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  • PANGAEA  (7,907)
  • 2020-2024  (7,907)
  • 1935-1939
  • 2023  (7,907)
Collection
Keywords
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  • 2020-2024  (7,907)
  • 1935-1939
Year
  • 1
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    Manganese and barium concentration data for water column samples collected during AL543 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Description: Barium (Ba) isotopes are a promising new tracer for riverine freshwater input to the ocean and marine biogeochemical cycling. However, many processes that affect Ba cycling at continental margins have not yet been investigated with respect to Ba isotope fractionation. We present a comprehensive data set of Ba concentration and isotope data for water column, pore water and sediment samples from Kiel Bight, a seasonally stratified and hypoxic fjord in the southwestern Baltic Sea.
    Keywords: AL543; AL543_10-1; AL543_13-1; AL543_8-1; Alkor (1990); Baltic Sea; Barium; CTD/Rosette; CTD-RO; Depth, bathymetric; DEPTH, water; Event label; Manganese; Saturation index; The Little Belt
    Type: Dataset
    Format: text/tab-separated-values, 120 data points
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    DATA PANGAEA
  • 2
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    Barium isotope data for water column samples collected during AL543 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Description: Barium (Ba) isotopes are a promising new tracer for riverine freshwater input to the ocean and marine biogeochemical cycling. However, many processes that affect Ba cycling at continental margins have not yet been investigated with respect to Ba isotope fractionation. We present a comprehensive data set of Ba concentration and isotope data for water column, pore water and sediment samples from Kiel Bight, a seasonally stratified and hypoxic fjord in the southwestern Baltic Sea.
    Keywords: AL543; AL543_10-1; AL543_13-1; AL543_8-1; Alkor (1990); Baltic Sea; Barium; CTD/Rosette; CTD-RO; DEPTH, water; Event label; Replicates; Standard deviation; The Little Belt; δ138Ba
    Type: Dataset
    Format: text/tab-separated-values, 68 data points
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    DATA PANGAEA
  • 3
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    Additional pore water geochemical and barium isotope data for sediment cores collected during AL543 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Description: Barium (Ba) isotopes are a promising new tracer for riverine freshwater input to the ocean and marine biogeochemical cycling. However, many processes that affect Ba cycling at continental margins have not yet been investigated with respect to Ba isotope fractionation. We present a comprehensive data set of Ba concentration and isotope data for water column, pore water and sediment samples from Kiel Bight, a seasonally stratified and hypoxic fjord in the southwestern Baltic Sea.
    Keywords: AL543; AL543_10-2; AL543_13-2; AL543_8-4; Alkor (1990); Baltic Sea; Barium; Comment; DEPTH, sediment/rock; Event label; Manganese; MIC; MiniCorer; Replicates; Saturation index; Standard deviation; Sulfate; The Little Belt; δ138Ba
    Type: Dataset
    Format: text/tab-separated-values, 271 data points
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    DATA PANGAEA
  • 4
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    Solid phase geochemical and barium isotope data for sediment cores collected during AL543 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Description: Barium (Ba) isotopes are a promising new tracer for riverine freshwater input to the ocean and marine biogeochemical cycling. However, many processes that affect Ba cycling at continental margins have not yet been investigated with respect to Ba isotope fractionation. We present a comprehensive data set of Ba concentration and isotope data for water column, pore water and sediment samples from Kiel Bight, a seasonally stratified and hypoxic fjord in the southwestern Baltic Sea.
    Keywords: AL543; AL543_10-2; AL543_13-2; AL543_8-4; Alkor (1990); Aluminium; Baltic Sea; Barium; Carbon, organic, total; Comment; DEPTH, sediment/rock; Event label; MIC; MiniCorer; Porosity; Replicates; Standard deviation; The Little Belt; Water content, wet mass; δ138Ba
    Type: Dataset
    Format: text/tab-separated-values, 282 data points
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  • 5
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    Geochemistry of coastal cliffs related to Alkor cruise AL543 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-14
    Description: Barium (Ba) isotopes are a promising new tracer for riverine freshwater input to the ocean and marine biogeochemical cycling. However, many processes that affect Ba cycling at continental margins have not yet been investigated with respect to Ba isotope fractionation. We present a comprehensive data set of Ba concentration and isotope data for water column, pore water and sediment samples from Kiel Bight, a seasonally stratified and hypoxic fjord in the southwestern Baltic Sea.
    Keywords: Aluminium; Barium; Distance; Event label; HAND; Replicates; Sampling by hand; Schoenhagen; Standard deviation; Stohl; δ138Ba
    Type: Dataset
    Format: text/tab-separated-values, 126 data points
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    DATA PANGAEA
  • 6
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    Grain size distribution for the sediment sample stations of the Elbe river campaign LP201508 in 2015 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-24
    Description: Transport, distribution and remobilization processes of sediments and suspended matter of the Wadden Sea or within the marine areas have a major influence on the pollution situation of the these areas. The combined analysis of element fingerprints and isotope ratios of selected elements is suitable to provide valuable insights into the origin, transport pathways and distribution of sediments and suspended sediments within the study area. Thus, sediment and freshwater samples were taken from the German part of the Elbe river and its tributaries in August and October 2015 to identify their elemental and isotopic fingerprint and to investigate potential inputs of this major river system into the German Bight. All sediment samples were taken using a box grab and were analyzed for their grain size distribution by laser diffraction.
    Keywords: Alte Harb. Elbbruecke; Artlenburg; BC; Belgern; Bielenberg Leuchtf.; Billwerder Inseln; Blankenese; Box corer; Breitenhagen rechts; Brunsbuettel Elbhafen; Bunthausspitze; Coswig rechts; DATE/TIME; DEPTH, sediment/rock; Device type; Doemitz rechts; Elbe and North Sea; Elbstorf; ELEVATION; Event label; Geesthacht; Glameyer; Glueckstadt; Grain size, LASER Particle Sizer; Grauerort; Havel/Kanalmuendung; Havel/Schleusenkanal; Helmholtz-Zentrum Hereon; Hereon; Hinzdorf rechts; Hohenwarthe rechts; Hohnstorf; Hollerwettern; Jahna; Koehlbrand; Kugelbake; LATITUDE; Lauenburg links; LONGITUDE; LP_2015_08_S_1; LP_2015_08_S_10; LP_2015_08_S_11; LP_2015_08_S_12; LP_2015_08_S_13; LP_2015_08_S_14; LP_2015_08_S_15; LP_2015_08_S_16; LP_2015_08_S_17; LP_2015_08_S_18; LP_2015_08_S_19; LP_2015_08_S_2; LP_2015_08_S_20; LP_2015_08_S_21; LP_2015_08_S_22; LP_2015_08_S_23; LP_2015_08_S_24; LP_2015_08_S_25; LP_2015_08_S_26; LP_2015_08_S_27; LP_2015_08_S_28_2; LP_2015_08_S_29; LP_2015_08_S_3; LP_2015_08_S_30_2; LP_2015_08_S_31_2; LP_2015_08_S_32; LP_2015_08_S_33_3; LP_2015_08_S_34_2; LP_2015_08_S_35_2; LP_2015_08_S_36; LP_2015_08_S_37_1; LP_2015_08_S_38_1; LP_2015_08_S_39_1; LP_2015_08_S_4; LP_2015_08_S_40_1; LP_2015_08_S_41_1; LP_2015_08_S_42_1; LP_2015_08_S_43_1; LP_2015_08_S_44_1; LP_2015_08_S_45_1; LP_2015_08_S_46_1; LP_2015_08_S_47_1; LP_2015_08_S_48; LP_2015_08_S_49_1; LP_2015_08_S_5; LP_2015_08_S_50_1; LP_2015_08_S_51_1; LP_2015_08_S_52_1; LP_2015_08_S_53_1; LP_2015_08_S_54_1; LP_2015_08_S_55_1; LP_2015_08_S_56_1; LP_2015_08_S_57_1; LP_2015_08_S_58_1; LP_2015_08_S_59_1; LP_2015_08_S_6; LP_2015_08_S_60_1; LP_2015_08_S_61_1; LP_2015_08_S_62; LP_2015_08_S_63; LP_2015_08_S_64; LP_2015_08_S_65_2; LP_2015_08_S_66; LP_2015_08_S_67; LP_2015_08_S_68_1; LP_2015_08_S_69; LP_2015_08_S_7; LP_2015_08_S_71_2; LP_2015_08_S_72_1; LP_2015_08_S_73_1; LP_2015_08_S_74; LP_2015_08_S_8; LP_2015_08_S_9; LP201508; Lt. Vogelsand; Ludwig Prandtl; Luehemuendung; Magdeburg rechts; Mueglitz Muendung; Muldemuendung; MULT; Multiple investigations; Neu Darchau rechts; Neue Elbbruecken; Neufeld; Neuland; Neumuehlen; Niegrip/Elbe-Havelkanal; Nienstedten; Optional event label; Oste; Otterndorf; Pegel Brockdorf; Pillnitz; Pretzsch rechts; Rosslau links; Rosslau rechts; Saale; Saalemuendung; Sample code/label; Sandau rechts; Scharfenberg; Scharhoernriff; Schmilka; Schnackenburg rechts; Schoenebeck rechts; Schulau; Schwarze Elster Muendung; Schwinge; Seemannshoeft; Size fraction 〈 0.020 mm; Size fraction 〈 0.020 mm, standard deviation; Size fraction 〈 0.063 mm, mud, silt+clay; Size fraction 〈 0.063 mm, mud, silt+clay, standard deviation; Size fraction 〈 0.125 µm; Size fraction 〈 0.125 mm, standard deviation; Size fraction 〈 0.250 mm; Size fraction 〈 0.250 mm, standard deviation; St. Magarethen; Stoer; Strehla rechts; Tangermuende rechts; Tonne 107; Tonne 112; Tonne 53; Tonne 57; Tonne 91 gruen; Tonne 96 rot; Torgau rechts; Triebisch; Wahrenberg rechts; Wittenberg rechts; Zehren; Zollenspieker
    Type: Dataset
    Format: text/tab-separated-values, 730 data points
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  • 7
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    Currents based on ADCP data in a small shallow Lake Vendyurskoe in Russia during 27 March-6 April 2020: AQ2_EXP1-202_b2 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-21
    Description: The measurements were carried out 27 March–6 April 2020 on small ice-covered shallow Lake Vendyurskoe (62.215784N, 33.262784E, North-Western of Russia). Surface area of the lake is 10.4 km2, its mean and maximal depths are 5.3 and 13.4 m. The maximal length of the lake is 7 km; and the average width is 1.5 km. The mooring equipment was deployed on the ice 350 m from the northern shore, with a location depth of ~ 7 m. The measuring complex included a pyranometer placed directly under the ice to measure downwelling irradiance (a M-80m universal pyranometer, produced in Russia, accuracy 1 W m-2, sampling frequency one minute), two Star-shaped pyranometers (Theodor Friderich & Co, Meteorologishe Gerate und Systeme, Germany, accuracy 0.2 W/m2, sampling interval one minute) placed at one meter above ice surface to measure downwelling and upwelling irradiance, thermistor chain (13 temperature sensors TR-1060 RBR, Canada, accuracy ±0.002°C, resolution ±0.00005°C, sampling frequency 10 s; sensors were placed at intervals of 0.5 m, starting from 0.2 m from the ice bottom to a depth of 6.2 m), and two ADCPs (2 MHz HR Aquadopp current velocity profiler, Nortek AS, Norway). The two ADCPs were mounted on a special retaining frame that rigidly fixed the instruments on the ice and to each other. Both devices were installed in a hole with emitters located 3 cm below the lower ice boundary. For the entire measurement period, the devices were set up as follows: signal discreteness was one minute (32 pulses with a frequency of 2 Hz), depth scanning range was 2.875 m (115 cells with a size of 25 mm). To exclude the mutual influence of the two ADCPs, the emitters were set in an asynchronous mode with a 30 s delay. In the first stage of experiment – from 17:00 LT (local time = UTC + 3 h) on 27 March 2020 to 09:30 LT on 30 March 2020 – the ADCPs emitters were located at a distance of 1.5 m from each other, and at a depth of 1.61 m one beam from each of the devices intersected. In the second stage – from 10:00 LT on 30 March 2020 to 10:00 LT on 6 April 2020 – the distance between the emitters was 0.75 m and two pairs of beams intersected at the same depth.
    Keywords: Acoustic Doppler Current Profiler; ADCP; Current velocity; DATE/TIME; Date/Time local; Field measurement; Number; Vendyurskoe2020
    Type: Dataset
    Format: text/tab-separated-values, 48285824 data points
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  • 8
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    Currents based on ADCP data in a small shallow Lake Vendyurskoe in Russia during 27 March-6 April 2020: AQ2_EXP1-202_b3 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-21
    Description: The measurements were carried out 27 March–6 April 2020 on small ice-covered shallow Lake Vendyurskoe (62.215784N, 33.262784E, North-Western of Russia). Surface area of the lake is 10.4 km2, its mean and maximal depths are 5.3 and 13.4 m. The maximal length of the lake is 7 km; and the average width is 1.5 km. The mooring equipment was deployed on the ice 350 m from the northern shore, with a location depth of ~ 7 m. The measuring complex included a pyranometer placed directly under the ice to measure downwelling irradiance (a M-80m universal pyranometer, produced in Russia, accuracy 1 W m-2, sampling frequency one minute), two Star-shaped pyranometers (Theodor Friderich & Co, Meteorologishe Gerate und Systeme, Germany, accuracy 0.2 W/m2, sampling interval one minute) placed at one meter above ice surface to measure downwelling and upwelling irradiance, thermistor chain (13 temperature sensors TR-1060 RBR, Canada, accuracy ±0.002°C, resolution ±0.00005°C, sampling frequency 10 s; sensors were placed at intervals of 0.5 m, starting from 0.2 m from the ice bottom to a depth of 6.2 m), and two ADCPs (2 MHz HR Aquadopp current velocity profiler, Nortek AS, Norway). The two ADCPs were mounted on a special retaining frame that rigidly fixed the instruments on the ice and to each other. Both devices were installed in a hole with emitters located 3 cm below the lower ice boundary. For the entire measurement period, the devices were set up as follows: signal discreteness was one minute (32 pulses with a frequency of 2 Hz), depth scanning range was 2.875 m (115 cells with a size of 25 mm). To exclude the mutual influence of the two ADCPs, the emitters were set in an asynchronous mode with a 30 s delay. In the first stage of experiment – from 17:00 LT (local time = UTC + 3 h) on 27 March 2020 to 09:30 LT on 30 March 2020 – the ADCPs emitters were located at a distance of 1.5 m from each other, and at a depth of 1.61 m one beam from each of the devices intersected. In the second stage – from 10:00 LT on 30 March 2020 to 10:00 LT on 6 April 2020 – the distance between the emitters was 0.75 m and two pairs of beams intersected at the same depth.
    Keywords: Acoustic Doppler Current Profiler; ADCP; Current velocity; DATE/TIME; Date/Time local; Field measurement; Number; Vendyurskoe2020
    Type: Dataset
    Format: text/tab-separated-values, 58739456 data points
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  • 9
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    Meiofauna communities from iron-enriched sediments at LTER HAUSGARTEN, with special foucs on nematodes (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-25
    Description: Impact of Local Iron Enrichment on the Small Benthic Biota in the deep Arctic Ocean The study assesses the impact of local iron enrichment on the small benthic biota (bacteria, meiofauna) together with environmental parameters indicating the input of food at the deep seafloor. To evaluate the hypothesis that abundance, distribution, and diversity of the small benthic biota varies in relation to a local input of structural steel at the seabed, we analyzed sediment samples and the associated infauna along a short transect with increasing distance to an iron source, i.e., corroding steel weights of a free-falling observational platform (bottom-lander), lying on the seafloor for approximately seven years. Iron-enriched surface sediments in the vicinity of the bottom-weight left in summer 2008 after a short-term deployment of a bottom-lander in 2433 m water depth at the LTER (Long-Term Ecological Research) observation HAUSGARTEN in eastern parts of the Fram Strait were sampled on 28th July 2015 using push-corer (PC) handled by the Remotely Operated Vehicle (ROV) QUEST 4000 (MARUM Center for Marine Environmental Sciences, Germany) during Dive 369 from board RV Polarstern. The block-shaped steel bottom-weights (30 x 30 x 6 cm) were sitting about half of the height sunken into the seafloor and thus, almost not affecting near-bottom currents. During sampling in 2015, the plates were largely corroded. Surface sediments around the plates had an orange-red color with a gradient of decreasing color intensity with increasing distance from the source, i.e., the bottom weight. A total of eight push-corer samples (PC1-8) were taken at approx. regular distances (on average every 18 cm) along a short transect (about 1.5 m) crossing the iron gradient. Push-corers PC1-4 retrieved sediment from heavily impacted sediments, while samples taken from push-corers PC5-8 were visually indistinguishable from background sediments in the wider area. After recovery of the ROV, sediment cores (8 cm in diameter, and 20-25 cm in height) were sub-sampled using plastic syringes with cut-off anterior ends for meiofauna and nematode communities as well as for environmental parameters. The position specified in the data sets (longitude / latitude) refers to the position of the ROV.
    Keywords: Deep sea; Hausgarten; iron; Long-term Investigation at AWI-Hausgarten off Svalbard; meiofauna; Nematoda; sediments
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 10
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    Clay mineral records for the last two glaciations at the East Siberian continental margin (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-08
    Description: Four gravity sediment cores used for clay mineral analysis were recovered from the East Siberian continental margin. Core M04 (172.199W,75.982N ), with a length of 560 cm, was recovered from the Chukchi Basin during the fifth Chinese National Arctic Research Expedition (CHINARE) in 2012; core C22 (154.581W, 77.164N ), with a length of 200 cm, from the Northwind Ridge during the sixth CHINARE in 2014; and cores E23 (179.715E, 77.060N ) and P13 (159.865W, 77.991N ), with lengths of 354 cm and 246 cm from the southern Mendeleev Ridge and Chukchi Plateau, respectively, during the seventh CHINARE in 2017. The fine-grained fractions (〈63 μm) of each sample were retained for clay mineral analysis. Decalcification and organic material removal were performed at room temperature with 1 M HCl and 30% H2O2, respectively. Clay fractions (〈2 μm) were determined using Stokes’ law (Liu et al., 2010 and references therein). Each sample was oriented by wet smearing onto two glass slides before being air-dried. One of the slides was used for qualitative identification of clay minerals, whereas the other was solvated with ethylene glycol in an under-pressurized desiccator for at least 36 h at 35 ℃. Clay minerals were then measured by a PANalytical diffractometer, scanning from 3°–35° 2θ with a step of 0.0167° 2θ, at the KLSG. Semi-quantitative estimates of peak areas for the main clay minerals were carried out on the ethylene-glycol solvated curve using MDI Jade 6.0 software, and the percentage of clay minerals was calculated using Biscaye’s (1965) weighting factors. The 17Å and 10Å peaks were used for quantification of smectite and illite, respectively. Kaolinite (3.58Å) and chlorite (3.54Å) peaks were identified to calculate their proportional percentage from the 7Å kaolinite + chlorite peak (Biscaye, 1965). Analytical precision was checked using 15 replicates with a standard deviation of 〈3%.
    Keywords: Arctic Ocean; clay mineral; East Siberian ice sheet; ice event
    Type: Dataset
    Format: application/zip, 4 datasets
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  • 11
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    Stable isotope from Miocene exceptionally preserved foraminifera from Tanzania (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-10
    Description: The dataset presents oxygen and carbon stable isotopes measured on multispecies planktonic foraminiferal shells and on Cibicidoides spp. and Uvigerina spp. (benthic foraminifera). The data were obtained from three samples from marine deposits outcropping in coastal Tanzania dating back to the early and middle Miocene. The samples are characterized by an exceptionally good preservation of foraminiferal shells.
    Keywords: Age model, Berggren et al (1995) BKSA95; Biostratigraphic zone; Calculated, δ18O; Event label; foraminiferal stable isotopes; Miocene; OUTCROP; Outcrop sample; paleotemperature; RAS99-38; RAS99-42; Site180906/1; Species; Tanzania; Temperature, calculated; δ13C; δ18O
    Type: Dataset
    Format: text/tab-separated-values, 205 data points
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  • 12
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    Detrital zircon U-Pb age and detrital rutile trace element data from the late Neogene Baode Red Clay sequence, Chinese Loess Plateau, and detrital rutile geochemistry from 14 potential source areas (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-01
    Description: The data were collected for a joint detrital zircon and detrital rutile provenance study of the late Neogene aeolian Baode Red Clay, located on the northern part of the Chinese Loess Plateau. The data consist of detrital zircon U-Pb ages of the 4.04–2.64 Ma Baode Red Clay (four samples from the Pliocene Jingle Formation and one sample from the 2.64 Ma Transitional Unit), and detrital rutile trace element geochemistry of the 6.91–2.64 Ma Baode Red Clay (three samples from the Miocene Baode Formation, five samples from the Pliocene Jingle Formation, and one sample from the Transitional Unit) and 14 potential sedimentary source areas in Central-East Asia. The data were collected using Nu Plasma AttoM single collector ICP-MS (Nu Instruments Ltd., Wrexham, UK) connected to an Analyte Excite 193 ArF laser ablation system (Photon Machines, San Diego, USA) at the Geological Survey of Finland. The rutiles were analysed for Li, Mg, Al, Si, P, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb, Mo, Sn, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Pb, Th, and U. The grain size fractions of the analysed grains were mostly 30–90 μm for the Red Clay zircons and rutiles, and 20–500 μm for the potential source area rutiles.
    Keywords: Chinese Loess Plateau; detrital rutile; detrital zircon; eolian sediment; Miocene; Pliocene; Provenance; Red Clay
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 13
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    Detrital zircon U-Pb age from the late Neogene Baode Red Clay sequence, Chinese Loess Plateau (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-01
    Description: The data consist of detrital zircon U-Pb ages of the 4.04–2.64 Ma Baode Red Clay (four samples from the Pliocene Jingle Formation and one sample from the 2.64 Ma Transitional Unit). The data were collected using Nu Plasma AttoM single collector ICP-MS (Nu Instruments Ltd., Wrexham, UK) connected to an Analyte Excite 193 ArF laser ablation system (Photon Machines, San Diego, USA) at the Geological Survey of Finland.
    Keywords: Age; Age, 206Pb/238U Lead-Uranium; Age, 207Pb/206Pb Lead-Lead; Age, 207Pb/235U Lead-Uranium; Age, error; Age, mineral; Baode; Chinese Loess Plateau; Comment; Correlation coefficient, isotope ratio error; Degree of concordance; detrital rutile; detrital zircon; eolian sediment; Fluence; Grain ID; Grain size, maximum; Grain size, minimum; Identification; LA-ICP-MS, Laser-ablation inductively coupled plasma mass spectrometer; LATITUDE; Lead; Lead-206; Lead-206/Lead-204 ratio; Lead-206/Uranium-238, error, relative; Lead-206/Uranium-238, standard deviation; Lead-206/Uranium-238 ratio; Lead-207/Lead-206 ratio; Lead-207/Lead-206 ratio, error, relative; Lead-207/Lead-206 ratio, standard deviation; Lead-207/Uranium-235, error, relative; Lead-207/Uranium-235, standard deviation; Lead-207/Uranium-235 ratio; Lithologic unit/sequence; LONGITUDE; Miocene; Pliocene; Preferred age; Provenance; Red Clay; Repetition rate; Sample ID; Sediment sample; SES; Spot size; Thorium; Uranium
    Type: Dataset
    Format: text/tab-separated-values, 53231 data points
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  • 14
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    Immobilisation dose rates for Weddell seals during expedition NEU2022 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-16
    Description: Weddell seals (Leptonychotes weddellii) were immobilised during expedition NEU2022 (ANT-Land 2022/2023) for the purpose of deployments with infrared (IR) camera loggers (CAM). The IR-CAM deployments (n=17) were located in the Atka-Bay in the vicinity to the Neumayer Station III (DE), eastern Weddell Sea. The seals were searcher by Skidoo and captured along tidal within the bay and immobilised and instrumented on fast (bay) ice with infrared video camera loggers (IR-CAMs, Little Leonardo, JP) to investigate their foraging behaviour in the context of sea ice features inside the bay.
    Keywords: Additives; ANT-Land_2022/2023; Atipamezol; Comment; DATE/TIME; Diazepam; Estimated; Event label; Girth, standard; immobilisation; Injection; Ketamine; LATITUDE; Length, standard; Leptonychotes weddellii; LONGITUDE; Marine endotherm; Mass; MET; NEU2022; NEU2022_wed_a_f_03; NEU2022_wed_a_f_05; NEU2022_wed_a_f_07; NEU2022_wed_a_m_01; NEU2022_wed_a_m_02; NEU2022_wed_a_m_04; NEU2022_wed_a_m_06; NEUMAYER III; Physical restraint; Premedication; Ruler tape; Sample code/label; SEAEIS; Seals and cryobenthic communities at the Ekström Ice Shelf; Southern Ocean; Species code; Time of day; Weddell seal; Xylazine
    Type: Dataset
    Format: text/tab-separated-values, 247 data points
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  • 15
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    Annual cloud cover anomaly over Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-11
    Keywords: CloudCA_Italy_Grid_1x1; Cloud cover anomaly; DATE/TIME; LATITUDE; LONGITUDE; Visual observation
    Type: Dataset
    Format: text/tab-separated-values, 3808 data points
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  • 16
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    Unknown
    Winter cloud cover anomaly over Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-11
    Keywords: CloudCA_Italy_Grid_1x1; Cloud cover anomaly; DATE/TIME; LATITUDE; LONGITUDE; Visual observation
    Type: Dataset
    Format: text/tab-separated-values, 3808 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 17
    facet.materialart.
