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  • 1
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    Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
    In:  EPIC3Expeditionsprogramm Polarstern, Bremerhaven, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 20 p., pp. 1-20
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Expedition program , notRev
    Format: application/pdf
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  • 2
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    Alfred-Wegener-Institute for Polar- and Marine Research
    In:  EPIC3Expeditionsprogramm Polarstern, Bremerhaven, Germany, Alfred-Wegener-Institute for Polar- and Marine Research, 45 p.
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Expedition program , notRev
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  • 3
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    Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
    In:  EPIC3Expeditionsprogramm Polarstern, Bremerhaven, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 40 p., pp. 1-40
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Expedition program , notRev
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  • 4
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    In:  Natural Hazards
    Publication Date: 2024-04-23
    Type: info:eu-repo/semantics/article
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  • 5
    Publication Date: 2024-04-23
    Description: DE: Dieser Datensatz besteht aus den Transkripten von vier Fokusgruppeninterviews der Migrantenwahlstudie. Ziel des Projektes war es, für die Bundestagswahl 2017 die erste deutsche Wahlstudie unter deutschen Staatsbürger/innen mit Migrationshintergrund durchzuführen, d.h. unter solchen Personen, die entweder selbst nach Deutschland immigriert sind oder die mindestens einen Elternteil mit eigener Migrationserfahrung haben. Die Migrantenwahlstudie umfasst eine qualitative und eine quantitative Phase. Ziel der ersten qualitativen Phase (Oktober 2016 bis Juli 2017) war der explorative Zugang zur Themen- und Kandidatenorientierung von Migrant/innen, um die Ergebnisse für eine Publikation sowie die Fragebogenentwicklung der quantitativen Phase zu nutzen: Welche Themenfelder werden als wichtig erachtet? Welche Vorstellung von Links-Rechts gibt es? Welche Kandidateneigenschaften sind besonders relevant? Wie stark ist die Bindung an das Herkunftsland? Als Methode haben wir dabei auf Gruppendiskussionen mit Russlanddeutschen zurückgegriffen, die in Duisburg und Köln durchgeführt wurden. Dabei haben wir mit etwa 5-6 Teilnehmer/innen jeweils knapp zwei Stunden lang diskutiert. Die Forschungsdaten der quantitativen Phase wurden beim Forschungsdatenzentrum GESIS archiviert. EN: This dataset is composed of the transcripts of four focus group interviews for the Immigrant German Election Study. The project aims to conduct the first Immigrant German Election Study for the federal election in 2017, targeting German citizens with an immigrant background, i.e. people who either migrated to Germany themselves (first generation) or have at least one parent who was born in another country (second generation). The Immigrant German Election Study encompasses a qualitative and a quantitative phase. The first qualitative stage of the project (October 2016 until July 2017) explored the issue and candidate orientations of migrants. The results were used for a publication as well as for the development of the questionnaire for the quantitative stage. The core questions are: Which political issues are classified as important to all Germans/all migrants from the same group? What political issues do Germans of immigrant origin perceive as "left" and "right"? What are the identity contents that Germans of migrant origin associate with being German? We used focus group interviews as the research method in the Duisburg/Cologne area that consisted of 5-6 participants each and lasted for about 90 minutes.The research data originating from the quantitative phase have been archived at GESIS Data Archive.
    Keywords: Age, social sciences; Das Wahlverhalten von Deutschen mit Migrationshintergrund; Data collection date; Data collection location; Data ID; File name; Fokusgruppeninterview; General data format; Germany, North Rhine-Westphalia; IMGES; IMGESQ; Interview. Fokusgruppeninterview; Language; Number of participants
    Type: Dataset
    Format: text/tab-separated-values, 40 data points
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  • 6
    Publication Date: 2024-04-23
    Description: The file includes both field measured and satellite derived high resolution LAI data obtained over the Honghe farm and Hailun site in northeastern China. The Honghe farm (centered at 47°39′N, 133°31′E) is located in the east of the Heilongjiang province, northeast China. Five plots in 400 m × 600 m were selected in the Honghe farm in 2012 and 2013. Within each plot, about 50 - 60 elementary sampling units (ESUs) about 20 m ×20 m in size were selected in different weeks with a moving sampling strategy to avoid the sampling disturbance. Field LAI measurements were performed weekly from June 11 to September 17, 2012, and from June 22 to August 27, 2013. All ESU measurements made with LAI-2200 within a plot were averaged to represent the plot LAI. The Hailun site (47°24′- 47°26′N, 126°47′- 126°51′E) is located in the western part of the Heilongjiang province. The main crop types are maize, soybean, and sorghum. Five crop plots in 100 m × 500 m were chosen for continuous LAI measurements. The plots cover an areas of about 30 km2 and an elevation of approximately 200-240 m above sea level with quite homogeneous surroundings. Three representative ESUs of approximately 20 m × 20 m were selected in each plot. Field LAI measurements were continuously carried out with LAI-2200 weekly at each plot from June 20 to September 22, 2016. The high-resolution LAI data were estimated with a look-up table (LUT) method from the HJ-1, Landsat 7 ETM+, and Sentinel-2A MSI reflectance data. The high resolution LAI data are consistent with the field measured LAI characterized by a slope close to the 1:1 line. The statistical results show R2 of 0.81 and 0.86, and RMSE of 0.62 and 0.70 for paddy rice and broadleaf crops, respectively. The scale factor is 0.01.
    Keywords: Field measurements; File format; File name; File size; High resolution LAI; leaf area index (LAI); NE China; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 4 data points
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  • 7
    Publication Date: 2024-04-23
    Keywords: Area/locality; Event label; File format; File name; File size; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; SAT; Satellite remote sensing; Transect; Transect1_LSAT8A; Transect2_LSAT8A; Transect3_LSAT8A; Transect4_LSAT8A; Transect5_LSAT8A; Uniform resource locator/link to file; Unit
    Type: Dataset
    Format: text/tab-separated-values, 307 data points
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  • 8
    Publication Date: 2024-04-23
    Keywords: 162-986A; 162-986B; 162-986C; Absorbance ratio; Average chain length, n-Alkanes, C25-C33; Baffin Bay; Carbon Preference Index, n-Alkanes (C17-C23); Carbon Preference Index, n-Alkanes (C25-C31); Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; DRILL; Drilling/drill rig; Event label; GC; Gravity corer; Håkon Mosby; HM79; HM79-08; HM83; HM83-03; HM83-06; HU76-029-036; HU77-027-002; HU87-025-07P; HU88-024-02P; Iceland Sea; James Clark Ross; Joides Resolution; JR20000727; JR51; JR51GC-08; JR51GC-10; JR51GC-11; JR51GC-12; JR51GC-30; JR51GC-31; JR51GC-32; Labrador Sea; Leg162; Location; Long-chain n-alkanes, C27-C29, relative abundance; Long-chain n-alkanes, C29-31; Long-chain n-alkanes, C29-31, relative abundance; n-Alkanes, total; North Greenland Sea; Norwegian Sea; PC; Piston corer; Porphyrins; Sample ID; Sediment type; Subsample ID; Unresolved complex mixture; Unresolved complex mixture/total n-Alkanes ratio
    Type: Dataset
    Format: text/tab-separated-values, 1210 data points
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  • 9
    Publication Date: 2024-04-23
    Keywords: Area/locality; Event label; File format; File name; File size; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Principal investigator; SAT; Satellite remote sensing; Transect; Transect2_LSAT8B; Transect4_LSAT8B; Transect5_LSAT8B; Transect6_LSAT8B; Transect7_LSAT8B; Uniform resource locator/link to file; Unit
    Type: Dataset
    Format: text/tab-separated-values, 267 data points
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  • 10
    Publication Date: 2024-04-23
    Description: This dataset contains the n-alkane data from a sedimentary record of an alpine lake (Laguna de Río Seco -LdRS-) located at 3020 masl in Sierra Nevada (southern Spain). This dataset includes the concentrations of the n-alkanes ranging from the C19 to the C35 chain lengths and the hydrogen isotopic composition of the C23, C25, C27, C29, C31, and C33 ones. Different indices have also been calculated to summarise 1) the n-alkane chain lengths: the average chain length (ACL), the carbon preference index (CPI), and the portion aquatic index (Paq), and 2) the hydrogen isotopic composition of the terrestrial n-alkanes (δDwax=δDC29 + δDC31), aquatic n-alkanes (δDaq= δDC23 + δDC25), and their difference ΔDwax-aq(δDwax-δDaq). The n-alkanes were analysed using a GC-FID (Shimadzu 2010) and a GC-MS (Shimadzu QP2010-Plus Mass Spectrometer interfaced with a Shimadzu 2010 GC). Hydrogen isotopic composition of sedimentary n-alkanes was measured with GC-IRMS using a GC-thermal conversion-IRMS system consisting of a HP 6890 GC connected to a Finnigan MAT delta Plus XL mass spectrometer.
    Keywords: AGE; Aquatic portion; Average chain length, n-Alkanes, C25-C33; Calculated; Carbon Preference Index, n-Alkanes (C25-C33); Depth, corrected; DEPTH, sediment/rock; Gas chromatography - Flame Ionization Detection (GC-FID) and Gas chromatography - Mass spectrometry (GC-MS); Gas chromatography - Isotope ratio mass spectrometer (GC-IRMS); Laboratory code/label; Laguna_de_Rio_Seco; n-Alkane, total per unit sediment mass; n-Alkane, ΔδD ((C29,C31 δD)-(C23,C25 δD)); n-Alkane C19, per unit sediment mass; n-Alkane C20, per unit sediment mass; n-Alkane C21, per unit sediment mass; n-Alkane C22, per unit sediment mass; n-Alkane C23,C25, δD; n-Alkane C23, per unit sediment mass; n-Alkane C23, δD; n-Alkane C23, δD, standard deviation; n-Alkane C24, per unit sediment mass; n-Alkane C25, per unit sediment mass; n-Alkane C25, δD; n-Alkane C25, δD, standard deviation; n-Alkane C26, per unit sediment mass; n-Alkane C27, per unit sediment mass; n-Alkane C27, δD; n-Alkane C27, δD, standard deviation; n-Alkane C28, per unit sediment mass; n-Alkane C29,C31, δD; n-Alkane C29, per unit sediment mass; n-Alkane C29, δD; n-Alkane C29, δD, standard deviation; n-Alkane C30, per unit sediment mass; n-Alkane C31, per unit sediment mass; n-Alkane C31, δD; n-Alkane C31, δD, standard deviation; n-Alkane C32, per unit sediment mass; n-Alkane C33, per unit sediment mass; n-Alkane C33, δD; n-Alkane C33, δD, standard deviation; n-Alkane C34, per unit sediment mass; n-Alkane C35, per unit sediment mass; n-alkanes; Sediment sample; SES; Sierra Nevada, Spain; southern Iberian Peninsula; Western Mediterranean
    Type: Dataset
    Format: text/tab-separated-values, 2618 data points
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  • 11
    Publication Date: 2024-04-23
    Description: Lake sediment samples were taken in April 2013 from the ice by drilling through lake ice and recovering an undisturbed core using a HON-Kajak sediment corer. Samples were analysed for pigments (University of Nottingham), carbon isotopes and C/N ratios (BGS, Keyworth), lipid biomarkers (Newcastle University) and compound-specific carbon isotopes (CUG, Wuhan). The purpose of the analyses was to develop an environmental reconstruction of carbon cycling for an upland lake (named Disko 2) to encompass the Little Ice Age to recent warming climate periods. Analyses were completed as part of Mark A. Stevenson's PhD research while based at the University of Nottingham, UK (Stevenson, 2017, http://eprints.nottingham.ac.uk/46579). ²¹⁰Pb, ²²⁶Ra, ¹³⁷Cs and ²⁴¹Am concentrations were measured by direct gamma assay in the Environmental Radiometric Facility at University College London (Dr Handong Yang), using an ORTEC HPGe GWL series well-type coaxial low background intrinsic germanium detector. Radiometric dating techniques follow Appleby et al, 1986 (doi: 10.1007/BF00026640), Appleby et al, 1992 (doi:10.1016/0168-583X(92)95328-O) and Appleby, 2001 (doi:10.1007/0-306-47669-X_9) with core extrapolation and linear interpolation used to derive an age depth model to the base of the core. The pigment β-carotene was analysed on an Agilent 1200 series high-performance liquid chromatography (HPLC) using separation conditions outlined in McGowan et al., 2012 (doi:10.1111/j.1365-2427.2011.02689.x). Bulk δ¹³C and C~org~/N ratios were analysed on acidified samples using a Costech ECS4010 elemental analyser (EA) coupled to a VG Triple Trap and a VG Optima dual-inlet mass spectrometer. Key lipid biomarkers (n-alkanes, n-alkanoic acids (as fatty acid methyl esters (FAMEs), n-alkanols and sterols) were analysed using an Agilent 7890A GC coupled to a 5975C MS according to Pearson et al., 2007 (doi:10.1016/j.orggeochem.2007.02.007) and are expressed as ratios, relative to the total of each compound class. Specific ratios were also calculated for CPI 2 n-alkanes (Marzi et al., 1993; doi:10.1016/0146-6380(93)90016-5), terrestrial aquatic ratio (TAR) for n-alkanes (Bourbonniere and Meyers, 1996; doi:10.1007/s002540050074), index of waxy n-alkanes to total hydrocarbons (PWAX) (Zheng et al., 2007; doi:10.1016/j.orggeochem.2007.06.012) and carbon preference index (CPI) for n-alkanoic acids (Matsuda and Koyama, 1977) (doi:10.1016/0016-7037(77)90214-9). Compound-specific δ¹³C on C~28:0~ fatty acid methyl ester (FAME) was analysed using a Thermo Finnigan Trace GC coupled to a Thermo Finnigan Delta Plus XP isotope ratio mass spectrometer using a combustion interface (GC-C-IRMS) according to conditions in Huang et al. (2018; doi:10.1038/s41467-018-03804-w). Acknowledgements: Mark Stevenson gratefully acknowledges the receipt of a NERC/ESRC studentship (ES/J500100/1). We acknowledge grants IP-1393-1113 & IP-1516-1114 from the NERC Isotope Geosciences laboratory (NIGL) for the analysis of δ¹³C~org~ & C/N ratios on sediment, soil and plant samples. Lipid and water chemistry analyses were funded by the Freshwater Biological Association's 2015 Gilson Le Cren Memorial Award to Mark Stevenson. We thank Teresa Needham, Christopher Kendrick, Julie Swales, Ian Conway, Graham Morris, Bernard Bowler, Paul Donohoe, Qingwei Song and Jiantao Xue for technical support. We acknowledge the support of Handong Yang for radiometric dating. Financial support for fieldwork was awarded via the INTERACT transnational access scheme (grant agreement No 262693) under the European Community's Seventh Framework Programme and UK RI NERC grant NE/K000276/1. Logistical support is acknowledged from University of Copenhagen Arktisk Station including Ole Stecher, Kjeld Mølgaard and Erik Wille.
    Keywords: 24-Methylcholesta-5,22E-dien-3beta-ol/sum sterols; Accumulation rate, carbon, per year; Accumulation rate, dry mass; Accumulation rate per year; Age; Age, standard error; Americium-241; Americium-241, error; Arctic; beta-Carotene, per unit mass total organic carbon; Caesium-137; Caesium-137, error; Calculated; Calculated after Bourbonniere and Meyers, 1996: (C27+C29+C31)/(C15+C17+C19); Calculated after Matsuda and Koyama, 1977: 0.5*((C12+C14+C16)+(C22+C24+C26+C28+C30))+((C14+C16+C18)+(C24+C26+C28+C30+C32))/((C13+C15+C17)+(C23+C25+C27+C29 +C31)); Calculated after Zheng et al., 2007; Calculation according to Marzi et al. (1993); Carbon, organic, total; Carbon/Nitrogen ratio; carbon isotope analysis; Carbon Preference Index, n-Alkanoic acids; Carbon Preference Index 2, n-Alkanes ((C23+C25+C27 )+(C25+C27+C29))/2*(C24+C26+C28); Coupled gas chromatography/mass spectrometry (GC/MS) on an Agilent Technologies 7890A GC linked to 5795C MS triple axis mass detector, equipped with a HP DB5-MS column; CRS model (Constant Rate of Supply); D2-K1-2013; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Disko Bay, Greenland; Disko Island; Dry mass per area; Element analyser CHN (ECS4010, Costech) coupled to a VG Triple Trap and a VG Optima dual-inlet mass spectrometer (MS); Greenland; High performance liquid chromatography (HPLC), Agilent 1200; HONK; HON-Kajak sediment corer; Index of waxy n-alkanes to total hydrocarbons (C27+C29+C31)/(C23+C25+C29+C31); Lake sediment; Lead-210; Lead-210, error; Lead-210, supported; Lead-210, supported, error; Lead-210, unsupported; Lead-210, unsupported, cumulative; Lead-210, unsupported, cumulative, error; Lead-210, unsupported, error; n-alkane C27/sum n-alkanes; n-Alkanoic acid C28:0, δ13C; n-Alkanoic acid C30/sum n-Alkanoic acid ratio; n-Alkanol C16/sum n-Alkanols; n-Alkanol C24/sum n-Alkanols; Organic Geochemistry; Reverse Coaxial Radiation Detector, ORTEC, HPGe GWL; Sedimentation rate, error; Sedimentation rate per year; Terrigenous/aquatic ratio; Thermo Trace GC coupled to ThermoFinnigan DELTAplus XP (GC-C-IRMS); δ13C, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 1040 data points
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  • 12
    Publication Date: 2024-04-23
    Description: Water was collected for net primary production (NPP) incubations and total and size-fractionated chlorophyll a from multiple depths spanning the euphotic zone, using a CTD-rosette equipped with 24 10L Niskin bottles. For NPP, water from 6 depths spanning the euphotic zone (0.1% of surface irradiance) were incubated in situ during the 5 Lagrangian cycles sampled during SalpPOOP. NPP was assessed using carbon-14 (14C) assays, with 24-hour incubations that integrated respiration/production balance over the dark and light periods of the diel cycle. Seawater samples (1.3 L) were collected into an acid-rinsed polycarbonate bottle from pre-dawn CTD casts (~2:00 h each day of each cycle) at six depths spanning the euphotic zone. The bottles were then spiked with 0.1 mCi 14C-bicarbonate (DHI, Denmark or Perkin-Elmer, USA) before triplicate controls on ethanolamine were taken to quantify initial radioactivity at each depth incubation. After gentle mixing, the 'hot' 1.3 L was dispensed into three light and one dark bottles (320 mL acid-cleaned polycarbonate) that were incubated in situ on the free-drifting array. After recovery, the entire content of the bottles were filtered onto 0.2 µm pore-size 25-mm polycarbonate filters and kept frozen until analysis. Once on land, filters were acidified with 200 µL 0.5 N HCL, Hi Safe 3 liquid scintillation cocktail was added and disintegrations per minute were then determined using a scintillation counter following procedures described in Gutiérrez‐Rodríguez et al (2020 doi:10.1029/2019JC015550). NPP of multiple casts (2 to 4) conducted during each experimental cycle were averaged to obtain cycle estimates of primary production. If only one estimate per depth was available, the std is indicated as n.d. Samples for total Chlorophyll a (Chl a) analysis were filtered on‐board on 25mm Whatman GF/F filters using low vacuum (〈200 mm Hg). The filters were folded, wrapped in aluminum foil, flash frozen in liquid nitrogen and kept at −80 °C until analysis. For size fractionated Chl a analysis (0.2–2, 2–20, and 〉20 μm), 250 ml of seawater were sequentially filtered through a 20 μm polycarbonate filter first (by gravity), and then sequentially through 2‐ and 0.2‐μm polycarbonate filters under low pressure vacuum. Filters were folded and stored in 1.5 ml cryovials, flash frozen in liquid nitrogen, and stored at −80 °C. Analyses was done following 90% acetone extraction using standard fluorometric methods with a Turner Design 10AU fluorometer after Strickland and Parsons (1972 doi:10.1002/iroh.19700550118).
