Format: text/tab-separated-values, 13808 data points
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  • 53
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    In:  Supplement to: Asmala, Eero; Haraguchi, Lumi; Markager, Stiig; Massicotte, Philippe; Riemann, Bo; Staehr, Peter A; Carstensen, Jacob (2018): Eutrophication leads to accumulation of recalcitrant autochthonous organic matter in coastal environment. Global Biogeochemical Cycles, 32(11), 1673-1687, https://doi.org/10.1029/2017GB005848
    Publication Date: 2023-03-14
    Description: Anthropogenic nutrient enrichment is changing the structure and the function of coastal ecosystems. These coastal zones are transitions between freshwater and marine systems where multiple biogeochemical processes remove, produce and transform organic matter. The extent to which the coastal zone is merely a conduit for terrestrial (allochthonous) organic matter, versus a distinct source of autochthonous organic matter fueled by eutrophication, is unclear. To address this issue, we characterized the freshwater and marine dissolved organic matter (DOM) pools in an eutrophic estuary with a long water residence time (Roskilde Fjord, Denmark) over an annual cycle. We combined elemental, optical (absorbance and fluorescence) and isotopic analyses to obtain insight about the bulk properties of the DOM pool during this period. We also used sediment traps to analyze the changes related to the exchange of organic matter between the particulate (POM) and dissolved (DOM) fractions. The results showed that labile autochthonous DOM from in situ primary production was rapidly transformed to more recalcitrant DOM that accumulated in the estuary despite continuous exchange with the open sea. Also, parts of the POM pool were degraded rapidly (within 24h) and transformed into the DOM pool. Accumulated DOM was characterized by relatively low molecular size and stable carbon isotopic value, and by high protein-like fluorescence. These results indicate that autotrophic material can be a major source of specific recalcitrant DOM in eutrophic coastal waters, contributing significantly to the flux of organic carbon to the ocean.
    Keywords: Absorption coefficient, 230 nm; Absorption coefficient, 254 nm; Absorption coefficient, 275 nm; Absorption coefficient, 295 nm; Absorption coefficient, 300 nm; Absorption coefficient, 350 nm; Absorption coefficient, 355 nm; Absorption coefficient, 375 nm; Absorption coefficient, 400 nm; Absorption coefficient, 440 nm; Ammonium; Biological index; Carbon, organic, dissolved; Carbon, organic, total; Chlorophyll a; Conductivity; DATE/TIME; DEPTH, water; Event label; Fluorescence, peak A; Fluorescence, peak C; Fluorescence, peak M; Fluorescence, peak T; Fluorescence index; Humification index; LATITUDE; LONGITUDE; Molecular mass; MULT; Multiple investigations; Nitrate; Nitrate and Nitrite; Nitrite; Nitrogen, inorganic, dissolved; Nitrogen, organic, dissolved; Nitrogen, total; Nitrogen, total dissolved; PDZ Europa ANCA-GSL elemental analyser; pH; Phosphate; Phosphorus, inorganic, dissolved; Phosphorus, organic, dissolved; Phosphorus, total; Phosphorus, total dissolved; Ratio; Roskilde_fjord_RF01; Roskilde_fjord_RF02; Roskilde_fjord_RF03; Roskilde_fjord_RF04; Roskilde_fjord_RF05; Roskilde_fjord_RF06; Roskilde_fjord_RF07; Roskilde_fjord_RF08; Salinity; Sample ID; SEC analyser; Silicate; Specific ultraviolet absorbance normalized to DOC, 254 nm; Spectral slope, 275-295 nm; Spectral slope, 300-650 nm; Spectral slope, 350-400 nm; Spectrophotometer UV/VIS (Shimadzu 2401PC); Temperature, water; Varian Cary Eclipse fluorometer (Agilent); Wet oxidation total organic carbon analyzer Shimadzu; δ13C, dissolved organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 7907 data points
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  • 54
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    In:  Supplement to: Juhlke, Tobias René; van Geldern, Robert; Huneau, Frédéric; Garel, Emilie; Santoni, Sébastien; Hemmerle, Hannes; Barth, Johannes A C (2019): Riverine carbon dioxide evasion along a high-relief watercourse derived from seasonal dynamics of the water-atmosphere gas exchange. Science of the Total Environment, 657, 1311-1322, https://doi.org/10.1016/j.scitotenv.2018.12.158
    Publication Date: 2023-03-14
    Description: The high-relief catchment of the Tavignanu River (Corsica Island, France) with an elevation range from sea level to 2622 m above sea level was investigated from January 2016 to April 2017 for its on-site parameters (T, pH, EC, TA), riverine carbon budget (TCO2) and stable carbon isotopes (d13C). Partial pressure of CO2 (pCO2) and annual carbon flux across the air-water boundary (FCO2) were calculated from analytical results. This data set contains the supplementary analytical data of the related publication.