    Unknown
    Fluxes and related variables from EddyPro®-Output from eddy-covariance measurements above the forest ecosystem Hohes Holz in 2016 (2023)
    PANGAEA
    In:  Helmholtz Centre for Environmental Research - UFZ
    Details
    Publication Date: 2023-04-04
    Description: Continuous measurements of carbon, water and energy fluxes are performed using the eddy covariance (EC) method in a mixed-beech forest ecosystem in central Germany (52° 5'12N, 11°13'20E, 193 m asl), accompanied by relevant abiotic measurements. The site was established in the Bode catchment as part of the TERENO Harz/Central German Lowland Observatory, a mesoscale water catchment within the Elbe river basin covering an area of approximately 3300 km². The forest area Hohes Holz is the only larger forested area in the otherwise agriculturally intensively-farmed western part of the Magdeburger Börde with an area of about 1500 ha [Wollschläger et al., 2017]. The forest is a protected area with the centre (150 ha) being a nature reserve (Natura 2000) and is dominated by common beech (Fagus sylvatica L.), sessile oak (Quercus petraea) and hornbeam (Carpinus betulus L.) of about 90 years in age, an average tree height of 23.5 m and a stand density of 260 trees/ha. The long term average of annual precipitation is 563 mm and mean annual temperature is 9.1 °C (1981 – 2010 DWD station Ummendorf, #5158). The eddy covariance system consists of a CSAT-3 anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI-7500 gas analyser (Li-Cor Inc., Lincoln, NE, USA), established in 2014 in 49 m on a scaffolding tower within the research area. Data presented here comprise energy, water (H and LE), and carbon fluxes (NEE) from the EC-system since 2015 as well as gross primary productivity (GPP) and ecosystem respiration (Reco) derived from partitioning of NEE-data. Complimentary data from the turbulence data set and prioritized driver variables as a basis for ecosystem process analysis are added. High-frequency data (20Hz) were acquired with a Campbell data logger and the Eddymeas data acquisition software [Kolle and Rebmann, 2007]. Flux computation from high frequency raw data was performed with the Eddy-Pro® software (v. 7.0.6). After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed according to Wutzler et al. [2018] with the REddyProc package. Full details of site instrumentation, metadata information and R-packages used for processing can be found in the supplementary material. Since January 2019 the site is approved as an ICOS ecosystem class 1 station (DE-HoH). ICOS standard procedures required an additional EC-setup consisting of a Gill HS-50 ultrasonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK) and a LI-7200 gas analyser which runs in parallel to the above described system (see ICOS carbon portal: https://www.icos-cp.eu/data-products/ecosystem-release).
    Keywords: Barometer, Setra, 278; Carbon dioxide, density; Carbon dioxide, flux; Carbon dioxide, flux, stationarity deviation; Carbon dioxide, flux, storage; Carbon dioxide, mole fraction; Carbon dioxide, mole fraction, variance; Carbon dioxide flux, random error; Carbon dioxide mixing ratio; DATE/TIME; Day of the year; Daytime Indicator; Density, water vapour; Distance, relative, X, offset; Distance, relative, X, peak; Distance, relative, X, percentage contribution; ECS; ECS_HohesHolz; ECS_Hohes Holz_Eggenstedt (Germany); Eddy covariance; Eddy covariance footprint, model type; Eddy Covariance Station; evapotranspiration; File name; Friction velocity; GPP; Heat, flux, latent; Heat, flux, latent, random error; Heat, flux, sensible; Heat, flux, sensible, random error; ICOS; Integrated Carbon Observation System; managed deciduous forest; Modeling Monitoring Events; MOMENT; Momentum, flux; Momentum, flux, random error; NEE; Net ecosystem exchange of carbon dioxide; Number of flagged records, chopper_LI-7500; Number of flagged records, detector_LI-7500; Number of flagged records, pll_LI-7500; Number of flagged records, sync_LI-7500; Number of records, total; Number of records, used; Obukhov stability parameter; Open Path CO2/H2O Gas Analyzer (LI-7500RS, LI-COR); Pressure, atmospheric; Quality flag, carbon dioxide, flux; Quality flag, carbon dioxide flux; Quality flag, heat, flux, latent; Quality flag, heat, flux, sensible; Quality flag, momentum flux; Quality flag, water vapour flux; Quality flag, wind speed; Reco; Signal strength; Sonic anemometer, CSAT3, Campbell Scientific; Sonic anemometer CSAT3 and CO2/H2O Gas Analyzer (LI-7500RS); Sonic temperature; Sonic temperature, variance; Spikes, carbon dioxide; Spikes, sonic temperature; Spikes, water vapour; Spikes, wind velocity, lateral; Spikes, wind velocity, longitudinal; Spikes, wind velocity, vertical; Temperature, air; TERENO; Terrestrial Environmental Observatories; Thermometer/Hygrometer, Vaisala, HMP155; Water vapour, density; Water vapour, flux; Water vapour, mole fraction; Water vapour, mole fraction, variance; Water vapour flux, random error; Water vapour mixing ratio; Wind direction; Wind speed; Wind velocity, lateral; Wind velocity, lateral, variance; Wind velocity, longitudinal; Wind velocity, longitudinal, variance; Wind velocity, vertical; Wind velocity, vertical, variance
    Type: Dataset
    Format: text/tab-separated-values, 1212192 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 18
    facet.materialart.
    Unknown
    Fluxes and related variables from EddyPro®-Output from eddy-covariance measurements above the forest ecosystem Hohes Holz in 2017 (2023)
    PANGAEA
    In:  Helmholtz Centre for Environmental Research - UFZ
    Details
    Publication Date: 2023-04-04
    Description: Continuous measurements of carbon, water and energy fluxes are performed using the eddy covariance (EC) method in a mixed-beech forest ecosystem in central Germany (52° 5'12N, 11°13'20E, 193 m asl), accompanied by relevant abiotic measurements. The site was established in the Bode catchment as part of the TERENO Harz/Central German Lowland Observatory, a mesoscale water catchment within the Elbe river basin covering an area of approximately 3300 km². The forest area Hohes Holz is the only larger forested area in the otherwise agriculturally intensively-farmed western part of the Magdeburger Börde with an area of about 1500 ha [Wollschläger et al., 2017]. The forest is a protected area with the centre (150 ha) being a nature reserve (Natura 2000) and is dominated by common beech (Fagus sylvatica L.), sessile oak (Quercus petraea) and hornbeam (Carpinus betulus L.) of about 90 years in age, an average tree height of 23.5 m and a stand density of 260 trees/ha. The long term average of annual precipitation is 563 mm and mean annual temperature is 9.1 °C (1981 – 2010 DWD station Ummendorf, #5158). The eddy covariance system consists of a CSAT-3 anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI-7500 gas analyser (Li-Cor Inc., Lincoln, NE, USA), established in 2014 in 49 m on a scaffolding tower within the research area. Data presented here comprise energy, water (H and LE), and carbon fluxes (NEE) from the EC-system since 2015 as well as gross primary productivity (GPP) and ecosystem respiration (Reco) derived from partitioning of NEE-data. Complimentary data from the turbulence data set and prioritized driver variables as a basis for ecosystem process analysis are added. High-frequency data (20Hz) were acquired with a Campbell data logger and the Eddymeas data acquisition software [Kolle and Rebmann, 2007]. Flux computation from high frequency raw data was performed with the Eddy-Pro® software (v. 7.0.6). After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed according to Wutzler et al. [2018] with the REddyProc package. Full details of site instrumentation, metadata information and R-packages used for processing can be found in the supplementary material. Since January 2019 the site is approved as an ICOS ecosystem class 1 station (DE-HoH). ICOS standard procedures required an additional EC-setup consisting of a Gill HS-50 ultrasonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK) and a LI-7200 gas analyser which runs in parallel to the above described system (see ICOS carbon portal: https://www.icos-cp.eu/data-products/ecosystem-release).
    Keywords: Barometer, Setra, 278; Carbon dioxide, density; Carbon dioxide, flux; Carbon dioxide, flux, stationarity deviation; Carbon dioxide, flux, storage; Carbon dioxide, mole fraction; Carbon dioxide, mole fraction, variance; Carbon dioxide flux, random error; Carbon dioxide mixing ratio; DATE/TIME; Day of the year; Daytime Indicator; Density, water vapour; Distance, relative, X, offset; Distance, relative, X, peak; Distance, relative, X, percentage contribution; ECS; ECS_HohesHolz; ECS_Hohes Holz_Eggenstedt (Germany); Eddy covariance; Eddy covariance footprint, model type; Eddy Covariance Station; evapotranspiration; File name; Friction velocity; GPP; Heat, flux, latent; Heat, flux, latent, random error; Heat, flux, sensible; Heat, flux, sensible, random error; ICOS; Integrated Carbon Observation System; managed deciduous forest; Modeling Monitoring Events; MOMENT; Momentum, flux; Momentum, flux, random error; NEE; Net ecosystem exchange of carbon dioxide; Number of flagged records, chopper_LI-7500; Number of flagged records, detector_LI-7500; Number of flagged records, pll_LI-7500; Number of flagged records, sync_LI-7500; Number of records, total; Number of records, used; Obukhov stability parameter; Open Path CO2/H2O Gas Analyzer (LI-7500RS, LI-COR); Pressure, atmospheric; Quality flag, carbon dioxide, flux; Quality flag, carbon dioxide flux; Quality flag, heat, flux, latent; Quality flag, heat, flux, sensible; Quality flag, momentum flux; Quality flag, water vapour flux; Quality flag, wind speed; Reco; Signal strength; Sonic anemometer, CSAT3, Campbell Scientific; Sonic anemometer CSAT3 and CO2/H2O Gas Analyzer (LI-7500RS); Sonic temperature; Sonic temperature, variance; Spikes, carbon dioxide; Spikes, sonic temperature; Spikes, water vapour; Spikes, wind velocity, lateral; Spikes, wind velocity, longitudinal; Spikes, wind velocity, vertical; Temperature, air; TERENO; Terrestrial Environmental Observatories; Thermometer/Hygrometer, Vaisala, HMP155; Water vapour, density; Water vapour, flux; Water vapour, mole fraction; Water vapour, mole fraction, variance; Water vapour flux, random error; Water vapour mixing ratio; Wind direction; Wind speed; Wind velocity, lateral; Wind velocity, lateral, variance; Wind velocity, longitudinal; Wind velocity, longitudinal, variance; Wind velocity, vertical; Wind velocity, vertical, variance
    Type: Dataset
    Format: text/tab-separated-values, 1208880 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 19
    facet.materialart.
    Unknown
    Fluxes and related variables from EddyPro®-Output from eddy-covariance measurements above the forest ecosystem Hohes Holz in 2018 (2023)
    PANGAEA
    In:  Helmholtz Centre for Environmental Research - UFZ
    Details
    Publication Date: 2023-04-04
    Description: Continuous measurements of carbon, water and energy fluxes are performed using the eddy covariance (EC) method in a mixed-beech forest ecosystem in central Germany (52° 5'12N, 11°13'20E, 193 m asl), accompanied by relevant abiotic measurements. The site was established in the Bode catchment as part of the TERENO Harz/Central German Lowland Observatory, a mesoscale water catchment within the Elbe river basin covering an area of approximately 3300 km². The forest area Hohes Holz is the only larger forested area in the otherwise agriculturally intensively-farmed western part of the Magdeburger Börde with an area of about 1500 ha [Wollschläger et al., 2017]. The forest is a protected area with the centre (150 ha) being a nature reserve (Natura 2000) and is dominated by common beech (Fagus sylvatica L.), sessile oak (Quercus petraea) and hornbeam (Carpinus betulus L.) of about 90 years in age, an average tree height of 23.5 m and a stand density of 260 trees/ha. The long term average of annual precipitation is 563 mm and mean annual temperature is 9.1 °C (1981 – 2010 DWD station Ummendorf, #5158). The eddy covariance system consists of a CSAT-3 anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI-7500 gas analyser (Li-Cor Inc., Lincoln, NE, USA), established in 2014 in 49 m on a scaffolding tower within the research area. Data presented here comprise energy, water (H and LE), and carbon fluxes (NEE) from the EC-system since 2015 as well as gross primary productivity (GPP) and ecosystem respiration (Reco) derived from partitioning of NEE-data. Complimentary data from the turbulence data set and prioritized driver variables as a basis for ecosystem process analysis are added. High-frequency data (20Hz) were acquired with a Campbell data logger and the Eddymeas data acquisition software [Kolle and Rebmann, 2007]. Flux computation from high frequency raw data was performed with the Eddy-Pro® software (v. 7.0.6). After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed according to Wutzler et al. [2018] with the REddyProc package. Full details of site instrumentation, metadata information and R-packages used for processing can be found in the supplementary material. Since January 2019 the site is approved as an ICOS ecosystem class 1 station (DE-HoH). ICOS standard procedures required an additional EC-setup consisting of a Gill HS-50 ultrasonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK) and a LI-7200 gas analyser which runs in parallel to the above described system (see ICOS carbon portal: https://www.icos-cp.eu/data-products/ecosystem-release).
    Keywords: Barometer, Setra, 278; Carbon dioxide, density; Carbon dioxide, flux; Carbon dioxide, flux, stationarity deviation; Carbon dioxide, flux, storage; Carbon dioxide, mole fraction; Carbon dioxide, mole fraction, variance; Carbon dioxide flux, random error; Carbon dioxide mixing ratio; DATE/TIME; Day of the year; Daytime Indicator; Density, water vapour; Distance, relative, X, offset; Distance, relative, X, peak; Distance, relative, X, percentage contribution; ECS; ECS_HohesHolz; ECS_Hohes Holz_Eggenstedt (Germany); Eddy covariance; Eddy covariance footprint, model type; Eddy Covariance Station; evapotranspiration; File name; Friction velocity; GPP; Heat, flux, latent; Heat, flux, latent, random error; Heat, flux, sensible; Heat, flux, sensible, random error; ICOS; Integrated Carbon Observation System; managed deciduous forest; Modeling Monitoring Events; MOMENT; Momentum, flux; Momentum, flux, random error; NEE; Net ecosystem exchange of carbon dioxide; Number of flagged records, chopper_LI-7500; Number of flagged records, detector_LI-7500; Number of flagged records, pll_LI-7500; Number of flagged records, sync_LI-7500; Number of records, total; Number of records, used; Obukhov stability parameter; Open Path CO2/H2O Gas Analyzer (LI-7500RS, LI-COR); Pressure, atmospheric; Quality flag, carbon dioxide, flux; Quality flag, carbon dioxide flux; Quality flag, heat, flux, latent; Quality flag, heat, flux, sensible; Quality flag, momentum flux; Quality flag, water vapour flux; Quality flag, wind speed; Reco; Signal strength; Sonic anemometer, CSAT3, Campbell Scientific; Sonic anemometer CSAT3 and CO2/H2O Gas Analyzer (LI-7500RS); Sonic temperature; Sonic temperature, variance; Spikes, carbon dioxide; Spikes, sonic temperature; Spikes, water vapour; Spikes, wind velocity, lateral; Spikes, wind velocity, longitudinal; Spikes, wind velocity, vertical; Temperature, air; TERENO; Terrestrial Environmental Observatories; Thermometer/Hygrometer, Vaisala, HMP155; Water vapour, density; Water vapour, flux; Water vapour, mole fraction; Water vapour, mole fraction, variance; Water vapour flux, random error; Water vapour mixing ratio; Wind direction; Wind speed; Wind velocity, lateral; Wind velocity, lateral, variance; Wind velocity, longitudinal; Wind velocity, longitudinal, variance; Wind velocity, vertical; Wind velocity, vertical, variance
    Type: Dataset
    Format: text/tab-separated-values, 1208880 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 20
    facet.materialart.
    Unknown
    Fluxes and related variables from EddyPro®-Output from eddy-covariance measurements above the forest ecosystem Hohes Holz in 2020 (2023)
    PANGAEA
    In:  Helmholtz Centre for Environmental Research - UFZ
    Details
    Publication Date: 2023-04-04
    Description: Continuous measurements of carbon, water and energy fluxes are performed using the eddy covariance (EC) method in a mixed-beech forest ecosystem in central Germany (52° 5'12N, 11°13'20E, 193 m asl), accompanied by relevant abiotic measurements. The site was established in the Bode catchment as part of the TERENO Harz/Central German Lowland Observatory, a mesoscale water catchment within the Elbe river basin covering an area of approximately 3300 km². The forest area Hohes Holz is the only larger forested area in the otherwise agriculturally intensively-farmed western part of the Magdeburger Börde with an area of about 1500 ha [Wollschläger et al., 2017]. The forest is a protected area with the centre (150 ha) being a nature reserve (Natura 2000) and is dominated by common beech (Fagus sylvatica L.), sessile oak (Quercus petraea) and hornbeam (Carpinus betulus L.) of about 90 years in age, an average tree height of 23.5 m and a stand density of 260 trees/ha. The long term average of annual precipitation is 563 mm and mean annual temperature is 9.1 °C (1981 – 2010 DWD station Ummendorf, #5158). The eddy covariance system consists of a CSAT-3 anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI-7500 gas analyser (Li-Cor Inc., Lincoln, NE, USA), established in 2014 in 49 m on a scaffolding tower within the research area. Data presented here comprise energy, water (H and LE), and carbon fluxes (NEE) from the EC-system since 2015 as well as gross primary productivity (GPP) and ecosystem respiration (Reco) derived from partitioning of NEE-data. Complimentary data from the turbulence data set and prioritized driver variables as a basis for ecosystem process analysis are added. High-frequency data (20Hz) were acquired with a Campbell data logger and the Eddymeas data acquisition software [Kolle and Rebmann, 2007]. Flux computation from high frequency raw data was performed with the Eddy-Pro® software (v. 7.0.6). After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed according to Wutzler et al. [2018] with the REddyProc package. Full details of site instrumentation, metadata information and R-packages used for processing can be found in the supplementary material. Since January 2019 the site is approved as an ICOS ecosystem class 1 station (DE-HoH). ICOS standard procedures required an additional EC-setup consisting of a Gill HS-50 ultrasonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK) and a LI-7200 gas analyser which runs in parallel to the above described system (see ICOS carbon portal: https://www.icos-cp.eu/data-products/ecosystem-release).
    Keywords: Barometer, Setra, 278; Carbon dioxide, density; Carbon dioxide, flux; Carbon dioxide, flux, stationarity deviation; Carbon dioxide, flux, storage; Carbon dioxide, mole fraction; Carbon dioxide, mole fraction, variance; Carbon dioxide flux, random error; Carbon dioxide mixing ratio; DATE/TIME; Day of the year; Daytime Indicator; Density, water vapour; Distance, relative, X, offset; Distance, relative, X, peak; Distance, relative, X, percentage contribution; ECS; ECS_HohesHolz; ECS_Hohes Holz_Eggenstedt (Germany); Eddy covariance; Eddy covariance footprint, model type; Eddy Covariance Station; evapotranspiration; File name; Friction velocity; GPP; Heat, flux, latent; Heat, flux, latent, random error; Heat, flux, sensible; Heat, flux, sensible, random error; ICOS; Integrated Carbon Observation System; managed deciduous forest; Modeling Monitoring Events; MOMENT; Momentum, flux; Momentum, flux, random error; NEE; Net ecosystem exchange of carbon dioxide; Number of flagged records, chopper_LI-7500; Number of flagged records, detector_LI-7500; Number of flagged records, pll_LI-7500; Number of flagged records, sync_LI-7500; Number of records, total; Number of records, used; Obukhov stability parameter; Open Path CO2/H2O Gas Analyzer (LI-7500RS, LI-COR); Pressure, atmospheric; Quality flag, carbon dioxide, flux; Quality flag, carbon dioxide flux; Quality flag, heat, flux, latent; Quality flag, heat, flux, sensible; Quality flag, momentum flux; Quality flag, water vapour flux; Quality flag, wind speed; Reco; Signal strength; Sonic anemometer, CSAT3, Campbell Scientific; Sonic anemometer CSAT3 and CO2/H2O Gas Analyzer (LI-7500RS); Sonic temperature; Sonic temperature, variance; Spikes, carbon dioxide; Spikes, sonic temperature; Spikes, water vapour; Spikes, wind velocity, lateral; Spikes, wind velocity, longitudinal; Spikes, wind velocity, vertical; Temperature, air; TERENO; Terrestrial Environmental Observatories; Thermometer/Hygrometer, Vaisala, HMP155; Water vapour, density; Water vapour, flux; Water vapour, mole fraction; Water vapour, mole fraction, variance; Water vapour flux, random error; Water vapour mixing ratio; Wind direction; Wind speed; Wind velocity, lateral; Wind velocity, lateral, variance; Wind velocity, longitudinal; Wind velocity, longitudinal, variance; Wind velocity, vertical; Wind velocity, vertical, variance
    Type: Dataset
    Format: text/tab-separated-values, 1212192 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 21
    facet.materialart.
    Unknown
    Spring cloud cover anomaly over Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-11
    Keywords: CloudCA_Italy_Grid_1x1; Cloud cover anomaly; DATE/TIME; LATITUDE; LONGITUDE; Visual observation
    Type: Dataset
    Format: text/tab-separated-values, 3808 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 22
    facet.materialart.
    Unknown
    Autumn cloud cover anomaly over Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-11
    Keywords: CloudCA_Italy_Grid_1x1; Cloud cover anomaly; DATE/TIME; LATITUDE; LONGITUDE; Visual observation
    Type: Dataset
    Format: text/tab-separated-values, 3808 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 23
    facet.materialart.
    Unknown
    Summer cloud cover anomaly over Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-11
    Keywords: CloudCA_Italy_Grid_1x1; Cloud cover anomaly; DATE/TIME; LATITUDE; LONGITUDE; Visual observation
    Type: Dataset
    Format: text/tab-separated-values, 3808 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 24
    facet.materialart.
    Unknown
    Fluxes and related variables from EddyPro®-Output from eddy-covariance measurements above the forest ecosystem Hohes Holz in 2015 (2023)
    PANGAEA
    In:  Helmholtz Centre for Environmental Research - UFZ
    Details
    Publication Date: 2023-04-04
    Description: Continuous measurements of carbon, water and energy fluxes are performed using the eddy covariance (EC) method in a mixed-beech forest ecosystem in central Germany (52° 5'12N, 11°13'20E, 193 m asl), accompanied by relevant abiotic measurements. The site was established in the Bode catchment as part of the TERENO Harz/Central German Lowland Observatory, a mesoscale water catchment within the Elbe river basin covering an area of approximately 3300 km². The forest area Hohes Holz is the only larger forested area in the otherwise agriculturally intensively-farmed western part of the Magdeburger Börde with an area of about 1500 ha [Wollschläger et al., 2017]. The forest is a protected area with the centre (150 ha) being a nature reserve (Natura 2000) and is dominated by common beech (Fagus sylvatica L.), sessile oak (Quercus petraea) and hornbeam (Carpinus betulus L.) of about 90 years in age, an average tree height of 23.5 m and a stand density of 260 trees/ha. The long term average of annual precipitation is 563 mm and mean annual temperature is 9.1 °C (1981 – 2010 DWD station Ummendorf, #5158). The eddy covariance system consists of a CSAT-3 anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI-7500 gas analyser (Li-Cor Inc., Lincoln, NE, USA), established in 2014 in 49 m on a scaffolding tower within the research area. Data presented here comprise energy, water (H and LE), and carbon fluxes (NEE) from the EC-system since 2015 as well as gross primary productivity (GPP) and ecosystem respiration (Reco) derived from partitioning of NEE-data. Complimentary data from the turbulence data set and prioritized driver variables as a basis for ecosystem process analysis are added. High-frequency data (20Hz) were acquired with a Campbell data logger and the Eddymeas data acquisition software [Kolle and Rebmann, 2007]. Flux computation from high frequency raw data was performed with the Eddy-Pro® software (v. 7.0.6). After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed according to Wutzler et al. [2018] with the REddyProc package. Full details of site instrumentation, metadata information and R-packages used for processing can be found in the supplementary material. Since January 2019 the site is approved as an ICOS ecosystem class 1 station (DE-HoH). ICOS standard procedures required an additional EC-setup consisting of a Gill HS-50 ultrasonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK) and a LI-7200 gas analyser which runs in parallel to the above described system (see ICOS carbon portal: https://www.icos-cp.eu/data-products/ecosystem-release).
    Keywords: Barometer, Setra, 278; Carbon dioxide, density; Carbon dioxide, flux; Carbon dioxide, flux, stationarity deviation; Carbon dioxide, flux, storage; Carbon dioxide, mole fraction; Carbon dioxide, mole fraction, variance; Carbon dioxide flux, random error; Carbon dioxide mixing ratio; DATE/TIME; Day of the year; Daytime Indicator; Density, water vapour; Distance, relative, X, offset; Distance, relative, X, peak; Distance, relative, X, percentage contribution; ECS; ECS_HohesHolz; ECS_Hohes Holz_Eggenstedt (Germany); Eddy covariance; Eddy covariance footprint, model type; Eddy Covariance Station; evapotranspiration; File name; Friction velocity; GPP; Heat, flux, latent; Heat, flux, latent, random error; Heat, flux, sensible; Heat, flux, sensible, random error; ICOS; Integrated Carbon Observation System; managed deciduous forest; Modeling Monitoring Events; MOMENT; Momentum, flux; Momentum, flux, random error; NEE; Net ecosystem exchange of carbon dioxide; Number of flagged records, chopper_LI-7500; Number of flagged records, detector_LI-7500; Number of flagged records, pll_LI-7500; Number of flagged records, sync_LI-7500; Number of records, total; Number of records, used; Obukhov stability parameter; Open Path CO2/H2O Gas Analyzer (LI-7500RS, LI-COR); Pressure, atmospheric; Quality flag, carbon dioxide, flux; Quality flag, carbon dioxide flux; Quality flag, heat, flux, latent; Quality flag, heat, flux, sensible; Quality flag, momentum flux; Quality flag, water vapour flux; Quality flag, wind speed; Reco; Signal strength; Sonic anemometer, CSAT3, Campbell Scientific; Sonic anemometer CSAT3 and CO2/H2O Gas Analyzer (LI-7500RS); Sonic temperature; Sonic temperature, variance; Spikes, carbon dioxide; Spikes, sonic temperature; Spikes, water vapour; Spikes, wind velocity, lateral; Spikes, wind velocity, longitudinal; Spikes, wind velocity, vertical; Temperature, air; TERENO; Terrestrial Environmental Observatories; Thermometer/Hygrometer, Vaisala, HMP155; Water vapour, density; Water vapour, flux; Water vapour, mole fraction; Water vapour, mole fraction, variance; Water vapour flux, random error; Water vapour mixing ratio; Wind direction; Wind speed; Wind velocity, lateral; Wind velocity, lateral, variance; Wind velocity, longitudinal; Wind velocity, longitudinal, variance; Wind velocity, vertical; Wind velocity, vertical, variance
    Type: Dataset
    Format: text/tab-separated-values, 1208875 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 25
    facet.materialart.