    Keywords: 14C incorporation; carbon export; Chatham Rise, east of New Zealand; Chlorophyll a; Chlorophyll a, size fraction 〉 20 µm; Chlorophyll a, size fraction 0.2 - 2 µm; Chlorophyll a, size fraction 2 - 20 µm; CTD; Date/Time local; Date/Time of event; DEPTH, water; Event label; Fluorometer, Turner Designs, 10-AU; Latitude of event; Longitude of event; Net primary production of carbon; Net primary production of carbon, standard deviation; Number; Salp Particle expOrt and Ocean Production; Salp Particle expOrt and Ocean Production (SalpPOOP); SalpPOOP; salps; Sample ID; TAN1810; TAN1810_015; TAN1810_024; TAN1810_039; TAN1810_051; TAN1810_069; TAN1810_090; TAN1810_1_015; TAN1810_1_024; TAN1810_1_039; TAN1810_1_051; TAN1810_1_069; TAN1810_1_090; TAN1810_137; TAN1810_150; TAN1810_176; TAN1810_188; TAN1810_193; TAN1810_2_137; TAN1810_2_150; TAN1810_2_176; TAN1810_2_188; TAN1810_207; TAN1810_223; TAN1810_266; TAN1810_283; TAN1810_298; TAN1810_3_193; TAN1810_3_207; TAN1810_3_223; TAN1810_324; TAN1810_339; TAN1810_353; TAN1810_4_266; TAN1810_4_283; TAN1810_4_298; TAN1810_5_324; TAN1810_5_339; TAN1810_5_353; Tangaroa
    Type: Dataset
    Format: text/tab-separated-values, 1263 data points
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  • 13
    Publication Date: 2024-04-23
    Description: Double oblique zooplankton net tows from 200 m water depth to the sea-surface were carried out using a 0.7 m diameter Bongo frame with paired 200 µm mesh nets, a General Oceanics Flow meter affixed to each net to measure the volume of water filtered, and a temperature-depth recorder. Tows were conducted at least twice daily (day and night), with one additional day per cycle of sampling every 2-3 hours for further studies of diel patterns. A quantitative subset of salp specimens were identified to species using keys (Foxton 1965, doi:10.1017/S0025315400016519; Bone 1998), classified into oozooid or blastozooid stage, and measured for total length and corrected to oral to atrial (OAL). For plots and calculations, Salpa thompsoni lengths were divided into 5 mm bins, and abundance was calculated for each size bin.
    Keywords: Biomass; BONGO; Bongo net; Chatham Rise, east of New Zealand; Cycle; Cycle description; Date/Time local; Date/Time of event; Date/Time of event 2; Day; DEPTH, water; Event label; Latitude of event; Longitude of event; Number; Salpa thompsoni, blastozooid, abundance; Salpa thompsoni, oozooid, abundance; Salp Particle expOrt and Ocean Production; Salp Particle expOrt and Ocean Production (SalpPOOP); SalpPOOP; salps; TAN1810; TAN1810_004; TAN1810_008; TAN1810_013; TAN1810_018; TAN1810_023; TAN1810_027; TAN1810_038; TAN1810_043; TAN1810_054; TAN1810_056; TAN1810_057; TAN1810_058; TAN1810_068; TAN1810_070; TAN1810_072; TAN1810_074; TAN1810_089; TAN1810_092; TAN1810_094; TAN1810_097; TAN1810_099; TAN1810_1_004; TAN1810_1_008; TAN1810_1_013; TAN1810_1_018; TAN1810_1_023; TAN1810_1_027; TAN1810_1_038; TAN1810_1_043; TAN1810_1_054; TAN1810_1_056; TAN1810_1_057; TAN1810_1_058; TAN1810_1_068; TAN1810_1_070; TAN1810_1_072; TAN1810_1_074; TAN1810_1_089; TAN1810_1_092; TAN1810_1_094; TAN1810_1_097; TAN1810_1_099; TAN1810_1_106; TAN1810_1_107; TAN1810_106; TAN1810_107; TAN1810_127; TAN1810_135; TAN1810_140; TAN1810_142; TAN1810_153; TAN1810_160; TAN1810_163; TAN1810_165; TAN1810_167; TAN1810_173; TAN1810_175; TAN1810_178; TAN1810_186; TAN1810_2_127; TAN1810_2_135; TAN1810_2_140; TAN1810_2_142; TAN1810_2_153; TAN1810_2_160; TAN1810_2_163; TAN1810_2_165; TAN1810_2_167; TAN1810_2_173; TAN1810_2_175; TAN1810_2_178; TAN1810_2_186; TAN1810_268; TAN1810_271; TAN1810_277; TAN1810_290; TAN1810_292; TAN1810_296; TAN1810_299; TAN1810_301; TAN1810_303; TAN1810_304; TAN1810_306; TAN1810_313; TAN1810_316; TAN1810_4_268; TAN1810_4_271; TAN1810_4_277; TAN1810_4_290; TAN1810_4_292; TAN1810_4_296; TAN1810_4_299; TAN1810_4_301; TAN1810_4_303; TAN1810_4_304; TAN1810_4_306; TAN1810_4_313; TAN1810_4_316; Tangaroa; VID; Visual identification; Water volume, filtered
    Type: Dataset
    Format: text/tab-separated-values, 2548 data points
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  • 14
    Publication Date: 2024-04-23
    Description: Here we present the data of the study by Strobel et al. (2020; doi:10.1016/j.scitotenv.2020.137045), who analysed topsoil samples from South Africa for their compound-specific hydrogen isotopic composition of leaf wax-derived n-alkanes (δ2Hn-alkane) and oxygen isotopic composition of hemicellulose-derived sugars (δ18Osugar). Apparent fractionation, which is the difference between δ2Hn-alkane and the isotopic signature of growing season precipitation δ2Hp (εapp 2H) and δ18Osugar and δ18Op (εapp 18O), was calculated. Coupling both δ2Hn-alkane and δ18O sugar using a 'paleohygrometer' approach enables the calculation of the plants-source water and relative humidity. For more details see Strobel et al. (2020; doi:10.1016/j.scitotenv.2020.137045).
    Keywords: Apparent fractionation; Apparent hydrogen isotope fractionation; Apparent hydrogen isotope fractionation, standard deviation; Arabinose, δ18O; Arabinose, δ18O standard deviation; Area/locality; Average chain length, odd; Biomarker; Carbon Preference Index; Compound-specific hydrogen isotopes; Compound-specific oxygen isotopes; DEPTH, soil; Elevation of event; Event label; Hemicellulose sugars; Humidity, relative; Latitude of event; leaf waxes; Longitude of event; Reconstructed; SOILS; Soil sample; South Africa; Sugar, δ18O; Sugar, δ18O, standard deviation; ZAT_1; ZAT_10; ZAT_14; ZAT_17; ZAT_2; ZAT_22; ZAT_24; ZAT_27; ZAT_28; ZAT_29; ZAT_3; ZAT_30; ZAT_31; ZAT_32; ZAT_34; ZAT_38; ZAT_39; ZAT_4; ZAT_40; ZAT_41; ZAT_42; ZAT_43; ZAT_44; ZAT_46; ZAT_47; ZAT_48; ZAT_49; ZAT_5; ZAT_50; ZAT_59; ZAT_6; ZAT_60; ZAT_63; ZAT_66; ZAT_67; ZAT_68; ZAT_69; ZAT_7; ZAT_70; ZAT_71; ZAT_72; ZAT_73; ZAT_74; ZAT_75; ZAT_76; ZAT_77; ZAT_78; ZAT_79; ZAT_8; ZAT_81; ZAT_82; ZAT_83; ZAT_85; ZAT_88; ZAT_89; ZAT_9; ZAT_94; ZAT_95; ZAT_98; δ18O, source water; δ Deuterium, n-alkanes; δ Deuterium, n-alkanes, standard deviation; δ Deuterium, source water
    Type: Dataset
    Format: text/tab-separated-values, 791 data points
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  • 15
    Publication Date: 2024-04-23
    Description: In this measurement campaign of five water bodies (lakes and reservoirs) several German research groups organised a joint effort to collect a data set for testing, evaluating, and potentially improving the abilities of satellite-based monitoring of water quality in standing waters. The strategy of the campaign is summarised in Figure 1 (documentation "Conceptual design of Inland Water Remote Sensing Validation Campaign 2017") and consists of three independently measured categories of data: (i) satellite-based monitoring, (ii) in situ monitoring, and (iii) bio-optical characterisation. The latter aspect, in particular, was intended in order to go beyond classical comparison of satellite-based and in-situ observations and to enable a more process-oriented and physically-based assessment of the observations made during the satellite overcasts. We concentrated our work on one week in summer 2017 and organised a synoptically measurement campaign on five lakes in Central Germany (Lake Arendsee, Lake Geiseltalsee, Kelbra Reservoir, Rappbode Reservoir, Lake Süßer See, see Tab. 1 in documentation "Main physical and limnological characteristics of the five water bodies from Inland Water Remote Sensing Validation Campaign 2017") based on various field and lab methods. The synoptically approach required the equipment of five sampling teams that are able to work independently from each other. Field- instruments used during the campaign (which required to be available in five sets) had been compared with each other in a separate intercalibration day. All lab-based measurements took place at the central lab of the Helmholtz-Centre for Environmental Research in Magdeburg using methods as outlined in Friese et al. (2014). The five water bodies were intentionally chosen because they reflect a broad range of temperate standing waters with respect to size, depth, trophic state, and the occurrence of cyanobacterial blooms. In addition, also natural and artificial water bodies are reflected by this set of lakes/reservoirs. To our knowledge, this is one of the rare multiple-teams efforts in remote sensing research on water quality making the collection of data in terms of their synoptic evaluation and broad methodological basis particularly useful and valuable.
    Keywords: IWRSVC-2017
    Type: Dataset
    Format: 9 datasets
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  • 16
    Publication Date: 2024-04-23
    Description: We analysed IODP Expedition 341 Site U1417 to understand the palaeoceanography in the Gulf of Alaska across the Pliocene and early Pleistocene (4-1.7 Ma). The data submitted here are productivity-related biomarkers (alkenone and brassicasterol accumulation rates), siliceous microfossils (total diatoms and silicoflagellate accumulation rates and diatom assemblages accumulation rates and relative abundance), biogenic silica accumulation rates, bulk carbon and nitrogen accumulation rates and stable isotope ratios (δ13C and δ15N), terrestrial and aquatic n-alkane accumulation rates, the Shannon-Weaver index and preservation value of diatoms (prev. value). The diatom assemblages include pelagic high productivity, pelagic warm water, coastal high productivity, coastal moderate productivity, benthic and freshwater habitats.
    Keywords: 341-U1417D; Abundance estimate; Accumulation rate, 24-Methylcholesta-5,22E-dien-3beta-ol; Accumulation rate, alkenones; Accumulation rate, diatoms; Accumulation rate, diatoms, benthic; Accumulation rate, diatoms, coastal high productivity; Accumulation rate, diatoms, coastal moderate productivity; Accumulation rate, diatoms, freshwater; Accumulation rate, diatoms, pelagic high productivity; Accumulation rate, diatoms, pelagic warm water; Accumulation rate, n-Alkanes, aquatic; Accumulation rate, n-Alkanes, terrestrial; Accumulation rate, nitrogen; Accumulation rate, opal; Accumulation rate, silicoflagellates; Accumulation rate, total organic carbon; after Jaeger et al. (2014); After Sánchez-Montes et al. (2019); After Sánchez-Montes et al. (2020); Age; Age, error; alkenone MAR; aquatic n-alkane MAR; benthic diatoms; biogenic silica MAR; brassicasterol MAR; Calculated; Calculated after Sánchez-Montes et al. (2019); Calculated after Sánchez-Montes et al. (2020); Carbon, organic, total; carbon isotope ratio (δ13C); coastal high productivity diatoms; coastal moderate productivity diatoms; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; diatoms; Diatoms, benthic; Diatoms, coastal high productivity; Diatoms, coastal moderate productivity; Diatoms, pelagic high productivity; Diatoms, pelagic warm water; Diatoms freshwater; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Exp341; freshwater diatoms; Gulf of Alaska; IODP 341; Joides Resolution; Nitrogen; nitrogen isotope ratio (δ15N); Opal, biogenic silica; paleoceanography; pelagic high productivity diatoms; pelagic warm water diatoms; Pleistocene; Pliocene; Preservation value; prev. value; Sample code/label; Sample ID; Sedimentation rate; Shannon Diversity Index; Shannon-Weaver index; silicoflagellate; Site U1417; Southern Alaska Margin; Southern Alaska Margin: Tectonics, Climate and Sedimentation; terrigenous n-alkane MAR; TN; TOC; δ13C; δ15N
    Type: Dataset
    Format: text/tab-separated-values, 6501 data points
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  • 17
    Publication Date: 2024-04-23
    Description: Field measured structural variables, particularly continuous in-situ data are pivotal for mechanism study and remote sensing validation. Multiple continuous field measurement campains were conducted in northeastern China crop fields: Honghe (2012, 2013, 2019) and Hailun (2016) . The Honghe site (47.65°N, 133.51°E) is covered with large homogeneous paddy rice and the Hailun site (47.41°N, 126.82°E) is planted with maize, sorghum, and soybean. Continuous measurements were made throughout almost the entire growing season, ranging from day of year (DOY) 160 to 280. For each site, five plots were selected. Typically, four elemental sampling units (approximately 15 m*15 m) were sampled for each plot to reduce random sampling error. Destructive sampling, Digital hemispheric photography (DHP), LAI-2200 canopy analyzer, and AccuPAR meausrements were carried out in the field measurement simultaneously. The green leaf area index (GAI), yellow leaf area index (YAI), and plant area index (PAI, including the area of leaf, stem, and ear) were measured with destructive sampling. The effective leaf area index (LAIeff), leaf area index (LAI), fractional of vegetation cover (FCOVER), and clumping index (CI) were derived from DHP and LAI-2200. Since DHP and LAI-2200 cannot separate the stem from leaf, the LAI obtained from DHP and LAI-2200 can be regarded as the PAI. The fraction of absorbed photosynthetically active radiation (FAPAR) was measured with AccuPAR using the four flux method. High-resolution LAI reference maps of these sites were also produced from cloud-free HJ-1 (30 m), Landsat 7 ETM+ (30 m), and Sentinel-2A MSI (20 m) images. The HJ-1 images were rectified to the Landsat 7 ETM+ images and were atmospherically corrected and transformed to surface reflectance data. The high-resolution reference LAI was derived from surface reflectance using the look-up table approach based on the ACRM model simulation. The reference LAI shows high consistency with field LAI (R2=0.85, bias=0.22, and RMSE=0.66). The reference LAI mean and standard deviation values within an extent of 3 km * 3 km in each site were extracted and provided in the dataset. The dataset is useful for the study of vegetation structural parameters and the validation of remote sensing products.
    Keywords: China crops; clumping index (CI); Field measurement; fractional of vegetation cover (FCOVER); fraction of absorbed photosynthetically active radiation (FAPAR); Hailun_site; Honghe_site; leaf area index(LAI); Northeastern China
    Type: Dataset
    Format: application/zip, 197.3 kBytes
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  • 18
    Publication Date: 2024-04-23
    Description: Raw data acquired by position sensors on board RV METEOR during expedition M174 were processed to receive a validated master track which can be used as reference of further expedition data. During M174 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and two C and C Technologies GPS receivers C-NAV3050 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
    Keywords: Calculated; Course; CT; DAM_Underway; DAM Underway Research Data; DATE/TIME; LATITUDE; LONGITUDE; M174; M174-track; Meteor (1986); Speed; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 13628 data points
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  • 19
    Publication Date: 2024-04-23
    Description: The dataset includes global specific vegetation cover (SVC), base clumping index (BCI), full clumping index (FCI), and leaf projection function (G) derived from clumping index (CI), leaf area index (LAI), and fractional vegetation cover (FVC) remote sensing products. The SVC, defined as the ratio of FVC to LAI, was proposed to characterize the ability of vegetation to cover the ground and has great potential for vegetation characterization and phenology studies. In this dataset, the global monthly SVC was generated with FVC and LAI products from 2003–2017. Theoretically, SVC varies from 0 to 1. SVC 〉1.0 reveals inconsistent retrievals for FVC and LAI. Therefore, we also map the spatial distribution and frequency of SVC outying pixels based on above monthly SVC product. The BCI refers to the hypothetical minimum CI during leaf emergence when both the FVC and LAI are close to zero. The FCI represents the CI when the ground is completely covered by vegetation (FVC=1.0) or the pixel LAI reaches its maximum (assumed to be 7.0). The BCI and FCI values indicate the seasonal CI variations and would greatly facilitate canopy modeling and parameter retrieval studies. The global BCI and FCI with a spatial resolution of 0.05° were both estimated using the exponential relationships between CI and FVC or between CI and LAI, respectively. The nadir leaf projection function (G(0)) is defined as the average projection of the unit leaf area in the nadir direction. The global monthly G(0) maps at 0.05° spatial resolution were generated for the first time from the global CI, FVC, and LAI products based on the Beer-Lambert equation under the assumption that the whole CI can be approximated as nadir CI. It can be used as a benchmark for biophysical parameter retrieval and land surface modeling studies. The remote sensing products used for generating this dataset include the CAS-CI V1.1 (Wei et al., 2019), the GEOV2 FVC (Verger, A., 2019; https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_ATBD_LAI1km-V2_I1.41.pdf), and the MODIS LAI C6 (Myneni et al., 2015). In order to facilitate further analysis by users, the global monthly average CI, FVC, and LAI data at 0.05° are also provided in this dataset. Moreover, we share the statistical results about the variations of CI, FVC, LAI, and SVC with seasonal, latitude, and altitude. For more details about this dataset, please refer to (Fang et al. (2021) do:10.1016/j.srs.2021.100027).