    Keywords: Alkalinity, total; Carbon, inorganic, total; Carbon dioxide, flux; Carbon dioxide, flux, standard deviation; Carbon dioxide, partial pressure; Carbon dioxide, partial pressure, standard deviation; Conductivity, electrical; DATE/TIME; Distance; Elevation of event; Event label; I_Orta; II_Zincaghju; III_Vechju; IV_Corsigliese; Latitude of event; Longitude of event; pH; Restonica_R1; Restonica_R2; Restonica_R3; Restonica_R4; Restonica_R5; Restonica_R6; Site; Tavignanu_1; Tavignanu_10; Tavignanu_2; Tavignanu_3; Tavignanu_4; Tavignanu_5; Tavignanu_6; Tavignanu_7; Tavignanu_8; Tavignanu_9; Temperature, water; V_Tagnone; Water sample; WS; δ13C, dissolved inorganic carbon
    Type: Dataset
    Format: text/tab-separated-values, 920 data points
    Location Call Number Expected Availability
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  • 55
    Publication Date: 2023-03-14
    Keywords: Acid titration; Area/locality; Aufsess; Bicarbonate ion; Calcium; Chloride; Conductivity, electrolytic; DATE/TIME; Distance; Ion chromatography; Kainach; LATITUDE; Leinleiter; LONGITUDE; Magnesium; Nitrate; Oxygen; pH; Phosphate; Potassium; Puettlach; Sample ID; Site; Sodium; Southern Germany; Sulfate; Temperature, water; Trubach; Truppach; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 590 data points
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  • 56
    Publication Date: 2023-03-14
    Keywords: Acid titration; Area/locality; Bicarbonate ion; Calcium; Chloride; Conductivity, electrolytic; DATE/TIME; Distance; Ion chromatography; LATITUDE; LONGITUDE; Magnesium; Nitrate; Oxygen; pH; Phosphate; Potassium; Sample ID; Site; Sodium; Southern Germany; Sulfate; Temperature, water; Water sample; Wiesent_River; WS
    Type: Dataset
    Format: text/tab-separated-values, 1063 data points
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  • 57
    Publication Date: 2023-03-14
    Keywords: Ammonium; Bicarbonate ion; Carbon, inorganic, total; Carbon, organic, dissolved; Carbon, organic, particulate; Carbon, organic, total; Carbon, total; Carbon dioxide; Chlorophyll a; Conductivity, electrical; DATE/TIME; DEPTH, water; Flow velocity, water; Laguna_LaConceja; Location; MULT; Multiple investigations; Nitrate; Nitrite; Nitrogen, inorganic, total; Nitrogen, organic, dissolved; Nitrogen, organic, total; Nitrogen, total; Nitrogen, total dissolved; pH; Phosphorus, reactive soluble; Phosphorus, total; River discharge; River level; River width; Spain; Suspended particulate matter; Visibility; Volume
    Type: Dataset
    Format: text/tab-separated-values, 1963 data points
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  • 58
    Publication Date: 2023-03-14
    Keywords: Bicarbonate; Carbon dioxide; Carbon dioxide, flux; Carbon dioxide, gradient at air/water interface; Carbon dioxide, partial pressure; Carbon dioxide (water) partial pressure; DATE/TIME; DEPTH, water; Laguna_LaConceja; MULT; Multiple investigations; pH; Piston velocity; Salinity; Spain; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 315 data points
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  • 59
    Publication Date: 2023-03-14
    Keywords: Alkalinity, total; Experiment day; Nitrate; Nitrite; Nitrogen, inorganic, dissolved; Nitrogen in ammonium; Number; pH; Phosphate; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 840 data points
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  • 60
    Publication Date: 2023-03-14
    Keywords: Aluminium; Automatic water sampler (ISCO 3700); AWI_PerDyn; AWS_ISCO; Barium 2+; Bicarbonate ion; Bromide; Calcium; Carbon, organic, dissolved; Chloride; Conductivity, electrical; DATE/TIME; Fluoride; Herschel Island, Yukon Territory, Canada; Ice_Creek_West; Iron; Magnesium; Manganese 2+; Nitrate; Permafrost Research (Periglacial Dynamics) @ AWI; pH; Phosphorus; Potassium; Sample code/label; Silicon; Sodium; Strontium 2+; Sulfate
    Type: Dataset
    Format: text/tab-separated-values, 1260 data points
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  • 61
    Publication Date: 2023-03-14
    Keywords: Aluminium; Automatic water sampler (ISCO 3700); AWI_PerDyn; AWS_ISCO; Barium 2+; Bicarbonate ion; Bromide; Calcium; Carbon, organic, dissolved; Chloride; Conductivity, electrical; DATE/TIME; Fluoride; Herschel Island, Yukon Territory, Canada; Ice_Creek_West; Iron; Magnesium; Manganese 2+; Nitrate; Nitrogen, total dissolved; Oxygen saturation; Permafrost Research (Periglacial Dynamics) @ AWI; pH; Phosphate; Phosphorus; Potassium; Sample code/label; Silicon; Sodium; Strontium 2+; Sulfate; Suspended particulate matter
    Type: Dataset
    Format: text/tab-separated-values, 896 data points
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  • 62
    Publication Date: 2023-03-14
    Keywords: Alkalinity, total; Calculated; Carbon, inorganic, dissolved; Carbon dioxide, partial pressure; CO2 acid liberation; Conductivity, electrical; Czech Republic; DATE/TIME; Element analyser isotope ratio mass spectrometer (EA-IRMS); Elevation of event; Event label; Isotope ratio mass spectrometry; Latitude of event; Longitude of event; Multiparameter instrument; Original value; Oxidation reduction (RedOx) potential; Oxygen; Oxygen saturation; pH; Sample ID; Temperature, water; Uhlirska_DST; Uhlirska_HST; Uhlirska_P84; Water sample; WS; δ13C, dissolved inorganic carbon; δ13C, particulate organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 772 data points
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  • 63
    Publication Date: 2023-03-14
    Keywords: Alkalinity, total; Calculated; Carbon, inorganic, dissolved; Carbon dioxide, partial pressure; CO2 acid liberation; Conductivity, electrical; Czech Republic; DATE/TIME; DEPTH, water; Element analyser isotope ratio mass spectrometer (EA-IRMS); Isotope ratio mass spectrometry; Multiparameter instrument; Original value; Oxidation reduction (RedOx) potential; Oxygen; Oxygen saturation; pH; River discharge; Sample ID; Temperature, water; Uhlirska_UHL; V-notch weir; Water sample; WS; δ13C, dissolved inorganic carbon; δ13C, particulate organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 855 data points
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  • 64
    Publication Date: 2023-03-14
    Keywords: Alkalinity, total; Aragonite saturation state; Bicarbonate ion; Calcite saturation state; Calculated using CO2SYS; Carbonate ion; Carbon dioxide (water) partial pressure; DATE/TIME; Dry air column-averaged mixing ratio of carbon dioxide; Kristineberg_Loven-Centre; Number; Oxygen; pH; Research station; RS; Salinity; Sweden; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 404 data points
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  • 65
    Publication Date: 2023-03-14
    Keywords: Classification; Color, a*; Color, b*; Color, L*, lightness; Conductivity; DEPTH, sediment/rock; Elba; Elba_F-I; F-I; Gravel; Gravel classification according to Ad-Hoc-AG Boden 2005 (KA5); Hand-held checker, 1:2.5 sample:ddH2O solution; Hand-held checker, 1:2.5 sample:KCl solution; Loss on ignition; Magnetic susceptibility, frequency dependence; Magnetic susceptibility, frequency dependence, standard deviation; Magnetic susceptibility, low-field; MS2B sensor; pH; Portable spectrophotometer Minolta CM-2500d; VC; Vibro corer
    Type: Dataset
    Format: text/tab-separated-values, 1076 data points
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  • 66
    Publication Date: 2023-03-14
    Keywords: CHN +S analyser; Classification; Color, a*; Color, b*; Color, L*, lightness; Conductivity; DEPTH, sediment/rock; Elba; Elba_S-I; Gravel; Gravel classification according to Ad-Hoc-AG Boden 2005 (KA5); Hand-held checker, 1:2.5 sample:ddH2O solution; Hand-held checker, 1:2.5 sample:KCl solution; Loss on ignition; Magnetic susceptibility, frequency dependence; Magnetic susceptibility, frequency dependence, standard deviation; Magnetic susceptibility, low-field; MS2B sensor; pH; Portable spectrophotometer Minolta CM-2500d; S-I; Sulfur, total; VC; Vibro corer
    Type: Dataset
    Format: text/tab-separated-values, 1351 data points
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  • 67
    Publication Date: 2023-03-14
    Keywords: Africa, Ethiopia; Branched and isoprenoid tetraether index; Calculated; Cyclization ratio of branched tetraethers; DEPTH, soil; Depth, soil, maximum; Depth, soil, minimum; Elevation of event; Event label; I; II; III; IV; Jimma_zone_I; Jimma_zone_II; Jimma_zone_III; Jimma_zone_IV; Jimma_zone_V; Jimma_zone_VI; Land use; Methylation index of dominant branched tetraethers; MULT; Multiple investigations; Optional event label; pH; Temperature, annual mean; V; VI
    Type: Dataset
    Format: text/tab-separated-values, 144 data points
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  • 68
    Publication Date: 2023-03-14
    Keywords: Alkalinity, total; Autotitrator Metrohm 785 and electrode; Barium/Calcium ratio; Barium/Calcium ratio, standard deviation; Comment; Experiment; ICP-MS, Agilent 7500-ce; Number of observations; pH; Strontium-86/Strontium-88, standard deviation; Strontium-86/Strontium-88 ratio; Strontium-87/Strontium-88, standard deviation; Strontium-87/Strontium-88 ratio; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 98 data points
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  • 69
    Publication Date: 2023-03-14
    Keywords: Barium/Calcium ratio; Experiment; ICP-MS, Agilent 7500-ce; LA-ICP-MS, Laser-ablation inductively coupled plasma mass spectrometer; Neogloboquadrina dutertrei, Barium/Calcium ratio; pH; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 325 data points
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  • 70
    facet.materialart.