    Unknown
    Fluxes and related variables from EddyPro®-Output from eddy-covariance measurements above the forest ecosystem Hohes Holz in 2019 (2023)
    PANGAEA
    In:  Helmholtz Centre for Environmental Research - UFZ
    Details
    Publication Date: 2023-04-04
    Description: Continuous measurements of carbon, water and energy fluxes are performed using the eddy covariance (EC) method in a mixed-beech forest ecosystem in central Germany (52° 5'12N, 11°13'20E, 193 m asl), accompanied by relevant abiotic measurements. The site was established in the Bode catchment as part of the TERENO Harz/Central German Lowland Observatory, a mesoscale water catchment within the Elbe river basin covering an area of approximately 3300 km². The forest area Hohes Holz is the only larger forested area in the otherwise agriculturally intensively-farmed western part of the Magdeburger Börde with an area of about 1500 ha [Wollschläger et al., 2017]. The forest is a protected area with the centre (150 ha) being a nature reserve (Natura 2000) and is dominated by common beech (Fagus sylvatica L.), sessile oak (Quercus petraea) and hornbeam (Carpinus betulus L.) of about 90 years in age, an average tree height of 23.5 m and a stand density of 260 trees/ha. The long term average of annual precipitation is 563 mm and mean annual temperature is 9.1 °C (1981 – 2010 DWD station Ummendorf, #5158). The eddy covariance system consists of a CSAT-3 anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI-7500 gas analyser (Li-Cor Inc., Lincoln, NE, USA), established in 2014 in 49 m on a scaffolding tower within the research area. Data presented here comprise energy, water (H and LE), and carbon fluxes (NEE) from the EC-system since 2015 as well as gross primary productivity (GPP) and ecosystem respiration (Reco) derived from partitioning of NEE-data. Complimentary data from the turbulence data set and prioritized driver variables as a basis for ecosystem process analysis are added. High-frequency data (20Hz) were acquired with a Campbell data logger and the Eddymeas data acquisition software [Kolle and Rebmann, 2007]. Flux computation from high frequency raw data was performed with the Eddy-Pro® software (v. 7.0.6). After removing physically unrealistic flux values from the time series, subsequent post-processing steps such as estimating the u*-threshold, gap-filling and flux partitioning were performed according to Wutzler et al. [2018] with the REddyProc package. Full details of site instrumentation, metadata information and R-packages used for processing can be found in the supplementary material. Since January 2019 the site is approved as an ICOS ecosystem class 1 station (DE-HoH). ICOS standard procedures required an additional EC-setup consisting of a Gill HS-50 ultrasonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, UK) and a LI-7200 gas analyser which runs in parallel to the above described system (see ICOS carbon portal: https://www.icos-cp.eu/data-products/ecosystem-release).
    Keywords: Barometer, Setra, 278; Carbon dioxide, density; Carbon dioxide, flux; Carbon dioxide, flux, stationarity deviation; Carbon dioxide, flux, storage; Carbon dioxide, mole fraction; Carbon dioxide, mole fraction, variance; Carbon dioxide flux, random error; Carbon dioxide mixing ratio; DATE/TIME; Day of the year; Daytime Indicator; Density, water vapour; Distance, relative, X, offset; Distance, relative, X, peak; Distance, relative, X, percentage contribution; ECS; ECS_HohesHolz; ECS_Hohes Holz_Eggenstedt (Germany); Eddy covariance; Eddy covariance footprint, model type; Eddy Covariance Station; evapotranspiration; File name; Friction velocity; GPP; Heat, flux, latent; Heat, flux, latent, random error; Heat, flux, sensible; Heat, flux, sensible, random error; ICOS; Integrated Carbon Observation System; managed deciduous forest; Modeling Monitoring Events; MOMENT; Momentum, flux; Momentum, flux, random error; NEE; Net ecosystem exchange of carbon dioxide; Number of flagged records, chopper_LI-7500; Number of flagged records, detector_LI-7500; Number of flagged records, pll_LI-7500; Number of flagged records, sync_LI-7500; Number of records, total; Number of records, used; Obukhov stability parameter; Open Path CO2/H2O Gas Analyzer (LI-7500RS, LI-COR); Pressure, atmospheric; Quality flag, carbon dioxide, flux; Quality flag, carbon dioxide flux; Quality flag, heat, flux, latent; Quality flag, heat, flux, sensible; Quality flag, momentum flux; Quality flag, water vapour flux; Quality flag, wind speed; Reco; Signal strength; Sonic anemometer, CSAT3, Campbell Scientific; Sonic anemometer CSAT3 and CO2/H2O Gas Analyzer (LI-7500RS); Sonic temperature; Sonic temperature, variance; Spikes, carbon dioxide; Spikes, sonic temperature; Spikes, water vapour; Spikes, wind velocity, lateral; Spikes, wind velocity, longitudinal; Spikes, wind velocity, vertical; Temperature, air; TERENO; Terrestrial Environmental Observatories; Thermometer/Hygrometer, Vaisala, HMP155; Water vapour, density; Water vapour, flux; Water vapour, mole fraction; Water vapour, mole fraction, variance; Water vapour flux, random error; Water vapour mixing ratio; Wind direction; Wind speed; Wind velocity, lateral; Wind velocity, lateral, variance; Wind velocity, longitudinal; Wind velocity, longitudinal, variance; Wind velocity, vertical; Wind velocity, vertical, variance
    Type: Dataset
    Format: text/tab-separated-values, 1208880 data points
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  • 26
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    Astronomical forcing on loess deposition in the Junggar Basin since the late Pliocene (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-18
    Description: Herein, we present paleomagnetic, GR and Rb/Sr data over the past 3.5 Ma from the ZK01 core in the southern Junggar Basin, Northwestern China. We took 300 oriented samples along the core (ca. 1 sample every 1.5 m) for magnetostratigraphic dating. The corresponding boundaries of the Brunhes/Matuyama and Matuyama/Gauss transitions are at about 59 m and 315 m interval in the ZK01 borehole, respectively. To complete the lithological description of this core, we selected 17 samples between levels 100 and 425 m for grain size analysis. Our grain size shows an eolian deposit. Gamma ray (GR; ~5 cm sampling resolution) well log data and Rb/Sr ratios in the ZK01 core were used to carry out astronomical tuning. Our results show that GR data are dominated by 405 kyr cycles before ~2.8 Ma; ~100 kyr short eccentricity and obliquity cycles are significantly enhanced between 2.8 Ma and 1.4 Ma; the obliquity signal finally strengthens between 1.4 and ~0.55 Ma. By contrast, our Rb/Sr data show monotonic, dominant 405-kyr cycles with weak 100-kyr eccentricity, obliquity (41 kyr) and precession (20 kyr) cycles in the past 3.5 Ma. To complete the lithological description of the core, we selected 17 samples between levels 100 and 425 m for grain size analysis. We selected 2 samples for scanning electron microscope (SEM) analysis of quartz particles at 317 m and 415 m, in order to investigate potential marks for aeolian transport of the sediment. We took 300 oriented samples along the core (ca. 1 sample every 1.5 m) for magnetostratigraphic dating. A total of 1201 power samples were measured with an Innov-X Systems X-ray fluorescence spectrometer in geochemistry mode using beam 1 (50 kv) and beam 2 (10 kv) to acquire the abundance of rubidium (Rb) and strontium (Sr) at the Chinese University of Geosciences (Wuhan). The continuous GR data were measured using FD-3019 γ logging tool based on EJ/T 611-2005 gamma logging specification through pipes.
    Keywords: Core; CORE; Junggar_Basin_ZK01; Northwestern China
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 27
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    Globigerinoides ruber stable oxygen isotope and Mg/Ca ratios record from IODP Site 363-U1483 (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-18
    Description: We carry out our work on a total of 202 sediment samples spanning the past ~400 kyr from IODP Site 363-U1483 (13.09°S, 121.80°E; water depth: 1733 m), which is located close to the southern margin of the ITCZ's latitudinal displacement in the outflow area of the Indonesian Throughflow. In this study, we present Mg/Ca ratios and stable oxygen isotope from tests of planktonic foraminiferal Globigerinoides ruber, respectively.
    Keywords: 363-U1483; AGE; COMPCORE; Composite Core; Exp363; Globigerinoides ruber; Globigerinoides ruber, Magnesium/Calcium ratio; Globigerinoides ruber, δ18O; Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES); Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP; IODP Site 363-U1483; Joides Resolution; Mass spectrometer, Finnigan, MAT 253; Mg/Ca ratios; North west Australian continental margin; stable oxygen isotope
    Type: Dataset
    Format: text/tab-separated-values, 414 data points
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  • 28
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    Gamma ray resolution of core ZK01 from the southern Junggar Basin, Northwestern China (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-18
    Keywords: Core; CORE; DEPTH, sediment/rock; Gamma spectrometer; GSPEC; Junggar_Basin_ZK01; Natural gamma ray; Northwestern China
    Type: Dataset
    Format: text/tab-separated-values, 8408 data points
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  • 29
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    Paleomagnetic age determination of core ZK01 from the southern Junggar Basin, Northwestern China (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-18
    Keywords: Core; CORE; DEPTH, sediment/rock; Inclination; Junggar_Basin_ZK01; MAG; Magnetometer; Maximum angular deviation; Northwestern China
    Type: Dataset
    Format: text/tab-separated-values, 526 data points
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  • 30
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    Daily soil temperature records (2019-2020) for active layer sites (GEC-1901 and GEC-1902) in the Argentinian Andes at 32°S (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-22
    Description: Soil temperature (°C) was collected in the uppermost levels of the active layer of "El Candidato" rock glacier, from 2019 to 2020. "El Candidato" is an active rock glacier placed in the Argentinian Central Andes at 4012 m a.s.l. (-31.898672°S; -70.178486°W). The purpose of the data set was to analyze the ground temperature in different pits, in terms of active layer depth estimation. The dataset includes the temperatures of two observation sites (GEC-1901 and GEC-1902) at different depths: 0 cm; 25 cm; 50 cm and 90 cm. The iButtons DS1922L (Maxim Integrated) took 6 measurements per day, at 02:00, 06:00, 10:00, 14:00, 18:00 and 22:00 hours (GMT-3), from 23/03/2019 to 02/03/2020. Soil temperature was calculated as the average of the six daily data.
    Keywords: active layer temperature and moisture; Andes mountains; Argentina; frozen ground; rock glacier; soil temperature
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 31
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    Depth profiles of trace gases, biomass and phytoplankton group composition during RV POLARSTERN cruise PS92 (2023)
    PANGAEA
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    Publication Date: 2023-03-25
    Description: This data set contains the concentrations of chlorohyll a (chla) and the phytoplankton fuctional types from the CTD stations during PS 92, which were calculated from marker pigment ratios using the CHEMTAX program (Mackey et al, 1996).Pigment ratios were constrained as suggested by Higgins et al. (2011) based on microscopic examination of representative samples during the cruise, and the input matrix published by Fragoso et al. (2016) was applied. The resulting phytoplankotn group composition is represented in chl a concentrations. From the same bottles various trace gases were measured as carbon monoxide (CO) and the Volatile Organic Compounds (VOCs) as Dimethyl sulphide (DMS), methanethiol (MeSH) and isoprene.
    Keywords: Arctic Ocean; ARK-XXIX/1, TRANSSIZ; Artic; AWI_BioOce; Biological Oceanography @ AWI; Carbon monoxide; Cast number; Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata + Cryptophyta; Chlorophyll a, Haptophyta + Chrysophyta + Cyanobacteria; Chlorophyll a, Phaeocystis; Chlorophyll a, Prasinophyta + Chlorophyta; Cruise/expedition; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Diagnostic Pigment Analysis (DPA); Dimethyl sulfide; DPA; ELEVATION; Event label; GASC; Gas chromatograph; High Performance Liquid Chromatography (HPLC); Isoprene; LATITUDE; LONGITUDE; Methanethiol; phytoplankton functional types; Polarstern; Pressure, water; Proton Transfer Mass Spectrometer; PS92; PS92/019-5; PS92/027-3; PS92/031-3; PS92/032-5; PS92/039-8; PS92/043-5; PS92/046-2; PS92/047-4; PTRMS; Sea ice; Station label; Time in seconds; trace gases; Type; vertical profile; volatile organic compounds
    Type: Dataset
    Format: text/tab-separated-values, 888 data points
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  • 32
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    Lake surface water temperatures measured with infrared radiation thermometers from Lake Geiseltal (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Device type; Direction; Event label; Geiseltalsee-0811_THERM; Geiseltalsee-1034_THERM; Germany; Height; Infrared radiation pyrometer, Heitronics, KT19.85II; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Temperature, water; water temperature
    Type: Dataset
    Format: text/tab-separated-values, 535 data points
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  • 33
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    Lake surface water temperatures measured with infrared radiation thermometers from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Device type; Direction; Event label; Germany; Height; Infrared radiation pyrometer, Heitronics, KT19.85II; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Suessersee-1211_THERM; Suessersee-1415_THERM; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 685 data points
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  • 34
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    Phytoplankton composition, classification and biomass from Lake Arendsee (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
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    Publication Date: 2023-03-31
    Keywords: Area/locality; Arendsee-S0856_PHYTO; Arendsee-S0857_PHYTO; Biovolume; Cell; Class; Comment; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; Event label; Germany, Saxony-Anhalt; Identification; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; PHYTONET; Phytoplankton net; Principal investigator; Quantitative phytoplankton method (Utermöhl, 1958); Taxon/taxa; Time Stamp
    Type: Dataset
    Format: text/tab-separated-values, 336 data points
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  • 35
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    Meteorological measurements within intercalibration campaign on Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Date/time end; Date/time start; Dew/frost point; Event label; Germany; Humidity, relative; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Multiparameter sensor, Ahlborn, ALMEMO D6; Pressure, atmospheric; Principal investigator; Suessersee-0902_MPS; Suessersee-1001_MPS; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 17 data points
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  • 36
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    Phytoplankton composition, classification and biomass from Lake Geiseltal (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Area/locality; Biovolume; Cell; Class; Comment; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; Event label; Geiseltalsee-S0858_PHYTO; Geiseltalsee-S0859_PHYTO; Germany, Saxony-Anhalt; Identification; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; PHYTONET; Phytoplankton net; Principal investigator; Quantitative phytoplankton method (Utermöhl, 1958); Taxon/taxa; Time Stamp
    Type: Dataset
    Format: text/tab-separated-values, 588 data points
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  • 37
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    Phytoplankton composition, classification and biomass from Kelbra Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
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    Publication Date: 2023-03-31
    Keywords: Area/locality; Biovolume; Cell; Class; Comment; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; Event label; Germany, Saxony-Anhalt; Identification; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Kelbra-S0862_PHYTO; Kelbra-S0863_PHYTO; PHYTONET; Phytoplankton net; Principal investigator; Quantitative phytoplankton method (Utermöhl, 1958); Taxon/taxa; Time Stamp
    Type: Dataset
    Format: text/tab-separated-values, 276 data points
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  • 38
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    Phytoplankton composition, classification and biomass from the Rappbode Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
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    Publication Date: 2023-03-31
    Keywords: Area/locality; Biovolume; Cell; Class; Comment; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; Event label; Germany, Saxony-Anhalt; Identification; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; PHYTONET; Phytoplankton net; Principal investigator; Quantitative phytoplankton method (Utermöhl, 1958); Rappbode-S0860_PHYTO; Rappbode-S0861_PHYTO; Taxon/taxa; Time Stamp
    Type: Dataset
    Format: text/tab-separated-values, 756 data points
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  • 39
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    Phytoplankton composition, classification and biomass from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Area/locality; Biovolume; Cell; Class; Comment; DATE/TIME; Depth, bottom/max; Depth, top/min; DEPTH, water; Depth comment; Event label; Germany, Saxony-Anhalt; Identification; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; PHYTONET; Phytoplankton net; Principal investigator; Quantitative phytoplankton method (Utermöhl, 1958); Suessersee-S0864_PHYTO; Suessersee-S0865_PHYTO; Suessersee-S0866_PHYTO; Suessersee-S0867_PHYTO; Suessersee-S0868_PHYTO; Suessersee-S0869_PHYTO; Suessersee-S0870_PHYTO; Suessersee-S0871_PHYTO; Suessersee-S0873_PHYTO; Suessersee-S0884_PHYTO; Suessersee-S0885_PHYTO; Taxon/taxa; Time Stamp
    Type: Dataset
    Format: text/tab-separated-values, 3339 data points
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  • 40
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    Physical, chemical and optical multiparameter measurements from Lake Geiseltal (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
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    Publication Date: 2023-03-31
    Keywords: Chlorophyll a; Conductivity; CTD profiles; DATE/TIME; Event label; Geiseltalsee-0800_SS_MPS1; Geiseltalsee-0830_SS_MPS1; Geiseltalsee-0845_SS_MPS1; Geiseltalsee-0900_SS_MPS1; Geiseltalsee-0901_SS_MPS1; Geiseltalsee-0958_SS_MPS1; Geiseltalsee-1000_SS_MPS1; Geiseltalsee-1019_SS_MPS1; Geiseltalsee-1031_SS_MPS1; Geiseltalsee-1059_SS_MPS1; Geiseltalsee-1100_SS_MPS1; Geiseltalsee-1143_SS_MPS1; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Multi parameter probe (CTD), Sea & Sun Technology GmbH, CTD90M; Oxygen, dissolved; Oxygen saturation; pH; Phycocyanin; Phycoerythrin; Pressure, water; Principal investigator; Salinity; Sound velocity in water; Temperature, water; Turbidity (Formazin Turbidity Unit)
    Type: Dataset
    Format: text/tab-separated-values, 210676 data points
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  • 41
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    Lake surface water temperatures measured with infrared radiation thermometers within intercalibration campaign from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Device type; Direction; Event label; Germany; Height; Infrared radiation pyrometer, Heitronics, KT19.85II; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Suessersee-0823_THERM; Suessersee-0903_THERM; Suessersee-0952_THERM; Suessersee-1115_THERM; Suessersee-1145_THERM; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 1220 data points
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  • 42
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    Chemical analysis of surface water samples from Lake Arendsee (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
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    Publication Date: 2023-03-31
    Keywords: Acid/Base capacity analysis (DIN 38409 -H7); Acid/Base DIN 38409 -H7; Acid capacity 4.3; Acid capacity 8.2; Alloxanthin; Ammonium-nitrogen; Arendsee-1_WS_28; Arendsee-1_WS_30; AS-1_WS_28; AS-1_WS_30; Base capacity 4.3; Base capacity 8.2; beta-Carotene; Calcium; Carbon, inorganic, dissolved; Carbon, inorganic, total; Carbon, organic, dissolved; Carbon, organic, total; Carbon analyzer; CFA; Chl (DIN 38412 L16); Chloride; Chlorophyll a; Chlorophyll b; Chlorophyll determination (DIN 38412 L16); Continuous Flow Analysis; Date/Time of event; Diadinoxanthin; Diatoxanthin; DIMA-IC; Echinenone; Event label; Filtered (QF20); Filtration basic analysis - quartz fiber round filter (QF20); Fucoxanthin; Germany; HPLCO; ICP-AES; Inductively coupled plasma atomic emission spectroscopy; Inland Water Remote Sensing Validation Campaign 2017; Iron; IWRSVC-2017; Liquid ion chromatography (DIN EN ISO 10304-1); Location; Lutein; Magnesium; Manganese; Nitrate-nitrogen; Nitrite-nitrogen; Nitrogen, total; Optional event label; Peridinin; Pheophytin a; Pheophytin b; Phosphorus, reactive soluble; Phosphorus, total; Phosphorus, total dissolved; Photometric; Photometry; Pigments analysis by HPLC (UV and FLD); Potassium; Principal investigator; Sample comment; Sample ID; Silicon; Sodium; Sulfate; Suspended matter, total; Violaxanthin; Water sample; WS; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 165 data points
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  • 43
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    Chemical analysis of surface water samples from Lake Geiseltal (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Acid/Base capacity analysis (DIN 38409 -H7); Acid/Base DIN 38409 -H7; Acid capacity 4.3; Acid capacity 8.2; Alloxanthin; Ammonium-nitrogen; Base capacity 4.3; Base capacity 8.2; beta-Carotene; Calcium; Carbon, inorganic, dissolved; Carbon, inorganic, total; Carbon, organic, dissolved; Carbon, organic, total; Carbon analyzer; CFA; Chl (DIN 38412 L16); Chloride; Chlorophyll a; Chlorophyll b; Chlorophyll determination (DIN 38412 L16); Continuous Flow Analysis; Date/Time of event; Diadinoxanthin; Diatoxanthin; DIMA-IC; Echinenone; Event label; Filtered (QF20); Filtration basic analysis - quartz fiber round filter (QF20); Fucoxanthin; Geiseltalsee-1_WS_28; Geiseltalsee-1_WS_30; Germany; GTS-1_WS_28; GTS-1_WS_30; HPLCO; ICP-AES; Inductively coupled plasma atomic emission spectroscopy; Inland Water Remote Sensing Validation Campaign 2017; Iron; IWRSVC-2017; Liquid ion chromatography (DIN EN ISO 10304-1); Location; Lutein; Magnesium; Manganese; Nitrate-nitrogen; Nitrite-nitrogen; Nitrogen, total; Optional event label; Peridinin; Pheophytin a; Pheophytin b; Phosphorus, reactive soluble; Phosphorus, total; Phosphorus, total dissolved; Photometric; Photometry; Pigments analysis by HPLC (UV and FLD); Potassium; Principal investigator; Sample comment; Sample ID; Silicon; Sodium; Sulfate; Suspended matter, total; Violaxanthin; Water sample; WS; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 166 data points
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  • 44
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    Chemical analysis of surface water samples from Kelbra Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Acid/Base capacity analysis (DIN 38409 -H7); Acid/Base DIN 38409 -H7; Acid capacity 4.3; Acid capacity 8.2; Alloxanthin; Ammonium-nitrogen; Base capacity 4.3; Base capacity 8.2; beta-Carotene; Calcium; Carbon, inorganic, dissolved; Carbon, inorganic, total; Carbon, organic, dissolved; Carbon, organic, total; Carbon analyzer; CFA; Chl (DIN 38412 L16); Chloride; Chlorophyll a; Chlorophyll b; Chlorophyll determination (DIN 38412 L16); Continuous Flow Analysis; Date/Time of event; Diadinoxanthin; Diatoxanthin; DIMA-IC; Echinenone; Event label; Filtered (QF20); Filtration basic analysis - quartz fiber round filter (QF20); Fucoxanthin; Germany; HPLCO; ICP-AES; Inductively coupled plasma atomic emission spectroscopy; Inland Water Remote Sensing Validation Campaign 2017; Iron; IWRSVC-2017; Kelbra-1_WS_28; Kelbra-1_WS_30; Liquid ion chromatography (DIN EN ISO 10304-1); Location; Lutein; Magnesium; Manganese; Nitrate-nitrogen; Nitrite-nitrogen; Nitrogen, total; Optional event label; Peridinin; Pheophytin a; Pheophytin b; Phosphorus, reactive soluble; Phosphorus, total; Phosphorus, total dissolved; Photometric; Photometry; Pigments analysis by HPLC (UV and FLD); Potassium; Principal investigator; Sample comment; Sample ID; Silicon; Sodium; Sulfate; Suspended matter, total; TSK-1_WS_28; TSK-1_WS_30; Violaxanthin; Water sample; WS; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 152 data points
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  • 45
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    Chemical analysis of surface water samples from Rappbode Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Acid/Base capacity analysis (DIN 38409 -H7); Acid/Base DIN 38409 -H7; Acid capacity 4.3; Acid capacity 8.2; Alloxanthin; Ammonium-nitrogen; Base capacity 4.3; Base capacity 8.2; beta-Carotene; Calcium; Carbon, inorganic, dissolved; Carbon, inorganic, total; Carbon, organic, dissolved; Carbon, organic, total; Carbon analyzer; CFA; Chl (DIN 38412 L16); Chloride; Chlorophyll a; Chlorophyll b; Chlorophyll determination (DIN 38412 L16); Continuous Flow Analysis; Date/Time of event; Diadinoxanthin; Diatoxanthin; DIMA-IC; Echinenone; Event label; Filtered (QF20); Filtration basic analysis - quartz fiber round filter (QF20); Fucoxanthin; Germany; HPLCO; ICP-AES; Inductively coupled plasma atomic emission spectroscopy; Inland Water Remote Sensing Validation Campaign 2017; Iron; IWRSVC-2017; Liquid ion chromatography (DIN EN ISO 10304-1); Location; Lutein; Magnesium; Manganese; Nitrate-nitrogen; Nitrite-nitrogen; Nitrogen, total; Optional event label; Peridinin; Pheophytin a; Pheophytin b; Phosphorus, reactive soluble; Phosphorus, total; Phosphorus, total dissolved; Photometric; Photometry; Pigments analysis by HPLC (UV and FLD); Potassium; Principal investigator; Rappbode_WS_28; Rappbode_WS_30; Sample comment; Sample ID; Silicon; Sodium; Sulfate; Suspended matter, total; Violaxanthin; Water sample; WS; YR1_WS_28; YR1_WS_30; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 151 data points
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  • 46
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    Chemical analysis of surface water samples from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-31
    Keywords: Acid/Base capacity analysis (DIN 38409 -H7); Acid/Base DIN 38409 -H7; Acid capacity 4.3; Acid capacity 8.2; Alloxanthin; Ammonium-nitrogen; Base capacity 4.3; Base capacity 8.2; beta-Carotene; Calcium; Carbon, inorganic, dissolved; Carbon, inorganic, total; Carbon, organic, dissolved; Carbon, organic, total; Carbon analyzer; CFA; Chl (DIN 38412 L16); Chloride; Chlorophyll a; Chlorophyll b; Chlorophyll determination (DIN 38412 L16); Continuous Flow Analysis; Date/Time of event; Diadinoxanthin; Diatoxanthin; DIMA-IC; Echinenone; Event label; Filtered (QF20); Filtration basic analysis - quartz fiber round filter (QF20); Fucoxanthin; Germany; HPLCO; ICP-AES; Inductively coupled plasma atomic emission spectroscopy; Inland Water Remote Sensing Validation Campaign 2017; Iron; IWRSVC-2017; Liquid ion chromatography (DIN EN ISO 10304-1); Location; Lutein; Magnesium; Manganese; Nitrate-nitrogen; Nitrite-nitrogen; Nitrogen, total; Optional event label; Peridinin; Pheophytin a; Pheophytin b; Phosphorus, reactive soluble; Phosphorus, total; Phosphorus, total dissolved; Photometric; Photometry; Pigments analysis by HPLC (UV and FLD); Potassium; Principal investigator; Sample comment; Sample ID; Silicon; Sodium; Suessersee-TP_WS_28; Suessersee-TP_WS_29; Suessersee-TP_WS_30; Sulfate; Suspended matter, total; SÜS-TP_WS_28; SÜS-TP_WS_29; SÜS-TP_WS_30; Violaxanthin; Water sample; WS; Zeaxanthin
    Type: Dataset
    Format: text/tab-separated-values, 742 data points
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  • 47
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    Microplastic concentration and composition in surface waters of the Weddell Sea, Antarctica (2023)
    PANGAEA
    Details
    Publication Date: 2023-02-15
    Description: The dataset provides information about the microplastic contamination in surface waters of the Weddell Sea, Antarctica. Samples (n = 34) were collected during two expeditions on RV Polarstern, PS111 (2018) and PS117 (2018/2019), to the Antarctic Weddell Sea, taking place during austral summer. Samples were collected with a surface trawl (manta trawl; 5gyres Institute, Los Angeles, California; 60 x18 cm² rectangular aperture) equipped with a 300 µm mesh and a mechanical flowmeter. On average 234.6 (±83.7 SD) m³ of seawater were filtered per sample (total filtered volume: 7974 m³). Putative microplastic particles were sorted visually under a dissecting microscope (magnification 6.7-45x) and analyzed by means of by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy.