    Keywords: Base clumping index (BCI); Full clumping index (FCI); Leaf projection function (G); Specific vegetation cover (SVC)
    Type: Dataset
    Format: application/zip, 922.4 MBytes
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  • 20
    Publication Date: 2024-04-23
    Description: The dataset is composed of hyperspectral imagery HySpex VNIR 1600 acquired during gyrocopter overflights on August 28th, 2017 consisting of 80 spectral bands in the wavelength range from 400 – 1.000 nm. The data were acquired in the framework of Inland Water Remote Sensing Validation Campaign 2017. The hyperspectral data represents the at sensor radiance in W/nm sr m² converted from DN. Furthermore, the data was georeferenced and rectified with the software PARGE. From a flight altitude of about 2,800 m the resulting GSD is 2.5 m. The listed data consists of a Band Sequential Image File [*] and a file header [*.hdr]. The first file can easily import to the opensource GIS QGIS. The digital surface model (ger. digitales Oberflächenmodell (DOM)) was captured during gyrocopter overflights on August 28th, 2017 acquired in the framework of Inland Water Remote Sensing Validation Campaign 2017. The flight altitude was about 2,800 m. Each pixel value represents the surface height. The GSD is about 0,52 m. The digital orthophoto were captured during gyrocopter overflights on August 28th, 2017 acquired in the framework of Inland Water Remote Sensing Validation Campaign 2017. The flight altitude was about 2,800 m. The GSD is about 0,25 m.
    Keywords: Area/locality; Binary Object; Data type; DATE/TIME; Digital surface model; DSM; Event label; Geiseltalsee_DOM; Geiseltalsee_HYPSPEC; Geiseltalsee_ORTHO; HSCAM; Hyperspectral camera; IWRSVC-17_GTS_DOM; IWRSVC-17_GTS_HYPSPEC; IWRSVC-17_GTS_ORTHO; IWRSVC-17_KELB_DOM; IWRSVC-17_KELB_HYPSPEC; IWRSVC-17_KELB_ORTHO; IWRSVC-17_SUESS_DOM; IWRSVC-17_SUESS_HYPSPEC; IWRSVC-17_SUESS_ORTHO; Kelbra_DOM; Kelbra_HYPSPEC; Kelbra_ORTHO; LATITUDE; LONGITUDE; ORTHO; Orthoimagery/Orthophotography; Suessersee_DOM; Suessersee_HYPSPEC; Suessersee_ORTHO; Transect
    Type: Dataset
    Format: text/tab-separated-values, 92 data points
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  • 21
    Publication Date: 2024-04-23
    Description: Concentrations of alkalinity (TA) and dissolved inorganic carbon (DIC) in porewater as well as in surface water measured during timeseries (fixed location) and spatial surveys (fixed time period) were compiled from 38 mangrove- and 8 saltmarsh-dominated creeks and estuaries. We used data from creeks that were predominantly surrounded by mangrove or saltmarsh vegetation and with minimal confounding factors such as mixed vegetation or large catchments. These creeks were located in either pristine or anthropologically impacted estuaries or coastal areas. Anthropologically impacted areas were defined as areas that were affected by nearby urban or agricultural activities, potentially delivering pollutants, e.g., sewage or fertilizers, to creeks. We also included pristine mangrove- and saltmarsh dominated estuaries. When available, environmental parameters were also recorded, i.e., season, salinity, temperature, pH, dissolved oxygen (DO), water level, porewater tracer radon (222Rn), partial pressure of carbon dioxide (pCO2), dissolved organic carbon (DOC), particulate organic carbon (POC), nitrate and nitrite (NOx), ammonium (NH4), total nitrogen (TN), phosphate (PO4), and total phosphorus (TP). Methods used to determine parameters are explained in each corresponding reference.
    Keywords: Alkalinity; Alkalinity, total; Alkalinity, total/Carbon, inorganic, dissolved ratio; Ammonium; Australia; Australia_M29; Australia_M30; Australia_M31; Australia_M32; Australia_M33; Australia_M34; Australia_M35; Australia_M36; Australia_M37; Australia_M38; blue carbon; Boron hydroxide; Brazil; Brazil_M18; Brazil_M19; Brazil_M20; Brazil_M21; CA_USA_S02; Carbon, inorganic, dissolved; Carbon, organic, dissolved; Carbon, organic, particulate; Carbon dioxide, partial pressure; China; China_M03; China_S06; China_S07; China_S08; Condition; Country; DATE/TIME; Date/Time local; Dissolved inorganic carbon; Ecosystem; Ecuador; Ecuador_M22; Event label; French_Guiana_M17; French Guiana; GA_USA_S04; Identification; India; India_M04; India_M05; India_M06; India_M07; India_M08; India_M09; Japan; Japan_M02; Kenya; Kenya_M23; Kenya_M24; LATITUDE; LONGITUDE; MA_USA_S01; Madagascar; Madagascar_M28; mangroves; Nitrogen, total; Nitrogen oxide; Oxygen, dissolved; Palau; Palau_M15; Palau_M16; Papua_New_Guinea_M25; Papua New Guinea; pH; Philippines; Philippines_M10; Phosphate; Phosphorus, total; Radon-222; Reference/source; Salinity; saltmarshes; Sample type; SC_USA_S03; Season; Site; Spain; Spain_S05; Tanzania; Tanzania_M26; Tanzania_M27; Temperature, water; Thailand; Thailand_M14; USA; USA_M01; Vietnam; Vietnam_M11; Vietnam_M12; Vietnam_M13; Water level; Water sample; WS
    Type: Dataset
    Format: text/tab-separated-values, 67107 data points
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  • 22
    Publication Date: 2024-04-23
    Description: As result of population growth and human development, many low latitude river-ocean systems are undergoing fundamental changes. The RV METEOR cruise M174 (https://doi.org/10.1594/PANGAEA.935041) was dedicated to understanding the links between the Amazon River and the adjacent tropical ocean, which are critical components of any attempt to address the ecosystem's health and productivity. This CTD dataset covers the Amazon River plume on the Brazilian shelf, the shelf of French Guinea and the adjacent offshore area as an example for such ecosystems. The data set has been obtained with a CTD SBE 911plus in April and May 2021, and contains vertical profiles of pressure, temperature, practical salinity, dissolved oxygen concentration, turbidity, and nitrate concentration.
    Keywords: Amazon River; Canarias Sea; Conductivity; CTD, Sea-Bird, SBE 911plus; CTD/Rosette; CTD profiles; CTD-RO; DATE/TIME; DEPTH, water; Event label; LATITUDE; LONGITUDE; M174; M174_10-2; M174_10-6; M174_1-1; M174_11-1; M174_12-2; M174_13-3; M174_14-2; M174_15-1; M174_16-1; M174_17-1; M174_17-10; M174_17-13; M174_17-15; M174_17-16; M174_17-19; M174_17-24; M174_17-29; M174_17-32; M174_17-37; M174_17-40; M174_17-42; M174_17-43; M174_17-45; M174_17-48; M174_17-50; M174_17-51; M174_17-6; M174_18-1; M174_18-4; M174_19-2; M174_19-5; M174_20-2; M174_20-4; M174_2-1; M174_21-14; M174_21-21; M174_21-24; M174_21-28; M174_21-3; M174_21-31; M174_21-33; M174_21-34; M174_21-37; M174_21-42; M174_21-47; M174_21-5; M174_21-50; M174_21-54; M174_21-57; M174_21-6; M174_21-9; M174_22-1; M174_23-1; M174_24-1; M174_24-5; M174_2-5; M174_25-1; M174_25-6; M174_26-19; M174_26-28; M174_26-3; M174_26-31; M174_26-34; M174_26-5; M174_26-9; M174_28-1; M174_28-6; M174_28-8; M174_29-1; M174_29-4; M174_30-11; M174_30-15; M174_30-18; M174_30-25; M174_30-28; M174_30-3; M174_30-30; M174_30-35; M174_30-39; M174_30-6; M174_3-1; M174_31-10; M174_31-14; M174_31-17; M174_31-24; M174_31-27; M174_31-29; M174_31-3; M174_31-34; M174_31-38; M174_31-44; M174_31-48; M174_31-5; M174_31-52; M174_31-53; M174_32-2; M174_32-5; M174_32-8; M174_3-4; M174_4-1; M174_4-5; M174_4-6; M174_5-3; M174_5-7; M174_7-2; M174_7-3; M174_8-2; M174_8-6; M174_9-2; M174_9-6; Meteor (1986); Nitrate; Oxygen; Pressure, water; river plume; Salinity; South Atlantic Ocean; Temperature, water; Time in seconds; tropical Atlantic
    Type: Dataset
    Format: text/tab-separated-values, 192083 data points
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  • 23
    Publication Date: 2024-04-23
    Description: The Amazon River is known as a region of intense biochemical reactivity. As a result of increased anthropogenic impacts in its catchment (e.g. population growth, agriculture, and deforestation), The Amazon River is undergoing fundamental changes. The RV METEOR cruise M174 (doi:10.1594/PANGAEA.935041) aimed to provide an integrated overview of nitrogen cycling processes in areas influenced by the Amazon River, which is crucial to estimate the ecosystem's health and productivity. During this cruise, six stations were visited to create an updated nitrogen budget of the Amazon and Pará estuaries. This dataset presents the results of the budget calculated for 6 stations of varying turbidity. Nitrification, nitrate, ammonium and amino acid uptake rates (in mol/day) are taken into account in this budget, and compared to riverine dissolved inorganic nitrogen inputs. Dissolved inorganic carbon uptake rates are also presented.
    Keywords: According to Damashek et al. (2016); According to Dugdale and Goering (1967); Amazon River; Amino acids, uptake rate, integrated; Amino acids, uptake rate, integrated, standard deviation; Ammonium, dark, uptake rate, integrated; Ammonium, dark, uptake rate, integrated, standard deviation; Ammonium, uptake rate, integrated; Ammonium, uptake rate, integrated, standard deviation; Area; Budget; Calculated; CTD, Sea-Bird, SBE 911plus; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Dissolved organic carbon, uptake rate, integrated; Dissolved organic carbon, uptake rate, integrated, standard deviation; Event label; export flux; Latitude, center; Latitude of event; Longitude, center; Longitude of event; M174; M174_10-2; M174_12-2; M174_13-3; M174_14-2; M174_17-16; M174_9-2; Meteor (1986); Nitrate, uptake rate, integrated; Nitrate, uptake rate, integrated, standard deviation; Nitrification, integrated; Nitrification, integrated, standard deviation; Nitrogen; Nitrogen, inorganic, dissolved, loading; Optical backscatter sensor, D&A, OBS-3; South Atlantic Ocean; Turbidity
    Type: Dataset
    Format: text/tab-separated-values, 138 data points
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  • 24
    Publication Date: 2024-04-23
    Description: The Amazon River is known as a region of intense biochemical reactivity. As a result of increased anthropogenic impacts in its catchment (e.g. population growth, agriculture, and deforestation), The Amazon River is undergoing fundamental changes. The RV METEOR cruise M174 (doi:10.1594/PANGAEA.935041) aimed to provide an integrated overview of nitrogen cycling processes in areas influenced by the Amazon River, which is crucial to estimate the ecosystem's health and productivity. During this cruise, six stations were visited to explore the significance of denitrification as a dominant pathway in the watercolumn of the Amazon and Pará estuaries. This dataset presents the stable isotopic composition of nitrate (d15N- d18O-NO3), as well as d2H and d18O of ambient water.
    Keywords: According to Böttcher & Schmiedinger (2021); According to Sigman et al. (2001) and Weigand et al. (2016); Amazon; Amazon estuary; Amazon River; Calculated; CTD, Sea-Bird, SBE 911plus; CTD/Rosette; CTD-RO; d15N; d18O; d2H; Date/Time of event; Event label; Latitude of event; Longitude of event; M174; M174_10-2; M174_12-2; M174_13-3; M174_14-2; M174_17-16; M174_9-2; Meteor (1986); Methods of Seawater Analysis, 3rd edition (Grasshoff et al., 1999); nitrate; Nitrate; Nitrate, standard deviation; Salinity; South Atlantic Ocean; Stable isotopes; Temperature, water; water; δ15N, nitrate; δ15N, nitrate, standard deviation; δ18O, nitrate; δ18O, nitrate, standard deviation; δ18O, water; δ18O, water, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 60 data points
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  • 25
    Publication Date: 2024-04-23
    Description: The data were generated during an experiment simulating different frequencies of heatwaves (zero, one and three) in late spring/summer 2015. The experiment was carried out at the Kiel Outdoor Benthocosm (KOB) of GEOMAR Helmholtz Centre for Ocean Research Kiel, located at the Kiel Fjord. The biomass of filamentous algae was quantified from the most abundant genus occurring inside the tanks, i.e. Ceramium sp. The biomass of Zostera marina and Fucus vesiculosus was estimated from growth rates measurements carried out every 15 days. The biomass of all macrophytes was converted to carbon using specific carbon contents measured concomitantly with stable isotopes (https://doi.pangaea.de/10.1594/PANGAEA.966179). Respiration and primary production measurements were carried out on 09.08.2015 for filamentous algae, and on 12.08.2015 for Fucus vesiculosus. To execute these measurements, organisms were kept in gas-tight cylindrical chambers equipped with sensor spots for non-invasive oxygen measurements, which allowed continuous oxygen logging. Throughout the measurements, the chambers were kept inside the KOB tanks to maintain the temperature. The oxygen values were converted to carbon and normalized by the area of the tank (1.53 m2) per day. Note that the data of net primary production and respiration rates of the Z. marina were previously published (https://doi.org/10.1594/PANGAEA.904632). The carbon flux refers to the exports, i.e. biomass that was floating in the tanks, which was considered as carbon leaving (i.e. exported outside of) the system but still usable. The material to quantify the exports was collected every seven days, separated accounting for the contribution of each macrophyte group, dried at 80 °C until the biomass was constant and weighted. The dry weight was converted to carbon using the specific carbon contents measured concomitantly with stable isotopes (https://doi.pangaea.de/10.1594/PANGAEA.966179), and normalized by the area of the tank (1.53 m2) per day.