    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Description: This data set was collected during the R. V. Polarstern cruise PS82. Outside territorial waters, current velocities were measured nearly continuously along the ship's track with a vessel-mounted TRD Instruments 153.6-kHz Ocean Surveyor ADCP. The transducers were located 11 meters below the water line and were protected against ice floes by an acoustically transparent plastic window. The current measurements used a pulse of 2 seconds and vertical bin length of 4 meters. The ship's velocity was calculated from position fixes obtained by the Global Positioning System (GPS). Heading, roll and pitch data from the ship's gyro platforms and the navigation data were used to convert the ADCP velocities into earth coordinates. The accuracy of the ADCP velocities mainly depends on the quality of the position fixes and the ship's heading data. Further errors stem from a misalignment of the transducer with the ship's centerline. The ADCP data were processed using the Ocean Surveyor Sputum Interpreter (OSSI) software developed by GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel. The reference layer was set to avoid near surface effects. The averaging interval was set to 120 seconds. Sampling interval setting: 2s; Number of bins: 80; Bin length: 4m; Pulse length: 4m; Blank beyond transmit length: 4m. Data processing setting: Upper depth of reference layer: 30m; Lower depth of reference layer: 80m; Time average: 120s; Misalignment angle: 0.9200 (from 2014-01-01T09:22:00 to 2014-02-04T15:34:00 Misalignment angle: 0.8600); amplitude: 1.0225. The precision for single ping and 4m cell size reported by TRDI is 0.30m/s. Resulting from the single ping precision and the number of pings (most of the time 36) during 120seconds the velocity accuracy is nearly 0.05m/s. (Velocity accuracy = single ping precision divided by square root of the number of pings).
    Keywords: Acoustic Doppler Current Profiling (ADCP), TRDI Ocean Surveyor, 153.6 kHz; ANT-XXIX/9; AWI_PhyOce; CT; Current velocity, east-west; Current velocity, north-south; DATE/TIME; DEPTH, water; LATITUDE; LONGITUDE; Physical Oceanography @ AWI; Polarstern; PS82; PS82-track; Quality; Underway cruise track measurements; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 8047893 data points
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  • 71
    Publication Date: 2023-03-16
    Keywords: Arctic Ocean; ARK-XXVI/3; DATE/TIME; DEPTH, water; Elevation of event; Event label; FRAM; FRontiers in Arctic marine Monitoring; Laptev Sea; Latitude of event; Longitude of event; Polarstern; PS78/216-2; PS78/226-2; PS78/227-3; PS78/230-3; PS78/235-3; PS78/242-2; PS78/245-4; PS78/259-2; PS78/262-2; PS78/265-2; PS78/270-3; PS78/272-1; PS78/276-2; PS78/280-2; PS78/300-2; PS78 TransArc; RAMSES; RAMSES-ACC-VIS hyperspectral radiometer and RAMSES-ARC hyperspectral radiometer, TriOS; RAMSES hyperspectral radiometer; Remote sensing reflectance at 350 nm; Remote sensing reflectance at 351 nm; Remote sensing reflectance at 352 nm; Remote sensing reflectance at 353 nm; Remote sensing reflectance at 354 nm; Remote sensing reflectance at 355 nm; Remote sensing reflectance at 356 nm; Remote sensing reflectance at 357 nm; Remote sensing reflectance at 358 nm; Remote sensing reflectance at 359 nm; Remote sensing reflectance at 360 nm; Remote sensing reflectance at 361 nm; Remote sensing reflectance at 362 nm; Remote sensing reflectance at 363 nm; Remote sensing reflectance at 364 nm; Remote sensing reflectance at 365 nm; Remote sensing reflectance at 366 nm; Remote sensing reflectance at 367 nm; Remote sensing reflectance at 368 nm; Remote sensing reflectance at 369 nm; Remote sensing reflectance at 370 nm; Remote sensing reflectance at 371 nm; Remote sensing reflectance at 372 nm; Remote sensing reflectance at 373 nm; Remote sensing reflectance at 374 nm; Remote sensing reflectance at 375 nm; Remote sensing reflectance at 376 nm; Remote sensing reflectance at 377 nm; Remote sensing reflectance at 378 nm; Remote sensing reflectance at 379 nm; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 381 nm; Remote sensing reflectance at 382 nm; Remote sensing reflectance at 383 nm; Remote sensing reflectance at 384 nm; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 386 nm; Remote sensing reflectance at 387 nm; Remote sensing reflectance at 388 nm; Remote sensing reflectance at 389 nm; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 391 nm; Remote sensing reflectance at 392 nm; Remote sensing reflectance at 393 nm; Remote sensing reflectance at 394 nm; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 396 nm; Remote sensing reflectance at 397 nm; Remote sensing reflectance at 398 nm; Remote sensing reflectance at 399 nm; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 401 nm; Remote sensing reflectance at 402 nm; Remote sensing reflectance at 403 nm; Remote sensing reflectance at 404 nm; Remote sensing reflectance at 405 nm; Remote sensing reflectance at 406 nm; Remote sensing reflectance at 407 nm; Remote sensing reflectance at 408 nm; Remote sensing reflectance at 409 nm; Remote sensing reflectance at 410 nm; Remote sensing reflectance at 411 nm; Remote sensing reflectance at 412 nm; Remote sensing reflectance at 413 nm; Remote sensing reflectance at 414 nm; Remote sensing reflectance at 415 nm; Remote sensing reflectance at 416 nm; Remote sensing reflectance at 417 nm; Remote sensing reflectance at 418 nm; Remote sensing reflectance at 419 nm; Remote sensing reflectance at 420 nm; Remote sensing reflectance at 421 nm; Remote sensing reflectance at 422 nm; Remote sensing reflectance at 423 nm; Remote sensing reflectance at 424 nm; Remote sensing reflectance at 425 nm; Remote sensing reflectance at 426 nm; Remote sensing reflectance at 427 nm; Remote sensing reflectance at 428 nm; Remote sensing reflectance at 429 nm; Remote sensing reflectance at 430 nm; Remote sensing reflectance at 431 nm; Remote sensing reflectance at 432 nm; Remote sensing reflectance at 433 nm; Remote sensing reflectance at 434 nm; Remote sensing reflectance at 435 nm; Remote sensing reflectance at 436 nm; Remote sensing reflectance at 437 nm; Remote sensing