    Keywords: Acrylates, polyurethanes, varnish; Antarctica; ANT-XXXIII/2; Campaign of event; DATE/TIME; DEPTH, water; Event label; FTIR spectroscopy; LATITUDE; Lazarev Sea; LONGITUDE; Manta trawl; Microplastic abundance; Microplastic abundance per area; Microplastic particles; Microplastics; MTR; Polarstern; Polyamide; Polyester; Polyethylene; Polypropylene; Polystyrene; PS111; PS111_10-1; PS111_12-1; PS111_13-1; PS111_19-1; PS111_20-1; PS111_28-4; PS111_64-1; PS111_74-5; PS111_85-2; PS111_9-1; PS117; PS117_100-2; PS117_14-3; PS117_15-7; PS117_17-2; PS117_22-3; PS117_26-3; PS117_32-2; PS117_3-4; PS117_34-6; PS117_35-1; PS117_36-2; PS117_38-2; PS117_41-11; PS117_41-14; PS117_41-5; PS117_41-7; PS117_48-3; PS117_56-7; PS117_57-5; PS117_64-3; PS117_81-3; PS117_83-1; PS117_95-2; PS117_99-4; Resins; Sample ID; Sample volume; Scotia Sea; Silicone; South Atlantic Ocean; Southern Ocean; surface water; Total area; Vinyl chloride, vinyl acetate, maleic acid, terpolymer; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 1122 data points
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  • 48
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    Calcium, carbon, and oxygen isotope values from planktic foraminifera from Gubbio, Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-02-21
    Keywords: DEPTH, sediment/rock; Foraminifera; Foraminifera, planktic, shell Feret diameter; Foraminifera, planktic, δ44/40Ca; Foraminifera, right coiling direction; Foraminiferal Abnormality Index; GUB; Gubbio; Italy; Ocean acidification; paleoclimatology; Paleooceanography; Precision, internal; Rotalipora cushmani, δ13C; Rotalipora cushmani, δ18O; Sample ID; Species
    Type: Dataset
    Format: text/tab-separated-values, 192 data points
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  • 49
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    Calcium, carbon, and oxygen isotope values from bulk carbon from Gubbio, Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-02-21
    Keywords: DEPTH, sediment/rock; Foraminifera; GUB; Gubbio; Italy; Ocean acidification; paleoclimatology; Paleooceanography; Precision, internal; Sample ID; δ13C, bulk carbonate; δ18O, bulk carbonate; δ44/40 Ca, bulk carbonate
    Type: Dataset
    Format: text/tab-separated-values, 178 data points
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  • 50
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    Age model used to place the data in context of time relative to Oceanic Anoxic Event 2, Gubbio, Italy (2023)
    PANGAEA
    Details
    Publication Date: 2023-02-21
    Keywords: Age, relative; Foraminifera; Foraminifera, planktic, δ44/40Ca; GUB; Gubbio; Italy; Ocean acidification; paleoclimatology; Paleooceanography; δ44/40 Ca, bulk carbonate
    Type: Dataset
    Format: text/tab-separated-values, 69 data points
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  • 51
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    Calcium, and carbon isotope values from bulk carbon from the Angus Aristocrat Core from the Western Interior Seaway in Colorado, USA (2023)
    PANGAEA
    Details
    Publication Date: 2023-02-21
    Keywords: Aristocrat_Angus_core_1; CDRILL; Colorado, United States of America; Core drilling; DEPTH, sediment/rock; Foraminifera; Ocean acidification; paleoclimatology; Paleooceanography; Precision, internal; δ13C, bulk carbonate; δ44/40 Ca, bulk carbonate
    Type: Dataset
    Format: text/tab-separated-values, 125 data points
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  • 52
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    Isotope and elemental geochemistry of tap waters from several major US metropolitan areas (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-29
    Description: Tap waters were collected from major metropolitan areas of the western United States. Tap waters were sampled between 2012-2015 from seven metropolitan areas: Los Angeles-Long Beach-Santa Ana (CA), Phoenix-Mesa-Glendale (AZ), Salt Lake City (UT), San Diego-Carlsbad-San Marcos (CA), San Francisco-Oakland-Fremont (CA), San Jose-Sunnyvale-Santa Clara (CA), and Riverside-San Bernardino-Ontario (CA). These areas represent some of the most populous in the US and employ a diversity of water management practices. Here hydrogen (d2H) and oxygen (d18O) isotope values along with strontium isotope ratios (87Sr/86Sr) and element abundances were measured. d2H and d18O of 2039 tap waters were measured following Tipple et al., 2017 (Water Research, 119, 212-224). 87Sr/86Sr and elemental compositions of 820 and 806 waters were analyzed following Tipple et al., 2018 (Scientific Reports, 8, 2224), respectively. The purpose of these data was to assess spatial, temporal, and climatic dynamics in isotope and elemental geochemistry of tap waters. We found that the isotope and elemental geochemistry of tap waters corresponded to the water sources (e.g., transported water, local surface water, groundwater, etc.) and management practices (e.g., storage in open reservoirs, mixing, etc.) for discrete areas within the larger metropolitan areas.
    Keywords: drought; elemental composition; hydrogen; hydrology; Oxygen; Strontium
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 53
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    Hydrogen and oxygen isotope values of tap waters from several major US metropolitan areas (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-29
    Keywords: Area/locality; Arizona_tap_water; California_tap_water; DATE/TIME; drought; elemental composition; Event label; hydrogen; hydrology; LATITUDE; Location; LONGITUDE; One-time_collection_tap_water; Oxygen; Salt_Lake_Valley_tap_water; Sample ID; Strontium; United States of America; Water sample; WS; δ18O, water; δ18O, water, standard deviation; δ Deuterium, water; δ Deuterium, water, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 11414 data points
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  • 54
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    Strontium isotope ratios (87Sr/86Sr) of tap waters from several major US metropolitan areas (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-29
    Keywords: Area/locality; Arizona_tap_water; California_tap_water; DATE/TIME; drought; elemental composition; Event label; hydrogen; hydrology; LATITUDE; Location; LONGITUDE; One-time_collection_tap_water; Oxygen; Salt_Lake_Valley_tap_water; Sample ID; Strontium; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, standard deviation; United States of America; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 3286 data points
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  • 55
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    Element abundances of tap waters from several major US metropolitan areas (2023)
    PANGAEA
    Details
    Publication Date: 2023-03-29
    Keywords: Aluminium; Antimony; Area/locality; Arizona_tap_water; Arsenic; Barium; Beryllium; Boron; Cadmium; Caesium; Calcium; California_tap_water; Cerium; Chromium; Cobalt; Copper; DATE/TIME; drought; elemental composition; Europium; Event label; hydrogen; hydrology; Iron; Lanthanum; LATITUDE; Lead; Lithium; Location; LONGITUDE; Magnesium; Manganese; Molybdenum; Neodymium; Nickel; One-time_collection_tap_water; Oxygen; Potassium; Salt_Lake_Valley_tap_water; Sample ID; Scandium; Selenium; Sodium; Strontium; Thorium; United States of America; Uranium; Vanadium; Water sample; WS; Yttrium; Zinc
    Type: Dataset
    Format: text/tab-separated-values, 16295 data points
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  • 56
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    Pollen and non-pollen palynomorph counts from Lake Van, Turkey (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-17
    Description: We illustrate the response of local and regional vegetation, aquatic ecosystem, and fire activity to volcanic eruptions in close connection to prevailing climate conditions. To understand these complex responses, we selected five volcaniclastic depositions in the Lake Van (Turkey) sediments from different interglacial/glacial periods. We analyzed high-resolution pollen data, non-pollen palynomorphs (NPPs), and microscopic charcoal particles (〉20 µm) from annually laminated lacustrine sediments with precise age control, allowing us to quantify recovery time in varve years. This study highlights that the extent and duration of the volcanic event, the thickness of subsequent volcanic deposits, the respective climatic conditions strongly influence the impact on terrestrial and aquatic ecosystems.
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 57
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    Rubidium (Rb) and strontium (Sr) determination of core ZK01 from the southern Junggar Basin, Northwestern China (2023)
    PANGAEA
    Details
    Publication Date: 2023-04-18
    Keywords: Core; CORE; DEPTH, sediment/rock; Junggar_Basin_ZK01; Northwestern China; Rubidium; Rubidium, standard deviation; Rubidium/Strontium ratio; Strontium; Strontium, standard deviation; X-ray fluorescence (XRF)
    Type: Dataset
    Format: text/tab-separated-values, 6005 data points
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  • 58
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    Fine-root biomass, fine-root production and environmental variables in four forested site types under undrained and drained conditions in Lakkasuo peatland, Southern Finland (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-10
    Description: We estimated fine-root biomass (FRB) and production (FRP) and their depth distribution and plant functional type (PFT) composition in four forested boreal peatland site types that varied in soil nutrient and water-table level regimes, ground vegetation and tree stand characteristics. Two were pine-dominated nutrient-poor sites (dwarf-shrub pine bog, tall-sedge pine fen) and two spruce-dominated nutrient-rich sites (Vaccinium myrtillus spruce swamp, herb-rich hardwood-spruce swamp). Measurements were done in two sites per site type: one undrained site and one site that had been drained for forestry. In each of the eight sites, we established three measurement plots. FRB was estimated by separating and visually identifying roots from soil cores extending down to 50-cm depth. The cores were taken in late August, 2016. FRP was estimated using ingrowth cores covering the same depth, and the separated roots were identified using Fourier transform infrared spectroscopy (FTIR). The ingrowth cores were incubated for two years, starting in November 2015 and ending in November 2017. Tree-stand basal area and stem volume per species, and projection cover of ground vegetation per species were determined in summer 2018. We monitored the soil water-table level and soil temperatures in 5 and 30 cm depths with dataloggers. Soil pH, bulk density, and carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, boron, zinc, and copper concentrations were measured from peat cores extending down to 50-cm depth and taken simultaneously with the FRB cores. FRB, FRP and peat properties are presented for 10-cm depth segments. FRB, FRP and peat properties are presented for 10-cm depth segments. Peat cores were taken with a box-shaped 65 mm x 37 mm peat corer, except in the wet TP site where a 60 mm x 60 mm corer was used.
    Keywords: peatland drainage; Peatland Ecology; peatlands; root biomass; rooting depth; root production; soil temperature; vegetation; Water table depth
    Type: Dataset
    Format: application/zip, 6 datasets
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  • 59
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    Morphological measurements of coccoliths from surface samples of R/V SONNE cruise SO95 (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-09
    Description: The dataset compiles morphological measurements of coccoliths (Gephyrocapsa spp.) from one sample from the Western Equatorial Pacific. The morphological measurements were applied to evaluate coccolithophore preservation aspects under different dissolution intensities in a laboratory experiment. For the dissolution experiment, we added different amounts of Calgon solution (0, 0.4, 0.8, 2, 4, and 6 ml) in subsamples prepared from one sediment sample (ODP 807A-2H2W57-59 cm). The length, volume, area, and mass were obtained using the C-Calcita software from images taken under a cross-polarized microscope (Zeiss AX10). Thickness and the shape factor ks were calculated. The columns include the sample name, how much Calgon was added into sediment suspension, the number of coccoliths measured per sample, length, mean ks shape factor and standard deviation of mean ks/mean ks.
    Keywords: Calculated after Gerotto et al. (2022); Calculated average/mean values; Coccoliths; Coccoliths, length; Coccoliths, mass; Coccoliths, shape factor; Coccoliths, shape factor, standard deviation; Coccoliths, thickness; Coccoliths, volume; dissolution experiment; Elevation of event; Emiliania huxleyi; Event label; Gephyrocapsa oceanica; Giant box corer; GIK17925-2; GIK17930-1; GIK17931-1; GIK17932-1; GIK17934-1; GIK17936-1; GIK17937-1; GIK17938-1; GIK17939-1; GIK17940-1; GIK17941-1; GIK17943-1; GIK17944-1; GIK17945-1; GIK17946-1; GIK17949-1; GIK17951-1; GIK17955-1; GIK17956-1; GIK17957-1; GIK17958-1; GIK17959-1; GIK17960-1; GIK17961-1; GIK17962-1; GIK17963-2; GIK17964-1; GIK17965-1; GKG; Latitude of event; Longitude of event; Method/Device of event; MONITOR MONSUN; morphological measurements; SO95; Sonne; South China Sea; surface sediments
    Type: Dataset
    Format: text/tab-separated-values, 196 data points
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  • 60
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    Monthly series of zooplankton abundance and biomass collected between 1989 and 2016 at A Coruña (NW Spain) (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-09
    Description: Zooplankton sampled approximately at monthly intervals between April 1988 and December 2016 at Station E2CO (A Coruña, NW Spain) with double-oblique tows of a 50-cm diameter Juday-Bogorov plankton net with 250 μm (until 1997) or 200 μm (from 1997 onwards) mesh size. The net was equipped with a General Oceanic Flowmeter for the calculation of water filtered and a depth recorder. All samples were collected between 10:00 and 14:00 o'clock (local time) and were preserved in 2−4% sodium borate-buffered formaldehyde. Sub-samples were taken to estimate total zooplankton abundances (in ind × m−3) by direct examination using a stereo microscope, and biomass (in μg DW × L−1) by weighting dried aliquots (50 ºC, 48 h). Species names referenced to the Word Register of Marine Species (last access: June 2019). Zooplankton identification and counts were made by M.T. Álvarez-Ossorio (until 2015), E. Rey and M.A. Louro (2016). These series are part of the long-term observational project RADIALES (Instituto Español de Oceanografía, IEO, Spain).
    Keywords: Abundance; Biomass; Bottle, Niskin; Coastal; DATE/TIME; Depth, bottom/max; Depth, top/min; E2CO; Galicia Margin; Mesh size; NE Atlantic; NIS; NW Spain; Phytoplankton; RADIALES; seRies temporAles De oceanografIA en eL norte de ESpaña; Upwelling; Zooplankton; Zooplankton, biomass, dry mass
    Type: Dataset
    Format: text/tab-separated-values, 1656 data points
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  • 61
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    Meteorological Observations at Schiaparelli Glacier Automatic Weather Station (AWSrock), Cordillera Darwin, Chile, 2015-2020 (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-09
    Description: The data set contains meteorological measurements from an automatic weather station installed on rock next to the glacier front (92 m a.s.l.) of Schiaparelli Glacier, Cordillera Darwin, Chile. The station has been installed by the Humboldt University Berlin in September 2015. It measures 2m air temperature (T), relative humidity (RH), solar radiation (SWin), wind speed (WS), wind direction (Dir) and precipitation (RRR) in an hourly interval (averaged, precipitation is accumulated). The data provided here has been corrected for outliers, radiative heating of the temperature sensor and sensor freezing.
    Keywords: Automated weather station (AWS); AWSrock_Schiaparelli; Campbell Scientific CS215 Temperature & Relative Humidity Sensor; DATE/TIME; Humidity, relative; Precipitation; Pyranometer, Campbell Scientific, CS300-L; Short-wave downward (GLOBAL) radiation; Temperature, air; Tipping bucket rain gauge, R. M. Young, 52203; Wind direction; Wind monitor, R.M. Young, model 05103; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 236727 data points
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  • 62
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    Fine-root production in four forested site types under undrained and drained conditions in Lakkasuo peatland, Southern Finland (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-10
    Description: Ingrowth cores were used according to Bhuiyan et al. (2017), and incubated for two years from November 2015 to November 2017. Roots were manually separated and identified to species group level with FTIR according to Straková et al. (2020). Drying temperature for roots was 40 °C.
    Keywords: Coniferophyta; Coniferophyta, root biomass production; DEPTH, soil; Depth, soil, maximum; Depth, soil, minimum; Forbs; Forbs, root biomass production; Graminoids; Graminoids, root biomass production; LATITUDE; LONGITUDE; peatland drainage; Peatland Ecology; peatlands; Plot; Replicates; root biomass; Root biomass production, fine roots; rooting depth; root production; Shrubs and Birch; Shrubs and Birch, root biomass production; Site; soil temperature; Subplot; vegetation; Water table depth
    Type: Dataset
    Format: text/tab-separated-values, 8194 data points
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  • 63
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    Soil water-table level in four forested site types under undrained and drained conditions in Lakkasuo peatland, Southern Finland (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-10
    Description: Measurements between November 2015 - November 2017.
    Keywords: Odyssey Capacitance Water Level Logger; peatland drainage; Peatland Ecology; peatlands; Percentile 10; Percentile 25; Percentile 50; Percentile 75; Percentile 90; Plot; root biomass; rooting depth; root production; Site; soil temperature; vegetation; Water table depth; Water table level, maximum; Water table level, mean; Water table level, minimum; Water table level, range; Water table level, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 384 data points
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  • 64
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    Soil temperature in four forested site types under undrained and drained conditions in Lakkasuo peatland, Southern Finland (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-10
    Description: Dataloggers: T: i-Button DS1921G, Maxim Integrated Products. Cumulative soil temperature sums were calculated from daily mean temperatures, using 5 °C as the threshold temperature. Measurements between November 2015 - November 2017.
    Keywords: Calculated; DEPTH, soil; peatland drainage; Peatland Ecology; peatlands; Plot; root biomass; rooting depth; root production; Site; soil temperature; Temperature, soil, cumulative; Temperature, soil, maximum; Temperature, soil, mean; Temperature, soil, median; Temperature, soil, minimum; Temperature, soil, range; Temperature, soil, standard deviation; vegetation; Water table depth
    Type: Dataset
    Format: text/tab-separated-values, 515 data points
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  • 65
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    Area and volume data of four small mountain lakes in the Northern Black Forest (Germany) (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-04
    Keywords: Area; Area/locality; Black Forest; Comment; DEPTH, water; Depth layer, volume; Event label; Herrenwieser_See; Huzenbacher_See; Mummelsee; Schurmsee; Volume
    Type: Dataset
    Format: text/tab-separated-values, 292 data points
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  • 66
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    Stable isotopes in water in the Fram Strait from POLARSTERN cruises PS92 and PS93/1 in 2015 (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-04
    Description: Measurements of stable water isotopes (δ Deuterium, δ18O, δ13C) from the water column in the Fram Strait from the POLARSTERN cruises PS92 (ARK-XXIX/1, TRANSSIZ) and PS93.1 (ARK-XXIX/2.1) in May to July 2015. δ Deuterium and δ18O were measured with a Picarro 2130i CRDS System, δ13C with a Finnigan MAT 252 gas isotope ratio mass spectrometer with Gasbench 2 at MARUM - Center for Marine Environmental Sciences. The measurements were conducted in 2020/2021.
    Keywords: Arctic Ocean; ARK-XXIX/1, TRANSSIZ; ARK-XXIX/2.1; Comment; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Event label; Expendable CTD; Fram Strait; Giant box corer; GKG; ICE; Ice station; Isotope analyzer L2130-i, Picarro Inc.; Latitude of event; Longitude of event; Mass spectrometer Finnigan MAT 252; MUC; MultiCorer; Multicorer with television; North Greenland Sea; Polarstern; PS92; PS92/010-1; PS92/019-15; PS92/019-18; PS92/019-5; PS92/019-6; PS92/020-1; PS92/027-13; PS92/027-14; PS92/027-2; PS92/027-3; PS92/027-6; PS92/028-1; PS92/031-12; PS92/031-14; PS92/032-14; PS92/032-15; PS92/032-5; PS92/039-10; PS92/039-5; PS92/039-8; PS92/040-1; PS92/040-2; PS92/043-1; PS92/043-20; PS92/043-5; PS92/046-14; PS92/046-15; PS92/046-2; PS92/047-19; PS92/047-20; PS92/047-4; PS92/056-3; PS92/056-5; PS93/011-1; PS93/011-4; PS93/016-1; PS93/016-5; PS93/017-1; PS93/017-5; PS93/018-4; PS93/020-2; PS93/020-5; PS93/023-4; PS93/024-1; PS93/024-6; PS93/030-1; PS93/030-4; PS93/031-2; PS93/031-3; PS93/039-1; PS93/039-7; PS93/041-2; PS93/046-1; PS93/046-4; PS93.1; Sample comment; stable carbon isotope; stable oxygen isotope; stable water isotopes; TVMUC; Underwater video profiler; UVP; WP2; WP-2 towed closing plankton net; XCTD; δ13C; δ13C, uncertainty; δ18O, water; δ18O, water, uncertainty; δ Deuterium, water; δ Deuterium, water, uncertainty
    Type: Dataset
    Format: text/tab-separated-values, 2224 data points
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  • 67
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    Mg/Ca of Globigerinoides ruber albus and Turborotalita clarkei from water column and core top samples in the hypersaline Gulf of Aqaba (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-16
    Description: We provide here supplementary data of Levy et al. (2023). To investigate influencing factors of Planktic Foraminifera (PF) Mg uptake in hypersaline regions, we measured the Mg/Ca of two flux dominating PF species, Globigerinoides ruber albus and Turborotalita clarkei with its two phenotypes 'big' and 'encrusted', derived from a monthly resolved time series of sediment traps in the Gulf of Aqaba (GOA), northern Red Sea. Presented are a summary of average (mean) individual planktic Foraminifera specimen Mg/Ca as a function of depth and time. Sediment traps were deployed at depths of 120 m, 220 m, 350 m, 450 m and 570 m and a core top sample. The data provided is at a near-monthly resolution between June 2014 and June 2015. Mg/Ca was measured using single chamber Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) to account for the differences between succeeding chambers. The 'min' Mg/Ca and 'max' Mg/Ca represent distance from mean to extremities (i.e., range) for LA-ICP-MS measurements.
    Keywords: DATE/TIME; DEPTH, water; Event label; GoA_2013_MultiCorer; GoA_2014_SedTrap; Gulf of Aqaba; LA-ICP-MS; Laser ablation, Inductively coupled plasma mass spectrometry; Magnesium/Calcium ratio; Magnesium/Calcium ratio, maximum; Magnesium/Calcium ratio, minimum; Mg/Ca; MUC; MultiCorer; Sample method; Sampling date; Sea surface temperature; Sediment trap; Species; Species, unique identification; Species, unique identification (Semantic URI); Species, unique identification (URI)
    Type: Dataset
    Format: text/tab-separated-values, 1092 data points
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  • 68
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    Daily streamflow of Rofenache (Vent) 1967-2016 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-19
    Description: Daily streamflow runoff observations of the Rofenache at gauge Vent.
    Keywords: daily; DATE/TIME; Gauge station; gauge Vent; GS; River discharge, daily mean; Rofenache; Rofental; Rofental, Ötztaler Alpen, Austria; streamflow
    Type: Dataset
    Format: text/tab-separated-values, 18263 data points
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  • 69
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    Trace metals concentrations, nutrients and 𝛿3He in hydrothermal plumes from Macauley and Brothers volcano (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-19
    Description: Hydrothermal vents are a source of many trace metals to the oceans. Compared to mid ocean ridges, hydrothermal vent systems at arcs occur in shallower water depth and are much more diverse in fluid composition, resulting in highly variable water column trace metal concentrations. However, only few studies have focused on trace metal dynamics in hydrothermal plumes at volcanic arcs. During R/V Sonne cruise SO253 in 2016/2017, hydrothermal plumes from two hydrothermally active submarine volcanoes along the Kermadec arc in the Southwest Pacific Ocean were sampled for trace metals and nutrients: (1) Macauley, a magmatic dominated vent site located in water depths between 300 and 680 m, and (2) Brothers, located between 1,200 and 1,600 m water depth, where hydrothermalism influenced by water rock interactions and magmatically influenced vent sites occur near each other.
    Keywords: Brothers volcano; Cadmium; Center for Marine Environmental Sciences; Cobalt; Copper; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Elevation of event; Event label; HYDROTHERMADEC; hydrothermalism at intraoceanic arcs; Iron; Lanthanum; Latitude of event; Lead; Location; Longitude of event; Macauely volcano; Manganese; MARUM; Nickel; Nitrogen oxide; Phosphate; plume dispersion; Sample code/label; Silicon dioxide; SO253; SO253_10-1; SO253_12-1; SO253_4-1; SO253_46-1; SO253_49-1; SO253_53-1; SO253_54-1; SO253_57-1; SO253_58-1; SO253_60-1; SO253_6-1; SO253_62-1; SO253_70-1; SO253_83-1; Sonne_2; South Pacific Ocean; trace metals; Zinc; δ Helium-3
    Type: Dataset
    Format: text/tab-separated-values, 1783 data points
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  • 70
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    Daily soil temperature records from site GEC-1901 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-19
    Keywords: active layer temperature and moisture; Andes mountains; Argentina; Calculated; Candidato_rock_glacier_GEC-1901; Central Andes, Argentina; DATE/TIME; DEPTH, soil; ELEVATION; frozen ground; GEC-1901; GLAC; Latitude of event; Location; Longitude of event; rock glacier; Sample ID; Sampling/measurements on glacier; soil temperature; Temperature, soil, daily mean
    Type: Dataset
    Format: text/tab-separated-values, 4104 data points
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  • 71
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    Daily soil temperature records from site GEC-1902 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-19
    Keywords: active layer temperature and moisture; Andes mountains; Argentina; Candidato_rock_glacier_GEC-1902; Central Andes, Argentina; DATE/TIME; DEPTH, soil; ELEVATION; frozen ground; GEC-1902; GLAC; Latitude of event; Location; Longitude of event; rock glacier; Sample ID; Sampling/measurements on glacier; soil temperature; Temperature, soil, daily mean
    Type: Dataset
    Format: text/tab-separated-values, 4104 data points
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  • 72
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    Water inherent optical properties and concentrations of water constituents from the German Bight and adjacent regions (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-19
    Description: A regional data set of water constituent concentrations and inherent optical properties (light absorption and scattering coefficient) for the German Bight and adjacent waters (River Elbe, North Sea, UK waters, and Southern Norwegian Sea) is presented. The data provide high quality results of in situ measurements and laboratory analysis of samples taken at sea, mainly from the mixed layer, during the years 2008 to 2021. Parameters of the water constituents include concentrations of chlorophyll a, particulate organic and dissolved organic carbon (POC, DOC), total suspended matter (TSM), organic suspended matter (OSM) together with water depth, temperature, salinity, and turbidity. Inherent optical properties (IOPs) are given spectrally as light attenuation, scattering and absorption coefficients. This includes coefficients of light attenuation by all non-water matter (cgp) and particulate matter alone (cp), light absorption by all non-water matter (agp), particulate (ap) and dissolved matter (Gelbstoff, ag), non-algal matter (anap) and phytoplankton (aph), and total scattering (bp) and backscattering (bbp) by particulate matter. The combination of concentrations and IOPS is used to determine specific IOPs of German Bight water and in optical modelling of coastal waters to interpret surface reflectance spectra like in satellite remote sensing approaches.