    Keywords: Amphipoda; Benthic biota; Biomass as carbon, export; Biomass as carbon, total per area; biomass estimation; carbon content; carbon fluxes; Experimental treatment; Fucus vesiculosus; Gastropoda; Isopoda; Kiel-Outdoor-Benthocosms; KOB; MESO; mesocosm experiment; Mesocosm experiment; net primary production; Net primary production of carbon; Respiration; Respiration rate, carbon; Stable carbon isotope (δ13C); stable nitrogen isotope (δ15N); Tank number; Taxa; Type of study; Zostera marina
    Type: Dataset
    Format: text/tab-separated-values, 264 data points
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  • 26
    Publication Date: 2024-04-23
    Keywords: 175-1075A; 175-1079A; 175-1082A; 175-1084A; Accumulation rate, calcium carbonate; Accumulation rate, n-Alkanes; Accumulation rate, n-alkanol; Accumulation rate, total organic carbon; AGE; Angola Basin; Benguela Current, South Atlantic Ocean; Calculated; Calculated, see reference(s); Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanols; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Event label; GeoB; GeoB1008-3; GeoB1016-3; GeoB1028-5; GeoB1710-3; GeoB1722-1; Geosciences, University of Bremen; Gravity corer (Kiel type); Higher Plant Alkanes index; Joides Resolution; Latitude of event; Leg175; Longitude of event; M20/2; M6/6; Meteor (1986); n-Alkane, average chain length; n-Alkanol average chain length; Namibia continental slope; Ocean Drilling Program; ODP; Sample code/label; SL; Walvis Ridge, Southeast Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 85 data points
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  • 27
    Publication Date: 2024-04-23
    Keywords: 175-1075A; 175-1079A; 175-1082A; 175-1084A; Accumulation rate, calcium carbonate; Accumulation rate, n-Alkanes; Accumulation rate, n-alkanol; Accumulation rate, total organic carbon; Age model; Angola Basin; Benguela Current, South Atlantic Ocean; Calculated, see reference(s); Carbon Preference Index, n-Alkanes (C27-C33); Carbon Preference Index, n-Alkanols; Center for Marine Environmental Sciences; DEPTH, sediment/rock; DRILL; Drilling/drill rig; Elevation of event; Event label; GeoB1008-3; GeoB1016-3; GeoB1028-5; GeoB1710-3; GeoB1722-1; Gravity corer (Kiel type); Higher Plant Alkanes index; Joides Resolution; Latitude of event; Leg175; Longitude of event; M20/2; M6/6; MARUM; Meteor (1986); n-Alkane, average chain length; n-Alkanol average chain length; Namibia continental slope; Ocean Drilling Program; ODP; Sea surface temperature, annual mean; see reference(s); SL; Walvis Ridge, Southeast Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 99 data points
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  • 28
    Publication Date: 2024-04-23
    Keywords: 175-1075A; 175-1079A; 175-1082A; 175-1084A; Accumulation rate, calcium carbonate; Accumulation rate, n-Alkanes; Accumulation rate, n-alkanol; Accumulation rate, total organic carbon; Age model; Angola Basin; Benguela Current, South Atlantic Ocean; Calculated, see reference(s); Carbon Preference Index, n-Alkanes (C27-C33); Carbon Preference Index, n-Alkanols; Center for Marine Environmental Sciences; DEPTH, sediment/rock; DRILL; Drilling/drill rig; Elevation of event; Event label; GeoB1008-3; GeoB1016-3; GeoB1028-5; GeoB1710-3; GeoB1722-1; Gravity corer (Kiel type); Higher Plant Alkanes index; Joides Resolution; Latitude of event; Leg175; Longitude of event; M20/2; M6/6; MARUM; Meteor (1986); n-Alkane, average chain length; n-Alkanol average chain length; Namibia continental slope; Ocean Drilling Program; ODP; Sea surface temperature, annual mean; see reference(s); SL; Walvis Ridge, Southeast Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 99 data points
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  • 29
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, even numbered n-Alkanoic acids; Accumulation rate, odd numbered n-alkanes; AGE; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; DEPTH, sediment/rock; Gas chromatography - Flame Ionization Detection (GC-FID); KALMAR II; PC; Piston corer; Shirshov Ridge; SO201/2; SO201-2-114KL; Sonne; Sum even numbered n-Alkanoic acids, per unit mass total organic carbon; Sum even numbered n-Alkanoic acids, per unit sediment mass; Sum odd numbered n-alkanes, per unit mass total organic carbon; Sum odd numbered n-alkanes, per unit sediment mass
    Type: Dataset
    Format: text/tab-separated-values, 756 data points
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  • 30
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, branched glycerol dialkyl glycerol tetraethers; Accumulation rate, even numbered n-Alkanoic acids; Accumulation rate, odd numbered n-alkanes; AGE; Aquatic portion; Bering Sea; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; DEPTH, sediment/rock; Gas chromatography - Flame Ionization Detection (GC-FID); High Performance Liquid Chromatography (HPLC-APCI-MS); INOPEX; KL; Piston corer (BGR type); SO202/1; SO202/1_18-3; Sonne; Sum branched glycerol dialkyl glycerol tetraether, per unit mass total organic carbon; Sum branched glycerol dialkyl glycerol tetraether, per unit sediment mass; Sum even numbered n-Alkanoic acids, per unit mass total organic carbon; Sum even numbered n-Alkanoic acids, per unit sediment mass; Sum odd numbered n-alkanes, per unit mass total organic carbon; Sum odd numbered n-alkanes, per unit sediment mass
    Type: Dataset
    Format: text/tab-separated-values, 456 data points
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  • 31
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, branched glycerol dialkyl glycerol tetraethers; Accumulation rate, even numbered n-Alkanoic acids; Accumulation rate, odd numbered n-alkanes; AGE; Aquatic portion; Bering Sea; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; DEPTH, sediment/rock; Gas chromatography - Flame Ionization Detection (GC-FID); High Performance Liquid Chromatography (HPLC-APCI-MS); INOPEX; KAL; Kasten corer; SO202/1; SO202/1_18-6; Sonne; Sum branched glycerol dialkyl glycerol tetraether, per unit mass total organic carbon; Sum branched glycerol dialkyl glycerol tetraether, per unit sediment mass; Sum even numbered n-Alkanoic acids, per unit mass total organic carbon; Sum even numbered n-Alkanoic acids, per unit sediment mass; Sum odd numbered n-alkanes, per unit mass total organic carbon; Sum odd numbered n-alkanes, per unit sediment mass
    Type: Dataset
    Format: text/tab-separated-values, 308 data points
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  • 32
    Publication Date: 2024-04-23
    Keywords: AGE; Average chain length, n-Alkanes, C25-C33; Calypso Square Core System; Carbon Preference Index, n-Alkanes (C25-C33); CASQS; Center for Marine Environmental Sciences; DEPTH, sediment/rock; IMAGES XII - MARCO POLO; Leibniz Centre for Tropical Marine Research; Marion Dufresne (1995); MARUM; MD052905; MD05-2905; MD147; n-Alkane C29; n-Alkane C29,C31, δ13C; n-Alkane C29,C31, δ13C, standard deviation; n-Alkane C29,C31, δD; n-Alkane C29,C31, δD, standard deviation; n-Alkane C29, δ13C; n-Alkane C29, δ13C, standard deviation; n-Alkane C29, δD; n-Alkane C29, δD, standard deviation; n-Alkane C31; n-Alkane C31, δ13C; n-Alkane C31, δ13C, standard deviation; n-Alkane C31, δD; n-Alkane C31, δD, standard deviation; South China Sea; Thermo Trace GC - Finnigan MAT 252 (GC/IR-MS); ZMT
    Type: Dataset
    Format: text/tab-separated-values, 1928 data points
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  • 33
    Publication Date: 2024-04-23
    Keywords: Aerosol angstrom exponent; Aerosol angstrom exponent, standard deviation; Aerosol optical thickness at 465 nm; Aerosol optical thickness at 465 nm, standard deviation; Aerosol optical thickness at 540 nm; Aerosol optical thickness at 540 nm, standard deviation; Aerosol optical thickness at 550 nm; Aerosol optical thickness at 550 nm, standard deviation; Aerosol optical thickness at 619 nm; Aerosol optical thickness at 619 nm, standard deviation; Ångström turbidity coefficient; Ångström turbidity coefficient, standard deviation; Date/Time of event; Effective particle radius; Effective particle radius, standard deviation; Event label; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Latitude of event; Longitude of event; Principal investigator; Sample code/label; Suessersee-0800_CALITOO_141-150; Suessersee-0830_CALITOO_151-160; Suessersee-0900_CALITOO_161-170; Suessersee-0900_CALITOO_58-70; Suessersee-0930_CALITOO_171-180; Suessersee-0930_CALITOO_71-80; Suessersee-1000_CALITOO_181-190; Suessersee-1000_CALITOO_81-90; Suessersee-1030_CALITOO_191-200; Suessersee-1030_CALITOO_91-100; Suessersee-1057_CALITOO_2-11; Suessersee-1100_CALITOO_101-110; Suessersee-1100_CALITOO_201-210; Suessersee-1130_CALITOO_111-120; Suessersee-1130_CALITOO_12-22; Suessersee-1130_CALITOO_211-220; Suessersee-1150_CALITOO_23-35; Suessersee-1200_CALITOO_121-140; Suessersee-1315_CALITOO_36-46; Suessersee-1400_CALITOO_47-57; Sun/Aerosol photometer, Tenum, Calitoo
    Type: Dataset
    Format: text/tab-separated-values, 303 data points
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  • 34
    Publication Date: 2024-04-23
    Keywords: Banagi; Carbon, organic, total; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; DEPTH, soil; Depth comment; Elemental Analyzer (EA-CRDS), G2131-i, Picarro, relative to USGS-40; Elevation of event; Event label; Gas chromatography - Flame Ionization Detection (GC-FID) relative to in house standards; grassland; Kemarishe; Kirawira; Latitude of event; Leaf wax; Longitude of event; Makoma; Malambo Road; MULT; Multiple investigations; Musabi; Naabi Hill; n-Alkane, average chain length; n-Alkane C23; n-Alkane C23-33; n-Alkane C23-33, total organic carbon; n-Alkane C24; n-Alkane C25; n-Alkane C26; n-Alkane C27; n-Alkane C28; n-Alkane C29; n-Alkane C30; n-Alkane C31; n-Alkane C32; n-Alkane C33; n-Alkane C34; n-Alkane C35; n-Alkanoic acid, average chain length; n-Alkanoic acid C16; n-Alkanoic acid C17; n-Alkanoic acid C18; n-Alkanoic acid C19; n-Alkanoic acid C20; n-Alkanoic acid C21; n-Alkanoic acid C22; n-Alkanoic acid C22-C32; n-Alkanoic acid C22-C32 , total organic carbon; n-Alkanoic acid C23; n-Alkanoic acid C24; n-Alkanoic acid C25; n-Alkanoic acid C26; n-Alkanoic acid C27; n-Alkanoic acid C28; n-Alkanoic acid C29; n-Alkanoic acid C30; n-Alkanoic acid C31; n-Alkanoic acid C32; n-Alkanoic acid C33; n-Alkanoic acid C34; Ndabakal; Nyaruswiga; Optional event label; Sample code/label; Serengeti; Serengeti_soil_Banagi; Serengeti_soil_Kemarishe; Serengeti_soil_Kirawira; Serengeti_soil_Makoma; Serengeti_soil_Malambo_Road; Serengeti_soil_Musabi; Serengeti_soil_Naabi_Hill; Serengeti_soil_Ndabaka; Serengeti_soil_Nyaruswiga; Serengeti_soil_Shifting_Sands; Serengeti_soil_Simba_Kopjes; Shifting Sands; Simba Kopjes; soil carbonates
    Type: Dataset
    Format: text/tab-separated-values, 1118 data points
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  • 35
    Publication Date: 2024-04-23
    Keywords: Aerosol angstrom exponent; Aerosol angstrom exponent, standard deviation; Aerosol optical thickness at 1020 nm; Aerosol optical thickness at 1020 nm, standard deviation; Aerosol optical thickness at 380 nm; Aerosol optical thickness at 380 nm, standard deviation; Aerosol optical thickness at 440 nm; Aerosol optical thickness at 440 nm, standard deviation; Aerosol optical thickness at 500 nm; Aerosol optical thickness at 500 nm, standard deviation; Aerosol optical thickness at 550 nm; Aerosol optical thickness at 550 nm, standard deviation; Aerosol optical thickness at 675 nm; Aerosol optical thickness at 675 nm, standard deviation; Aerosol optical thickness at 870 nm; Aerosol optical thickness at 870 nm, standard deviation; Ångström turbidity coefficient; Ångström turbidity coefficient, standard deviation; Date/Time of event; Effective particle radius; Effective particle radius, standard deviation; Event label; Germany; Inland Water Remote Sensing Validation Campaign 2017; IWRSVC-2017; Latitude of event; Longitude of event; Ozone total; Ozone total, standard deviation; Precipitable water content; Precipitable water content, standard deviation; Principal investigator; Sample code/label; Suessersee-0800_SONNE_161-170; Suessersee-0830_SONNE_171-180; Suessersee-0900_SONNE_181-190; Suessersee-0900_SONNE_76-85; Suessersee-0930_SONNE_191-200; Suessersee-0930_SONNE_86-95; Suessersee-1000_SONNE_0-10; Suessersee-1000_SONNE_201-210; Suessersee-1000_SONNE_96-110; Suessersee-1029_SONNE_10-20; Suessersee-1030_SONNE_111-120; Suessersee-1030_SONNE_211-220; Suessersee-1057_SONNE_21-30; Suessersee-1100_SONNE_121-130; Suessersee-1100_SONNE_221-230; Suessersee-1130_SONNE_131-140; Suessersee-1130_SONNE_231-240; Suessersee-1130_SONNE_35-45; Suessersee-1150_SONNE_46-55; Suessersee-1200_SONNE_142-160; Suessersee-1315_SONNE_46-55; Suessersee-1400_SONNE_66-75; Sun photometer, Microtops; Temperature, standard deviation; Temperature, technical
    Type: Dataset
    Format: text/tab-separated-values, 729 data points
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  • 36
    Publication Date: 2024-04-23
    Description: A number of studies expect an increase of carbon export by rivers to the Arctic Ocean due to rapidly changing climate in the Arctic One possible reason for the increase of carbon export is thawing permafrost, which can lead to a mobilization of previously frozen dissolved organic matter (DOM). Scarce measurements of DOC and the coloured fraction of DOM (CDOM) (〈 8 samples/year) were previously used to estimate fluxes to the Arctic Ocean for the whole year. Here, we present a new high frequency sampling program and its viability to monitor export fluxes of DOM and its biogeochemistry in the Lena River. This dataset includes measurements of several water parameters, such as temperature, electric conductivity, dissolved organic carbon (DOC), spectral CDOM absorption (aCDOM), stable water isotopes and major ions. The high sampling frequency throughout the whole year facilitats the identification of the main drivers behind the seasonality of DOM concentration and biogeochemistry of the Lena River. The high number of samples throughout the whole year allows flux calculations that are independently from load models that likely lead to a large variation of earlier studies. Optical indices reveal changing composition and sources of DOM throughout the year. This dataset represents the first year (April 2018 to April 2019) of a planned long-term monitoring program at the Research Station Samoylov Island and provides a baseline data set against which future change of this large integrative system may be measured. A continuous sampling of Arctic River water will facilitate to identify intra and inter-annual trends with ongoing climate change.
    Keywords: Absorption coefficient, colored dissolved organic matter at given wavelength; Aluminium; AWI Arctic Land Expedition; Barium 2+; biogeochemistry; Bromide; Calcium; Calculated; Carbon, organic, dissolved; CDOM; Chloride; Conductivity, electrical; DATE/TIME; DEPTH, water; Deuterium excess; DOC; DOM; Fluoride; Handheld meter, WTW, 340i, Conductivity; Identification; Inductively coupled plasma optical emission spectrometry (ICP-OES), Perkin-Elmer, Optima 8300DV; Ion chromatography (Thermo-Fischer ICS 2100); Iron; Latitude of event; Lena 2018; Lena 2019; Longitude of event; Magnesium; major ions; Manganese; Mass spectrometer Finnigan Delta-S/equilibration device; Nitrate; Olenekskaya_Ch; Phosphate; Phosphorus; Potassium; RIVER; RU-Land_2018_Lena; RU-Land_2018_Lena_Olenekskaya; RU-Land_2019_Lena; RU-Land_2019_Lena_Olenekskaya; Sampling river; Shimadzu TOC-VCPH total organic carbon analyzer; Silicon; Sodium; Specific ultraviolet absorbance normalized to DOC; Spectrophotometer UV/VIS (PerkinElmer Lambda 950); stable water isotopes; Strontium 2+; Sulfate; Temperature, water; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 42031 data points
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  • 37
    Publication Date: 2024-04-23
    Keywords: Area/locality; climate; Clumped isotopes; Comment; Continental; Eocene; LATITUDE; LONGITUDE; Oxygen isotopes; Paleocene; Paleolatitude; Reference/source; siderite; Time point, descriptive; Uncertainty; Δδ18O; Δδ Deuterium; δ18O, precipitation; δ Deuterium, n-alkanes; δ Deuterium, precipitation
    Type: Dataset
    Format: text/tab-separated-values, 388 data points
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  • 38
    Publication Date: 2024-04-23
    Keywords: Alkenone, C37:2, per unit mass organic carbon; Alkenone, C37:2 per unit sediment mass; Alkenone, unsaturation index UK'37; Average chain length, n-Alkanes, C27-C33; Average chain length, n-fatty acid, C24-C30; Carbon, inorganic, total; Carbon, organic; Carbon/Nitrogen ratio; Carbon Preference Index, n-Alkanes (C26-C34); Carbon Preference Index, n-fatty acid (C23-C31); Center for Marine Environmental Sciences; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; GeoB7807-2; M52/3; MARUM; Meteor (1986); MUC; MultiCorer; n-Alkane C29, δ13C; n-Alkane C29, δD; n-Alkane C29-C31, per unit mass organic carbon; n-Alkane C29-C31, per unit sediment mass; n-Alkane C31, δ13C; n-Alkane C31, δD; n-fatty acid C16, per unit mass organic carbon; n-fatty acid C16, δD; n-fatty acid C16:0, δ13C; n-fatty acid C16 per unit sediment mass; n-fatty acid C24, per unit mass organic carbon; n-fatty acid C24, δD; n-fatty acid C24:0, δ13C; n-fatty acid C24 per unit sediment mass; n-fatty acid C26, δD; n-fatty acid C26:0, δ13C; n-fatty acid C28, per unit mass organic carbon; n-fatty acid C28, δD; n-fatty acid C28:0, δ13C; n-fatty acid C28 per unit sediment mass; n-fatty acid C30, δ13C; n-fatty acid C30, δD; Nitrogen; Northern Red Sea, Shaban Deep Area; Sea surface temperature, annual mean; δ13C, total organic carbon; δ Deuterium, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 805 data points
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  • 39
    Publication Date: 2024-04-23
    Keywords: AGE; Alkenones; Arabian Sea; Average chain length, n-Alkanes, C27-C33; CAHOL; CAME-II_CAHOL; Carbon Preference Index, n-Alkanes (C27-C33); Crossing Climatic Tipping Points - Central Asian Holocene Climate; DEPTH, sediment/rock; Grain Size; KAL; Kasten corer; n-Alkane, C31/(C29+C31) ratio; n-Alkane, C33/(C29+C33) ratio; n-Alkane C23 of total alkanes; n-Alkane C24 of total alkanes; n-Alkane C25 of total alkanes; n-Alkane C26 of total alkanes; n-Alkane C27 of total alkanes; n-Alkane C28 of total alkanes; n-Alkane C29 of total alkanes; n-Alkane C30 of total alkanes; n-Alkane C31 of total alkanes; n-Alkane C32 of total alkanes; n-Alkane C33 of total alkanes; n-Alkane C34 of total alkanes; n-Alkane C35 of total alkanes; n-alkanes; PAKOMIN; SO90; SO90_63KA; Sonne
    Type: Dataset
    Format: text/tab-separated-values, 2788 data points
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  • 40
    Publication Date: 2024-04-23
    Keywords: AGE; compound-specific stable isotope analysis; DEPTH, sediment/rock; Geochemistry; Holocene climate; MHC; Motor hammer corer; n-alkanes; sea-level change; South Africa; VOV16-1; δ Deuterium, n-alkanes; δ Deuterium, n-alkanes, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 146 data points
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  • 41
    Publication Date: 2024-04-23
    Description: Water isotopes (δ²H and δ¹⁸O) were analyzed in samples from a range of relatively small lakes and ponds in northeastern Germany. The sampled water bodies are not connected to major river systems but are either fed by groundwater or small creeks. Water chemical parameters were determined in-situ with a portable WTW-multiparameter probe. Water samples were collected in different seasons of the years 2020, 2022 and 2023. Here, composite samples were taken from the lake centers with a Ruttner Water Sampler in 2 m intervals from the lake surface to bottom. In some cases, only a surface sample was collected from 50 cm depth using a pipette. Samples were filtered and transferred into a measurement vial. Stable isotope analysis was conducted at IGB Berlin, using a Picarro L2130-i cavity ring-down spectrometer. Measurement uncertainty was quantified to 〈0.5 ‰ for δ²H and 〈0.2 ‰ for δ¹⁸O. The data give information about the seasonal and spatial stable isotope variability at the sampled lacustrine systems.