reflectance at 438 nm; Remote sensing reflectance at 439 nm; Remote sensing reflectance at 440 nm; Remote sensing reflectance at 441 nm; Remote sensing reflectance at 442 nm; Remote sensing reflectance at 443 nm; Remote sensing reflectance at 444 nm; Remote sensing reflectance at 445 nm; Remote sensing reflectance at 446 nm; Remote sensing reflectance at 447 nm; Remote sensing reflectance at 448 nm; Remote sensing reflectance at 449 nm; Remote sensing reflectance at 450 nm; Remote sensing reflectance at 451 nm; Remote sensing reflectance at 452 nm; Remote sensing reflectance at 453 nm; Remote sensing reflectance at 454 nm; Remote sensing reflectance at 455 nm; Remote sensing reflectance at 456 nm; Remote sensing reflectance at 457 nm; Remote sensing reflectance at 458 nm; Remote sensing reflectance at 459 nm; Remote sensing reflectance at 460 nm; Remote sensing reflectance at 461 nm; Remote sensing reflectance at 462 nm; Remote sensing reflectance at 463 nm; Remote sensing reflectance at 464 nm; Remote sensing reflectance at 465 nm; Remote sensing reflectance at 466 nm; Remote sensing reflectance at 467 nm; Remote sensing reflectance at 468 nm; Remote sensing reflectance at 469 nm; Remote sensing reflectance at 470 nm; Remote sensing reflectance at 471 nm; Remote sensing reflectance at 472 nm; Remote sensing reflectance at 473 nm; Remote sensing reflectance at 474 nm; Remote sensing reflectance at 475 nm; Remote sensing reflectance at 476 nm; Remote sensing reflectance at 477 nm; Remote sensing reflectance at 478 nm; Remote sensing reflectance at 479 nm; Remote sensing reflectance at 480 nm; Remote sensing reflectance at 481 nm; Remote sensing reflectance at 482 nm; Remote sensing reflectance at 483 nm; Remote sensing reflectance at 484 nm; Remote sensing reflectance at 485 nm; Remote sensing reflectance at 486 nm; Remote sensing reflectance at 487 nm; Remote sensing reflectance at 488 nm; Remote sensing reflectance at 489 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 491 nm; Remote sensing reflectance at 492 nm; Remote sensing reflectance at 493 nm; Remote sensing reflectance at 494 nm; Remote sensing reflectance at 495 nm; Remote sensing reflectance at 496 nm; Remote sensing reflectance at 497 nm; Remote sensing reflectance at 498 nm; Remote sensing reflectance at 499 nm; Remote sensing reflectance at 500 nm; Remote sensing reflectance at 501 nm; Remote sensing reflectance at 502 nm; Remote sensing reflectance at 503 nm; Remote sensing reflectance at 504 nm; Remote sensing reflectance at 505 nm; Remote sensing reflectance at 506 nm; Remote sensing reflectance at 507 nm; Remote sensing reflectance at 508 nm; Remote sensing reflectance at 509 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 511 nm; Remote sensing reflectance at 512 nm; Remote sensing reflectance at 513 nm; Remote sensing reflectance at 514 nm; Remote sensing reflectance at 515 nm; Remote sensing reflectance at 516 nm; Remote sensing reflectance at 517 nm; Remote sensing reflectance at 518 nm; Remote sensing reflectance at 519 nm; Remote sensing reflectance at 520 nm; Remote sensing reflectance at 521 nm; Remote sensing reflectance at 522 nm; Remote sensing reflectance at 523 nm; Remote sensing reflectance at 524 nm; Remote sensing reflectance at 525 nm; Remote sensing reflectance at 526 nm; Remote sensing reflectance at 527 nm; Remote sensing reflectance at 528 nm; Remote sensing reflectance at 529 nm; Remote sensing reflectance at 530 nm; Remote sensing reflectance at 531 nm; Remote sensing reflectance at 532 nm; Remote sensing reflectance at 533 nm; Remote sensing reflectance at 534 nm; Remote sensing reflectance at 535 nm; Remote sensing reflectance at 536 nm; Remote sensing reflectance at 537 nm; Remote sensing reflectance at 538 nm; Remote sensing reflectance at 539 nm; Remote sensing reflectance at 540 nm; Remote sensing reflectance at 541 nm; Remote sensing reflectance at 542 nm; Remote sensing reflectance at 543 nm; Remote sensing reflectance at 544 nm; Remote sensing reflectance at 545 nm; Remote sensing reflectance at 546
    Type: Dataset
    Format: text/tab-separated-values, 6015 data points
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  • 72
    Publication Date: 2023-03-16
    Keywords: Arctic Ocean; ARK-XXVI/3; DATE/TIME; DEPTH, water; Elevation of event; Event label; FRAM; FRontiers in Arctic marine Monitoring; Laptev Sea; Latitude of event; Longitude of event; Polarstern; PS78/216-2; PS78/226-2; PS78/227-3; PS78/230-3; PS78/235-3; PS78/242-2; PS78/245-4; PS78/259-2; PS78/262-2; PS78/265-2; PS78/270-3; PS78/272-1; PS78/276-2; PS78/280-2; PS78/300-2; PS78 TransArc; RAMSES; RAMSES-ACC-VIS hyperspectral radiometer and RAMSES-ARC hyperspectral radiometer, TriOS; RAMSES hyperspectral radiometer; Remote sensing reflectance at 350 nm; Remote sensing reflectance at 351 nm; Remote sensing reflectance at 352 nm; Remote sensing reflectance at 353 nm; Remote sensing reflectance at 354 nm; Remote sensing reflectance at 355 nm; Remote sensing reflectance at 356 nm; Remote sensing reflectance at 357 nm; Remote sensing reflectance at 358 nm; Remote sensing reflectance at 359 nm; Remote sensing reflectance at 360 nm; Remote sensing reflectance at 361 nm; Remote sensing reflectance at 362 nm; Remote sensing reflectance at 363 nm; Remote sensing reflectance at 364 nm; Remote sensing reflectance at 365 nm; Remote sensing reflectance at 366 nm; Remote sensing reflectance at 367 nm; Remote sensing reflectance at 368 nm; Remote sensing reflectance at 369 nm; Remote sensing reflectance at 370 nm; Remote sensing reflectance at 371 nm; Remote sensing reflectance at 372 nm; Remote sensing reflectance at 373 nm; Remote sensing reflectance at 374 nm; Remote sensing reflectance at 375 nm; Remote sensing reflectance at 376 nm; Remote sensing reflectance at 377 nm; Remote sensing reflectance at 378 nm; Remote sensing reflectance at 379 nm; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 381 nm; Remote sensing reflectance at 382 nm; Remote sensing reflectance at 383 nm; Remote sensing reflectance at 384 nm; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 386 nm; Remote sensing reflectance at 387 nm; Remote sensing reflectance at 388 nm; Remote sensing