    Keywords: German Bight; inherent optical properties; S3VT-OC; Sentinel-3 Validation Ocean Colour
    Type: Dataset
    Format: application/zip, 9 datasets
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  • 73
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    Varve thickness record of Diss Mere (UK) (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-10
    Description: The sedimentary record of Diss Mere, a lake in the UK, is 15 m long. Annul laminations (varves) are preserved between 9 and 13 m of sediment depth covering most of the Holocene from ca. 2100 to 10,300 cal BP. Varves consist of a pale lamina made of authigenic calcite crystals deposited in summer, and a dark lamina composed of, primarily, crysophyceae cyst, planktonic centric diatoms, filaments of organic matter and micrite, which represents lake sedimentation during autumn - winter. This dataset contains the varve (annual) thickness record and thickness of the seasonal layers. Data were collected in 2018-2019 and covers a time interval from 2069 to 10290 cal. a BP. Data are at annual resolution. Detailed microfacies analysis, varve counting, and varve thickness measurements were performed on the petrographic thin sections using a Leica (M205C) stereo-zoom petrological microscope with plane- and cross-polarised light, at 80x. Varve counting and varve thickness measurements were performed for each seasonal layer along the ca 4.2 m long sequence of varved sediments.
    Keywords: AGE; Age model, varve counting; DISS16; Diss Mere; East Anglia, UK; Holocene; PCUWI; Piston corer, UWITEC; Varve age; Varve counting; varve thickness; Varve thickness; Varve thickness, summer calcite layer; Varve thickness, winter organic layer
    Type: Dataset
    Format: text/tab-separated-values, 32888 data points
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  • 74
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    Ablation Measurements at Schiaparelli Glacier, Cordillera Darwin, Chile, with an automatic ablation sensor, 2016-2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-08
    Description: The data set contains measurements of surface ablation recorded by an automatic ablation sensor at the frontal part of Schiaparelli Glacier, Cordillera Darwin, Chile. The sensor was installed by the Universidade Federal do Rio Grande, Brazil, in September 2016 and uninstalled in November 2017. The sensor records the time passed for every 15 cm of surface lowering. Unrealistic records have been excluded. The data set gives the name, latitude (latitude), longitude (longitude), start of the period (Date1), end of the period (Date2) and total ablation during that period (ablation).
    Keywords: Ablation, water equivalent; Ablation sensor; DATE/TIME; LATITUDE; LONGITUDE; Name; Schiaparelli_AblationSensor
    Type: Dataset
    Format: text/tab-separated-values, 70 data points
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  • 75
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    Glacier surface velocities of Gepatschferner and Taschachferner, 2021/2022 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-03
    Description: Glacier surface velocities are measured on four neighbouring glaciers in the Ötztal Alps (Austria). Measurements of the annual horizontal flow velocity (Δs/a [m/a]) on Hintereisferner (HEF) were started in 1885 at stone lines (cross-profiles). Annual values for the stone lines are given as mean values from the stones at the cross-profiles. On Kesselwandferner, the annual horizontal (Δs/a [m/a]) and vertical velocities (Δv/a [m/a], positive upwards and negative downwards) are measured at ablation and accumulation stakes since 1965. On Taschachferner (TSF) and Gepatschferner (GPF), the records of annual and subseasonal horizontal flow velocities at ablation stakes were started in 2009. This data series is a continuation of: doi:10.1594/PANGAEA.896741
    Keywords: Austria; DATE/TIME; Event label; Gepatschferner; GLAC; Glacial flow velocity, horizontal, annual; LATITUDE; LONGITUDE; Number; Sampling/measurements on glacier; Taschachferner_E; Tirol, Austria; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; Years
    Type: Dataset
    Format: text/tab-separated-values, 60 data points
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  • 76
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    Glacier surface velocities per day of Gepatschferner and Taschachferner, 2021 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-03
    Description: Glacier surface velocities are measured on four neighbouring glaciers in the Ötztal Alps (Austria). Measurements of the annual horizontal flow velocity (Δs/a [m/a]) on Hintereisferner (HEF) were started in 1885 at stone lines (cross-profiles). Annual values for the stone lines are given as mean values from the stones at the cross-profiles. On Kesselwandferner, the annual horizontal (Δs/a [m/a]) and vertical velocities (Δv/a [m/a], positive upwards and negative downwards) are measured at ablation and accumulation stakes since 1965. On Taschachferner (TSF) and Gepatschferner (GPF), the records of annual and subseasonal horizontal flow velocities at ablation stakes were started in 2009. This data series is a continuation of: doi:10.1594/PANGAEA.896741
    Keywords: DATE/TIME; Gepatschferner; GLAC; Glacial flow velocity, horizontal, annual; Sampling/measurements on glacier; Tirol, Austria
    Type: Dataset
    Format: text/tab-separated-values, 4 data points
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  • 77
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    Radiocarbon dates from North Cantal Peatlands (France) (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-19
    Description: Dataset of radiocarbon dates obtained on peat and lacustrine/palsutrine sediment from several wetlands of North Cantal (Massif Central, France). All these data were acquired during the collective research project (PCR) on the archaeology of the Sianne and Sumène Valleys during the Bronze Age and the 1st Iron Age (Haute-Auvergne) coordinated by F. Delrieu (DRAC ARA, Service Régional de l'Archéologie, F-63000 Clermont-Ferrand).
    Keywords: Age, 14C AMS; Age, 14C calibrated, IntCal20 (Reimer et al. 2020); Age, comment; Age, dated; Age, dated material; Age, dated standard deviation; BG; calculated, 1 sigma; Calendar age, maximum/old; Calendar age, minimum/young; Chastel-Marlhac; Chastel-Marlhac, Le Montel, France; Commune; Date/Time of event; DEPTH, sediment/rock; Elevation of event; Etang_de_Majonenc; Etang de Majonenc , Riom-es-Montagnes, France; Event label; Laboratory code/label; Lac_Long; Lac Long / Lac Lant, Espalem, France; Lacustrine-palustrine sediment; Latitude of event; LBS; Le_Vern; Les_Brougues; Les Brougues, Saint-Etienne-de-Chomeil, France; Le Vern, Moledes, France; LL; LMC; Longitude of event; MAJ; Massif Central; MER; Merigot; Merigot, Saint-Etienne-de-Chomeil, France; MUR; Muratet; Muratet, Saint-Etienne-de-Chomeil, France; PD; peat; Peat drill; radiocarbon dating; Sagne_Gousseau; Sagne Gousseau, Allanche, France; Sample code/label; Sample comment; Sample thickness; SG; Sianne; Site; Sources de la Sianne, Anzat-le-Luguet, France; Tronque; Tronque, Trizac, France; TTT; δ13C, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 336 data points
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  • 78
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    XRF core scanning, TOC, CN, 13C, and macro-charcoal counts from a fresh water mountain lake (Cueifong Lake), northeastern Taiwan (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-18
    Description: The dataset comprises X-ray fluorescent (XRF) core scanning, TOC, C/N, δ13Corg, and macro-charcoal counts of bulk sediment from the sediment core CFL-3. The purpose of this dataset is to reconstruct the sedimentation environment change after the large-scale deforestation. The lake sediment core CFL-3 was taken in Cueifong Lake, northeastern Taiwan in 2017, with a Russian Corer set. The XRF core scanning signals were normalized as described in Lin et al., 2023. The age model was established with 210Pb dating results, augmented by 137Cs dating results. The experiment and analyze detail were described Lin et al., 2023.
    Keywords: 13C; Anthropogenic disturbances; Anthropogenic impact; C/N; charcoal; Deforestation; freshwater lake; Lake sediment core; mountain lakes; Taiwan; TOC; XRF core scanner data; XRF-core scanning
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 79
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    CTD (RBR, XR-420) data collected during the EMSO physical access project TRIPLE-VTESTS at the EMSO-ERIC observatory OBSEA (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-18
    Description: CTD data collected in coastal regions are often dynamically changing which is a major impediment to derive any process related information from those data. Only longer time series and comparison with nearby coastal stations can help to detect events or trends in data. In this case the data were collected with the idea to what extent mobile platforms can assist in this process, i.e. how data that are collected from vehicles sent out from the buoy station to locations nearby can contribute to a more comprehensive picture of on-going processes. The calm weather conditions that lasted for several days during the time the data had been collected allowed for a detection of a warm current, possibly an eddy, passing the OBSEA station in a distance of a few kilometers. A series of CTD profiles had been recorded during a filed test campaign at EMSO-ERIC observatory OBSEA (https://emso.eu/observatories-node/obsea/). The maximum water depth was at 30 m. Date: February 16, 2023, Time: Between 11:30 and 13.30 CET, Instrument RBR XR-420 deployed from board a RIB boat at 5 different position with two repeated profiles each
    Keywords: Coastal; Conductivity; CTD, RBR, XR-420; CTD profiles; DATE/TIME; EMSO ERIC Physical Access, TRIPLE-VTESTS; Event label; Mediterranean; OBSEA_EMSO_TNA_1143; OBSEA_EMSO_TNA_1151; OBSEA_EMSO_TNA_1227; OBSEA_EMSO_TNA_1232; OBSEA_EMSO_TNA_1239; OBSEA_EMSO_TNA_1243; OBSEA_EMSO_TNA_1307; OBSEA_EMSO_TNA_1311; OBSEA_EMSO_TNA_1320; OBSEA_EMSO_TNA_1326; Pressure, water; Salinity; Sound velocity in water; Temperature, water; TRIPLE-VTESTS; XR420
    Type: Dataset
    Format: text/tab-separated-values, 14210 data points
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  • 80
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    TOC, TOC/TN, and δ13C results from bulk sediment of the top 32 cm of the lake sediment core CFL-3 (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-18
    Keywords: 13C; AGE; Anthropogenic disturbances; Anthropogenic impact; C/N; Carbon, organic, total; Carbon, organic, total/Nitrogen, total ratio; CFL-3; charcoal; Cueifong Lake; Deforestation; DEPTH, sediment/rock; freshwater lake; Lake sediment core; mountain lakes; RUSC; Russian corer; Taiwan; TOC; XRF core scanner data; XRF-core scanning; δ13C, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 30 data points
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  • 81
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    Marco-charcoal counts from the top 32 cm of the lake sediment core CFL-3 (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-18
    Keywords: 13C; AGE; Anthropogenic disturbances; Anthropogenic impact; C/N; CFL-3; charcoal; Counting; Cueifong Lake; Deforestation; DEPTH, sediment/rock; freshwater lake; Lake sediment core; Macrocharcoal; mountain lakes; RUSC; Russian corer; Taiwan; TOC; XRF core scanner data; XRF-core scanning
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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  • 82
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    Isotope and elemental geochemistry of human hair from the United States (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Description: Hair samples were collected throughout the United States, with particular focus on major metropolitan areas of the western United States. Hair samples were collected in 2004 as well as between 2013-2015. Here hydrogen (d2H) and oxygen (d18O) isotope values along with strontium isotope ratios (87Sr/86Sr) and element abundances were measured. d2H and d18O values, 87Sr/86Sr, and elemental compositions of 560, 385 and 306 hair samples were analyzed following Tipple et al., 2018 (Scientific Reports, 8, 2224), respectively. The purpose of these data was to assess geospatial variations in isotope and elemental geochemistry of human hair. We found that the isotope and elemental geochemistry of human hair largely corresponded to the geochemistry of drinking and bathing water, which in turn varied by water source and management practice. These data provide a foundation to reconstruct human movements using the geochemistry of modern or ancient human hair.
    Keywords: anthropogenic tracers; provenance analysis; stable isotope analysis; strontium isotopes; trace element; water chemistry; water isotopes; water management
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 83
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    Seasonal variations in the compound specific isotope analyses of amino acids from co-occurring clams in the San Francisco Bay-Sacramento/San Joaquin Delta (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Description: Amino acids were isolated from the muscle tissue of Corbicula fluminea and Potamocorbula amurensis; two co-occurring invasive clams within the San Francisco Bay-Delta system. Clam specimens were collected near Montezuma Slough (Contra Costa County, California) at the confluence of the Sacramento and San Joaquin Rivers twice during the hydrological extremes of 2010 water year (November, 2009 and May, 2010). Carbon and nitrogen isotope values of individual amino acids were measured. Clam specimens were collected at USGS Sites 2.1 and processed following Stewart et al. (2013; doi:10.3354/meps10503). Amino acids were hydrolyzed from clam muscle tissue, derivatized, and isolated following Vokhshoori et al. (2013; doi:10.3354/meps10746). The measurement of the carbon and nitrogen isotope values of individual amino acids were conducted following Vokhshoori et al., 2013 and Vokhshoori and McCarthy, 2013 (doi:10.1371/journal.pone.0098087), respectively. The purpose of this study was to monitor biogeochemistry during 2010 water year and assess dietary difference between two co-occurring invasive species. This study focused on two species, C. fluminea and P. amurensis, benthic sessile primary consumers that can inhabit the same environment. USGS Site 2.1 was specifically selected as it exhibited the environmental conditions where both clam species co-existed during 2010 water year. This design allowed for interspecies variation to be explored. Nitrogen isotopes of amino acids were used to isolate variations in nutrient baseline from dietary changes across the season between the two species. Carbon isotopes of amino acid were utilized to understand the diet of the two species at two points in the season.
    Keywords: amino acids; biogeochemistry; Bivalve; carbon isotope; diet; Estuary; invasive species; nitrogen isotope; stable isotope analysis
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 84
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    A twenty-year record of biogeochemical changes in the San Francisco Bay Estuary: Timeseries of compound specific isotope analyses of amino acids from a benthic filter-feeder, Potamocorbula amurensis (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Description: Amino acids were isolated from the muscle tissue of Potamocorbula amurensis, an invasive clam species, collected from two locations in the northern portion of the San Francisco Bay. Clam specimens were collected biannually in 1997, 2002, and from 2009-2017 at both locations. The carbon and nitrogen isotope values of individual amino acids were measured. Clam specimens were collected at USGS Sites 4.1 (Suisun Bay) and 8.1 (Carquinez Strait) in the San Francisco Bay and processed as described in Stewart et al. (2013; doi:10.3354/meps10503). Amino acids were hydrolyzed from P. amurensis, derivatized, and isolated following Vokhshoori et al. (2013; doi:10.3354/meps10746). Carbon and nitrogen isotope values of individual amino acids were measured following Vokhshoori et al., 2013 and Vokhshoori and McCarthy, 2013 (doi:10.1371/journal.pone.0098087), respectively. The purpose of this study was to assess long-term changes in the biogeochemistry of the San Francisco Bay estuary following the arrival of invasive P. amurensis. Sites were selected both due to species occurrence as well as significantly different salinity ranges. This design allowed for intraspecies and site-specific variations to be explored. Nitrogen isotopes of amino acids were used to isolate variations in nutrient baseline over the twenty-year period. Carbon isotopes of amino acid were utilized to understand long-term changes in dietary sources and/or changes in the baseline carbon isotope value of the estuary's food-web.
    Keywords: amino acids; biogeochemistry; Biological sample; BIOS; Bivalve; carbon isotope; compound-specific isotope analysis; ecology; invasive species; nitrogen isotope; San Francisco Bay, California; Site 4.1; Site 8.1; stable isotope analysis; USGS_4-1; USGS_8-1
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 85
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    Hydrogen (d2H) and oxygen (d18O) isotopes of human hair from the United States (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Keywords: anthropogenic tracers; Area/locality; Arizona_1156; Arizona_1161; Arizona_1197; Arizona_1199; Arizona_570; Arizona_577; Arizona_579; Arizona_585; Arizona_602; Arizona_611; Arizona_625; Arizona_630; Arizona_635; Arizona_640; Arizona_661; Arizona_677; Arizona_678; California_1280; California_1287; California_198; California_200; California_201; California_202; California_205; California_208; California_470; California_473; California_480; California_485; California_487; California_491; California_495; California_542; California_549; California_561; California_562; California_563; California_564; California_731; California_733; California_738; California_756; California_772; California_779; California_781; California_785; California_798; California_808; California_839; California_840; California_853; California_855; California_858; California_862; California_872; California_879; California_882; California_883; California_884; California_885; California_887; California_888; California_889; California_898; California_901; California_904; California_909; California_913; California_914; California_917; DATE/TIME; Event label; HHS; Human hair sample; LATITUDE; Location ID; LONGITUDE; One-time_collection_1349; One-time_collection_1350; One-time_collection_1352; One-time_collection_1353; One-time_collection_1354; One-time_collection_1355; One-time_collection_1356; One-time_collection_1357; One-time_collection_1358; One-time_collection_1359; One-time_collection_1360; One-time_collection_1361; One-time_collection_1363; One-time_collection_1364; One-time_collection_1365; One-time_collection_1366; One-time_collection_1367; One-time_collection_1368; One-time_collection_1369; One-time_collection_1370; One-time_collection_1371; One-time_collection_1372; One-time_collection_1373; One-time_collection_1374; One-time_collection_1375; One-time_collection_1376; One-time_collection_1377; One-time_collection_1378; One-time_collection_1379; One-time_collection_1380; One-time_collection_1381; One-time_collection_1382; One-time_collection_1383; One-time_collection_1384; One-time_collection_1386; One-time_collection_1388; One-time_collection_1389; One-time_collection_1390; One-time_collection_1392; One-time_collection_1393; One-time_collection_1395; One-time_collection_1396; One-time_collection_1397; One-time_collection_1398; One-time_collection_1400; One-time_collection_1401; One-time_collection_1402; One-time_collection_1403; One-time_collection_1404; One-time_collection_1405; One-time_collection_1406; One-time_collection_1407; One-time_collection_1408; One-time_collection_1409; One-time_collection_1410; One-time_collection_1411; One-time_collection_1412; One-time_collection_1413; One-time_collection_1415; One-time_collection_1416; One-time_collection_1417; One-time_collection_1418; One-time_collection_1419; One-time_collection_1420; One-time_collection_1421; One-time_collection_1422; provenance analysis; Salt_Lake_Valley_1000; Salt_Lake_Valley_1001; Salt_Lake_Valley_1002; Salt_Lake_Valley_1003; Salt_Lake_Valley_1004; Salt_Lake_Valley_1005; Salt_Lake_Valley_1006; Salt_Lake_Valley_1007; Salt_Lake_Valley_1008; Salt_Lake_Valley_1009; Salt_Lake_Valley_1010; Salt_Lake_Valley_1011; Salt_Lake_Valley_1012; Salt_Lake_Valley_1013; Salt_Lake_Valley_1014; Salt_Lake_Valley_1015; Salt_Lake_Valley_1016; Salt_Lake_Valley_1017; Salt_Lake_Valley_1018; Salt_Lake_Valley_1019; Salt_Lake_Valley_248; Salt_Lake_Valley_249; Salt_Lake_Valley_250; Salt_Lake_Valley_251; Salt_Lake_Valley_341; Salt_Lake_Valley_342; Salt_Lake_Valley_382; Salt_Lake_Valley_396; Salt_Lake_Valley_413; Salt_Lake_Valley_420; Salt_Lake_Valley_421; Salt_Lake_Valley_432; Salt_Lake_Valley_448; Salt_Lake_Valley_996; Salt_Lake_Valley_997; Salt_Lake_Valley_998; Salt_Lake_Valley_999; Sample ID; stable isotope analysis; strontium isotopes; TC/EA-IRMS; trace element; United States; water chemistry; water isotopes; water management; Year of observation; δ18O; δ18O, standard deviation; δ Deuterium; δ Deuterium, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 3134 data points
    Location Call Number Expected Availability
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  • 86
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    Unknown
    Element abundances of human hair from the United States (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Keywords: Aluminium; anthropogenic tracers; Antimony; Area/locality; Arizona_1156; Arizona_1161; Arizona_1197; Arizona_1199; Arizona_570; Arizona_577; Arizona_579; Arizona_585; Arizona_602; Arizona_611; Arizona_625; Arizona_630; Arizona_635; Arizona_640; Arizona_661; Arizona_677; Arizona_678; Arsenic; Barium; Beryllium; Boron; Cadmium; Caesium; Calcium; California_198; California_200; California_201; California_202; California_205; California_208; California_470; California_473; California_480; California_485; California_487; California_491; California_495; California_542; California_549; California_561; California_562; California_563; California_564; California_731; California_733; California_738; California_756; California_772; California_779; California_781; California_785; California_872; California_879; California_882; California_883; California_884; California_885; California_887; California_888; California_889; California_898; California_901; California_904; California_909; California_913; California_914; California_917; Cerium; Chromium; Cobalt; Copper; DATE/TIME; Europium; Event label; HHS; Human hair sample; ICP-MS; Iron; Lanthanum; LATITUDE; Lead; Lithium; Location ID; LONGITUDE; Magnesium; Manganese; Molybdenum; Neodymium; Nickel; Potassium; provenance analysis; Salt_Lake_Valley_1000; Salt_Lake_Valley_1001; Salt_Lake_Valley_1002; Salt_Lake_Valley_1003; Salt_Lake_Valley_1004; Salt_Lake_Valley_1005; Salt_Lake_Valley_1006; Salt_Lake_Valley_1007; Salt_Lake_Valley_1008; Salt_Lake_Valley_1009; Salt_Lake_Valley_1010; Salt_Lake_Valley_1011; Salt_Lake_Valley_1012; Salt_Lake_Valley_1013; Salt_Lake_Valley_1014; Salt_Lake_Valley_1015; Salt_Lake_Valley_1016; Salt_Lake_Valley_1017; Salt_Lake_Valley_1018; Salt_Lake_Valley_1019; Salt_Lake_Valley_248; Salt_Lake_Valley_249; Salt_Lake_Valley_250; Salt_Lake_Valley_251; Salt_Lake_Valley_342; Salt_Lake_Valley_413; Salt_Lake_Valley_421; Salt_Lake_Valley_432; Salt_Lake_Valley_996; Salt_Lake_Valley_997; Salt_Lake_Valley_998; Salt_Lake_Valley_999; Sample ID; Selenium; Sodium; stable isotope analysis; Strontium; strontium isotopes; Thorium; trace element; United States; Uranium; Vanadium; water chemistry; water isotopes; water management; Year of observation; Yttrium; Zinc
    Type: Dataset
    Format: text/tab-separated-values, 5779 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 87
    facet.materialart.
    Unknown
    Strontium isotope ratios (87Sr/86Sr) of human hair from the United States (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Keywords: anthropogenic tracers; Area/locality; Arizona_1156; Arizona_1161; Arizona_1197; Arizona_1199; Arizona_570; Arizona_577; Arizona_579; Arizona_585; Arizona_602; Arizona_611; Arizona_625; Arizona_630; Arizona_635; Arizona_640; Arizona_661; Arizona_677; Arizona_678; California_198; California_200; California_201; California_202; California_205; California_208; California_470; California_473; California_480; California_485; California_487; California_491; California_495; California_542; California_549; California_561; California_562; California_563; California_564; California_731; California_733; California_738; California_756; California_772; California_779; California_781; California_785; California_872; California_879; California_882; California_883; California_884; California_885; California_887; California_888; California_889; California_898; California_901; California_904; California_909; California_913; California_914; California_917; DATE/TIME; Event label; HHS; Human hair sample; LATITUDE; Location ID; LONGITUDE; MC-ICP-MS; One-time_collection_1349; One-time_collection_1350; One-time_collection_1352; One-time_collection_1354; One-time_collection_1355; One-time_collection_1357; One-time_collection_1358; One-time_collection_1359; One-time_collection_1360; One-time_collection_1361; One-time_collection_1363; One-time_collection_1364; One-time_collection_1365; One-time_collection_1366; One-time_collection_1367; One-time_collection_1368; One-time_collection_1369; One-time_collection_1370; One-time_collection_1371; One-time_collection_1372; One-time_collection_1373; One-time_collection_1374; One-time_collection_1375; One-time_collection_1376; One-time_collection_1377; One-time_collection_1378; One-time_collection_1379; One-time_collection_1380; One-time_collection_1381; One-time_collection_1382; One-time_collection_1383; One-time_collection_1386; One-time_collection_1389; One-time_collection_1390; One-time_collection_1395; One-time_collection_1396; One-time_collection_1397; One-time_collection_1401; One-time_collection_1403; One-time_collection_1404; One-time_collection_1405; One-time_collection_1406; One-time_collection_1407; One-time_collection_1408; One-time_collection_1409; One-time_collection_1410; One-time_collection_1411; One-time_collection_1412; One-time_collection_1413; One-time_collection_1415; One-time_collection_1416; One-time_collection_1417; One-time_collection_1418; One-time_collection_1419; One-time_collection_1420; provenance analysis; Salt_Lake_Valley_1000; Salt_Lake_Valley_1001; Salt_Lake_Valley_1002; Salt_Lake_Valley_1003; Salt_Lake_Valley_1004; Salt_Lake_Valley_1005; Salt_Lake_Valley_1006; Salt_Lake_Valley_1007; Salt_Lake_Valley_1008; Salt_Lake_Valley_1009; Salt_Lake_Valley_1010; Salt_Lake_Valley_1011; Salt_Lake_Valley_1012; Salt_Lake_Valley_1013; Salt_Lake_Valley_1014; Salt_Lake_Valley_1015; Salt_Lake_Valley_1016; Salt_Lake_Valley_1017; Salt_Lake_Valley_1018; Salt_Lake_Valley_1019; Salt_Lake_Valley_248; Salt_Lake_Valley_249; Salt_Lake_Valley_250; Salt_Lake_Valley_251; Salt_Lake_Valley_342; Salt_Lake_Valley_413; Salt_Lake_Valley_421; Salt_Lake_Valley_432; Salt_Lake_Valley_996; Salt_Lake_Valley_997; Salt_Lake_Valley_998; Salt_Lake_Valley_999; Sample ID; stable isotope analysis; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, standard deviation; strontium isotopes; trace element; United States; water chemistry; water isotopes; water management; Year of observation
    Type: Dataset
    Format: text/tab-separated-values, 1655 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 88
    facet.materialart.
    Unknown
    A twenty-year record of carbon isotope values of amino acids from Potamocorbula amurensis (2023)
    PANGAEA
    Details
    Publication Date: 2023-05-24
    Description: Amino acids were isolated from the muscle tissue of Potamocorbula amurensis, an invasive clam species, collected from two locations in the northern portion of the San Francisco Bay. Clam specimens were collected biannually in 1997, 2002, and from 2009-2017 at both locations. The carbon isotope values of individual amino acids were measured. Clam specimens were collected at USGS Sites 4.1 (Suisun Bay) and 8.1 (Carquinez Strait) in the San Francisco Bay and processed as described in Stewart et al. (2013; doi:10.3354/meps10503). Amino acids were hydrolyzed from P. amurensis, derivatized, isolated and the carbon isotope values were measured following Vokhshoori et al. (2013; doi:10.3354/meps10746). The purpose of this study was to assess long-term changes in dietary sources and/or changes in the baseline carbon isotope value of the estuary's food-web following the invasion of P. amurensis.