    Keywords: Bottle, Ruttner; Calculated; Cavity ring-down spectroscopy (CRDS), L2130-i, Picarro Inc.; Chara_Seen; Chara Lakes; Conductivity, electrical; DATE/TIME; DEPTH, water; Depth, water, bottom/maximum; Depth, water, top/minimum; Depth of Secchi Disk; Dreetzsee; Dreetzsee, Germany; Dunkelsee; Dunkelsee, Germany; Event label; Germany; Giesenschlagsee_Middle; Giesenschlagsee_North; Giesenschlagsee_South; Giesenschlagsee Middle, Germany; Giesenschlagsee North, Germany; Giesenschlagsee South, Germany; Glambecksee; Glambecksee, Germany; Gottssee; Gottssee, Germany; Gr_Bodensee; Gr_Gollinsee; Gr_Griebchensee; Gr_Kronsee; Gr_Petznicksee; Gr_Tietzen; Gr_Weisser_See; Großer Bodensee, Germany; Großer Gollinsee, Germany; Großer Griebchensee, Germany; Großer Kronsee, Germany; Großer Petznicksee, Germany; Großer Tietzen, Germany; Großer Weißer See, Germany; Hinbergsee; Hinbergsee, Germany; Kl_Peetzigsee; Kleiner Peetzigsee, Germany; Krueselinsee; Krüselinsee, Germany; lakes; Latitude of event; Longitude of event; Oxygen, dissolved; Oxygen saturation; pH; ponds; Rohrhalmgrund; Rohrhalmgrund, Germany; RWS; Sabinensee; Sabinensee, Germany; SD; Secchi disk; Temperature, water; Warnitzsee; Warnitzsee, Germany; Waschsee; Waschsee, Germany; water isotopes; WTW probe, hand-held; Wuckersee; Wuckersee, Germany; δ18O, water; δ18O, water, standard deviation; δ Deuterium, water; δ Deuterium, water, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 628 data points
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  • 42
    Publication Date: 2024-04-23
    Keywords: Average chain length, n-Alkanes, C14-C37; Average chain length, n-Alkanes, C25-C37; Bulk organic geochemistry; Carbon Preference Index; China; Clumped isotope thermometry; Event label; GASC; Gas chromatograph; Instrument; Latitude of event; Lithology/composition/facies; Longitude of event; n-Alkane C27, δ13C; n-Alkane C27, δ13C, standard deviation; n-Alkane C27, δD; n-Alkane C27, δD, standard deviation; n-Alkane C28, δ13C; n-Alkane C28, δ13C, standard deviation; n-Alkane C28, δD; n-Alkane C28, δD, standard deviation; n-Alkane C29, δ13C; n-Alkane C29, δ13C, standard deviation; n-Alkane C29, δD; n-Alkane C29, δD, standard deviation; n-Alkane C30, δ13C; n-Alkane C30, δ13C, standard deviation; n-Alkane C30, δD; n-Alkane C30, δD, standard deviation; n-Alkane C31, δ13C; n-Alkane C31, δ13C, standard deviation; n-Alkane C31, δD; n-Alkane C31, δD, standard deviation; n-Alkane C32, δ13C; n-Alkane C32, δ13C, standard deviation; n-Alkane C32, δD; n-Alkane C32, δD, standard deviation; n-Alkane C33, δ13C; n-Alkane C33, δ13C, standard deviation; n-Alkane C33, δD; n-Alkane C33, δD, standard deviation; OUTCROP; Outcrop sample; palynology; pedogenic carbonate; Plant wax; Reference/source; Sample ID; Section; stable isotope geochemistry; Stratigraphic height; Uniform resource locator/link to reference; Xining_Basin_Bingling_Shan; Xining_Basin_Caijia; XRD
    Type: Dataset
    Format: text/tab-separated-values, 753 data points
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  • 43
    Publication Date: 2024-04-23
    Description: Water isotopes (δ²H and δ¹⁸O) were analyzed in samples from lakes and rivers in eastern Germany. This sub-dataset is derived from water samples collected from lake and river shores. Seasonal samples were collected in March, July, October 2022, and in March 2023, with a plastic syringe from 20-50 cm depth below water surface and directly filtered and transferred into a measurement vial. Stable isotope analysis was conducted at IGB Berlin, using a Picarro L2130-i cavity ring-down spectrometer. Measurement uncertainty was quantified to 〈0.5 ‰ for δ²H and 〈0.2 ‰ for δ¹⁸O. Water chemical parameters were determined in-situ with a portable WTW-multiparameter probe. The data give information about the seasonal isotope amplitude at the sampled spots and about spatial isotope variability in different branches of the associated river systems.
    Keywords: Calculated; Cavity ring-down spectroscopy (CRDS), L2130-i, Picarro Inc.; Conductivity, electrical; Dahme; Dahme_Berlin-Gruenau; Dahme Berlin-Grünau, Germany; DATE/TIME; DEPTH, water; Dolgensee; Dolgensee, Germany; Elbe_Lutherstadt-Wittenberg; Elbe Lutherstadt-Wittenberg, Germany; Elster; Elsterflutbett_Leipzig; Elsterflutbett Leipzig, Germany; Event label; GEPRIS_418096356; Gremminer_See; Gremminer See, Germany; Gröbener See, Germany; Groebener_See; Grosser_Goitzschesee; Großer Goitzschesee, Germany; Heidsee; Heidsee, Germany; Hoelzerner_See; Hölzerner See, Germany; Influence of environmental factors onto the hydrogen isotopic signature of aquatic plants; Klein_Koeriser_See; Klein Köriser See, Germany; Kulkwitzer_See; Kulkwitzer See, Germany; lakes; Latitude of event; Longitude of event; Mueggelsee_outflow; Müggelsee, Germany; Mulde; Mulde_Puch; Mulde Puch, Germany; Muldestausee; Muldestausee, Germany; pH; Pinnower_See; Pinnower See, Germany; Pipette; rivers; Schladitzer_See; Schladitzer See, Germany; Schweriner_See; Schweriner See, Germany; Spree; Spree_Luebbenau; Spree_Spremberg; Spree_Unterspreewald; Spree Lübbenau, Germany; Spree Spremberg, Germany; Spree Unterspreewald, Germany; Spremberg_Talsperre_; Spremberg_Talsperre_Aue; Spremberg Talsperre, Germany; Spremberg Talsperre Aue, Germany; Streganzer_See; Streganzer See, Germany; Syringe, plastic; Temperature, water; Teupitzer_See; Teupitzer See, Germany; water isotopes; Weisse_Elster_Leipzig; Weiße Elster Leipzig, Germany; Werbelliner_See; Werbelliner See, Germany; Wolziger_See; Wolziger See, Germany; WTW probe, hand-held; Zeuthener_See; Zeuthener See, Germany; δ18O, water; δ18O, water, standard deviation; δ Deuterium, water; δ Deuterium, water, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 386 data points
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  • 44
    Publication Date: 2024-04-23
    Description: Lake Arendsee originated from salt depressions (sinkhole) 822 A.D. and in 1685 (Scharf 1998). Due to the maximum depth of 49 m (mean depth 29 m) and a surface area of about 5 km², Lake Arendsee has a volume of approx. 150 Mio. m³. According to Scharf (1998), eutrophication dates back to 1970 when sewage loading of the town of Arendsee and drainage of Lake Fauler See into Lake Arendsee raised nutrient loading. Due to the long residence time of 114 years, no recovery of the lake occurred up to now although several restoration measures were applied (e.g. Stüben et al. 1998; Hupfer and Lewandowski 2005). Lake Arendsee regularly expresses, large, surface scum forming blooms of cyanobacteria. This publication series includes datasets collected on Lake Arendsee during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 45
    Publication Date: 2024-04-23
    Description: Lake Geiseltal is the largest lake of Saxony-Anhalt and the largest artificial lake of Germany (max. depth 78 m; mean depth 22.8 m; volume 423 Mio. m³; surface area 1853 ha) and can be classified as oligotrophic. It was created by the excavation of lignite in several former surface mines starting at industrial scale in 1906 (formerly only small-scale mining dated back to 1698; Knochenhauer 1996). Mining stopped 1993 after 1.4*109 tons of lignite and the same mass of overburden were excavated. To stabilize the slopes of the residual mine pits and to avoid acidification from mine drainage, a planned, large scale flooding of the residual mine pits started in 2003 by pumping water from River Saale that was cleaned up by sand filtration before (Fritz et al. 2001). In 2011, the flooding of the lake was completed (LMBV 2018). Algal productivity in the lake is low and water transparency high, the littoral compartments along the shores harbor large stocks of submerged macrophytes. This publication series includes datasets collected on Lake Geiseltal during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).
    Type: Dataset
    Format: application/zip, 7 datasets
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  • 46
    Publication Date: 2024-04-23
    Description: Kelbra Reservoir is a shallow lowland reservoir (approx. 35 Mio. m³ volume; max. depth 5,5 m, mean depth 3.5 m; surface area 600 ha), which was constructed 1962-1966 for flood protection for the downstream reaches of the river “Helme” and came into operation 1969. It is also used for irrigation and recreational purposes. Until the beginning of the 1990s, the production of carp was also initiated and played an important role (Ziemann 1986). Immediately after commissioning, the reservoir became an important resting place for diverse migrating birds (Görner et al. 1983; Scheuer 2003). In the last 30 years, cranes, during their migration from Scandinavia to Africa, occurred in high numbers (up to 10,000 individuals; Höpfner 2003; Scheuer et al. 2019) so that bird protection measures became a vital component of the reservoir management. During winter, the reservoir is emptied to have its full capacity for flood protection. The highest floods occur in river “Helme” typically from December through March (LHW 2016). Kelbra Reservoir has been highly eutrophic from the very beginning (Ziemann 1986) until now although waste water treatment was improved and carp production was stopped in the 1990s. Still, external and internal phosphorus loading is at a high level. Due to very high total phosphorous concentrations cyanobacterial blooms appear regularly in summer time and the trophic state can be characterized to be hypertrophic. This publication series includes datasets collected on Kelbra Reservoir during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).
    Type: Dataset
    Format: application/zip, 11 datasets
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  • 47
    Publication Date: 2024-04-23
    Description: Lake Süßer See is a eutrophic to hypertrophic medium-sized lake (max. depth 8.2 m, mean depth 4.3 m; volume 11.6 Mio. m³, surface area 268 ha) which receives water from the south-eastern foothills of the Harz Mountains and a former mining area for copper shale (Mansfelder Land) by the stream “Böse Sieben” (Becker et al. 2001; Lewandowski et al. 2003). The lake basin represents a sinkhole that has been formed by dissolution of underlying Permian evaporates (Wennrich et al. 2007). High P load by the inflows are a result of extensive fertilizer application in the catchment further supporting the high trophic state of the lake. Although phosphorus removal by aluminum sulfate application between 1976 and 1992 reduced internal P loading from the sediments high external, diffuse-source TP loads prevented substantial water quality improvements (TPlake about 200 µg/L) and annually occurring algae blooms of cyanobacteria persist. This publication series includes datasets collected on Lake Süßer See during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).
    Type: Dataset
    Format: application/zip, 15 datasets
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  • 48
    Publication Date: 2024-04-23
    Description: Current warming, shifting hydrological regimes and accelerated permafrost thaw in the catchment of the Arctic rivers will affect their water biogeochemistry. The Lena River is the second largest Arctic river and 71 % of its catchment is characterized by continuous permafrost. Monitoring of Arctic rivers will enable to observe expected changes in matter transport such as an increase of dissolved organic matter (DOM) re-mobilization from permafrost. A number of biogeochemical variables are presented here in a unique high frequency throughout the whole year. The sampling of Lena River water is done near the Research Station Samoylov Island in the central Lena River Delta. The Samoylov research station allows a unique chance for continuous sampling since it operates throughout the year.
    Keywords: biogeochemistry; CDOM; DOC; DOM; major ions; Olenekskaya_Ch; RIVER; Sampling river; stable water isotopes
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 49
    Publication Date: 2024-04-23
    Description: The Rappbode Reservoir was constructed between 1952 and 1959 and is the largest drinking water reservoir (by volume) in Germany (max. depth 89 m, mean depth 28.6; max. volume 113 Mio. m³, surface area 395 ha). It is located in the eastern Harz Mountains (51.74N, 10.89E), supplies about 1 Mio. people with drinking water and is also used for flood protection, recreation and hydropower. The reservoir receives inflows from the streams Rappbode and Hassel and by a controlled water transfer from Bode River. All this water is passed through pre-dams which were constructed to trap nutrients, sediments and particulate matter thereby reducing the external nutrient load (Rinke et al. 2013, Friese et al. 2014). The water body of is dimictic and nowadays at oligotrophic to mesotrophic state with low P concentrations (TP 10 to 20 µg/L) (Wentzky et al. 2018) but historically underwent eutrophication before 1990. Phytoplankton biomass is relatively low and the algal community is largely dominated by diatoms. This publication series includes datasets collected on Rappbode Reservoir during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).
    Type: Dataset
    Format: application/zip, 6 datasets
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  • 50
    Publication Date: 2024-04-23
    Description: On Aug. 29, 2017, the central intercalibration campaign was conducted on Lake Süßer See, in which all groups were participating that had been involved in the field measurements included in the overall project campaigns. The main goal of this intercalibration campaign was to realise a direct comparison of all involved field and lab sensors/analysis in order to assess their accuracy, comparability, and reproducibility. For this purpose, all used instruments were used simultaneously at the measuring point on Lake Süßer See. This fully parallel application enabled us to directly compare the results under identical in-situ conditions and to detect and quantify instrumental deviations.
    Keywords: interdisciplinary; IWRSVC-2017
    Type: Dataset
    Format: application/zip, 22 datasets
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  • 51
    Publication Date: 2024-04-23
    Description: These data have been obtained on two cores ARA04C/37 (70°38.0212'N, 139°22.0749'W; 1173 m) and JPC15 (71°06.222'N, 135°08.129'W; 690 m). The datasets contain thermal maturity proxies, specifically of Carbon Preference Index (CPI), fractional abundance of homohopane isomer C31ββR, and Tmax, and OI, HI from the Rock-Eval pyrolysis. Additionally, compound-specific radiocarbon concentrations of bulk organic carbon and long-chain n-alkanoic acids with 26 and 28 carbon atoms have been measured and expressed as F14C with a 1 sigma uncertainty, and counts of Pediastrum spp. An additional dataset contains carbon cycle model simulations using the box model BICYCLE to describe the effect of CO2 release from deglacial terrestrial organic carbon release.
    Keywords: Arctic Ocean; Beaufort Sea; BICYCLE-SE carbon cycle model; Biomarker; radiocarbon isotope (Fm); Rock-Eval
    Type: Dataset
    Format: application/zip, 7 datasets
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  • 52
    facet.materialart.
    Unknown
    PANGAEA
    In:  Zentrum für Marine und Atmosphärische Wissenschaften, Hamburg
    Publication Date: 2024-04-23
    Keywords: Adriatic Sea; CTD, Sea-Bird SBE 911plus; CTD, underway; CTD/Rosette; CTD-RO; CTD-UW; CTD with attached oxygen sensor; Date/Time of event; DEPTH, water; Elevation of event; Event label; Latitude of event; Longitude of event; Mediterranean Sea; Oxygen; POS468; POS468_181-1; POS468_210-1; POS468_224-1; POS468_254-1; POS468_269-1; POS468_293-1; POS468_302-1; POS468_316-1; POS468_335-1; POS468_342-1; POS468_351-1; POS468_365-1; POS468_386-1; POS468_403-1; POS468_417-1; POS468_452-1; POS468_459-1; POS468_467-1; POS468_477-1; POS468_489-1; POS468_496-1; POS468_504-1; POS468_508-1; POS468_511-1; POS468_516-1; POS468_521-1; POS468_534-1; POS468_536-1; POS468_538-1; POS468_541-1; POS468_554-1; POS468_558-1; POS468_560-1; POS468_562-1; Poseidon; Pressure, water; Salinity; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 313424 data points
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  • 53
    Publication Date: 2024-04-23
    Description: Water isotopes (δ2H and δ18O) were analyzed in samples collected in lakes associated to major riverine systems in northeastern Germany throughout 2020. The dataset is derived from water samples taken at a) lake shores (sampled in March and July 2020); b) buoys temporarily installed in deep parts of the lake (sampled monthly from March to October 2020); c) multiple spatially distributed spots in four selected lakes (in September 2020); d) the outflow of Müggelsee (sampled biweekly from March 2020 to January 2021). At shores, water was sampled with a pipette from 40-60 cm below water surface and directly transferred into a measurement vial, while at buoys a Limnos water sampler was used to obtain samples from 1 m below surface. Isotope analysis was conducted at IGB Berlin, using a Picarro L2130-i cavity ring-down spectrometer. The data give information about the seasonal isotope amplitude in the sampled lakes and about spatial isotope variability in different branches of the associated riverine systems.
    Keywords: CONNECT; Connectivity and synchronization of lake ecosystems in space and time; Dahme; GEPRIS_418096356; Havel; Influence of environmental factors onto the hydrogen isotopic signature of aquatic plants; Müritz; Spree; Ucker; δ18O; δ2H
    Type: Dataset
    Format: application/zip, 6 datasets
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  • 54
    facet.materialart.
    Unknown
    PANGAEA
    In:  SIO7 Data Center, J-CORES Database; Ocean Drilling Program, Center for Deep Earth Exploration (JAMSTEC)
    Publication Date: 2024-04-23
    Keywords: 337-C0020A; C27 ster-4-enes + ster-5-enes; C27 sterane; C28 ster-4-enes + ster-5-enes; C28 sterane; C29 ster-4-enes + ster-5-enes; C29 sterane; C29 steranes; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; Chikyu; Deep Coalbed Biosphere off Shimokita; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Exp337; Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP; n-Alkanoic acids, methyl branched; Northwest Pacific; Phytane/n-C18 ratio; Pristane/Phytane ratio; Sample code/label; Sample code/label 2; Sterane/sterene ratio; Sum n-Alkanes C18-C32; Sum n-Alkanoic acids C14-C32; Sum n-Alkanoic acids C16-C18; Time Stamp; δ34S, sulfur, water-insoluble
    Type: Dataset
    Format: text/tab-separated-values, 441 data points
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  • 55
    facet.materialart.
    Unknown
    PANGAEA
    In:  Zentrum für Marine und Atmosphärische Wissenschaften, Hamburg
    Publication Date: 2024-04-23
    Keywords: CT; Current velocity, east-west; Current velocity, north-south; DATE/TIME; DEPTH, water; LATITUDE; LONGITUDE; Mediterranean Sea; POS468; POS468-track; Poseidon; Sample code/label; Shipboard Acoustic Doppler Current Profiling (SADCP); Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 358530 data points
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  • 56
    facet.materialart.