reflectance at 389 nm; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 391 nm; Remote sensing reflectance at 392 nm; Remote sensing reflectance at 393 nm; Remote sensing reflectance at 394 nm; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 396 nm; Remote sensing reflectance at 397 nm; Remote sensing reflectance at 398 nm; Remote sensing reflectance at 399 nm; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 401 nm; Remote sensing reflectance at 402 nm; Remote sensing reflectance at 403 nm; Remote sensing reflectance at 404 nm; Remote sensing reflectance at 405 nm; Remote sensing reflectance at 406 nm; Remote sensing reflectance at 407 nm; Remote sensing reflectance at 408 nm; Remote sensing reflectance at 409 nm; Remote sensing reflectance at 410 nm; Remote sensing reflectance at 411 nm; Remote sensing reflectance at 412 nm; Remote sensing reflectance at 413 nm; Remote sensing reflectance at 414 nm; Remote sensing reflectance at 415 nm; Remote sensing reflectance at 416 nm; Remote sensing reflectance at 417 nm; Remote sensing reflectance at 418 nm; Remote sensing reflectance at 419 nm; Remote sensing reflectance at 420 nm; Remote sensing reflectance at 421 nm; Remote sensing reflectance at 422 nm; Remote sensing reflectance at 423 nm; Remote sensing reflectance at 424 nm; Remote sensing reflectance at 425 nm; Remote sensing reflectance at 426 nm; Remote sensing reflectance at 427 nm; Remote sensing reflectance at 428 nm; Remote sensing reflectance at 429 nm; Remote sensing reflectance at 430 nm; Remote sensing reflectance at 431 nm; Remote sensing reflectance at 432 nm; Remote sensing reflectance at 433 nm; Remote sensing reflectance at 434 nm; Remote sensing reflectance at 435 nm; Remote sensing reflectance at 436 nm; Remote sensing reflectance at 437 nm; Remote sensing reflectance at 438 nm; Remote sensing reflectance at 439 nm; Remote sensing reflectance at 440 nm; Remote sensing reflectance at 441 nm; Remote sensing reflectance at 442 nm; Remote sensing reflectance at 443 nm; Remote sensing reflectance at 444 nm; Remote sensing reflectance at 445 nm; Remote sensing reflectance at 446 nm; Remote sensing reflectance at 447 nm; Remote sensing reflectance at 448 nm; Remote sensing reflectance at 449 nm; Remote sensing reflectance at 450 nm; Remote sensing reflectance at 451 nm; Remote sensing reflectance at 452 nm; Remote sensing reflectance at 453 nm; Remote sensing reflectance at 454 nm; Remote sensing reflectance at 455 nm; Remote sensing reflectance at 456 nm; Remote sensing reflectance at 457 nm; Remote sensing reflectance at 458 nm; Remote sensing reflectance at 459 nm; Remote sensing reflectance at 460 nm; Remote sensing reflectance at 461 nm; Remote sensing reflectance at 462 nm; Remote sensing reflectance at 463 nm; Remote sensing reflectance at 464 nm; Remote sensing reflectance at 465 nm; Remote sensing reflectance at 466 nm; Remote sensing reflectance at 467 nm; Remote sensing reflectance at 468 nm; Remote sensing reflectance at 469 nm; Remote sensing reflectance at 470 nm; Remote sensing reflectance at 471 nm; Remote sensing reflectance at 472 nm; Remote sensing reflectance at 473 nm; Remote sensing reflectance at 474 nm; Remote sensing reflectance at 475 nm; Remote sensing reflectance at 476 nm; Remote sensing reflectance at 477 nm; Remote sensing reflectance at 478 nm; Remote sensing reflectance at 479 nm; Remote sensing reflectance at 480 nm; Remote sensing reflectance at 481 nm; Remote sensing reflectance at 482 nm; Remote sensing reflectance at 483 nm; Remote sensing reflectance at 484 nm; Remote sensing reflectance at 485 nm; Remote sensing reflectance at 486 nm; Remote sensing reflectance at 487 nm; Remote sensing reflectance at 488 nm; Remote sensing reflectance at 489 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 491 nm; Remote sensing reflectance at 492 nm; Remote sensing reflectance at 493 nm; Remote sensing reflectance at 494 nm; Remote sensing reflectance at 495 nm; Remote sensing reflectance at 496 nm; Remote sensing reflectance at 497 nm; Remote sensing reflectance at 498 nm; Remote sensing reflectance at 499 nm; Remote sensing reflectance at 500 nm; Remote sensing reflectance at 501 nm; Remote sensing reflectance at 502 nm; Remote sensing reflectance at 503 nm; Remote sensing reflectance at 504 nm; Remote sensing reflectance at 505 nm; Remote sensing reflectance at 506 nm; Remote sensing reflectance at 507 nm; Remote sensing reflectance at 508 nm; Remote sensing reflectance at 509 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 511 nm; Remote sensing reflectance at 512 nm; Remote sensing reflectance at 513 nm; Remote sensing reflectance at 514 nm; Remote sensing reflectance at 515 nm; Remote sensing reflectance at 516 nm; Remote sensing reflectance at 517 nm; Remote sensing reflectance at 518 nm; Remote sensing reflectance at 519 nm; Remote sensing reflectance at 520 nm; Remote sensing reflectance at 521 nm; Remote sensing reflectance at 522 nm; Remote sensing reflectance at 523 nm; Remote sensing reflectance at 524 nm; Remote sensing reflectance at 525 nm; Remote sensing reflectance at 526 nm; Remote sensing reflectance at 527 nm; Remote sensing reflectance at 528 nm; Remote sensing reflectance at 529 nm; Remote sensing reflectance at 530 nm; Remote sensing reflectance at 531 nm; Remote sensing reflectance at 532 nm; Remote sensing reflectance at 533 nm; Remote sensing reflectance at 534 nm; Remote sensing reflectance at 535 nm; Remote sensing reflectance at 536 nm; Remote sensing reflectance at 537 nm; Remote sensing reflectance at 538 nm; Remote sensing reflectance at 539 nm; Remote sensing reflectance at 540 nm; Remote sensing reflectance at 541 nm; Remote sensing reflectance at 542 nm; Remote sensing reflectance at 543 nm; Remote sensing reflectance at 544 nm; Remote sensing reflectance at 545 nm; Remote sensing reflectance at 546
    Type: Dataset
    Format: text/tab-separated-values, 6015 data points
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  • 73
    Publication Date: 2023-03-16
    Keywords: AGE; Arctic Ocean; ARK-XXIX/1, TRANSSIZ; AWI_Paleo; Calcium carbonate; Carbon, organic, total; Carbon, total; Carbon/Nitrogen ratio; DEPTH, sediment/rock; KAL; Kasten corer; Nitrogen, total; Paleoenvironmental Reconstructions from Marine Sediments @ AWI; Polarstern; PS92; PS92/039-2; Sulfur, total