    Keywords: Alanine, δ13C; Alanine, δ13C, standard deviation; amino acids; Aspartic acid, δ13C; Aspartic acid, δ13C, standard deviation; biogeochemistry; Biological sample; BIOS; Bivalve; carbon isotope; compound-specific isotope analysis; DATE/TIME; ecology; Glutamic acid, δ13C; Glutamic acid δ13C, standard deviation; Glycine, δ13C; Glycine, δ15N, standard deviation; invasive species; Isoleucine, δ13C; Isoleucine, δ13C, standard deviation; LATITUDE; Leucine, δ13C; Leucine, δ13C, standard deviation; LONGITUDE; Lysine, δ13C; Lysine, δ13C, standard deviation; nitrogen isotope; Phenylalanine, δ13C; Phenylalanine, δ13C, standard deviation; Proline, δ13C; Proline, δ13C, standard deviation; Sample ID; San Francisco Bay, California; see description in data abstract; Serine, δ13C; Serine, δ13C, standard deviation; Site; Site 4.1; Site 8.1; Species; stable isotope analysis; Threonine, δ13C; Threonine, δ13C, standard deviation; USGS_4-1; USGS_8-1; Valine, δ13C; Valine, δ13C, standard deviation; δ13C, bulk muscle tissue
    Type: Dataset
    Format: text/tab-separated-values, 1176 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 89
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    Unknown
    Seawater oxygen isotope data in the western Pacific Ocean analysed on samples from the R/V Hakuho-Maru KH-14-6 (GEOTRACES, GP19) cruise in 2014-2015 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-08
    Description: This study presents a new set of paired d18Osw and salinity data (n = 83) from a large region of the western Pacific (mainly the western south Pacific) collected during the 2014–2015 El Niño period. We determined regional salinity–d18Osw relationships for three ocean regions (40º–20ºN, 20ºN–30ºS, and 30º–66ºS) and found that all three regions exhibited highly linear relationships between d18Osw and salinity. The 40º–20ºN and 20ºN–30ºS regions had relatively low slopes (0.33 and 0.37 ‰ psu-1, respectively), while the 30º–66ºS region had a relatively high slope (0.54 ‰ psu-1). Each regional regression was statistically different from those of the GEOSECS (1973–1974 La Niña) data, indicating that inter-annual ENSO variability has an impact on salinity–d18Osw relationships over a wide area of the western Pacific.
    Keywords: DATE/TIME; DEPTH, water; Event label; GEOSECS; GEOTRACES; Global marine biogeochemical cycles of trace elements and their isotopes; GP19; Hakuho-Maru; IRMS; Isotope ratio mass spectrometer; KH-14-6; KH14-6_SSW001; KH14-6_SSW002; KH14-6_SSW003; KH14-6_SSW004; KH14-6_SSW005; KH14-6_SSW006; KH14-6_SSW007; KH14-6_SSW008; KH14-6_SSW009; KH14-6_SSW010; KH14-6_SSW011; KH14-6_SSW012; KH14-6_SSW013; KH14-6_SSW014; KH14-6_SSW015; KH14-6_SSW016; KH14-6_SSW017; KH14-6_SSW018; KH14-6_SSW019; KH14-6_SSW020; KH14-6_SSW021; KH14-6_SSW022; KH14-6_SSW023; KH14-6_SSW024; KH14-6_SSW025; KH14-6_SSW026; KH14-6_SSW027; KH14-6_SSW028; KH14-6_SSW029; KH14-6_SSW030; KH14-6_SSW031; KH14-6_SSW032; KH14-6_SSW033; KH14-6_SSW034; KH14-6_SSW036; KH14-6_SSW037; KH14-6_SSW038; KH14-6_SSW039; KH14-6_SSW040; KH14-6_SSW041; KH14-6_SSW042; KH14-6_SSW043; KH14-6_SSW044; KH14-6_SSW045; KH14-6_SSW046; KH14-6_SSW047; KH14-6_SSW048; KH14-6_SSW049; KH14-6_SSW050; KH14-6_SSW051; KH14-6_SSW052; KH14-6_SSW053; KH14-6_SSW054; KH14-6_SSW055; KH14-6_SSW056; KH14-6_SSW057; KH14-6_SSW058; KH14-6_SSW060; KH14-6_SSW061; KH14-6_SSW062; KH14-6_SSW064; KH14-6_SSW066; KH14-6_SSW067; KH14-6_SSW068; KH14-6_SSW069; KH14-6_SSW070; KH14-6_SSW071; KH14-6_SSW072; KH14-6_SSW073; KH14-6_SSW074; KH14-6_SSW075; KH14-6_SSW076; KH14-6_SSW077; KH14-6_SSW078; KH14-6_SSW079; KH14-6_SSW080; KH14-6_SSW082; KH14-6_SSW083; KH14-6_SSW084; KH14-6_SSW085; KH14-6_SSW086; KH14-6_SSW087; KH14-6_SSW088; LATITUDE; LONGITUDE; Method comment; oxygen isotopes; Salinity; Salinity, standard error; salinity–d18Osw relationship; seawater; Surface water sample; SWS; Temperature, water; Thermosalinograph; TSG; Western Pacific; δ18O, water; δ18O, water, standard error
    Type: Dataset
    Format: text/tab-separated-values, 498 data points
    Location Call Number Expected Availability
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  • 90
    facet.materialart.
    Unknown
    Sortable silt data from core MD03-2679 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-27
    Description: Sortable silt data was obtained from core MD03-2679 located in the North Atlantic. The sediment was analysed with a laser diffractometer at GEOsciences Paris-Sud (GEOPS), in order to provide informations on past changes in intensity of the Iceland-Scotland Overflow Water (ISOW) over the Marine Isotopic Stages 7 and 9. The original sortable silt data (percent and mean) were used for comparison with the new method of correcting for ice-rafted debris (IRD) influence on sortable silt described in Stevenard et al. (submitted).
    Keywords: CALYPSO; Calypso Corer; DEPTH, sediment/rock; Gardar Drift; Grain size, Mastersizer 2000, Malvern Instrument Inc.; IMAGES XI - P.I.C.A.S.S.O.; Marion Dufresne (1995); MD032679; MD03-2679; MD132; Size fraction 0.063-0.010 mm, sortable silt; sortable silt; Sortable-silt mean; Zr/Rb
    Type: Dataset
    Format: text/tab-separated-values, 530 data points
    Location Call Number Expected Availability
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  • 91
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    Unknown
    Spectral particulate absorption and attenuation coefficients derived from unterwater measurements from Lake Geiseltal (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-27
    Description: Measurements were taken from Wetlabs AC-S a spectral absoption and attenuation sensor and Wetlabs FL3W an underwater fluorometer with three channels(chlorophyll, pycoerythrin, CDOM flurorescence). Data is quality assured. The measured parameters from AC-S are attenuation and absorption coefficients (latter with scattering error). Results of attenuation channel used, results of absorption channel not used. Data are corrected for temperature and salinity, blank measurements of HE488 was used for blanking, remaining temperature effects are removed manual, choosen a correct temperature differences. Detector angle error of attenuation channel not corrected. All results are averages of measurements over 10-20 min. Total scattering coefficients provided, from AC-S attenuation minus PSICAM absorption results. The measured parameters from FL3W are using calibration of manufacturer. All results are averages of measurements over 10-20 min. For both sensors no standard deviations provided, but data are available.
    Keywords: AC-S; Attenuation coefficient, 402 nm; Attenuation coefficient, 404 nm; Attenuation coefficient, 406 nm; Attenuation coefficient, 408 nm; Attenuation coefficient, 410 nm; Attenuation coefficient, 412 nm; Attenuation coefficient, 414 nm; Attenuation coefficient, 416 nm; Attenuation coefficient, 418 nm; Attenuation coefficient, 420 nm; Attenuation coefficient, 422 nm; Attenuation coefficient, 424 nm; Attenuation coefficient, 426 nm; Attenuation coefficient, 428 nm; Attenuation coefficient, 430 nm; Attenuation coefficient, 432 nm; Attenuation coefficient, 434 nm; Attenuation coefficient, 436 nm; Attenuation coefficient, 438 nm; Attenuation coefficient, 440 nm; Attenuation coefficient, 442 nm; Attenuation coefficient, 444 nm; Attenuation coefficient, 446 nm; Attenuation coefficient, 448 nm; Attenuation coefficient, 450 nm; Attenuation coefficient, 452 nm; Attenuation coefficient, 454 nm; Attenuation coefficient, 456 nm; Attenuation coefficient, 458 nm; Attenuation coefficient, 460 nm; Attenuation coefficient, 462 nm; Attenuation coefficient, 464 nm; Attenuation coefficient, 466 nm; Attenuation coefficient, 468 nm; Attenuation coefficient, 470 nm; Attenuation coefficient, 472 nm; Attenuation coefficient, 474 nm; Attenuation coefficient, 476 nm; Attenuation coefficient, 478 nm; Attenuation coefficient, 480 nm; Attenuation coefficient, 482 nm; Attenuation coefficient, 484 nm; Attenuation coefficient, 486 nm; Attenuation coefficient, 488 nm; Attenuation coefficient, 490 nm; Attenuation coefficient, 492 nm; Attenuation coefficient, 494 nm; Attenuation coefficient, 496 nm; Attenuation coefficient, 498 nm; Attenuation coefficient, 500 nm; Attenuation coefficient, 502 nm; Attenuation coefficient, 504 nm; Attenuation coefficient, 506 nm; Attenuation coefficient, 508 nm; Attenuation coefficient, 510 nm; Attenuation coefficient, 512 nm; Attenuation coefficient, 514 nm; Attenuation coefficient, 516 nm; Attenuation coefficient, 518 nm; Attenuation coefficient, 520 nm; Attenuation coefficient, 522 nm; Attenuation coefficient, 524 nm; Attenuation coefficient, 526 nm; Attenuation coefficient, 528 nm; Attenuation coefficient, 530 nm; Attenuation coefficient, 532 nm; Attenuation coefficient, 534 nm; Attenuation coefficient, 536 nm; Attenuation coefficient, 538 nm; Attenuation coefficient, 540 nm; Attenuation coefficient, 542 nm; Attenuation coefficient, 544 nm; Attenuation coefficient, 546 nm; Attenuation coefficient, 548 nm; Attenuation coefficient, 550 nm; Attenuation coefficient, 552 nm; Attenuation coefficient, 554 nm; Attenuation coefficient, 556 nm; Attenuation coefficient, 558 nm; Attenuation coefficient, 560 nm; Attenuation coefficient, 562 nm; Attenuation coefficient, 564 nm; Attenuation coefficient, 566 nm; Attenuation coefficient, 568 nm; Attenuation coefficient, 570 nm; Attenuation coefficient, 572 nm; Attenuation coefficient, 574 nm; Attenuation coefficient, 576 nm; Attenuation coefficient, 578 nm; Attenuation coefficient, 580 nm; Attenuation coefficient, 582 nm; Attenuation coefficient, 584 nm; Attenuation coefficient, 586 nm; Attenuation coefficient, 588 nm; Attenuation coefficient, 590 nm; Attenuation coefficient, 592 nm; Attenuation coefficient, 594 nm; Attenuation coefficient, 596 nm; Attenuation coefficient, 598 nm; Attenuation coefficient, 600 nm; Attenuation coefficient, 602 nm; Attenuation coefficient, 604 nm; Attenuation coefficient, 606 nm; Attenuation coefficient, 608 nm; Attenuation coefficient, 610 nm; Attenuation coefficient, 612 nm; Attenuation coefficient, 614 nm; Attenuation coefficient, 616 nm; Attenuation coefficient, 618 nm; Attenuation coefficient, 620 nm; Attenuation coefficient, 622 nm; Attenuation coefficient, 624 nm; Attenuation coefficient, 626 nm; Attenuation coefficient, 628 nm; Attenuation coefficient, 630 nm; Attenuation coefficient, 632 nm; Attenuation coefficient, 634 nm; Attenuation coefficient, 636 nm; Attenuation coefficient, 638 nm; Attenuation coefficient, 640 nm; Attenuation coefficient, 642 nm; Attenuation coefficient, 644 nm; Attenuation coefficient, 646 nm; Attenuation coefficient, 648 nm; Attenuation coefficient, 650 nm; Attenuation coefficient, 652 nm; Attenuation coefficient, 654 nm; Attenuation coefficient, 656 nm; Attenuation coefficient, 658 nm; Attenuation coefficient, 660 nm; Attenuation coefficient, 662 nm; Attenuation coefficient, 664 nm; Attenuation coefficient, 666 nm; Attenuation coefficient, 668 nm; Attenuation coefficient, 670 nm; Attenuation coefficient, 672 nm; Attenuation coefficient, 674 nm; Attenuation coefficient, 676 nm; Attenuation coefficient, 678 nm; Attenuation coefficient, 680 nm; Attenuation coefficient, 682 nm; Attenuation coefficient, 684 nm; Attenuation coefficient, 686 nm; Attenuation coefficient, 688 nm; Attenuation coefficient, 690 nm; Attenuation coefficient, 692 nm; Attenuation coefficient, 694 nm; Attenuation coefficient, 696 nm; Attenuation coefficient, 698 nm; Attenuation coefficient, 700 nm; Attenuation coefficient, 702 nm; Attenuation coefficient, 704 nm; Attenuation coefficient, 706 nm; Attenuation coefficient, 708 nm; Attenuation coefficient, 710 nm; Attenuation coefficient, 712 nm; Attenuation coefficient, 714 nm; Attenuation coefficient, 716 nm; Attenuation coefficient, 718 nm; Attenuation coefficient, 720 nm; Attenuation coefficient, 722 nm; Attenuation coefficient, 724 nm; Attenuation coefficient, 726 nm; Attenuation coefficient, 728 nm; Attenuation coefficient, 730 nm; Attenuation coefficient, 732 nm; Attenuation coefficient, 734 nm; Attenuation coefficient, 736 nm; Attenuation coefficient, 738 nm; Attenuation coefficient, 740 nm; Chlorophyll a; Chlorophyll a, standard deviation; DATE/TIME; Event label; Fluorometer, WET Labs, FL3; Geiseltalsee-0808_AC-S; Geiseltalsee-0826_AC-S; Geiseltalsee-1227_AC-S; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Organic matter, colored dissolved; Organic matter, colored dissolved, standard deviation; Phycoerythrin; Phycoerythrin, standard deviation; Principal investigator; Spectral Absorption and Attenuation Sensor, AC-S; Spectrophotometer, WET Labs, Inc., AC-S; Total scattering coefficient, 400 nm; Total scattering coefficient, 402 nm; Total scattering coefficient, 404 nm; Total scattering coefficient, 406 nm; Total scattering coefficient, 408 nm; Total scattering coefficient, 410 nm; Total scattering coefficient, 412 nm; Total scattering coefficient, 414 nm; Total scattering coefficient, 416 nm; Total scattering coefficient, 418 nm; Total scattering coefficient, 420 nm; Total scattering coefficient, 422 nm; Total scattering coefficient, 424 nm; Total scattering coefficient, 426 nm; Total scattering coefficient, 428 nm; Total scattering coefficient, 430 nm; Total scattering coefficient, 432 nm; Total scattering coefficient, 434 nm; Total scattering coefficient, 436 nm; Total scattering coefficient, 438 nm; Total scattering coefficient, 440 nm; Total scattering coefficient, 442 nm; Total scattering coefficient, 444 nm; Total scattering coefficient, 446 nm; Total scattering coefficient, 448 nm; Total scattering coefficient, 450 nm; Total scattering coefficient, 452 nm; Total scattering coefficient, 454 nm; Total scattering coefficient, 456 nm; Total scattering coefficient, 458 nm; Total scattering coefficient, 460 nm; Total scattering coefficient, 462 nm; Total scattering coefficient, 464 nm; Total scattering coefficient, 466 nm; Total scattering coefficient, 468 nm; Total scattering coefficient, 470 nm; Total scattering coefficient, 472 nm; Total scattering coefficient, 474 nm; Total scattering coefficient, 476 nm; Total scattering coefficient, 478 nm; Total scattering coefficient, 480 nm; Total scattering coefficient, 482 nm; Total scattering coefficient, 484 nm; Total scattering coefficient, 486 nm; Total scattering coefficient, 488 nm; Total scattering coefficient, 490 nm; Total scattering coefficient, 492 nm; Total scattering coefficient, 494 nm; Total scattering coefficient, 496 nm; Total scattering
    Type: Dataset
    Format: text/tab-separated-values, 1044 data points
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  • 92
    facet.materialart.
    Unknown
    Spectral particulate absorption and attenuation coefficients derived from unterwater measurements within intercalibration campaign from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-27
    Description: Measurements were taken from Wetlabs AC-S a spectral absoption and attenuation sensor and Wetlabs FL3W an underwater fluorometer with three channels(chlorophyll, pycoerythrin, CDOM flurorescence). Data is quality assured. The measured parameters from AC-S are attenuation and absorption coefficients (latter with scattering error). Results of attenuation channel used, results of absorption channel not used. Data are corrected for temperature and salinity, blank measurements of HE488 was used for blanking, remaining temperature effects are removed manual, choosen a correct temperature differences. Detector angle error of attenuation channel not corrected. All results are averages of measurements over 10-20 min. Total scattering coefficients provided, from AC-S attenuation minus PSICAM absorption results. The measured parameters from FL3W are using calibration of manufacturer. All results are averages of measurements over 10-20 min. For both sensors no standard deviations provided, but data are available.
    Keywords: AC-S; Attenuation coefficient, 400 nm; Attenuation coefficient, 402 nm; Attenuation coefficient, 404 nm; Attenuation coefficient, 406 nm; Attenuation coefficient, 408 nm; Attenuation coefficient, 410 nm; Attenuation coefficient, 412 nm; Attenuation coefficient, 414 nm; Attenuation coefficient, 416 nm; Attenuation coefficient, 418 nm; Attenuation coefficient, 420 nm; Attenuation coefficient, 422 nm; Attenuation coefficient, 424 nm; Attenuation coefficient, 426 nm; Attenuation coefficient, 428 nm; Attenuation coefficient, 430 nm; Attenuation coefficient, 432 nm; Attenuation coefficient, 434 nm; Attenuation coefficient, 436 nm; Attenuation coefficient, 438 nm; Attenuation coefficient, 440 nm; Attenuation coefficient, 442 nm; Attenuation coefficient, 444 nm; Attenuation coefficient, 446 nm; Attenuation coefficient, 448 nm; Attenuation coefficient, 450 nm; Attenuation coefficient, 452 nm; Attenuation coefficient, 454 nm; Attenuation coefficient, 456 nm; Attenuation coefficient, 458 nm; Attenuation coefficient, 460 nm; Attenuation coefficient, 462 nm; Attenuation coefficient, 464 nm; Attenuation coefficient, 466 nm; Attenuation coefficient, 468 nm; Attenuation coefficient, 470 nm; Attenuation coefficient, 472 nm; Attenuation coefficient, 474 nm; Attenuation coefficient, 476 nm; Attenuation coefficient, 478 nm; Attenuation coefficient, 480 nm; Attenuation coefficient, 482 nm; Attenuation coefficient, 484 nm; Attenuation coefficient, 486 nm; Attenuation coefficient, 488 nm; Attenuation coefficient, 490 nm; Attenuation coefficient, 492 nm; Attenuation coefficient, 494 nm; Attenuation coefficient, 496 nm; Attenuation coefficient, 498 nm; Attenuation coefficient, 500 nm; Attenuation coefficient, 502 nm; Attenuation coefficient, 504 nm; Attenuation coefficient, 506 nm; Attenuation coefficient, 508 nm; Attenuation coefficient, 510 nm; Attenuation coefficient, 512 nm; Attenuation coefficient, 514 nm; Attenuation coefficient, 516 nm; Attenuation coefficient, 518 nm; Attenuation coefficient, 520 nm; Attenuation coefficient, 522 nm; Attenuation coefficient, 524 nm; Attenuation coefficient, 526 nm; Attenuation coefficient, 528 nm; Attenuation coefficient, 530 nm; Attenuation coefficient, 532 nm; Attenuation coefficient, 534 nm; Attenuation coefficient, 536 nm; Attenuation coefficient, 538 nm; Attenuation coefficient, 540 nm; Attenuation coefficient, 542 nm; Attenuation coefficient, 544 nm; Attenuation coefficient, 546 nm; Attenuation coefficient, 548 nm; Attenuation coefficient, 550 nm; Attenuation coefficient, 552 nm; Attenuation coefficient, 554 nm; Attenuation coefficient, 556 nm; Attenuation coefficient, 558 nm; Attenuation coefficient, 560 nm; Attenuation coefficient, 562 nm; Attenuation coefficient, 564 nm; Attenuation coefficient, 566 nm; Attenuation coefficient, 568 nm; Attenuation coefficient, 570 nm; Attenuation coefficient, 572 nm; Attenuation coefficient, 574 nm; Attenuation coefficient, 576 nm; Attenuation coefficient, 578 nm; Attenuation coefficient, 580 nm; Attenuation coefficient, 582 nm; Attenuation coefficient, 584 nm; Attenuation coefficient, 586 nm; Attenuation coefficient, 588 nm; Attenuation coefficient, 590 nm; Attenuation coefficient, 592 nm; Attenuation coefficient, 594 nm; Attenuation coefficient, 596 nm; Attenuation coefficient, 598 nm; Attenuation coefficient, 600 nm; Attenuation coefficient, 602 nm; Attenuation coefficient, 604 nm; Attenuation coefficient, 606 nm; Attenuation coefficient, 608 nm; Attenuation coefficient, 610 nm; Attenuation coefficient, 612 nm; Attenuation coefficient, 614 nm; Attenuation coefficient, 616 nm; Attenuation coefficient, 618 nm; Attenuation coefficient, 620 nm; Attenuation coefficient, 622 nm; Attenuation coefficient, 624 nm; Attenuation coefficient, 626 nm; Attenuation coefficient, 628 nm; Attenuation coefficient, 630 nm; Attenuation coefficient, 632 nm; Attenuation coefficient, 634 nm; Attenuation coefficient, 636 nm; Attenuation coefficient, 638 nm; Attenuation coefficient, 640 nm; Attenuation coefficient, 642 nm; Attenuation coefficient, 644 nm; Attenuation coefficient, 646 nm; Attenuation coefficient, 648 nm; Attenuation coefficient, 650 nm; Attenuation coefficient, 652 nm; Attenuation coefficient, 654 nm; Attenuation coefficient, 656 nm; Attenuation coefficient, 658 nm; Attenuation coefficient, 660 nm; Attenuation coefficient, 662 nm; Attenuation coefficient, 664 nm; Attenuation coefficient, 666 nm; Attenuation coefficient, 668 nm; Attenuation coefficient, 670 nm; Attenuation coefficient, 672 nm; Attenuation coefficient, 674 nm; Attenuation coefficient, 676 nm; Attenuation coefficient, 678 nm; Attenuation coefficient, 680 nm; Attenuation coefficient, 682 nm; Attenuation coefficient, 684 nm; Attenuation coefficient, 686 nm; Attenuation coefficient, 688 nm; Attenuation coefficient, 690 nm; Attenuation coefficient, 692 nm; Attenuation coefficient, 694 nm; Attenuation coefficient, 696 nm; Attenuation coefficient, 698 nm; Attenuation coefficient, 700 nm; Attenuation coefficient, 702 nm; Attenuation coefficient, 704 nm; Attenuation coefficient, 706 nm; Attenuation coefficient, 708 nm; Attenuation coefficient, 710 nm; Attenuation coefficient, 712 nm; Attenuation coefficient, 714 nm; Attenuation coefficient, 716 nm; Attenuation coefficient, 718 nm; Attenuation coefficient, 720 nm; Attenuation coefficient, 722 nm; Attenuation coefficient, 724 nm; Attenuation coefficient, 726 nm; Attenuation coefficient, 728 nm; Attenuation coefficient, 730 nm; Attenuation coefficient, 732 nm; Attenuation coefficient, 734 nm; Attenuation coefficient, 736 nm; Attenuation coefficient, 738 nm; Attenuation coefficient, 740 nm; Chlorophyll a; Chlorophyll a, standard deviation; DATE/TIME; Event label; Fluorometer, WET Labs, FL3; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Organic matter, colored dissolved; Organic matter, colored dissolved, standard deviation; Phycoerythrin; Phycoerythrin, standard deviation; Principal investigator; Spectral Absorption and Attenuation Sensor, AC-S; Spectrophotometer, WET Labs, Inc., AC-S; Suessersee-0901_AC-S; Suessersee-0912_AC-S; Suessersee-0945_AC-S; Suessersee-1033_AC-S; Total scattering coefficient, 400 nm; Total scattering coefficient, 402 nm; Total scattering coefficient, 404 nm; Total scattering coefficient, 406 nm; Total scattering coefficient, 408 nm; Total scattering coefficient, 410 nm; Total scattering coefficient, 412 nm; Total scattering coefficient, 414 nm; Total scattering coefficient, 416 nm; Total scattering coefficient, 418 nm; Total scattering coefficient, 420 nm; Total scattering coefficient, 422 nm; Total scattering coefficient, 424 nm; Total scattering coefficient, 426 nm; Total scattering coefficient, 428 nm; Total scattering coefficient, 430 nm; Total scattering coefficient, 432 nm; Total scattering coefficient, 434 nm; Total scattering coefficient, 436 nm; Total scattering coefficient, 438 nm; Total scattering coefficient, 440 nm; Total scattering coefficient, 442 nm; Total scattering coefficient, 444 nm; Total scattering coefficient, 446 nm; Total scattering coefficient, 448 nm; Total scattering coefficient, 450 nm; Total scattering coefficient, 452 nm; Total scattering coefficient, 454 nm; Total scattering coefficient, 456 nm; Total scattering coefficient, 458 nm; Total scattering coefficient, 460 nm; Total scattering coefficient, 462 nm; Total scattering coefficient, 464 nm; Total scattering coefficient, 466 nm; Total scattering coefficient, 468 nm; Total scattering coefficient, 470 nm; Total scattering coefficient, 472 nm; Total scattering coefficient, 474 nm; Total scattering coefficient, 476 nm; Total scattering coefficient, 478 nm; Total scattering coefficient, 480 nm; Total scattering coefficient, 482 nm; Total scattering coefficient, 484 nm; Total scattering coefficient, 486 nm; Total scattering coefficient, 488 nm; Total scattering coefficient, 490 nm; Total scattering coefficient, 492 nm; Total scattering coefficient, 494 nm; Total
    Type: Dataset
    Format: text/tab-separated-values, 1396 data points
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  • 93
    facet.materialart.
    Unknown
    Surface transect data of trace gases, biomass and phytoplankton group composition during RV POLARSTERN cruise PS92 (2023)
    PANGAEA
    Details
    Publication Date: 2023-06-27
    Description: This data set includes the concentration of surface water volatile organic compounds and carbon monoxide, biomass concentrations and temperature from the ferry box system of RV POLARSTERN on the cruise track from Bremerhaven to the ice covered region north of Svalbard during RV POLARSTERN cruise PS92. Temperature and salinity were used to classify the sampled water masses based on the criteria applied in Tran et al., 2013.