    Unknown
    PANGAEA
    In:  Zentrum für Marine und Atmosphärische Wissenschaften, Hamburg
    Publication Date: 2024-04-23
    Keywords: Adriatic Sea; Alboran Sea; CTD, underway; CTD-Scanfish; CTD-SF; CTD-UW; Date/Time of event; Date/Time of event 2; DEPTH, water; Eastern Basin; Elevation of event; Elevation of event 2; Event label; Ionian Sea; Latitude of event; Latitude of event 2; Longitude of event; Longitude of event 2; Mediterranean Sea; POS468; POS468_153-1; POS468_154-1; POS468_155-1; POS468_156-1; POS468_157-1; POS468_158-1; POS468_159-1; POS468_160-1; POS468_161-1; POS468_162-1; POS468_163-1; POS468_164-1; POS468_165-1; POS468_166-1; POS468_167-1; POS468_168-1; POS468_169-1; POS468_170-1; POS468_171-1; POS468_172-1; POS468_173-1; POS468_174-1; POS468_175-1; POS468_176-1; POS468_177-1; POS468_178-1; POS468_179-1; POS468_180-1; POS468_182-1; POS468_183-1; POS468_184-1; POS468_185-1; POS468_186-1; POS468_187-1; POS468_188-1; POS468_189-1; POS468_190-1; POS468_191-1; POS468_192-1; POS468_193-1; POS468_194-1; POS468_195-1; POS468_196-1; POS468_197-1; POS468_198-1; POS468_199-1; POS468_200-1; POS468_201-1; POS468_202-1; POS468_203-1; POS468_204-1; POS468_205-1; POS468_206-1; POS468_207-1; POS468_208-1; POS468_209-1; POS468_211-1; POS468_212-1; POS468_213-1; POS468_214-1; POS468_215-1; POS468_216-1; POS468_217-1; POS468_218-1; POS468_219-1; POS468_221-1; POS468_222-1; POS468_223-1; POS468_225-1; POS468_226-1; POS468_227-1; POS468_228-1; POS468_229-1; POS468_232-1; POS468_233-1; POS468_234-1; POS468_235-1; POS468_236-1; POS468_237-1; POS468_238-1; POS468_239-1; POS468_240-1; POS468_241-1; POS468_242-1; POS468_243-1; POS468_244-1; POS468_245-1; POS468_246-1; POS468_247-1; POS468_248-1; POS468_249-1; POS468_250-1; POS468_251-1; POS468_252-1; POS468_253-1; POS468_256-1; POS468_257-1; POS468_258-1; POS468_259-1; POS468_260-1; POS468_261-1; POS468_262-1; POS468_263-1; POS468_264-1; POS468_265-1; POS468_266-1; POS468_267-1; POS468_268-1; POS468_270-1; POS468_271-1; POS468_272-1; POS468_274-1; POS468_275-1; POS468_276-1; POS468_277-1; POS468_278-1; POS468_279-1; POS468_280-1; POS468_281-1; POS468_282-1; POS468_283-1; POS468_284-1; POS468_285-1; POS468_286-1; POS468_287-1; POS468_288-1; POS468_289-1; POS468_290-1; POS468_291-1; POS468_292-1; POS468_294-1; POS468_295-1; POS468_296-1; POS468_297-1; POS468_298-1; POS468_299-1; POS468_300-1; POS468_301-1; POS468_303-1; POS468_304-1; POS468_305-1; POS468_306-1; POS468_307-1; POS468_308-1; POS468_309-1; POS468_310-1; POS468_311-1; POS468_312-1; POS468_313-1; POS468_314-1; POS468_315-1; POS468_317-1; POS468_318-1; POS468_319-1; POS468_320-1; POS468_321-1; POS468_322-1; POS468_323-1; POS468_324-1; POS468_325-1; POS468_326-1; POS468_327-1; POS468_328-1; POS468_329-1; POS468_330-1; POS468_331-1; POS468_332-1; POS468_333-1; POS468_334-1; POS468_336-1; POS468_337-1; POS468_338-1; POS468_339-1; POS468_340-1; POS468_341-1; POS468_343-1; POS468_344-1; POS468_345-1; POS468_346-1; POS468_347-1; POS468_348-1; POS468_349-1; POS468_350-1; POS468_352-1; POS468_353-1; POS468_354-1; POS468_355-1; POS468_356-1; POS468_357-1; POS468_358-1; POS468_359-1; POS468_360-1; POS468_361-1; POS468_362-1; POS468_363-1; POS468_364-1; POS468_366-1; POS468_367-1; POS468_368-1; POS468_369-1; POS468_370-1; POS468_371-1; POS468_372-1; POS468_373-1; POS468_374-1; POS468_375-1; POS468_376-1; POS468_377-1; POS468_378-1; POS468_379-1; POS468_380-1; POS468_381-1; POS468_382-1; POS468_383-1; POS468_383-2; POS468_384-1; POS468_385-1; POS468_387-1; POS468_388-1; POS468_389-1; POS468_390-1; POS468_391-1; POS468_392-1; POS468_393-1; POS468_394-1; POS468_395-1; POS468_396-1; POS468_397-1; POS468_398-1; POS468_399-1; POS468_400-1; POS468_401-1; POS468_402-1; POS468_403-2; POS468_404-1; POS468_405-1; POS468_406-1; POS468_407-1; POS468_408-1; POS468_409-1; POS468_410-1; POS468_411-1; POS468_412-1; POS468_413-1; POS468_414-1; POS468_415-1; POS468_416-1; POS468_418-1; POS468_419-1; POS468_420-1; POS468_421-1; POS468_422-1; POS468_423-1; POS468_424-1; POS468_425-1; POS468_426-1; POS468_427-1; POS468_428-1; POS468_429-1; POS468_430-1; POS468_431-1; POS468_432-1; POS468_433-1; POS468_434-1; POS468_435-1; POS468_436-1; POS468_437-1; POS468_438-1; POS468_439-1; POS468_440-1; POS468_441-1; POS468_442-1; POS468_443-1; POS468_444-1; POS468_445-1; POS468_446-1; POS468_447-1; POS468_448-1; POS468_449-1; POS468_450-1; POS468_451-1; POS468_453-1; POS468_454-1; POS468_455-1; POS468_456-1; POS468_457-1; POS468_458-1; POS468_460-1; POS468_461-1; POS468_462-1; POS468_463-1; POS468_464-1; POS468_465-1; POS468_466-1; POS468_467-2; POS468_468-1; POS468_469-1; POS468_470-1; POS468_471-1; POS468_472-1; POS468_473-1; POS468_474-1; POS468_475-1; POS468_476-1; POS468_477-2; POS468_478-1; POS468_480-1; POS468_481-1; POS468_482-1; POS468_483-1; POS468_484-1; POS468_485-1; POS468_486-1; POS468_487-1; POS468_488-1; POS468_490-1; POS468_491-1; POS468_492-1; POS468_493-1; POS468_494-1; POS468_495-1; POS468_497-1; POS468_498-1; POS468_499-1; POS468_500-1; POS468_501-1; POS468_502-1; POS468_503-1; POS468_505-1; POS468_506-1; POS468_507-1; POS468_508-2; POS468_509-1; POS468_510-1; POS468_512-1; POS468_513-1; POS468_514-1; POS468_515-1; POS468_517-1; POS468_518-1; POS468_519-1; POS468_520-1; POS468_522-1; POS468_523-1; POS468_524-1; POS468_525-1; POS468_526-1; POS468_527-1; POS468_528-1; POS468_529-1; POS468_530-1; POS468_531-1; POS468_532-1; POS468_533-1; POS468_535-1; POS468_537-1; POS468_539-1; POS468_540-1; POS468_542-1; POS468_543-1; POS468_544-1; POS468_545-1; POS468_546-1; POS468_547-1; POS468_548-1; POS468_549-1; POS468_550-1; POS468_551-1; POS468_552-1; POS468_553-1; POS468_554-2; POS468_556-1; POS468_557-1; POS468_559-1; POS468_561-1; POS468_563-1; POS468_565-1; POS468_567-1; Poseidon; Pressure, water; Salinity; Sample code/label; Strait of Sicilia; Temperature, water; Tirreno Sea; UnderwayCTD (UCTD), Oceanscience; Western Basin
    Type: Dataset
    Format: text/tab-separated-values, 947740 data points
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  • 57
    Publication Date: 2024-04-23
    Description: The estimation of gross primary productivity (GPP) and evapotranspiration (ET) would be effected by the spatial distribution of vegetation foliage, which can be described with the canopy clumping index (CI). And the CI is an important factor to characterize the terrestrial ecosystem and model land-surface processes. The multi-angle remote sensing data provide an effective way to produce long-term global CI data. Currently, the CI data products have been produced based on the correlation between CI and the normalized difference between hotspot and darkspot (NDHD). But the global CI data products do not take into consideration of the influence of solar zenith angle (SZA) and the surface reflectance model, seriously affecting the accuracy of the CI. In addition, because of lacking for long-term CI data and ignoring the seasonal and interannual changes, the current CI data products cannot meet the demand the research of CI. Aiming at the problems of current CI data products, Prof. Hongliang Fang and the research team, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), researched on the CI estimation algorithm and produced the new global CI data products (LIS-CI-A1). The research team analyzed the CI value on the different BRDF model and SZA using the field measured leaf area index and high spatial resolution image data and put forward the optimal BRDF model and SZA to estimate the CI, solving the problem of CI estimation in sparse vegetation areas and raising the global CI data product accuracy effectively. The research team has produced the long term global CI data products, supplying global maps of CI at 8-days steps and 500-m spatial resolution for 2001-2016. This study was supported by National Natural Science Foundation of China (41471295) and the National Key Research and Development Program of China (2016YFA0600201).
    Type: Dataset
    Format: application/zip, 962.5 MBytes
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  • 58
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Castañeda, Isla S; Caley, Thibaut; Dupont, Lydie M; Kim, Jung-Hyun; Malaizé, Bruno; Schouten, Stefan (2016): Middle to Late Pleistocene vegetation and climate change in subtropical southern East Africa. Earth and Planetary Science Letters, 450, 306-316, https://doi.org/10.1016/j.epsl.2016.06.049
    Publication Date: 2024-04-23
    Description: In this study we investigate Pleistocene vegetation and climate change in southern East Africa by examining plant leaf waxes in a marine sediment core that receives terrestrial runoff from the Limpopo River. The plant leaf wax records are compared to a multi-proxy sea surface temperature (SST) record and pollen assemblage data from the same site. We find that Indian Ocean SST variability, driven by high-latitude obliquity, exerted a strong control on the vegetation of southern East Africa during the past 800,000 yr. Interglacial periods were characterized by relatively wetter and warmer conditions, increased contributions of C3 vegetation, and higher SST, whereas glacial periods were marked by cooler and arid conditions, increased contributions of C4 vegetation, and lower SST. We find that Marine Isotope Stages (MIS) 5e, 11c, 15e and 7a-7c are strongly expressed in the plant leaf wax records but MIS 7e is absent while MIS 9 is rather weak. Our plant leaf wax records also record the climate transition associated with the Mid-Brunhes Event (MBE) suggesting that the pre-MBE interval (430-800 ka) was characterized by higher inputs from grasses in comparison to relatively higher inputs from trees in the post-MBE interval (430 to 0 ka). Differences in vegetation and SST of southern East Africa between the pre- and post-MBE intervals appear to be related to shifts in the location of the Subtropical Front. Comparison with vegetation records from tropical East Africa indicates that the vegetation of southern East Africa, while exhibiting glacial-interglacial variability and notable differences between the pre- and post-MBE portions of the record, likely did not experience such dramatic extremes as occurred to the north at Lake Malawi.
    Keywords: Accumulation rate, n-Alkanes C29-C33 per year; AGE; Average chain length; CALYPSO; Calypso Corer; DEPTH, sediment/rock; Marion Dufresne (1995); MD104; MD96-2048; n-Alkane C27, per unit sediment mass; n-Alkane C29, per unit sediment mass; n-Alkane C31, per unit sediment mass; n-Alkane C31, δ13C; n-Alkane C31, δ13C, standard deviation; n-Alkane C33, per unit sediment mass; PEGASE; Ratio
    Type: Dataset
    Format: text/tab-separated-values, 1508 data points
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  • 59
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    PANGAEA
    In:  Supplement to: Häggi, Christoph; Eglinton, Timothy Ian; Zech, Wolfgang; Sosin, Piotr; Zech, Roland (2019): A 250 ka leaf-wax δD record from a loess section in Darai Kalon, Southern Tajikistan. Quaternary Science Reviews, 208, 118-128, https://doi.org/10.1016/j.quascirev.2019.01.019
    Publication Date: 2024-04-23
    Description: The data set contains plant wax biomarker and CaCO3 concentration data from the Darai Kalon loess section in Southern Tajikistan over the time-period of the last 250 thousand years. It includes long-chain n-alkane ACL data, long-chain n-carboxylic acid Index-C data as well as CPI and compound-specific δD data for both long-chain n-alkanes and long-chain n-carboxylic acids.
    Keywords: AGE; Average chain length, n-Alkanes, C27-C33; Biomarker; CaCO3; Calcium carbonate; Calculated; Carbon Preference Index, n-Alkanes (C26-C34); Carbon Preference Index, n-carboxylic acid (C23-C31); Central Asia; Compound-specific δD; Coulometer CM5012 CO2; Darai_Kalon_loess_section; Darai Kalon loess section; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Glacial; Index-C of long-chain n-carboxylic acids; Interglacial; Leaf wax; Loess; Long-chain n-alkanes; Long-chain n-carboxylic acids; n-Alkane C29, δD; n-Alkane C29-C31 average, δD; n-Alkane C31, δD; n-caboxylic acid C28, δD; Plant wax; Pleistocene; Precipitation seasonality; Sample code/label; Stable isotopes; Tajik depression; Tajikistan; Vegetation
    Type: Dataset
    Format: text/tab-separated-values, 1298 data points
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  • 60
    Publication Date: 2024-04-23
    Description: The Amazon River is known as a region of intense biochemical reactivity. As a result of increased anthropogenic impacts in its catchment (e.g. population growth, agriculture, and deforestation), the Amazon River is undergoing fundamental changes. During the RV METEOR cruise M174 (doi:10.1594/PANGAEA.935041), six stations were visited to study the nitrogen cycling and productivity of the Amazon and Pará estuaries, which is crucial to estimate the ecosystem's health. This dataset presents rates of various nitrogen cycle processes (nitrification, nitrate, ammonium, and amino acid uptake rates) as well as associated dissolved inorganic carbon uptake rates. Rates are presented as volumetric rates, as well as rates integrated over the euphotic zone (3 to 10 meters).
    Keywords: According to Damashek et al. (2016); According to Dugdale and Goering (1967); Amazon; Amazon estuary; Amino acids, uptake rate; Amino acids, uptake rate, integrated; Amino acids, uptake rate, integrated, standard deviation; Ammonium, uptake rate, integrated; Ammonium, uptake rate, integrated, standard deviation; Ammonium uptake rate; Area/locality; Calculated; Carbon, inorganic, dissolved, uptake rate; Carbon, inorganic, dissolved, uptake rate, integrated; Carbon, inorganic, dissolved, uptake rate, integrated, standard deviation; Carbon, inorganic, dissolved, uptake rate, standard deviation; CTD, Sea-Bird, SBE 911plus; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Depth comment; Event label; Incubation type; Latitude of event; Longitude of event; M174; M174_10-2; M174_12-2; M174_13-3; M174_14-2; M174_17-16; M174_9-2; Meteor (1986); Nitrate, uptake rate, integrated; Nitrate, uptake rate, integrated, standard deviation; Nitrate uptake rate; Nitrate uptake rate, standard deviation; Nitrification, integrated; Nitrification, integrated, standard deviation; Nitrification rate; Nitrification rate, standard deviation; Nitrogen; South Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 228 data points
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  • 61
    Publication Date: 2024-04-23
    Keywords: AGE; Average chain length, n-Alkanes, C25-C33; Carbon Preference Index, n-Alkanes (C25-C33); Center for Marine Environmental Sciences; DEPTH, sediment/rock; GeoB16601-5; INVERS; Leibniz Centre for Tropical Marine Research; MARUM; MUC; MultiCorer; n-Alkane C29; n-Alkane C29,C31, δ13C; n-Alkane C29,C31, δ13C, standard deviation; n-Alkane C29,C31, δD; n-Alkane C29,C31, δD, standard deviation; n-Alkane C29, δ13C; n-Alkane C29, δ13C, standard deviation; n-Alkane C29, δD; n-Alkane C29, δD, standard deviation; n-Alkane C31; n-Alkane C31, δ13C; n-Alkane C31, δ13C, standard deviation; n-Alkane C31, δD; n-Alkane C31, δD, standard deviation; SO221; Sonne; Thermo Trace GC - Finnigan MAT 252 (GC/IR-MS); ZMT
    Type: Dataset
    Format: text/tab-separated-values, 471 data points
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  • 62
    Publication Date: 2024-04-23
    Description: This global database (CoastDOM v.1) contains both previously published and unpublished measurements of Dissolved organic carbon (DOC), nitrogen (DON) and phosphorus (DOP) in coastal waters. The dataset also contains hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e.g., Chlorophyll-a, inorganic nutrients) and the inorganic carbon system (e.g., dissolved inorganic carbon and total alkalinity). The data included were collected from 1978 to 2022 and consist of 62339 data points for DOC, 20360 for DON and 13440 for DOP.
    Keywords: Alkalinity, total; Ammonium; Analytical method; Carbon, inorganic, dissolved; Carbon, organic, dissolved; Carbon, organic, particulate; Chlorophyll a; Coastal waters; Comment; DATE/TIME; DEPTH, water; Dissolved Organic Carbon; Dissolved Organic Matter; dissolved organic nitrogen; dissolved organic phosphorus; ELEVATION; global database;; Hydrogen phosphate; Institution; LATITUDE; Location; LONGITUDE; Nitrate and Nitrite; Nitrogen, organic, dissolved; Nitrogen, particulate; Nitrogen, total dissolved; Phosphorus, organic, dissolved; Phosphorus, particulate; Phosphorus, total dissolved; Principal investigator; Quality flag, alkalinity, total; Quality flag, ammonium; Quality flag, carbon, inorganic, dissolved; Quality flag, carbon, organic, dissolved; Quality flag, carbon, organic, particulate; Quality flag, chlorophyll a; Quality flag, hydrogen phosphate; Quality flag, nitrate and nitrite; Quality flag, nitrogen, particulate; Quality flag, nitrogen, total dissolved; Quality flag, phosphorus, particulate; Quality flag, phosphorus, total dissolved; Reference/source; Salinity; Sample ID; Suspended solids, total; Temperature, water; World Oceans Circulation Experiment (WOCE) quality flags according to Jiang et al. (2022)
    Type: Dataset
    Format: text/tab-separated-values, 1286555 data points
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  • 63
    Publication Date: 2024-04-23
    Description: The collection of in situ data is generally a costly process, with the Arctic being no exception. Indeed, there has been a perception that the Arctic lacks for in situ sampling; however, after many years of concerted effort and international collaboration, the Arctic is now rather well sampled with many cruise expeditions every year. For example, the GLODAP product has a greater density of in situ sample points within the Arctic than along the equator. While this is useful for open ocean processes, the fjords of the Arctic, which serve as crucially important intersections of terrestrial, coastal, and marine processes, are sampled in a much more ad hoc process. This is not to say they are not well sampled, but rather that the data are more difficult to source and combine for further analysis. It was therefore noted that the fjords of the Arctic are lacking in FAIR (Findable, Accessible, Interoperable, and Reusable) data. To address this issue a single dataset has been created from publicly available, predominantly in situ data from a number of online platforms. After finding and accessing the data, they were amalgamated into a single project-wide standard, ensuring their interoperability. The dataset was then uploaded to PANGAEA so that it itself can be findable and reusable into the future. The focus of the data collection was driven by the key drivers of change in Arctic fjords identified in a companion review paper. After receiving feedback on this process and the dataset itself, a second version (v2.0) has been created.