    Type: Dataset
    Format: text/tab-separated-values, 1006 data points
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  • 74
    Publication Date: 2023-03-16
    Keywords: AWI_Paleo; File format; File name; File size; Paleoenvironmental Reconstructions from Marine Sediments @ AWI; 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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  • 75
    facet.materialart.
    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Keywords: DATE/TIME; DEPTH, sediment/rock; MULT; Multiple investigations; Oxygen; PotterCove_Isla_D_2015; Potter Cove, King George Island, Antarctic Peninsula
    Type: Dataset
    Format: text/tab-separated-values, 902 data points
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  • 76
    Publication Date: 2023-03-16
    Keywords: AWI_Envi; DATE/TIME; LATITUDE; LONGITUDE; MULT; Multiple investigations; ORDINAL NUMBER; Plot; Polar Terrestrial Environmental Systems @ AWI; Reflectance; Replicate; Season; Site; ToolikL_plot; Toolik Lake, Alaska; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vegetation type; Wavelength
    Type: Dataset
    Format: text/tab-separated-values, 3314520 data points
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  • 77
    Publication Date: 2023-03-16
    Keywords: Biological oxygen saturation anomalies; CT; DATE/TIME; Distance; LATITUDE; LONGITUDE; NCP_2016_sep; Net community production of oxygen; Sea surface salinity; Sea surface temperature; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 53400 data points
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  • 78
    Publication Date: 2023-03-16
    Keywords: AWI_Envi; Polar Terrestrial Environmental Systems @ AWI; Taymyr_peninsula
    Type: Dataset
    Format: application/zip, 659.4 MBytes
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  • 79
    Publication Date: 2023-03-16
    Keywords: Date/Time of event; Event label; Indian Ocean; Latitude of event; Longitude of event; Marion Dufresne (1995); MD206; MD206_OISO11; MD206_OISO14; MD206_OISO15; MD206_OISO16; MD206_OISO18; MD206_OISO2; MD206_OISO3; MD206_OISO37; MD206_OISO4; MD206_OISO6; MD206_OISO7; MD206_OISO9; MD206_OISOE; Method comment; MULT; Multiple investigations; Nitrogen; Nitrogen, total; Nitrogen-15, atmospheric; Nitrogen fixation rate; Replicate; Salinity; δ15N
    Type: Dataset
    Format: text/tab-separated-values, 792 data points
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  • 80
    Publication Date: 2023-03-16
    Keywords: AWI_Envi; DATE/TIME; Event label; Imnavait_vegGrid; LATITUDE; LONGITUDE; MULT; Multiple investigations; ORDINAL NUMBER; Plot; Polar Terrestrial Environmental Systems @ AWI; Reflectance; Replicate; Season; ToolikL_plot; Toolik Lake, Alaska; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vegetation type; Wavelength
    Type: Dataset
    Format: text/tab-separated-values, 9265320 data points
    Location Call Number Expected Availability
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  • 81
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    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Keywords: 19-Butanoyloxyfucoxanthin; 19-Hexanoyloxyfucoxanthin; Alloxanthin; alpha-Carotene; Antheraxanthin; beta-Carotene; Chlorophyll a; Chlorophyll b; Chlorophyll c1+c2; Chlorophyll c3; Chlorophyllide a; Date/Time of event; Diadinoxanthin; Diatoxanthin; Divinyl chlorophyll a; Event label; Fucoxanthin; Indian Ocean; Latitude of event; Longitude of event; Lutein; Marion Dufresne (1995); MD206; MD206_OISO10; MD206_OISO11; MD206_OISO12; MD206_OISO14; MD206_OISO15; MD206_OISO16; MD206_OISO18; MD206_OISO2; MD206_OISO3; MD206_OISO37; MD206_OISO4; MD206_OISO6; MD206_OISO7; MD206_OISO9; MD206_OISOE; MULT; Multiple investigations; Neoxanthin; Peridinin; Prasinoxanthin; Salinity; Timeslice; Violaxanthin; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 552 data points
    Location Call Number Expected Availability
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  • 82
    Publication Date: 2023-03-16
    Keywords: Ammonium; Ammonium uptake rate; Carbon fixation rate; Chlorophyll a; Date/Time of event; DEPTH, water; Event label; Fraction; Indian Ocean; Latitude of event; Longitude of event; Marion Dufresne (1995); MD206; MD206_OISO11; MD206_OISO14; MD206_OISO15; MD206_OISO16; MD206_OISO18; MD206_OISO2; MD206_OISO3; MD206_OISO37; MD206_OISO4; MD206_OISO6; MD206_OISO7; MD206_OISO9; MD206_OISOE; Method comment; Mixed layer depth; MULT; Multiple investigations; Nitrate; Nitrate uptake rate; Nitrite; Oxygen; Phosphate; Replicate; Salinity; Silicate; Temperature, water; Timeslice
    Type: Dataset
    Format: text/tab-separated-values, 1253 data points
    Location Call Number Expected Availability
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  • 83
    Publication Date: 2023-03-16
    Keywords: Aggregates; Center for Marine Environmental Sciences; Equivalent spherical diameter; Equivalent spherical diameter, standard deviation; Event label; Experiment; GeoB19401; GeoB19403; GeoB19406; High deposition; Latitude of event; Longitude of event; Low deposition; MARUM; MULT; Multiple investigations; POS481; Poseidon; ROBEX; Scavenging; SeaPump; Seasonal and regional food web interactions with the biological pump; Sinking velocity; Sinking velocity, standard deviation; Standard deviation; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 57 data points
    Location Call Number Expected Availability
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  • 84
    Publication Date: 2023-03-16
    Keywords: Aggregates; Aggregates, mean volume; Carbon, organic, particulate mass; Center for Marine Environmental Sciences; Equivalent spherical diameter; Equivalent spherical diameter, standard deviation; Event label; Experiment; GeoB19401; GeoB19403; GeoB19406; High deposition; Latitude of event; Longitude of event; Low deposition; MARUM; MULT; Multiple investigations; POS481; Poseidon; Ratio; ROBEX; Scavenging; SeaPump; Seasonal and regional food web interactions with the biological pump; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 141 data points