    Keywords: Acetaldehyde; Acetone; Acetonitrile; Arctic; ARK-XXIX/1, TRANSSIZ; AWI_BioOce; Biological Oceanography @ AWI; Biomass; Carbon monoxide; Chlorophyll a; Code; DATE/TIME; Dimethyl sulfide; FBOX; FerryBox; GASC; Gas chromatograph; Isoprene; LATITUDE; LONGITUDE; Methanethiol; Polarstern; Proton Transfer Mass Spectrometer; PS92; PS92_0_Underway-1; PTRMS; Temperate waters; Temperature, water; trace gases; transect
    Type: Dataset
    Format: text/tab-separated-values, 29918 data points
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  • 94
    facet.materialart.
    Unknown
    Aerosol absorption coefficients at seven wavelengths measured at Neumayer station in 2022 (2023)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Details
    Publication Date: 2023-06-27
    Description: We operate a 7-wavelength aethalometer (Model AE33, Magee Scientific) which is in operation since 23 January 2019 ongoing. The Aethalometer model AE33 collects aerosol particles continuously by drawing the aerosol-laden air stream through a spot on the filter tape. It analyzes the aerosol by measuring the transmission of light through one portion of the filter tape containing the sample, versus the transmission through an unloaded portion of the filter tape acting as a reference area. This analysis is done at seven optical wavelengths spanning the range from the near-infrared to the near-ultraviolet. The Aethalometer calculates the instantaneous concentration of optically-absorbing aerosols from the rate of change of the attenuation of light transmitted through the particle-laden filter.
    Keywords: aerosol; Aerosol absorption at 370 nm; Aerosol absorption at 470 nm; Aerosol absorption at 520 nm; Aerosol absorption at 590 nm; Aerosol absorption at 660 nm; Aerosol absorption at 880 nm; Aerosol absorption at 950 nm; aerosol absorption coefficient; Aethalometer, AE33, Magee Scientific; Air chemistry observatory; Air Chemistry Observatory; Atmospheric Chemistry @ AWI; AWI_AC; DATE/TIME; Dronning Maud Land, Antarctica; Duration; HEIGHT above ground; Neumayer_based; Neumayer_SPUSO; NEUMAYER III; Neumayer Station; Spuso; SPUSO
    Type: Dataset
    Format: text/tab-separated-values, 131400 data points
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  • 95
    facet.materialart.
    Unknown
    Remote-sensing reflectance determined from spectral data set from Kelbra Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-07-03
    Keywords: Angle; Date/time end; Date/time start; Event label; Flag; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Kelbra-0857_RAMSES1; Kelbra-0907_RAMSES1; Kelbra-0947_RAMSES1; Kelbra-0954_RAMSES1; Kelbra-1020_RAMSES1; Kelbra-1040_RAMSES1; Kelbra-1100_RAMSES1; Kelbra-1142_RAMSES1; Principal investigator; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 382.5 nm; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 387.5 nm; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 392.5 nm; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 397.5 nm; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 402.5 nm; Remote sensing reflectance at 405 nm; Remote sensing reflectance at 407.5 nm; Remote sensing reflectance at 410 nm; Remote sensing reflectance at 412.5 nm; Remote sensing reflectance at 415 nm; Remote sensing reflectance at 417.5 nm; Remote sensing reflectance at 420 nm; Remote sensing reflectance at 422.5 nm; Remote sensing reflectance at 425 nm; Remote sensing reflectance at 427.5 nm; Remote sensing reflectance at 430 nm; Remote sensing reflectance at 432.5 nm; Remote sensing reflectance at 435 nm; Remote sensing reflectance at 437.5 nm; Remote sensing reflectance at 440 nm; Remote sensing reflectance at 442.5 nm; Remote sensing reflectance at 445 nm; Remote sensing reflectance at 447.5 nm; Remote sensing reflectance at 450 nm; Remote sensing reflectance at 452.5 nm; Remote sensing reflectance at 455 nm; Remote sensing reflectance at 457.5 nm; Remote sensing reflectance at 460 nm; Remote sensing reflectance at 462.5 nm; Remote sensing reflectance at 465 nm; Remote sensing reflectance at 467.5 nm; Remote sensing reflectance at 470 nm; Remote sensing reflectance at 472.5 nm; Remote sensing reflectance at 475 nm; Remote sensing reflectance at 477.5 nm; Remote sensing reflectance at 480 nm; Remote sensing reflectance at 482.5 nm; Remote sensing reflectance at 485 nm; Remote sensing reflectance at 487.5 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 492.5 nm; Remote sensing reflectance at 495 nm; Remote sensing reflectance at 497.5 nm; Remote sensing reflectance at 500 nm; Remote sensing reflectance at 502.5 nm; Remote sensing reflectance at 505 nm; Remote sensing reflectance at 507.5 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 512.5 nm; Remote sensing reflectance at 515 nm; Remote sensing reflectance at 517.5 nm; Remote sensing reflectance at 520 nm; Remote sensing reflectance at 522.5 nm; Remote sensing reflectance at 525 nm; Remote sensing reflectance at 527.5 nm; Remote sensing reflectance at 530 nm; Remote sensing reflectance at 532.5 nm; Remote sensing reflectance at 535 nm; Remote sensing reflectance at 537.5 nm; Remote sensing reflectance at 540 nm; Remote sensing reflectance at 542.5 nm; Remote sensing reflectance at 545 nm; Remote sensing reflectance at 547.5 nm; Remote sensing reflectance at 550 nm; Remote sensing reflectance at 552.5 nm; Remote sensing reflectance at 555 nm; Remote sensing reflectance at 557.5 nm; Remote sensing reflectance at 560 nm; Remote sensing reflectance at 562.5 nm; Remote sensing reflectance at 565 nm; Remote sensing reflectance at 567.5 nm; Remote sensing reflectance at 570 nm; Remote sensing reflectance at 572.5 nm; Remote sensing reflectance at 575 nm; Remote sensing reflectance at 577.5 nm; Remote sensing reflectance at 580 nm; Remote sensing reflectance at 582.5 nm; Remote sensing reflectance at 585 nm; Remote sensing reflectance at 587.5 nm; Remote sensing reflectance at 590 nm; Remote sensing reflectance at 592.5 nm; Remote sensing reflectance at 595 nm; Remote sensing reflectance at 597.5 nm; Remote sensing reflectance at 600 nm; Remote sensing reflectance at 602.5 nm; Remote sensing reflectance at 605 nm; Remote sensing reflectance at 607.5 nm; Remote sensing reflectance at 610 nm; Remote sensing reflectance at 612.5 nm; Remote sensing reflectance at 615 nm; Remote sensing reflectance at 617.5 nm; Remote sensing reflectance at 620 nm; Remote sensing reflectance at 622.5 nm; Remote sensing reflectance at 625 nm; Remote sensing reflectance at 627.5 nm; Remote sensing reflectance at 630 nm; Remote sensing reflectance at 632.5 nm; Remote sensing reflectance at 635 nm; Remote sensing reflectance at 637.5 nm; Remote sensing reflectance at 640 nm; Remote sensing reflectance at 642.5 nm; Remote sensing reflectance at 645 nm; Remote sensing reflectance at 647.5 nm; Remote sensing reflectance at 650 nm; Remote sensing reflectance at 652.5 nm; Remote sensing reflectance at 655 nm; Remote sensing reflectance at 657.5 nm; Remote sensing reflectance at 660 nm; Remote sensing reflectance at 662.5 nm; Remote sensing reflectance at 665 nm; Remote sensing reflectance at 667.5 nm; Remote sensing reflectance at 670 nm; Remote sensing reflectance at 672.5 nm; Remote sensing reflectance at 675 nm; Remote sensing reflectance at 677.5 nm; Remote sensing reflectance at 680 nm; Remote sensing reflectance at 682.5 nm; Remote sensing reflectance at 685 nm; Remote sensing reflectance at 687.5 nm; Remote sensing reflectance at 690 nm; Remote sensing reflectance at 692.5 nm; Remote sensing reflectance at 695 nm; Remote sensing reflectance at 697.5 nm; Remote sensing reflectance at 700 nm; Remote sensing reflectance at 702.5 nm; Remote sensing reflectance at 705 nm; Remote sensing reflectance at 707.5 nm; Remote sensing reflectance at 710 nm; Remote sensing reflectance at 712.5 nm; Remote sensing reflectance at 715 nm; Remote sensing reflectance at 717.5 nm; Remote sensing reflectance at 720 nm; Remote sensing reflectance at 722.5 nm; Remote sensing reflectance at 725 nm; Remote sensing reflectance at 727.5 nm; Remote sensing reflectance at 730 nm; Remote sensing reflectance at 732.5 nm; Remote sensing reflectance at 735 nm; Remote sensing reflectance at 737.5 nm; Remote sensing reflectance at 740 nm; Remote sensing reflectance at 742.5 nm; Remote sensing reflectance at 745 nm; Remote sensing reflectance at 747.5 nm; Remote sensing reflectance at 750 nm; Remote sensing reflectance at 752.5 nm; Remote sensing reflectance at 755 nm; Remote sensing reflectance at 757.5 nm; Remote sensing reflectance at 760 nm; Remote sensing reflectance at 762.5 nm; Remote sensing reflectance at 765 nm; Remote sensing reflectance at 767.5 nm; Remote sensing reflectance at 770 nm; Remote sensing reflectance at 772.5 nm; Remote sensing reflectance at 775 nm; Remote sensing reflectance at 777.5 nm; Remote sensing reflectance at 780 nm; Remote sensing reflectance at 782.5 nm; Remote sensing reflectance at 785 nm; Remote sensing reflectance at 787.5 nm; Remote sensing reflectance at 790 nm; Remote sensing reflectance at 792.5 nm; Remote sensing reflectance at 795 nm; Remote sensing reflectance at 797.5 nm; Remote sensing reflectance at 800 nm; Remote sensing reflectance at 802.5 nm; Remote sensing reflectance at 805 nm; Remote sensing reflectance at 807.5 nm; Remote sensing reflectance at 810 nm; Remote sensing reflectance at 812.5 nm; Remote sensing reflectance at 815 nm; Remote sensing reflectance at 817.5 nm; Remote sensing reflectance at 820 nm; Remote sensing reflectance at 822.5 nm; Remote sensing reflectance at 825 nm; Remote sensing reflectance at 827.5 nm; Remote sensing reflectance at 830 nm; Remote sensing reflectance at 832.5 nm; Remote sensing reflectance at 835 nm; Remote sensing reflectance at 837.5 nm; Remote sensing reflectance at 840 nm; Remote sensing reflectance at 842.5 nm; Remote sensing reflectance at 845 nm; Remote sensing reflectance at 847.5 nm; Remote sensing reflectance at 850 nm; Remote sensing reflectance at 852.5 nm; Remote sensing reflectance at 855 nm; Remote sensing reflectance at 857.5 nm; Remote sensing reflectance at 860 nm; Remote sensing reflectance at 862.5 nm; Remote sensing reflectance at 865 nm;
    Type: Dataset
    Format: text/tab-separated-values, 1728 data points
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  • 96
    facet.materialart.
    Unknown
    Remote-sensing reflectance determined from spectral data set within intercalibration campaign from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-07-03
    Keywords: Date/time end; Date/time start; Flag; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Remote sensing reflectance at 320 nm; Remote sensing reflectance at 320 nm, standard deviation; Remote sensing reflectance at 321 nm; Remote sensing reflectance at 321 nm, standard deviation; Remote sensing reflectance at 322 nm; Remote sensing reflectance at 322 nm, standard deviation; Remote sensing reflectance at 323 nm; Remote sensing reflectance at 323 nm, standard deviation; Remote sensing reflectance at 324 nm; Remote sensing reflectance at 324 nm, standard deviation; Remote sensing reflectance at 325 nm; Remote sensing reflectance at 325 nm, standard deviation; Remote sensing reflectance at 326 nm; Remote sensing reflectance at 326 nm, standard deviation; Remote sensing reflectance at 327 nm; Remote sensing reflectance at 327 nm, standard deviation; Remote sensing reflectance at 328 nm; Remote sensing reflectance at 328 nm, standard deviation; Remote sensing reflectance at 329 nm; Remote sensing reflectance at 329 nm, standard deviation; Remote sensing reflectance at 330 nm; Remote sensing reflectance at 330 nm, standard deviation; Remote sensing reflectance at 331 nm; Remote sensing reflectance at 331 nm, standard deviation; Remote sensing reflectance at 332 nm; Remote sensing reflectance at 332 nm, standard deviation; Remote sensing reflectance at 333 nm; Remote sensing reflectance at 333 nm, standard deviation; Remote sensing reflectance at 334 nm; Remote sensing reflectance at 334 nm, standard deviation; Remote sensing reflectance at 335 nm; Remote sensing reflectance at 335 nm, standard deviation; Remote sensing reflectance at 336 nm; Remote sensing reflectance at 336 nm, standard deviation; Remote sensing reflectance at 337 nm; Remote sensing reflectance at 337 nm, standard deviation; Remote sensing reflectance at 338 nm; Remote sensing reflectance at 338 nm, standard deviation; Remote sensing reflectance at 339 nm; Remote sensing reflectance at 339 nm, standard deviation; Remote sensing reflectance at 340 nm; Remote sensing reflectance at 340 nm, standard deviation; Remote sensing reflectance at 341 nm; Remote sensing reflectance at 341 nm, standard deviation; Remote sensing reflectance at 342 nm; Remote sensing reflectance at 342 nm, standard deviation; Remote sensing reflectance at 343 nm; Remote sensing reflectance at 343 nm, standard deviation; Remote sensing reflectance at 344 nm; Remote sensing reflectance at 344 nm, standard deviation; Remote sensing reflectance at 345 nm; Remote sensing reflectance at 345 nm, standard deviation; Remote sensing reflectance at 346 nm; Remote sensing reflectance at 346 nm, standard deviation; Remote sensing reflectance at 347 nm; Remote sensing reflectance at 347 nm, standard deviation; Remote sensing reflectance at 348 nm; Remote sensing reflectance at 348 nm, standard deviation; Remote sensing reflectance at 349 nm; Remote sensing reflectance at 349 nm, standard deviation; Remote sensing reflectance at 350 nm; Remote sensing reflectance at 350 nm, standard deviation; Remote sensing reflectance at 351 nm; Remote sensing reflectance at 351 nm, standard deviation; Remote sensing reflectance at 352 nm; Remote sensing reflectance at 352 nm, standard deviation; Remote sensing reflectance at 353 nm; Remote sensing reflectance at 353 nm, standard deviation; Remote sensing reflectance at 354 nm; Remote sensing reflectance at 354 nm, standard deviation; Remote sensing reflectance at 355 nm; Remote sensing reflectance at 355 nm, standard deviation; Remote sensing reflectance at 356 nm; Remote sensing reflectance at 356 nm, standard deviation; Remote sensing reflectance at 357 nm; Remote sensing reflectance at 357 nm, standard deviation; Remote sensing reflectance at 358 nm; Remote sensing reflectance at 358 nm, standard deviation; Remote sensing reflectance at 359 nm; Remote sensing reflectance at 359 nm, standard deviation; Remote sensing reflectance at 360 nm; Remote sensing reflectance at 360 nm, standard deviation; Remote sensing reflectance at 361 nm; Remote sensing reflectance at 361 nm, standard deviation; Remote sensing reflectance at 362 nm; Remote sensing reflectance at 362 nm, standard deviation; Remote sensing reflectance at 363 nm; Remote sensing reflectance at 363 nm, standard deviation; Remote sensing reflectance at 364 nm; Remote sensing reflectance at 364 nm, standard deviation; Remote sensing reflectance at 365 nm; Remote sensing reflectance at 365 nm, standard deviation; Remote sensing reflectance at 366 nm; Remote sensing reflectance at 366 nm, standard deviation; Remote sensing reflectance at 367 nm; Remote sensing reflectance at 367 nm, standard deviation; Remote sensing reflectance at 368 nm; Remote sensing reflectance at 368 nm, standard deviation; Remote sensing reflectance at 369 nm; Remote sensing reflectance at 369 nm, standard deviation; Remote sensing reflectance at 370 nm; Remote sensing reflectance at 370 nm, standard deviation; Remote sensing reflectance at 371 nm; Remote sensing reflectance at 371 nm, standard deviation; Remote sensing reflectance at 372 nm; Remote sensing reflectance at 372 nm, standard deviation; Remote sensing reflectance at 373 nm; Remote sensing reflectance at 373 nm, standard deviation; Remote sensing reflectance at 374 nm; Remote sensing reflectance at 374 nm, standard deviation; Remote sensing reflectance at 375 nm; Remote sensing reflectance at 375 nm, standard deviation; Remote sensing reflectance at 376 nm; Remote sensing reflectance at 376 nm, standard deviation; Remote sensing reflectance at 377 nm; Remote sensing reflectance at 377 nm, standard deviation; Remote sensing reflectance at 378 nm; Remote sensing reflectance at 378 nm, standard deviation; Remote sensing reflectance at 379 nm; Remote sensing reflectance at 379 nm, standard deviation; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 380 nm, standard deviation; Remote sensing reflectance at 381 nm; Remote sensing reflectance at 381 nm, standard deviation; Remote sensing reflectance at 382 nm; Remote sensing reflectance at 382 nm, standard deviation; Remote sensing reflectance at 383 nm; Remote sensing reflectance at 383 nm, standard deviation; Remote sensing reflectance at 384 nm; Remote sensing reflectance at 384 nm, standard deviation; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 385 nm, standard deviation; Remote sensing reflectance at 386 nm; Remote sensing reflectance at 386 nm, standard deviation; Remote sensing reflectance at 387 nm; Remote sensing reflectance at 387 nm, standard deviation; Remote sensing reflectance at 388 nm; Remote sensing reflectance at 388 nm, standard deviation; Remote sensing reflectance at 389 nm; Remote sensing reflectance at 389 nm, standard deviation; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 390 nm, standard deviation; Remote sensing reflectance at 391 nm; Remote sensing reflectance at 391 nm, standard deviation; Remote sensing reflectance at 392 nm; Remote sensing reflectance at 392 nm, standard deviation; Remote sensing reflectance at 393 nm; Remote sensing reflectance at 393 nm, standard deviation; Remote sensing reflectance at 394 nm; Remote sensing reflectance at 394 nm, standard deviation; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 395 nm, standard deviation; Remote sensing reflectance at 396 nm; Remote sensing reflectance at 396 nm, standard deviation; Remote sensing reflectance at 397 nm; Remote sensing reflectance at 397 nm, standard deviation; Remote sensing reflectance at 398 nm; Remote sensing reflectance at 398 nm, standard deviation; Remote sensing reflectance at 399 nm; Remote sensing reflectance at 399 nm, standard deviation; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 400 nm, standard deviation; Remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 2325 data points
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  • 97
    facet.materialart.
    Unknown
    Remote-sensing reflectance determined from spectral data set within intercalibration campaign from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
    PANGAEA
    Details
    Publication Date: 2023-07-03
    Description: Radiometric measurement with Trios Ramses system. All Trios measurements averaged over reliable common times. The viewing zenith angle was approx. 45°. The surface reflectance factors rho are only given for 40 or 50°. If rho is to high, there is an overcorrection of Lw and Rrs. For this three methods for correction are applied: constant factor rho, Mobley 1999 and Mobley 2015 (Mobley 1999 related to 50°, Mobley 2015 related to 40°).