    Keywords: Arctic Biodiversity & Livelihoods; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); biology; chemical oceanography; Cryosphere; FACE-IT; File content; physical oceanography; Social-science
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
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  • 64
    Publication Date: 2024-04-23
    Keywords: 175-1075A; 175-1079A; 175-1082A; 175-1084A; Accumulation rate, calcium carbonate; Accumulation rate, n-Alkanes; Accumulation rate, n-alkanol; Accumulation rate, total organic carbon; Age model; Angola Basin; Benguela Current, South Atlantic Ocean; Calculated, see reference(s); Carbon Preference Index, n-Alkanes (C27-C33); Carbon Preference Index, n-Alkanols; Center for Marine Environmental Sciences; DEPTH, sediment/rock; DRILL; Drilling/drill rig; Elevation of event; Event label; GeoB1008-3; GeoB1016-3; GeoB1028-5; GeoB1710-3; GeoB1722-1; Gravity corer (Kiel type); Higher Plant Alkanes index; Joides Resolution; Latitude of event; Leg175; Longitude of event; M20/2; M6/6; MARUM; Meteor (1986); n-Alkane, average chain length; n-Alkanol average chain length; Namibia continental slope; Ocean Drilling Program; ODP; Sea surface temperature, annual mean; see reference(s); SL; Walvis Ridge, Southeast Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 99 data points
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  • 65
    Publication Date: 2024-04-23
    Keywords: 24-231; AGE; Carbon, organic, total; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; Deep Sea Drilling Project; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP; DSDP/ODP/IODP sample designation; Dust, aeolian; Glomar Challenger; Indian Ocean/Gulf of Aden/BASIN; Leg24; n-Alkane, average chain length; n-Alkane, C31/C29 ratio; n-Alkane C29, per unit sediment mass; n-Alkane C31, per unit sediment mass; n-Alkane C33, per unit sediment mass; n-Alkane modal chain length; n-Alkanoic acid, average chain length; n-Alkanoic acid C26 per unit sediment; n-Alkanoic acid C28 per unit sediment; n-Alkanoic acid C30 per unit sediment; n-Alkanoic acid modal chain length; n-Alkanoic acids/n-Alkane ratio; Sample code/label
    Type: Dataset
    Format: text/tab-separated-values, 102 data points
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  • 66
    Publication Date: 2024-04-23
    Keywords: Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; Kenya; Lithology/composition/facies; n-Alkane, average chain length; n-Alkane, C31/C29 ratio; n-Alkane C29, per unit sediment mass; n-Alkane C31, per unit sediment mass; n-Alkane C33, per unit sediment mass; n-Alkane modal chain length; n-Alkanoic acid, average chain length; n-Alkanoic acid C26 per unit sediment; n-Alkanoic acid C28 per unit sediment; n-Alkanoic acid C30 per unit sediment; n-Alkanoic acid modal chain length; n-Alkanoic acids/n-Alkane ratio; OUTCROP; Outcrop sample; Sample code/label; SECTION, height; Wargolo
    Type: Dataset
    Format: text/tab-separated-values, 40 data points
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  • 67
    Publication Date: 2024-04-23
    Keywords: 177-1090; Accumulation rate, n-Alkanes per year; AGE; Calculated; COMPCORE; Composite Core; Joides Resolution; Leg177; Ocean Drilling Program; ODP; South Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 992 data points
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  • 68
    Publication Date: 2024-04-23
    Description: The pelagic tunicate Salpa thompsoni is a widespread cold-water metazoan and a major grazer of phyto- and microzooplankton in the Southern Ocean. Long-term time series and spatiotemporal models predict that salps will expand their distribution towards higher latitudes over the next decades with ramifications for all food web components, including higher tropic level predators. Salps are potentially less nutritious and energy-rich than co-occurring euphausiids. In a changing Southern Ocean ecosystem, predators such as baleen whales, seabirds, and planktivorous fish that historically relied on an energy-rich and numerous food source (euphausiids) may face an uncertain future. This, however, may differ by season too. Whether S. thompsoni are a less nutritious prey item than euphausiids across an annual cycle at circumpolar mid and high latitudes (51–70°S) has not been investigated. We utilised published and new body composition data, i.e., organic content (ash-free dry weight as percent of dry weight, DW), carbon content (carbon weight as percent of DW), and proximate biochemical composition (carbohydrate, lipid, and protein weight as percent of DW), collected over the past forty years (1980–2020). Energy content values were calculated based on these parameters using published conversion factors. We corrected for residual water (water remaining in tissue after drying) with a published conversion factor of 12.9 %. Samples (N = 303, sometimes comprising of several salps) were collected in four seasons using a variety of large plankton nets and midwater trawls between the surface and 3200 m (mostly less than 400 m). Each specimen was sized (oral-atrial or total length) and staged (blastozooid, oozooid). The carbon-to-nitrogen ratio (C/N value) was reported for most (77 %) of the samples. Samples were used for the determination of sometimes one or several body composition parameters: organic content (N = 151), carbon content (N = 220), and proximate biochemical composition (N = 70). The weight-specific energy content ranged between 〈 0.1 and 20.5 kJ g DW⁻¹.
    Keywords: According to source references; ANT-XIII/4; ANT-XVIII/5b; ANT-XXI/4; ANT-XXIII/2; ANT-XXIII/6; ANT-XXXIII/3; BONGO; Bongo net; Container, mass; Cruise/expedition; DATE/TIME; DEPTH, water; Drake Passage; Energy content estimation according to Båmstedt (1981); Energy content estimation according to Doyle et al. (2007); Energy content estimation according to Platt et al. (1969); Event label; Gear; Hand net; HN; IKMT; invertebrate macro-grazer; Isaac-Kid-Midwater Trawl; KYM_1980_net; KYM_2000_net; LATITUDE; LONGITUDE; Mass; Midwater trawl; Month; MPD_1984_net; MPD_1986_net; MPD_1988_net; MSM-ANT-III_465; MSN; MULT; Multiple investigations; Multiple opening/closing net; MWT; NDA_1982_net; Number of specimens, range; Polar; Polarstern; proximate composition; PS112; PS112_106-17; PS112_109-2; PS112_111-16; PS112_25-40; PS112_25-49; PS112_27-2; PS112_50-11; PS22_net; PS40/048-2; PS40/086-5; PS40/099-2; PS40 06AQANTXIII_4; PS58; PS58/301-1; PS58/305-1; PS58/323-1; PS58/324-10; PS58/327-11; PS58/328-1; PS58/329-1; PS58/330-1; PS65; PS65/622-1; PS65/625-3; PS65/633-1; PS65/645-2; PS65/649-1; PS65/669-1; PS65/684-1; PS69; PS69/030-1; PS69/058-1; PS69/063-1; PS69/070-1; PS69/072-1; PS69/084-1; PS69/103-1; PS69/121-1; PS69/124-1; PS69/474-2; PS69/532-9; PS69/534-1; Rectangular midwater trawl; Reference/source; RMT; Salpa thompsoni, ash free dry mass; Salpa thompsoni, ash mass; Salpa thompsoni, carbohydrate content, per dry mass; Salpa thompsoni, carbohydrate content, per wet mass; Salpa thompsoni, carbon, per dry mass; Salpa thompsoni, carbon/nitrogen ratio; Salpa thompsoni, carbon mass; Salpa thompsoni, dry mass; Salpa thompsoni, energy content, per dry mass; Salpa thompsoni, life stage; Salpa thompsoni, lipid content, per dry mass; Salpa thompsoni, lipid content, per wet mass; Salpa thompsoni, nitrogen mass; Salpa thompsoni, oral-atrial length; Salpa thompsoni, protein content, per dry mass; Salpa thompsoni, protein content, per wet mass; Salpa thompsoni, wet mass; Scotia Sea; Season; South Atlantic; South Pacific Ocean; SPX1; SPX2; SPX3; SPX4; Station label; stoichiometry; Thaliacea; TT; Tucker trawl; Vessel; Year of sampling; ZNET; Zooplankton net
    Type: Dataset
    Format: text/tab-separated-values, 6271 data points
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  • 69
    Publication Date: 2024-04-23
    Keywords: Calculated; Carbon Preference Index, n-Alkanoic acids; Congo_River; DATE/TIME; Gas chromatography - Flame Ionization Detection (GC-FID); MULT; Multiple investigations; n-Alkanoic acid, average chain length; n-Alkanoic acid C16; n-Alkanoic acid C17; n-Alkanoic acid C18; n-Alkanoic acid C19; n-Alkanoic acid C20; n-Alkanoic acid C21; n-Alkanoic acid C22; n-Alkanoic acid C23; n-Alkanoic acid C24; n-Alkanoic acid C25; n-Alkanoic acid C26; n-Alkanoic acid C27; n-Alkanoic acid C28; n-Alkanoic acid C29; n-Alkanoic acid C30; n-Alkanoic acid C31; n-Alkanoic acid C32; n-Alkanoic acid C33; n-Alkanoic acid C34; n-Alkanoic acid C35; n-Alkanoic acid C36; Standard deviation; Sum n-Alkanoic acids C26-C36
    Type: Dataset
    Format: text/tab-separated-values, 1536 data points
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  • 70
    Publication Date: 2024-04-23
    Keywords: Carbon, organic, total; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; DEPTH, soil; Depth, soil, maximum; Depth, soil, minimum; Depth comment; Elevation of event; Event label; LA-ridgetop; LA-slopebase; Latitude of event; Longitude of event; MULT; Multiple investigations; n-Alkane, average chain length; n-Alkane C23; n-Alkane C23-33; n-Alkane C23-33, total organic carbon; n-Alkane C24; n-Alkane C25; n-Alkane C26; n-Alkane C27; n-Alkane C28; n-Alkane C29; n-Alkane C30; n-Alkane C31; n-Alkane C32; n-Alkane C33; n-Alkanoic acid, average chain length; n-Alkanoic acid C22; n-Alkanoic acid C22-C32; n-Alkanoic acid C22-C32 , total organic carbon; n-Alkanoic acid C23; n-Alkanoic acid C24; n-Alkanoic acid C25; n-Alkanoic acid C26; n-Alkanoic acid C27; n-Alkanoic acid C28; n-Alkanoic acid C29; n-Alkanoic acid C30; n-Alkanoic acid C31; n-Alkanoic acid C32; n-Alkanoic acid C33; n-Alkanoic acid C34; Peru; Peru_soil_LA-ridgetop; Peru_soil_LA-slopebase; Peru_soil_SP; Peru_soil_VC-ridgetop; Peru_soil_VC-slopebase; Peru_soil_WAY; Sample code/label; SP; VC-ridgetop; VC-slopebase; WAY
    Type: Dataset
    Format: text/tab-separated-values, 1040 data points
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  • 71
    Publication Date: 2024-04-23
    Keywords: 313; Accumulation rate, n-Alkanes C29-C33; AGE; Carbon; Center for Marine Environmental Sciences; charcoal; Density, dry bulk; DEPTH, sediment/rock; fire history; GeoB9528-3; Gravity corer (Kiel type); indicators; levoglucosan; M65/1; MARUM; Meteor (1986); Monsoon; NIOZ_UU; NIOZ Royal Netherlands Institute for Sea Research, and Utrecht University; organic aerosols; preservation; record; Sedimentation rate; sediments; SL; Sum n-alkanes C29-C33
    Type: Dataset
    Format: text/tab-separated-values, 748 data points
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  • 72
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, even numbered n-Alkanoic acids; Accumulation rate, odd numbered n-alkanes; AGE; Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanoic acids; DEPTH, sediment/rock; Gas chromatography - Flame Ionization Detection (GC-FID); KALMAR II; Kronotsky Peninsula; PC; Piston corer; SO201/2; SO201-2-12KL; Sonne; Sum even numbered n-Alkanoic acids, per unit mass total organic carbon; Sum even numbered n-Alkanoic acids, per unit sediment mass; Sum odd numbered n-alkanes, per unit mass total organic carbon; Sum odd numbered n-alkanes, per unit sediment mass
    Type: Dataset
    Format: text/tab-separated-values, 727 data points
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  • 73
    Publication Date: 2024-04-23
    Keywords: AGE; Average chain length, n-Alkanes, C27-C33; Branched GDGTs; Chinese Loess Plateau; DEPTH, sediment/rock; isotopes; Lingtai_section; Lingtai, China; Monsoon; n-Alkane C26 of total alkanes; n-Alkane C27 of total alkanes; n-Alkane C28 of total alkanes; n-Alkane C29 of total alkanes; n-Alkane C30 of total alkanes; n-Alkane C31 of total alkanes; n-Alkane C32 of total alkanes; n-Alkane C33 of total alkanes; Paleoclimate; plant waxes; Sample code/label; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 417 data points
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  • 74
    Publication Date: 2024-04-23
    Keywords: 306-U1313; Accumulation rate, C26-alkan-1-ol; Accumulation rate, n-Alkanes, non-reworked; AGE; C26-alkan-1-ol; Calculated; COMPCORE; Composite Core; Depth, composite; Depth, composite, adjusted; DEPTH, sediment/rock; DSDP/ODP/IODP sample designation; Exp306; Integrated Ocean Drilling Program / International Ocean Discovery Program; Intercore correlation; IODP; Joides Resolution; n-Alkane, non reworked; North Atlantic Climate 2; Sample code/label; Sample code/label 2; Sea surface temperature, annual mean; Sedimentation rate
    Type: Dataset
    Format: text/tab-separated-values, 21096 data points
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  • 75
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, n-Alkanes; AGE; ANT-XXVI/2; AWI_Paleo; Calculated; DEPTH, sediment/rock; Gas chromatograph, Hewlett-Packard 6891; KL; n-Alkane C29+C31; Paleoenvironmental Reconstructions from Marine Sediments @ AWI; Piston corer (BGR type); Polarstern; PS75/059-2; PS75 BIPOMAC; South Pacific Ocean
    Type: Dataset
    Format: text/tab-separated-values, 98 data points
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  • 76
    Publication Date: 2024-04-23
    Description: Compound-specific hydrogen isotopic composition of _n_-alkanes was measured in 1 cm resolution.
    Keywords: AGE; Altai region; compound-specific biomarker isotopes; DEPTH, sediment/rock; GCUWI; Gravity corer, UWITEC; KN18; Lake Khar Nuur; lake sediments; Late Holocene; n-Alkane C23, δD; n-Alkane C23, δD, standard deviation; n-Alkane C31, δD; n-Alkane C31, δD, standard deviation; Paleoclimate; Sample code/label; Δδ Deuterium, n-alkanes
    Type: Dataset
    Format: text/tab-separated-values, 818 data points
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  • 77
    Publication Date: 2024-04-23
    Keywords: 318-U1356A; Accumulation rate, hopane; Accumulation rate, n-Alkanes; Accumulation rate, total organic carbon; AGE; Average chain length; Biomarkers; C31 hopane beta beta/alpha beta+beta beta ratio; Carbon, organic, total; Carbon Preference Index; Density, dry bulk; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; DRILL; Drilling/drill rig; Dry mass; DSDP/ODP/IODP sample designation; Exp318; Hopane, total; Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP; Joides Resolution; n-Alkanes, total; Sample code/label; Sedimentation rate; Wilkes Land
    Type: Dataset
    Format: text/tab-separated-values, 1673 data points
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  • 78
    Publication Date: 2024-04-23
    Keywords: 37GVC1; AGE; ARA04C; ARA04C/37; Araon; Arctic Ocean; Beaufort Sea; BICYCLE-SE carbon cycle model; Biomarker; Carbon Preference Index, n-Alkanes (C22-C32); DEPTH, sediment/rock; GC; Gravity corer; radiocarbon isotope (Fm); Rock-Eval
    Type: Dataset
    Format: text/tab-separated-values, 31 data points
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  • 79
    Publication Date: 2024-04-23
    Keywords: Associated plant; Beerberg_peatland_2019_plants; Biomarkers; Calculated; Calculation according to Marzi et al. (1993); Calculation according to Poynter et al. (1989); Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanols; Carbon Preference Index, n-fatty acids; Central Germany; Gas chromatography - Flame Ionization Detection (GC-FID), Agilent, Agilent 7890B; relative to in-house standards; HAND; Index of aquatic macrophyte proxy of n-alkanes (C23+C25)/(C23+C25+C29+C31); Index of waxy n-alkanes to total hydrocarbons (C27+C29+C31)/(C23+C25+C27+C29+C31); Late Holocene; macrofossil; n-Alkane, average chain length; n-Alkane, C23/(C27+C31) ratio; n-Alkane, C23/C25 ratio; n-Alkane C19; n-Alkane C20; n-Alkane C21; n-Alkane C22; n-Alkane C23; n-Alkane C24; n-Alkane C26; n-Alkane C27; n-Alkane C28; n-Alkane C29; n-Alkane C30; n-Alkane C31; n-Alkane C32; n-Alkane C33; n-Alkanol average chain length; n-Alkanol C15; n-Alkanol C16; n-Alkanol C17; n-Alkanol C18; n-Alkanol C19; n-Alkanol C20; n-Alkanol C21; n-Alkanol C22; n-Alkanol C23; n-Alkanol C24; n-Alkanol C25; n-Alkanol C26; n-Alkanol C27; n-Alkanol C28; n-fatty acid C14; n-fatty acid C15; n-fatty acid C16; n-fatty acid C17; n-fatty acid C18; n-fatty acid C19; n-fatty acid C20; n-fatty acid C21; n-fatty acid C22; n-fatty acid C23; n-fatty acid C24; n-fatty acid C25; n-fatty acid C26; n-fatty acid C27; n-fatty acid C28; n-fatty acid C29; n-fatty acid C30; n-fatty acid C31; n-fatty acid C32; n-fatty acids, average chain length; peat; Pollen; radiocarbon; Sample type; Sampling by hand
    Type: Dataset
    Format: text/tab-separated-values, 2098 data points
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  • 80
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, alkenone; Accumulation rate, branched glycerol dialkyl glycerol tetraether; Accumulation rate, crenarchaeol; Accumulation rate, n-Alkanes; AGE; Alkenones; Branched and isoprenoid tetraether index; Branched glycerol dialkyl glycerol tetraether; Carbon Preference Index; Crenarchaeol; deglacial variability; Density, dry bulk; DEPTH, sediment/rock; foraminiferal geochemistry; GC; Gravity corer; land-ocean teleconnection; M125; M125_428-3; M125-35-3; Meteor (1986); n-Alkane; Organic Geochemistry; SAMBA; Sedimentation rate; South American Monsoon System; South Atlantic Convergence Zone; South Atlantic Ocean; XRF data
    Type: Dataset
    Format: text/tab-separated-values, 1167 data points
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  • 81
    Publication Date: 2024-04-23
    Description: The data sets contains n-alkane data of sediment core GeoTü SL167.he measurements of n-alkanes were carried out using Thermo Scientific Trace 1310 GC-FID and Thermo Scientific DSQ II (GC-MS). Gravity core GeoTü SL167, was retrieved at station no. 960 during R.V. METEOR cruise M74/1b in 2007 (Bohrmann et al., 2010) from the northwestern Arabian Sea off Oman, at 22°37.2'N, 59°41.5'E, 774 m water depth, core recovery 7.39 m. The sediment core was retrieved for the reconstruction of circulation and productivity changes in the eastern Mediterranean Sea during the late Quaternary with particular focus on changes in the Indian monsoon system.