    Location Call Number Expected Availability
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  • 85
    Publication Date: 2023-03-16
    Type: Dataset
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, 21.1 kBytes
    Location Call Number Expected Availability
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  • 86
    Publication Date: 2023-03-16
    Keywords: Aggregates; Aggregates, mean volume; Center for Marine Environmental Sciences; Equivalent spherical diameter; Event label; Experiment; GeoB19401; GeoB19403; GeoB19406; High deposition; Latitude of event; Longitude of event; Low deposition; MARUM; MULT; Multiple investigations; Number; POS481; Poseidon; ROBEX; Scavenging; SeaPump; Seasonal and regional food web interactions with the biological pump; Sinking velocity; Sinking velocity, standard deviation; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 1887 data points
    Location Call Number Expected Availability
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  • 87
    Publication Date: 2023-03-16
    Keywords: after Gamon and Field (1992); after Gitelson et al. (2001); after Merzlyak et al. (1999); Anthocyanin Reflectance Index 1; Anthocyanin Reflectance Index 2; AWI_Envi; Carotenoid Reflectance Index 1; Carotenoid Reflectance Index 2; Chlorophyll Carotenoid Index; DATE/TIME; Index; LATITUDE; LONGITUDE; MULT; Multiple investigations; Photochemical Reflectance Index; Pigment Specific Simple Ratio; Plant Senescence Reflectance Index; Plot; Polar Terrestrial Environmental Systems @ AWI; PSSRa after Blackburn (1998, 1999); PSSRb after Sims and Gamon (2002); Season; Site; ToolikL_plot; Toolik Lake, Alaska; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vegetation type
    Type: Dataset
    Format: text/tab-separated-values, 3288 data points
    Location Call Number Expected Availability
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  • 88
    Publication Date: 2023-03-16
    Keywords: Acyclic glycerol dialkyl glycerol tetraether, fractional abundance; Calcium carbonate, flux; Carbon, organic, flux; Crenarchaeol regio-isomer, fractional abundance; Cruise/expedition; DATE/TIME; Date/time end; DEPTH, water; Dicyclic glycerol dialkyl glycerol tetraether, fractional abundance; Duration, number of days; Elevation of event; Event label; Glycerol dialkyl glycerol tetraethers; Latitude of event; Lithogenic, flux; Longitude of event; LZ1-trap; LZ2-trap; Monocyclic glycerol dialkyl glycerol tetraether, fractional abundance; Nitrogen, total, flux; Opal, flux; Pentacyclic glycerol dialkyl glycerol tetraether, fractional abundance; Sample code/label; Tetraether index of 86 carbon atoms; Total mass, flux per day; Trap; TRAP; Tricyclic glycerol dialkyl glycerol tetraether, fractional abundance
    Type: Dataset
    Format: text/tab-separated-values, 308 data points
    Location Call Number Expected Availability
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  • 89
    Publication Date: 2023-03-16
    Keywords: after Gamon and Field (1992); after Gitelson et al. (2001); after Merzlyak et al. (1999); Anthocyanin Reflectance Index 1; Anthocyanin Reflectance Index 2; AWI_Envi; Carotenoid Reflectance Index 1; Carotenoid Reflectance Index 2; Carotenoids, standard deviation; Carotenoids per unit dry mass; Chlorophyll a, standard deviation; Chlorophyll a+b, standard deviation; Chlorophyll a+b per unit dry mass; Chlorophyll a per unit dry mass; Chlorophyll b, standard deviation; Chlorophyll b per unit dry mass; Chlorophyll Carotenoid Index; DATE/TIME; LATITUDE; LONGITUDE; MULT; Multiple investigations; Number; ORDINAL NUMBER; Photochemical Reflectance Index; Pigment Specific Simple Ratio; Plant Senescence Reflectance Index; Plot; Polar Terrestrial Environmental Systems @ AWI; PSSRa after Blackburn (1998, 1999); PSSRb after Sims and Gamon (2002); Ratio; Ratio, standard deviation; Season; Site; ToolikL_plot; Toolik Lake, Alaska; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vegetation type
    Type: Dataset
    Format: text/tab-separated-values, 1181 data points
    Location Call Number Expected Availability
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  • 90
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    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Keywords: Acyclic glycerol dialkyl glycerol tetraether, fractional abundance; Crenarchaeol regio-isomer, fractional abundance; Cruise/expedition; DATE/TIME; Date/time end; DEPTH, water; Dicyclic glycerol dialkyl glycerol tetraether, fractional abundance; Duration, number of days; Elevation of event; Event label; GBN3_trap; Glycerol dialkyl glycerol tetraethers; Latitude of event; Lithogenic, flux; Longitude of event; M9/4; Meteor (1986); Monocyclic glycerol dialkyl glycerol tetraether, fractional abundance; Pentacyclic glycerol dialkyl glycerol tetraether, fractional abundance; Sample code/label; Tetraether index of 86 carbon atoms; Total mass, flux per day; Trap; TRAP; Tricyclic glycerol dialkyl glycerol tetraether, fractional abundance
    Type: Dataset
    Format: text/tab-separated-values, 550 data points
    Location Call Number Expected Availability
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  • 91
    Publication Date: 2023-03-16
    Keywords: AWI_Envi; Carotenoids, standard deviation; Carotenoids per unit dry mass; Chlorophyll a, standard deviation; Chlorophyll a+b, standard deviation; Chlorophyll a+b per unit dry mass; Chlorophyll a per unit dry mass; Chlorophyll b, standard deviation; Chlorophyll b per unit dry mass; DATE/TIME; Index; LATITUDE; LONGITUDE; MULT; Multiple investigations; Number; ORDINAL NUMBER; Plot; Polar Terrestrial Environmental Systems @ AWI; Ratio; Ratio, standard deviation; Season; Site; ToolikL_plot; Toolik Lake, Alaska; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vegetation type
    Type: Dataset
    Format: text/tab-separated-values, 1060 data points
    Location Call Number Expected Availability
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  • 92
    Publication Date: 2023-03-16
    Keywords: AWI_BioOce; Biological Oceanography @ AWI; Day of experiment; Experimental treatment; Pontogammarus maeoticus; Proportion of survival; Replicate; Salinity; Survival