    Keywords: Angle; Flag; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Remote sensing reflectance at 380 nm; Remote sensing reflectance at 382.5 nm; Remote sensing reflectance at 385 nm; Remote sensing reflectance at 387.5 nm; Remote sensing reflectance at 390 nm; Remote sensing reflectance at 392.5 nm; Remote sensing reflectance at 395 nm; Remote sensing reflectance at 397.5 nm; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 402.5 nm; Remote sensing reflectance at 405 nm; Remote sensing reflectance at 407.5 nm; Remote sensing reflectance at 410 nm; Remote sensing reflectance at 412.5 nm; Remote sensing reflectance at 415 nm; Remote sensing reflectance at 417.5 nm; Remote sensing reflectance at 420 nm; Remote sensing reflectance at 422.5 nm; Remote sensing reflectance at 425 nm; Remote sensing reflectance at 427.5 nm; Remote sensing reflectance at 430 nm; Remote sensing reflectance at 432.5 nm; Remote sensing reflectance at 435 nm; Remote sensing reflectance at 437.5 nm; Remote sensing reflectance at 440 nm; Remote sensing reflectance at 442.5 nm; Remote sensing reflectance at 445 nm; Remote sensing reflectance at 447.5 nm; Remote sensing reflectance at 450 nm; Remote sensing reflectance at 452.5 nm; Remote sensing reflectance at 455 nm; Remote sensing reflectance at 457.5 nm; Remote sensing reflectance at 460 nm; Remote sensing reflectance at 462.5 nm; Remote sensing reflectance at 465 nm; Remote sensing reflectance at 467.5 nm; Remote sensing reflectance at 470 nm; Remote sensing reflectance at 472.5 nm; Remote sensing reflectance at 475 nm; Remote sensing reflectance at 477.5 nm; Remote sensing reflectance at 480 nm; Remote sensing reflectance at 482.5 nm; Remote sensing reflectance at 485 nm; Remote sensing reflectance at 487.5 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 492.5 nm; Remote sensing reflectance at 495 nm; Remote sensing reflectance at 497.5 nm; Remote sensing reflectance at 500 nm; Remote sensing reflectance at 502.5 nm; Remote sensing reflectance at 505 nm; Remote sensing reflectance at 507.5 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 512.5 nm; Remote sensing reflectance at 515 nm; Remote sensing reflectance at 517.5 nm; Remote sensing reflectance at 520 nm; Remote sensing reflectance at 522.5 nm; Remote sensing reflectance at 525 nm; Remote sensing reflectance at 527.5 nm; Remote sensing reflectance at 530 nm; Remote sensing reflectance at 532.5 nm; Remote sensing reflectance at 535 nm; Remote sensing reflectance at 537.5 nm; Remote sensing reflectance at 540 nm; Remote sensing reflectance at 542.5 nm; Remote sensing reflectance at 545 nm; Remote sensing reflectance at 547.5 nm; Remote sensing reflectance at 550 nm; Remote sensing reflectance at 552.5 nm; Remote sensing reflectance at 555 nm; Remote sensing reflectance at 557.5 nm; Remote sensing reflectance at 560 nm; Remote sensing reflectance at 562.5 nm; Remote sensing reflectance at 565 nm; Remote sensing reflectance at 567.5 nm; Remote sensing reflectance at 570 nm; Remote sensing reflectance at 572.5 nm; Remote sensing reflectance at 575 nm; Remote sensing reflectance at 577.5 nm; Remote sensing reflectance at 580 nm; Remote sensing reflectance at 582.5 nm; Remote sensing reflectance at 585 nm; Remote sensing reflectance at 587.5 nm; Remote sensing reflectance at 590 nm; Remote sensing reflectance at 592.5 nm; Remote sensing reflectance at 595 nm; Remote sensing reflectance at 597.5 nm; Remote sensing reflectance at 600 nm; Remote sensing reflectance at 602.5 nm; Remote sensing reflectance at 605 nm; Remote sensing reflectance at 607.5 nm; Remote sensing reflectance at 610 nm; Remote sensing reflectance at 612.5 nm; Remote sensing reflectance at 615 nm; Remote sensing reflectance at 617.5 nm; Remote sensing reflectance at 620 nm; Remote sensing reflectance at 622.5 nm; Remote sensing reflectance at 625 nm; Remote sensing reflectance at 627.5 nm; Remote sensing reflectance at 630 nm; Remote sensing reflectance at 632.5 nm; Remote sensing reflectance at 635 nm; Remote sensing reflectance at 637.5 nm; Remote sensing reflectance at 640 nm; Remote sensing reflectance at 642.5 nm; Remote sensing reflectance at 645 nm; Remote sensing reflectance at 647.5 nm; Remote sensing reflectance at 650 nm; Remote sensing reflectance at 652.5 nm; Remote sensing reflectance at 655 nm; Remote sensing reflectance at 657.5 nm; Remote sensing reflectance at 660 nm; Remote sensing reflectance at 662.5 nm; Remote sensing reflectance at 665 nm; Remote sensing reflectance at 667.5 nm; Remote sensing reflectance at 670 nm; Remote sensing reflectance at 672.5 nm; Remote sensing reflectance at 675 nm; Remote sensing reflectance at 677.5 nm; Remote sensing reflectance at 680 nm; Remote sensing reflectance at 682.5 nm; Remote sensing reflectance at 685 nm; Remote sensing reflectance at 687.5 nm; Remote sensing reflectance at 690 nm; Remote sensing reflectance at 692.5 nm; Remote sensing reflectance at 695 nm; Remote sensing reflectance at 697.5 nm; Remote sensing reflectance at 700 nm; Remote sensing reflectance at 702.5 nm; Remote sensing reflectance at 705 nm; Remote sensing reflectance at 707.5 nm; Remote sensing reflectance at 710 nm; Remote sensing reflectance at 712.5 nm; Remote sensing reflectance at 715 nm; Remote sensing reflectance at 717.5 nm; Remote sensing reflectance at 720 nm; Remote sensing reflectance at 722.5 nm; Remote sensing reflectance at 725 nm; Remote sensing reflectance at 727.5 nm; Remote sensing reflectance at 730 nm; Remote sensing reflectance at 732.5 nm; Remote sensing reflectance at 735 nm; Remote sensing reflectance at 737.5 nm; Remote sensing reflectance at 740 nm; Remote sensing reflectance at 742.5 nm; Remote sensing reflectance at 745 nm; Remote sensing reflectance at 747.5 nm; Remote sensing reflectance at 750 nm; Remote sensing reflectance at 752.5 nm; Remote sensing reflectance at 755 nm; Remote sensing reflectance at 757.5 nm; Remote sensing reflectance at 760 nm; Remote sensing reflectance at 762.5 nm; Remote sensing reflectance at 765 nm; Remote sensing reflectance at 767.5 nm; Remote sensing reflectance at 770 nm; Remote sensing reflectance at 772.5 nm; Remote sensing reflectance at 775 nm; Remote sensing reflectance at 777.5 nm; Remote sensing reflectance at 780 nm; Remote sensing reflectance at 782.5 nm; Remote sensing reflectance at 785 nm; Remote sensing reflectance at 787.5 nm; Remote sensing reflectance at 790 nm; Remote sensing reflectance at 792.5 nm; Remote sensing reflectance at 795 nm; Remote sensing reflectance at 797.5 nm; Remote sensing reflectance at 800 nm; Remote sensing reflectance at 802.5 nm; Remote sensing reflectance at 805 nm; Remote sensing reflectance at 807.5 nm; Remote sensing reflectance at 810 nm; Remote sensing reflectance at 812.5 nm; Remote sensing reflectance at 815 nm; Remote sensing reflectance at 817.5 nm; Remote sensing reflectance at 820 nm; Remote sensing reflectance at 822.5 nm; Remote sensing reflectance at 825 nm; Remote sensing reflectance at 827.5 nm; Remote sensing reflectance at 830 nm; Remote sensing reflectance at 832.5 nm; Remote sensing reflectance at 835 nm; Remote sensing reflectance at 837.5 nm; Remote sensing reflectance at 840 nm; Remote sensing reflectance at 842.5 nm; Remote sensing reflectance at 845 nm; Remote sensing reflectance at 847.5 nm; Remote sensing reflectance at 850 nm; Remote sensing reflectance at 852.5 nm; Remote sensing reflectance at 855 nm; Remote sensing reflectance at 857.5 nm; Remote sensing reflectance at 860 nm; Remote sensing reflectance at 862.5 nm; Remote sensing reflectance at 865 nm; Remote sensing reflectance at 867.5 nm; Remote sensing reflectance at 870 nm; Remote sensing reflectance at 872.5 nm; Remote sensing reflectance at 875 nm; Remote sensing reflectance at 877.5 nm; Remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 214 data points
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  • 98
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    Spectral irradiance at lake water level from Lake Süßer See (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
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    Publication Date: 2023-07-03
    Keywords: Angle; DATE/TIME; Event label; Flag; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Sensor height above water level; Spectral irradiance, downward at 1000.518 nm; Spectral irradiance, downward at 1001.304 nm; Spectral irradiance, downward at 1002.091 nm; Spectral irradiance, downward at 1002.877 nm; Spectral irradiance, downward at 1003.664 nm; Spectral irradiance, downward at 1004.45 nm; Spectral irradiance, downward at 1005.236 nm; Spectral irradiance, downward at 1006.023 nm; Spectral irradiance, downward at 1006.809 nm; Spectral irradiance, downward at 1007.595 nm; Spectral irradiance, downward at 1008.381 nm; Spectral irradiance, downward at 1009.167 nm; Spectral irradiance, downward at 1009.953 nm; Spectral irradiance, downward at 1010.738 nm; Spectral irradiance, downward at 1011.524 nm; Spectral irradiance, downward at 1012.31 nm; Spectral irradiance, downward at 1013.095 nm; Spectral irradiance, downward at 1013.881 nm; Spectral irradiance, downward at 1014.666 nm; Spectral irradiance, downward at 1015.451 nm; Spectral irradiance, downward at 1016.237 nm; Spectral irradiance, downward at 1017.022 nm; Spectral irradiance, downward at 1017.807 nm; Spectral irradiance, downward at 1018.592 nm; Spectral irradiance, downward at 1019.377 nm; Spectral irradiance, downward at 1020.162 nm; Spectral irradiance, downward at 353.585 nm; Spectral irradiance, downward at 354.408 nm; Spectral irradiance, downward at 355.231 nm; Spectral irradiance, downward at 356.055 nm; Spectral irradiance, downward at 356.878 nm; Spectral irradiance, downward at 357.702 nm; Spectral irradiance, downward at 358.525 nm; Spectral irradiance, downward at 359.348 nm; Spectral irradiance, downward at 360.172 nm; Spectral irradiance, downward at 360.995 nm; Spectral irradiance, downward at 361.818 nm; Spectral irradiance, downward at 362.642 nm; Spectral irradiance, downward at 363.465 nm; Spectral irradiance, downward at 364.288 nm; Spectral irradiance, downward at 365.111 nm; Spectral irradiance, downward at 365.935 nm; Spectral irradiance, downward at 366.758 nm; Spectral irradiance, downward at 367.581 nm; Spectral irradiance, downward at 368.405 nm; Spectral irradiance, downward at 369.228 nm; Spectral irradiance, downward at 370.051 nm; Spectral irradiance, downward at 370.874 nm; Spectral irradiance, downward at 371.698 nm; Spectral irradiance, downward at 372.521 nm; Spectral irradiance, downward at 373.344 nm; Spectral irradiance, downward at 374.167 nm; Spectral irradiance, downward at 374.991 nm; Spectral irradiance, downward at 375.814 nm; Spectral irradiance, downward at 376.637 nm; Spectral irradiance, downward at 377.46 nm; Spectral irradiance, downward at 378.283 nm; Spectral irradiance, downward at 379.106 nm; Spectral irradiance, downward at 379.93 nm; Spectral irradiance, downward at 380.753 nm; Spectral irradiance, downward at 381.576 nm; Spectral irradiance, downward at 382.399 nm; Spectral irradiance, downward at 383.222 nm; Spectral irradiance, downward at 384.045 nm; Spectral irradiance, downward at 384.868 nm; Spectral irradiance, downward at 385.692 nm; Spectral irradiance, downward at 386.515 nm; Spectral irradiance, downward at 387.338 nm; Spectral irradiance, downward at 388.161 nm; Spectral irradiance, downward at 388.984 nm; Spectral irradiance, downward at 389.807 nm; Spectral irradiance, downward at 390.63 nm; Spectral irradiance, downward at 391.453 nm; Spectral irradiance, downward at 392.276 nm; Spectral irradiance, downward at 393.099 nm; Spectral irradiance, downward at 393.922 nm; Spectral irradiance, downward at 394.745 nm; Spectral irradiance, downward at 395.568 nm; Spectral irradiance, downward at 396.391 nm; Spectral irradiance, downward at 397.214 nm; Spectral irradiance, downward at 398.037 nm; Spectral irradiance, downward at 398.86 nm; Spectral irradiance, downward at 399.683 nm; Spectral irradiance, downward at 400.506 nm; Spectral irradiance, downward at 401.329 nm; Spectral irradiance, downward at 402.152 nm; Spectral irradiance, downward at 402.974 nm; Spectral irradiance, downward at 403.797 nm; Spectral irradiance, downward at 404.62 nm; Spectral irradiance, downward at 405.443 nm; Spectral irradiance, downward at 406.266 nm; Spectral irradiance, downward at 407.089 nm; Spectral irradiance, downward at 407.911 nm; Spectral irradiance, downward at 408.734 nm; Spectral irradiance, downward at 409.557 nm; Spectral irradiance, downward at 410.38 nm; Spectral irradiance, downward at 411.203 nm; Spectral irradiance, downward at 412.025 nm; Spectral irradiance, downward at 412.848 nm; Spectral irradiance, downward at 413.671 nm; Spectral irradiance, downward at 414.493 nm; Spectral irradiance, downward at 415.316 nm; Spectral irradiance, downward at 416.139 nm; Spectral irradiance, downward at 416.961 nm; Spectral irradiance, downward at 417.784 nm; Spectral irradiance, downward at 418.607 nm; Spectral irradiance, downward at 419.429 nm; Spectral irradiance, downward at 420.252 nm; Spectral irradiance, downward at 421.074 nm; Spectral irradiance, downward at 421.897 nm; Spectral irradiance, downward at 422.72 nm; Spectral irradiance, downward at 423.542 nm; Spectral irradiance, downward at 424.365 nm; Spectral irradiance, downward at 425.187 nm; Spectral irradiance, downward at 426.01 nm; Spectral irradiance, downward at 426.832 nm; Spectral irradiance, downward at 427.655 nm; Spectral irradiance, downward at 428.477 nm; Spectral irradiance, downward at 429.299 nm; Spectral irradiance, downward at 430.122 nm; Spectral irradiance, downward at 430.944 nm; Spectral irradiance, downward at 431.767 nm; Spectral irradiance, downward at 432.589 nm; Spectral irradiance, downward at 433.411 nm; Spectral irradiance, downward at 434.234 nm; Spectral irradiance, downward at 435.056 nm; Spectral irradiance, downward at 435.878 nm; Spectral irradiance, downward at 436.7 nm; Spectral irradiance, downward at 437.523 nm; Spectral irradiance, downward at 438.345 nm; Spectral irradiance, downward at 439.167 nm; Spectral irradiance, downward at 439.989 nm; Spectral irradiance, downward at 440.811 nm; Spectral irradiance, downward at 441.634 nm; Spectral irradiance, downward at 442.456 nm; Spectral irradiance, downward at 443.278 nm; Spectral irradiance, downward at 444.1 nm; Spectral irradiance, downward at 444.922 nm; Spectral irradiance, downward at 445.744 nm; Spectral irradiance, downward at 446.566 nm; Spectral irradiance, downward at 447.388 nm; Spectral irradiance, downward at 448.21 nm; Spectral irradiance, downward at 449.032 nm; Spectral irradiance, downward at 449.854 nm; Spectral irradiance, downward at 450.676 nm; Spectral irradiance, downward at 451.498 nm; Spectral irradiance, downward at 452.32 nm; Spectral irradiance, downward at 453.142 nm; Spectral irradiance, downward at 453.964 nm; Spectral irradiance, downward at 454.786 nm; Spectral irradiance, downward at 455.608 nm; Spectral irradiance, downward at 456.429 nm; Spectral irradiance, downward at 457.251 nm; Spectral irradiance, downward at 458.073 nm; Spectral irradiance, downward at 458.895 nm; Spectral irradiance, downward at 459.716 nm; Spectral irradiance, downward at 460.538 nm; Spectral irradiance, downward at 461.36 nm; Spectral irradiance, downward at 462.181 nm; Spectral irradiance, downward at 463.003 nm; Spectral irradiance, downward at 463.825 nm; Spectral irradiance, downward at 464.646 nm; Spectral irradiance, downward at 465.468 nm; Spectral irradiance, downward at 466.289 nm; Spectral irradiance, downward at 467.111 nm; Spectral irradiance, downward at 467.933 nm; Spectral irradiance, downward at 468.754 nm; Spectral irradiance, downward at 469.576 nm; Spectral irradiance, downward at 470.397 nm; Spectral irradiance, downward at 471.218 nm; Spectral irradiance, downward at 472.04 nm; Spectral irradiance, downward at 472.861 nm; Spectral irradiance, downward at 473.683 nm; Spectral irradiance, downward at 474.
    Type: Dataset
    Format: text/tab-separated-values, 110390 data points
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  • 99
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    Spectral irradiance above and at lake water level from the Kelbra Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
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    Publication Date: 2023-07-03
    Keywords: Angle; Date/time end; Date/time start; Event label; Flag; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Kelbra-0857_RAMSES1; Kelbra-0907_RAMSES1; Kelbra-0947_RAMSES1; Kelbra-0954_RAMSES1; Kelbra-1020_RAMSES1; Kelbra-1040_RAMSES1; Kelbra-1100_RAMSES1; Kelbra-1142_RAMSES1; Principal investigator; Sensor height above water level; Spectral irradiance, downward at 380 nm; Spectral irradiance, downward at 380 nm, standard deviation; Spectral irradiance, downward at 382.5 nm; Spectral irradiance, downward at 382.5 nm, standard deviation; Spectral irradiance, downward at 385 nm; Spectral irradiance, downward at 385 nm, standard deviation; Spectral irradiance, downward at 387.5 nm; Spectral irradiance, downward at 387.5 nm, standard deviation; Spectral irradiance, downward at 390 nm; Spectral irradiance, downward at 390 nm, standard deviation; Spectral irradiance, downward at 392.5 nm; Spectral irradiance, downward at 392.5 nm, standard deviation; Spectral irradiance, downward at 395 nm; Spectral irradiance, downward at 395 nm, standard deviation; Spectral irradiance, downward at 397.5 nm; Spectral irradiance, downward at 397.5 nm, standard deviation; Spectral irradiance, downward at 400 nm; Spectral irradiance, downward at 400 nm, standard deviation; Spectral irradiance, downward at 402.5 nm; Spectral irradiance, downward at 402.5 nm, standard deviation; Spectral irradiance, downward at 405 nm; Spectral irradiance, downward at 405 nm, standard deviation; Spectral irradiance, downward at 407.5 nm; Spectral irradiance, downward at 407.5 nm, standard deviation; Spectral irradiance, downward at 410 nm; Spectral irradiance, downward at 410 nm, standard deviation; Spectral irradiance, downward at 412.5 nm; Spectral irradiance, downward at 412.5 nm, standard deviation; Spectral irradiance, downward at 415 nm; Spectral irradiance, downward at 415 nm, standard deviation; Spectral irradiance, downward at 417.5 nm; Spectral irradiance, downward at 417.5 nm, standard deviation; Spectral irradiance, downward at 420 nm; Spectral irradiance, downward at 420 nm, standard deviation; Spectral irradiance, downward at 422.5 nm; Spectral irradiance, downward at 422.5 nm, standard deviation; Spectral irradiance, downward at 425 nm; Spectral irradiance, downward at 425 nm, standard deviation; Spectral irradiance, downward at 427.5 nm; Spectral irradiance, downward at 427.5 nm, standard deviation; Spectral irradiance, downward at 430 nm; Spectral irradiance, downward at 430 nm, standard deviation; Spectral irradiance, downward at 432.5 nm; Spectral irradiance, downward at 432.5 nm, standard deviation; Spectral irradiance, downward at 435 nm; Spectral irradiance, downward at 435 nm, standard deviation; Spectral irradiance, downward at 437.5 nm; Spectral irradiance, downward at 437.5 nm, standard deviation; Spectral irradiance, downward at 440 nm; Spectral irradiance, downward at 440 nm, standard deviation; Spectral irradiance, downward at 442.5 nm; Spectral irradiance, downward at 442.5 nm, standard deviation; Spectral irradiance, downward at 445 nm; Spectral irradiance, downward at 445 nm, standard deviation; Spectral irradiance, downward at 447.5 nm; Spectral irradiance, downward at 447.5 nm, standard deviation; Spectral irradiance, downward at 450 nm; Spectral irradiance, downward at 450 nm, standard deviation; Spectral irradiance, downward at 452.5 nm; Spectral irradiance, downward at 452.5 nm, standard deviation; Spectral irradiance, downward at 455 nm; Spectral irradiance, downward at 455 nm, standard deviation; Spectral irradiance, downward at 457.5 nm; Spectral irradiance, downward at 457.5 nm, standard deviation; Spectral irradiance, downward at 460 nm; Spectral irradiance, downward at 460 nm, standard deviation; Spectral irradiance, downward at 462.5 nm; Spectral irradiance, downward at 462.5 nm, standard deviation; Spectral irradiance, downward at 465 nm; Spectral irradiance, downward at 465 nm, standard deviation; Spectral irradiance, downward at 467.5 nm; Spectral irradiance, downward at 467.5 nm, standard deviation; Spectral irradiance, downward at 470 nm; Spectral irradiance, downward at 470 nm, standard deviation; Spectral irradiance, downward at 472.5 nm; Spectral irradiance, downward at 472.5 nm, standard deviation; Spectral irradiance, downward at 475 nm; Spectral irradiance, downward at 475 nm, standard deviation; Spectral irradiance, downward at 477.5 nm; Spectral irradiance, downward at 477.5 nm, standard deviation; Spectral irradiance, downward at 480 nm; Spectral irradiance, downward at 480 nm, standard deviation; Spectral irradiance, downward at 482.5 nm; Spectral irradiance, downward at 482.5 nm, standard deviation; Spectral irradiance, downward at 485 nm; Spectral irradiance, downward at 485 nm, standard deviation; Spectral irradiance, downward at 487.5 nm; Spectral irradiance, downward at 487.5 nm, standard deviation; Spectral irradiance, downward at 490 nm; Spectral irradiance, downward at 490 nm, standard deviation; Spectral irradiance, downward at 492.5 nm; Spectral irradiance, downward at 492.5 nm, standard deviation; Spectral irradiance, downward at 495 nm; Spectral irradiance, downward at 495 nm, standard deviation; Spectral irradiance, downward at 497.5 nm; Spectral irradiance, downward at 497.5 nm, standard deviation; Spectral irradiance, downward at 500 nm; Spectral irradiance, downward at 500 nm, standard deviation; Spectral irradiance, downward at 502.5 nm; Spectral irradiance, downward at 502.5 nm, standard deviation; Spectral irradiance, downward at 505 nm; Spectral irradiance, downward at 505 nm, standard deviation; Spectral irradiance, downward at 507.5 nm; Spectral irradiance, downward at 507.5 nm, standard deviation; Spectral irradiance, downward at 510 nm; Spectral irradiance, downward at 510 nm, standard deviation; Spectral irradiance, downward at 512.5 nm; Spectral irradiance, downward at 512.5 nm, standard deviation; Spectral irradiance, downward at 515 nm; Spectral irradiance, downward at 515 nm, standard deviation; Spectral irradiance, downward at 517.5 nm; Spectral irradiance, downward at 517.5 nm, standard deviation; Spectral irradiance, downward at 520 nm; Spectral irradiance, downward at 520 nm, standard deviation; Spectral irradiance, downward at 522.5 nm; Spectral irradiance, downward at 522.5 nm, standard deviation; Spectral irradiance, downward at 525 nm; Spectral irradiance, downward at 525 nm, standard deviation; Spectral irradiance, downward at 527.5 nm; Spectral irradiance, downward at 527.5 nm, standard deviation; Spectral irradiance, downward at 530 nm; Spectral irradiance, downward at 530 nm, standard deviation; Spectral irradiance, downward at 532.5 nm; Spectral irradiance, downward at 532.5 nm, standard deviation; Spectral irradiance, downward at 535 nm; Spectral irradiance, downward at 535 nm, standard deviation; Spectral irradiance, downward at 537.5 nm; Spectral irradiance, downward at 537.5 nm, standard deviation; Spectral irradiance, downward at 540 nm; Spectral irradiance, downward at 540 nm, standard deviation; Spectral irradiance, downward at 542.5 nm; Spectral irradiance, downward at 542.5 nm, standard deviation; Spectral irradiance, downward at 545 nm; Spectral irradiance, downward at 545 nm, standard deviation; Spectral irradiance, downward at 547.5 nm; Spectral irradiance, downward at 547.5 nm, standard deviation; Spectral irradiance, downward at 550 nm; Spectral irradiance, downward at 550 nm, standard deviation; Spectral irradiance, downward at 552.5 nm; Spectral irradiance, downward at 552.5 nm, standard deviation; Spectral irradiance, downward at 555 nm; Spectral irradiance, downward at 555 nm, standard deviation; Spectral irradiance, downward at 557.5 nm; Spectral irradiance, downward at 557.5 nm, standard deviation; Spectral irradiance, downward at 560 nm; Spectral irradiance, downward at 560 nm,
    Type: Dataset
    Format: text/tab-separated-values, 21300 data points
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  • 100
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    Spectral irradiance above and at lake water level from the Rappbode Reservoir (Germany) during the Inland Water Remote Sensing Validation Campaign 2017 (2023)
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    Publication Date: 2023-07-03
    Description: Radiometric measurement with Trios Ramses system. All Trios measurements averaged over reliable common times. The viewing zenith angle was approx. 45°. The surface reflectance factors rho are only given for 40 or 50°. If rho is to high, there is an overcorrection of Lw and Rrs. For this three methods for correction are applied: constant factor rho, Mobley 1999 and Mobley 2015 (Mobley 1999 related to 50°, Mobley 2015 related to 40°).
    Keywords: Angle; Date/time end; Date/time start; Event label; Flag; Germany; Hyperspectral radiometer, TriOS Mess- und Datentechnik GmbH, RAMSES; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; Rappbode-YT1-0800_RAMSES2; Rappbode-YT1-0800B_RAMSES2; Rappbode-YT6-1114_RAMSES2; Rappbode-YTMETE-1045_RAMSES2; Sensor height above water level; Spectral irradiance, downward at 380 nm; Spectral irradiance, downward at 380 nm, standard deviation; Spectral irradiance, downward at 382.5 nm; Spectral irradiance, downward at 382.5 nm, standard deviation; Spectral irradiance, downward at 385 nm; Spectral irradiance, downward at 385 nm, standard deviation; Spectral irradiance, downward at 387.5 nm; Spectral irradiance, downward at 387.5 nm, standard deviation; Spectral irradiance, downward at 390 nm; Spectral irradiance, downward at 390 nm, standard deviation; Spectral irradiance, downward at 392.5 nm; Spectral irradiance, downward at 392.5 nm, standard deviation; Spectral irradiance, downward at 395 nm; Spectral irradiance, downward at 395 nm, standard deviation; Spectral irradiance, downward at 397.5 nm; Spectral irradiance, downward at 397.5 nm, standard deviation; Spectral irradiance, downward at 400 nm; Spectral irradiance, downward at 400 nm, standard deviation; Spectral irradiance, downward at 402.5 nm; Spectral irradiance, downward at 402.5 nm, standard deviation; Spectral irradiance, downward at 405 nm; Spectral irradiance, downward at 405 nm, standard deviation; Spectral irradiance, downward at 407.5 nm; Spectral irradiance, downward at 407.5 nm, standard deviation; Spectral irradiance, downward at 410 nm; Spectral irradiance, downward at 410 nm, standard deviation; Spectral irradiance, downward at 412.5 nm; Spectral irradiance, downward at 412.5 nm, standard deviation; Spectral irradiance, downward at 415 nm; Spectral irradiance, downward at 415 nm, standard deviation; Spectral irradiance, downward at 417.5 nm; Spectral irradiance, downward at 417.5 nm, standard deviation; Spectral irradiance, downward at 420 nm; Spectral irradiance, downward at 420 nm, standard deviation; Spectral irradiance, downward at 422.5 nm; Spectral irradiance, downward at 422.5 nm, standard deviation; Spectral irradiance, downward at 425 nm; Spectral irradiance, downward at 425 nm, standard deviation; Spectral irradiance, downward at 427.5 nm; Spectral irradiance, downward at 427.5 nm, standard deviation; Spectral irradiance, downward at 430 nm; Spectral irradiance, downward at 430 nm, standard deviation; Spectral irradiance, downward at 432.5 nm; Spectral irradiance, downward at 432.5 nm, standard deviation; Spectral irradiance, downward at 435 nm; Spectral irradiance, downward at 435 nm, standard deviation; Spectral irradiance, downward at 437.5 nm; Spectral irradiance, downward at 437.5 nm, standard deviation; Spectral irradiance, downward at 440 nm; Spectral irradiance, downward at 440 nm, standard deviation; Spectral irradiance, downward at 442.5 nm; Spectral irradiance, downward at 442.5 nm, standard deviation; Spectral irradiance, downward at 445 nm; Spectral irradiance, downward at 445 nm, standard deviation; Spectral irradiance, downward at 447.5 nm; Spectral irradiance, downward at 447.5 nm, standard deviation; Spectral irradiance, downward at 450 nm; Spectral irradiance, downward at 450 nm, standard deviation; Spectral irradiance, downward at 452.5 nm; Spectral irradiance, downward at 452.5 nm, standard deviation; Spectral irradiance, downward at 455 nm; Spectral irradiance, downward at 455 nm, standard deviation; Spectral irradiance, downward at 457.5 nm; Spectral irradiance, downward at 457.5 nm, standard deviation; Spectral irradiance, downward at 460 nm; Spectral irradiance, downward at 460 nm, standard deviation; Spectral irradiance, downward at 462.5 nm; Spectral irradiance, downward at 462.5 nm, standard deviation; Spectral irradiance, downward at 465 nm; Spectral irradiance, downward at 465 nm, standard deviation; Spectral irradiance, downward at 467.5 nm; Spectral irradiance, downward at 467.5 nm, standard deviation; Spectral irradiance, downward at 470 nm; Spectral irradiance, downward at 470 nm, standard deviation; Spectral irradiance, downward at 472.5 nm; Spectral irradiance, downward at 472.5 nm, standard deviation; Spectral irradiance, downward at 475 nm; Spectral irradiance, downward at 475 nm, standard deviation; Spectral irradiance, downward at 477.5 nm; Spectral irradiance, downward at 477.5 nm, standard deviation; Spectral irradiance, downward at 480 nm; Spectral irradiance, downward at 480 nm, standard deviation; Spectral irradiance, downward at 482.5 nm; Spectral irradiance, downward at 482.5 nm, standard deviation; Spectral irradiance, downward at 485 nm; Spectral irradiance, downward at 485 nm, standard deviation; Spectral irradiance, downward at 487.5 nm; Spectral irradiance, downward at 487.5 nm, standard deviation; Spectral irradiance, downward at 490 nm; Spectral irradiance, downward at 490 nm, standard deviation; Spectral irradiance, downward at 492.5 nm; Spectral irradiance, downward at 492.5 nm, standard deviation; Spectral irradiance, downward at 495 nm; Spectral irradiance, downward at 495 nm, standard deviation; Spectral irradiance, downward at 497.5 nm; Spectral irradiance, downward at 497.5 nm, standard deviation; Spectral irradiance, downward at 500 nm; Spectral irradiance, downward at 500 nm, standard deviation; Spectral irradiance, downward at 502.5 nm; Spectral irradiance, downward at 502.5 nm, standard deviation; Spectral irradiance, downward at 505 nm; Spectral irradiance, downward at 505 nm, standard deviation; Spectral irradiance, downward at 507.5 nm; Spectral irradiance, downward at 507.5 nm, standard deviation; Spectral irradiance, downward at 510 nm; Spectral irradiance, downward at 510 nm, standard deviation; Spectral irradiance, downward at 512.5 nm; Spectral irradiance, downward at 512.5 nm, standard deviation; Spectral irradiance, downward at 515 nm; Spectral irradiance, downward at 515 nm, standard deviation; Spectral irradiance, downward at 517.5 nm; Spectral irradiance, downward at 517.5 nm, standard deviation; Spectral irradiance, downward at 520 nm; Spectral irradiance, downward at 520 nm, standard deviation; Spectral irradiance, downward at 522.5 nm; Spectral irradiance, downward at 522.5 nm, standard deviation; Spectral irradiance, downward at 525 nm; Spectral irradiance, downward at 525 nm, standard deviation; Spectral irradiance, downward at 527.5 nm; Spectral irradiance, downward at 527.5 nm, standard deviation; Spectral irradiance, downward at 530 nm; Spectral irradiance, downward at 530 nm, standard deviation; Spectral irradiance, downward at 532.5 nm; Spectral irradiance, downward at 532.5 nm, standard deviation; Spectral irradiance, downward at 535 nm; Spectral irradiance, downward at 535 nm, standard deviation; Spectral irradiance, downward at 537.5 nm; Spectral irradiance, downward at 537.5 nm, standard deviation; Spectral irradiance, downward at 540 nm; Spectral irradiance, downward at 540 nm, standard deviation; Spectral irradiance, downward at 542.5 nm; Spectral irradiance, downward at 542.5 nm, standard deviation; Spectral irradiance, downward at 545 nm; Spectral irradiance, downward at 545 nm, standard deviation; Spectral irradiance, downward at 547.5 nm; Spectral irradiance, downward at 547.5 nm, standard deviation; Spectral irradiance, downward at 550 nm; Spectral irradiance, downward at 550 nm, standard deviation; Spectral irradiance, downward at 552.5 nm; Spectral irradiance, downward at 552.5 nm, standard deviation; Spectral irradiance, downward at 555 nm; Spectral irradiance, downward at 555 nm, standard deviation; Spectral irradiance, downward at 557.5 nm; Spectral irradiance, downward at 557.5 nm, standard deviation; Spectral irradiance, downward at 560 nm; Spectral irradiance, downward at 560 nm, standard deviation; Spectral irradiance, downward at 562.
    Type: Dataset
    Format: text/tab-separated-values, 1282 data points
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