    Type: Dataset
    Format: text/tab-separated-values, 9390 data points
    Location Call Number Expected Availability
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  • 93
    Publication Date: 2023-03-16
    Keywords: AWI_BioOce; Batch; Biological Oceanography @ AWI; Experimental treatment; Experiment week; Pontogammarus maeoticus; Pontogammarus maeoticus, cephalon length; Replicate; Salinity; Survival
    Type: Dataset
    Format: text/tab-separated-values, 18478 data points
    Location Call Number Expected Availability
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  • 94
    Publication Date: 2023-03-16
    Keywords: IMCOAST/IMCONet; Impact of climate induced glacier melt on marine coastal systems, Antarctica
    Type: Dataset
    Format: application/zip, 1.9 kBytes
    Location Call Number Expected Availability
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  • 95
    Publication Date: 2023-03-16
    Description: Sea ice drift, surface temperature, and barometric pressure were measured by Surface Velocity Profiler 2016P18 drifting on Antarctic sea ice. The buoy was deployed on multi year ice without drogue during POLARSTERN cruise ANT-XXXI/2 (PS96). The time series describes the position and additional parameters of the buoy between 18 Jan 2016 and 22 Aug 2017 in sample intervals of 1 hour. The data set has been processed, including the removal of obvious inconsistencies (missing values).
    Keywords: 2016P18; ACROSS; Advanced Remote Sensing – Ground-Truth Demo and Test Facilities; Buoy; BUOY; DATE/TIME; LATITUDE; LONGITUDE; Pressure, atmospheric; Temperature, water; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 27972 data points
    Location Call Number Expected Availability
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  • 96
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    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Description: Sea ice drift, surface temperature, and barometric pressure were measured by Surface Velocity Profiler 2015P12 drifting on Arctic sea ice. The buoy was deployed on first year ice without drogue during POLARSTERN cruise ARK-XXIX/3 (PS94). The time series describes the position and additional parameters of the buoy between 02 Sep 2015 and 12 Sep 2017 in sample intervals of 1 hour. The data set has been processed, including the removal of obvious inconsistencies (missing values).
    Keywords: 2015P12; Arctic Ocean; Buoy; BUOY; DATE/TIME; FRAM; FRontiers in Arctic marine Monitoring; LATITUDE; LONGITUDE; Pressure, atmospheric; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 35026 data points
    Location Call Number Expected Availability
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  • 97
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    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Description: Sea ice drift, surface temperature, and barometric pressure were measured by Surface Velocity Profiler 2017P24 drifting on Arctic sea ice. The buoy was deployed on first year ice without drogue during POLARSTERN cruise ARK-XXXI/1 (PS106). The time series describes the position and additional parameters of the buoy between 07 Jun 2017 and 21 Jun 2017 in sample intervals of 1 hour. The data set has been processed, including the removal of obvious inconsistencies (missing values).
    Keywords: 2017P24; Arctic Ocean; Buoy; BUOY; DATE/TIME; FRAM; FRontiers in Arctic marine Monitoring; LATITUDE; LONGITUDE; Pressure, atmospheric; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 426 data points
    Location Call Number Expected Availability
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  • 98
    Publication Date: 2023-03-16
    Description: Sea ice drift, surface temperature, and barometric pressure were measured by Surface Velocity Profiler 2016P20 drifting on Antarctic sea ice. The buoy was deployed on multi year ice without drogue during POLARSTERN cruise ANT-XXXI/2 (PS96). The time series describes the position and additional parameters of the buoy between 18 Jan 2016 and 09 Jul 2017 in sample intervals of 1 hour. The data set has been processed, including the removal of obvious inconsistencies (missing values).
    Keywords: 2016P20; ACROSS; Advanced Remote Sensing – Ground-Truth Demo and Test Facilities; Buoy; BUOY; DATE/TIME; LATITUDE; LONGITUDE; Pressure, atmospheric; Temperature, water; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 26002 data points
    Location Call Number Expected Availability
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  • 99
    Publication Date: 2023-03-16
    Description: Sea ice drift, surface temperature, and barometric pressure were measured by Surface Velocity Profiler 2015P21 drifting on Antarctic sea ice. The buoy was deployed on multi year ice without drogue during POLARSTERN cruise ANT-XXXI/2 (PS96). The time series describes the position and additional parameters of the buoy between 02 Sep 2015 and 12 Sep 2017 in sample intervals of 1 hour. The data set has been processed, including the removal of obvious inconsistencies (missing values).
    Keywords: 2015P21; ACROSS; Advanced Remote Sensing – Ground-Truth Demo and Test Facilities; Buoy; BUOY; DATE/TIME; LATITUDE; LONGITUDE; Pressure, atmospheric; Temperature, water; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 29396 data points
    Location Call Number Expected Availability
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  • 100
    facet.materialart.
    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2023-03-16
    Description: Sea ice drift was measured by Surface Velocity Profiler 2017P38 drifting on Arctic sea ice. The buoy was deployed without drogue during POLARSTERN cruise ARK-XXXI/1 (PS106). The time series describes the position of the buoy between 12 Jun 2017 and 26 Jun 2017 in sample intervals of 5 minutes. The data set has been processed, including the removal of obvious inconsistencies (missing values).
    Keywords: 2017P38; Arctic Ocean; Buoy; BUOY; DATE/TIME; FRAM; FRontiers in Arctic marine Monitoring; LATITUDE; LONGITUDE
    Type: Dataset
    Format: text/tab-separated-values, 0 data points
    Location Call Number Expected Availability
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