    Keywords: (Lycopane + n-alkane C35)/n-alkane C31 ratio; AGE; Age model; Arabian Sea; Average chain length, n-Alkanes, C27-C33; Carbon Preference Index, n-Alkanes (C27-C33); CLICCS; Cluster of Excellence: Climate, Climatic Change, and Society; Denitrification; DEPTH, sediment/rock; Depth, sediment/rock, bottom/maximum; Depth, sediment/rock, top/minimum; Gas chromatograph, Thermo Scientific, Trace 1310 (GC-FID) and Thermo Scientific DSQ II (GC-MS); Gravity corer (Kiel type); M74/1b; M74/1b_960-1; Meteor (1986); n-alkanes; Oman Margin; OMZ; Quaternary; SL; SL 167
    Type: Dataset
    Format: text/tab-separated-values, 1095 data points
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  • 82
    Publication Date: 2024-04-23
    Description: The collection of in situ data is generally a costly process, with the Arctic being no exception. Indeed, there has been a perception that the Arctic lacks for in situ sampling; however, after many years of concerted effort and international collaboration, the Arctic is now rather well sampled with many cruise expeditions every year. For example, the GLODAP product has a greater density of in situ sample points within the Arctic than along the equator. While this is useful for open ocean processes, the fjords of the Arctic, which serve as crucially important intersections of terrestrial, coastal, and marine processes, are sampled in a much more ad hoc process. This is not to say they are not well sampled, but rather that the data are more difficult to source and combine for further analysis. It was therefore noted that the fjords of the Arctic are lacking in FAIR (Findable, Accessible, Interoperable, and Reusable) data. To address this issue a single dataset has been created from publicly available, predominantly in situ data from a number of online platforms. After finding and accessing the data, they were amalgamated into a single project-wide standard, ensuring their interoperability. The dataset was then uploaded to PANGAEA so that it itself can be findable and reusable into the future. The focus of the data collection was driven by the key drivers of change in Arctic fjords identified in a companion review paper. This dataset is a work in progress and as new datasets containing the relevant key drivers are released they will be added to an updated version planned for late 2024.
    Keywords: Arctic Biodiversity & Livelihoods; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); biology; chemical oceanography; Cryosphere; FACE-IT; File content; physical oceanography; Social-science
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
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  • 83
    Publication Date: 2024-04-23
    Description: This dataset contains bulk geochemical information (TOC, S, Tmax, Hydrogen and Oxygen Indices), relative and absolute abundances of selected plant wax and bacterial hopanoid biomarkers as well as stable carbon and hydrogen isotope signatures of selected compounds. Samples were analysed with a Leco C/S analyser, a Rock-Eval 6 pyrolysis unit, a gas chromatograph-triple quad mass spectrometer and gas chromatograph isotope ratio mass spectrometer (for d13C and d2H). The samples originate from two onshore outcrops at the Stenkul Fiord (Ellesmere Island) and are mostly coal samples of the lignite thermal maturity stage. The age of the samples are Paleogene and cover the Paleocene Eocene Thermal Maximum (PETM; Margaret Formation), and they were taken in 2017 (BGR CASE 19 expedition). The data were generated to reconstruct the terrestrial paleovenvironment in this high-latitude setting. More information on the studied sections can be found elswhere (Reinhardt et al., 2022).
    Keywords: 17alpha,21beta-homohopane, δ13C; 17beta,21beta-homohopane, δ13C; 2-methyl hop-17(21)-ene/hop-17(21)-ene ratio; Biomarkers; Carbon, organic, total; Carbon Preference Index, n-Alkanes; CASE19; Diterpenoids, total, per unit mass total organic carbon; Element analyser CS, LECO; Ellesmere Island; Epoch; Gas chromatography - Isotope ratio mass spectrometer (GC-IRMS); Gas chromatography triple quadrupole mass spectrometry (GC-QQQ-MS); Hop-17(21)-ene, per unit mass total organic carbon; Hop-17(21)-ene, δ13C; Hopanoids; Hopene, total, per unit mass total organic carbon; Hydrogen index, mass HC, per unit mass total organic carbon; Isopimarane, per unit mass total organic carbon; Isopimarane, δ13C; Location; LR17_Stenkul_Fiord_earlyEocene; n-Alkane, average chain length; n-Alkane, sum, per unit mass total organic carbon; n-Alkane C25, δD; n-Alkane C27, δD; n-Alkane C29, δD; n-Alkane C31, δ13C; neohop-13(18)-ene, δ13C; Nor-Isopimarane, δ13C; OUTCROP; Outcrop sample; Oxygen index, mass CO2, per unit mass total organic carbon; PETM; Pyrolysis temperature maximum; Rock-Eval analysis; Sample ID; SECTION, height; Site; Stable carbon and hydrogen isotopes of plant-waxes; Stable carbon isotope (δ13C); Sulfur, total; δ Deuterium, n-alkanes; δ Deuterium, n-alkanes, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 781 data points
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  • 84
    Publication Date: 2024-04-23
    Keywords: AGE; Bay of Bengal; Bengal Sea Level; Center for Marine Environmental Sciences; DEPTH, sediment/rock; KL; Leibniz Centre for Tropical Marine Research; MARUM; Piston corer (BGR type); SO188/2; SO188/2_342; Sonne; Thermo Trace GC - Finnigan MAT 252 (GC/IR-MS); Thermo Trace GC - Thermo Fischer MAT 253 (GC/IR-MS); ZMT; δ13C, n-alkanes; δ13C, standard deviation; δ Deuterium, n-alkanes; δ Deuterium, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 393 data points
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  • 85
    Publication Date: 2024-04-23
    Keywords: Campaign of event; Center for Marine Environmental Sciences; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Elevation of event; Event label; GeoB3701-1; GeoB3705-3; GeoB3706-3; GeoB3713-1; GeoB3715-2; GeoB4904-6; GeoB4906-4; GeoB4909-3; GeoB4912-3; GeoB4913-3; GeoB4915-2; GeoB4916-3; GeoB4917-4; Latitude of event; Longitude of event; M34/2; M41/1; MARUM; Meteor (1986); MUC; MultiCorer; Northern Cape Basin; northern Congo fan; Number; off Angola; off Gabun; off northern Gabun; off south Gabun; Replicates; δ Deuterium, n-alkanes; δ Deuterium, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 156 data points
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  • 86
    Publication Date: 2024-04-23
    Keywords: 230Th-normalised; Accumulation rate, alkenone per year; Accumulation rate, branched glycerol dialkyl glycerol tetraether per year; Accumulation rate, isoprenoid glycerol dialkyl glycerol tetraether per year; Accumulation rate, n-Alkanes per year; ANT-XXVI/2; AWI_MarGeoChem; AWI_Paleo; Date/Time of event; DEPTH, sediment/rock; Elevation of event; Event label; KL; Latitude of event; Longitude of event; Marine Geochemistry @ AWI; MUC; MultiCorer; Paleoenvironmental Reconstructions from Marine Sediments @ AWI; Piston corer (BGR type); Polarstern; PS75/046-1; PS75/051-2; PS75/053-1; PS75/063-1; PS75/070-1; PS75/072-3; PS75/076-1; PS75/080-2; PS75/082-2; PS75/084-1; PS75/085-2; PS75/088-3; PS75/092-1; PS75/094-3; PS75/097-5; PS75/099-1; PS75/100-1; PS75/101-2; PS75/104-2; PS75/105-1; PS75 BIPOMAC; SO213/1; SO213/1_14-1; SO213/1_22-4; SO213/2; SO213/2_26-1; SO213/2_54-4; SO213/2_76-1; SO213/2_78-1; SO213/2_79-1; SO213/2_84-2; SO213/2_87-1; Sonne; SOPATRA; South Pacific Ocean
    Type: Dataset
    Format: text/tab-separated-values, 116 data points
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  • 87
    Publication Date: 2024-04-23
    Description: Arctic marine ecosystems are experiencing rapid environmental change with respect to warming. This is leading to an increased frequency, duration, and intensity of marine heatwaves. The impact of these stochastic heatwave events have the potential to negatively effect temperature-sensitive, habitat forming, kelp, that exist in the lower Arctic region. We tested the potential impacts of two heatwave events on mixed kelp communities occurring in the lower Arctic by conducting a 1-month ex situ mesocosm experiment in Tromsø, Norway. Each mesocosm was stocked with ~ 2.5 kg fw (fresh weight) of kelp, 200 g fw of snails and mussels, and ~ 750 g of sea urchins. Three experimental conditions were tested: a constant high temperature which was + 1.76°C above a dynamic control, and two heatwave scenarios. Scenario 1 was a long duration at + 2.8°C above the control for 2 weeks, and scenario 2 was a high frequency and magnitude treatment with conditions + 3.8°C above the control. This occurred at two peaks that were one weak apart and returned to + 1.76°C in-between. Three-hour incubations were performed to examine net community productivity (NCP) for the mixed kelp communities. We identified that both heatwave scenarios diminished the total gross production over the experimental period compared to the control and between scenario 1 and scenario 2. Scenario 1 appeared to exhibit the lowest total gross community production over the experimental period.
    Keywords: Analysis date/time, experiment; Arctic Biodiversity & Livelihoods; Climate change; Conductivity digital sensor, Aqualabo, PC4E; ecosystems; Experiment; FACE-IT; Incubation duration; kelp; marine heatwaves (MHWs); MESO; Mesocosm experiment; Net community production; Oxygen; Oxygen Optical digital sensor, Aqualabo, PODOC; Photosynthetic Active Radiation Logger (PAR), Odyssey®; Radiation, photosynthetically active per minute; Replicates; Treatment; Treatment: salinity; Treatment: temperature; Tromsø_Mesocosm_2022; Tromsø, Norway
    Type: Dataset
    Format: text/tab-separated-values, 196010 data points
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  • 88
    Publication Date: 2024-04-23
    Keywords: Arctic Ocean; ARK-XIII/2; ARK-XV/2; ARK-XVI/1; ARK-XVI/2; AWI_Paleo; Carbon Preference Index, n-Alkanes (C22-C32); DEPTH, sediment/rock; East Greenland continental slope; Elevation of event; Event label; Fram Strait; Giant box corer; GKG; KAL; Kasten corer; Latitude of event; Longitude of event; MUC; MultiCorer; n-Alkane, per unit mass total organic carbon; n-Alkane, sum, assigned to total organic carbon; n-Docosane, per unit mass total organic carbon; n-Dotriacontane, per unit mass total organic carbon; n-Eicosane, per unit mass total organic carbon; n-Heneicosane, per unit mass total organic carbon; n-Hentriacontane, per unit mass total organic carbon; n-Heptacosane, per unit mass total organic carbon; n-Heptadecane, per unit mass total organic carbon; n-Hexacosane, per unit mass total organic carbon; n-Hexadecane, per unit mass total organic carbon; n-Nonacosane, per unit mass total organic carbon; n-Nonadecane, per unit mass total organic carbon; n-Octacosane, per unit mass total organic carbon; n-Octadecane, per unit mass total organic carbon; North Greenland Sea; n-Pentacosane, per unit mass total organic carbon; n-Pentadecane, per unit mass total organic carbon; n-Tetracosane, per unit mass total organic carbon; n-Triacontane, per unit mass total organic carbon; n-Tricosane, per unit mass total organic carbon; Paleoenvironmental Reconstructions from Marine Sediments @ AWI; Polarstern; PS2830-8; PS2831-10; PS2832-13; PS2834-7; PS2835-5; PS2836-6; PS2837-6; PS2838-8; PS2847-3; PS2848-3; PS2849-6; PS2851-2; PS2853-9; PS2854-2; PS2855-9; PS2856-7; PS2857-10; PS2858-6; PS2859-10; PS2860-7; PS2861-11; PS2863-2; PS2864-4; PS2865-2; PS44; PS44/057; PS44/058; PS44/059; PS44/062; PS44/063; PS44/064; PS44/065; PS44/067; PS44/076; PS44/077; PS44/079; PS44/082; PS44/084; PS44/085; PS44/087; PS44/088; PS44/089; PS44/090; PS44/091; PS44/092; PS44/093A; PS44/095; PS44/096; PS44/097; PS55; PS55/077-4; PS55/092-4; PS55/093-2; PS55/095-2; PS55/097-2; PS55/098-2; PS55/158-1; PS57; PS57/123-2; PS57/131-2; PS57/153-1; PS57/157-1; PS57/160-1; PS57/161-3; PS57 ARKTIEF; Sample comment; W Spitzbergen; Yermak Plateau
    Type: Dataset
    Format: text/tab-separated-values, 850 data points
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  • 89
    Publication Date: 2024-04-23
    Keywords: 175-1075A; 175-1079A; 175-1082A; 175-1084A; Accumulation rate, calcium carbonate; Accumulation rate, n-Alkanes; Accumulation rate, n-alkanol; Accumulation rate, total organic carbon; Age model; Angola Basin; Benguela Current, South Atlantic Ocean; Calculated, see reference(s); Carbon Preference Index, n-Alkanes (C27-C33); Carbon Preference Index, n-Alkanols; Center for Marine Environmental Sciences; DEPTH, sediment/rock; DRILL; Drilling/drill rig; Elevation of event; Event label; GeoB1008-3; GeoB1016-3; GeoB1028-5; GeoB1710-3; GeoB1722-1; Gravity corer (Kiel type); Higher Plant Alkanes index; Joides Resolution; Latitude of event; Leg175; Longitude of event; M20/2; M6/6; MARUM; Meteor (1986); n-Alkane, average chain length; n-Alkanol average chain length; Namibia continental slope; Ocean Drilling Program; ODP; Sea surface temperature, annual mean; see reference(s); SL; Walvis Ridge, Southeast Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 99 data points
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  • 90
    Publication Date: 2024-04-23
    Keywords: 133; Alkenone, C37:2, per unit mass organic carbon; Alkenone, C37:2+C37:3, δ13C; Alkenone, C37:2+C37:3, δD; Alkenone, C37:2 per unit sediment mass; Alkenone, unsaturation index UK'37; Alkenones; Average chain length, n-Alkanes, C27-C33; Average chain length, n-fatty acid, C24-C30; Carbon, inorganic, total; Carbon, organic; Carbon/Nitrogen ratio; Carbon Preference Index, n-Alkanes (C26-C34); Carbon Preference Index, n-fatty acid (C23-C31); Center for Marine Environmental Sciences; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; fatty acids; GeoB7805-1; lipid biomarkers; M52/3; marine organic matter degradation; MARUM; Meteor (1986); MUC; MultiCorer; n-Alkane C29, δ13C; n-Alkane C29, δD; n-Alkane C29-C31, per unit mass organic carbon; n-Alkane C29-C31, per unit sediment mass; n-Alkane C31, δ13C; n-Alkane C31, δD; n-alkanes; n-fatty acid C16, per unit mass organic carbon; n-fatty acid C16, δD; n-fatty acid C16:0, δ13C; n-fatty acid C16 per unit sediment mass; n-fatty acid C24, per unit mass organic carbon; n-fatty acid C24, δD; n-fatty acid C24:0, δ13C; n-fatty acid C24 per unit sediment mass; n-fatty acid C26, δD; n-fatty acid C26:0, δ13C; n-fatty acid C28, per unit mass organic carbon; n-fatty acid C28, δD; n-fatty acid C28:0, δ13C; n-fatty acid C28 per unit sediment mass; n-fatty acid C30, δ13C; n-fatty acid C30, δD; Nitrogen; Northern Red Sea, Shaban Deep Area; particulate organic matter; Red Sea; Sea surface temperature, annual mean; Shaban Deep; stable carbon isotopes δ13C; stable hydrogen isotopes δ2H; δ13C, total organic carbon; δ Deuterium, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 902 data points
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  • 91
    Publication Date: 2024-04-23
    Keywords: African Summer Monsoon; Calendar age; Compound-specific carbon isotopes; Compound-specific hydrogen isotopes; Compound-specific oxygen isotopes; coupled δ2Hn-alkane–δ18Osugar paleohygrometer; DEPTH, sediment/rock; elemental geochemistry; Gas chromatography - Isotope ratio mass spectrometer (GC-IRMS); Grain Size; hydrology; leaf-wax n-alkane; Paleoclimate; PCUWI; peat geochemistry; Piston corer, UWITEC; precipitation reconstruction; Reference/source; relative humidity; South Africa; sugars; Vankervelsvlei; Westerlies; δ Deuterium, n-alkanes; δ Deuterium, n-alkanes, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 374 data points
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  • 92
    Publication Date: 2024-04-23
    Keywords: Beerberg_peatland_2019; Biomarkers; Calculated; Calculation according to Marzi et al. (1993); Calculation according to Poynter et al. (1989); Carbon Preference Index, n-Alkanes; Carbon Preference Index, n-Alkanols; Carbon Preference Index, n-fatty acids; Central Germany; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Gas chromatography - Flame Ionization Detection (GC-FID), Agilent, Agilent 7890B; relative to in-house standards; Index of aquatic macrophyte proxy of n-alkanes (C23+C25)/(C23+C25+C29+C31); Index of waxy n-alkanes to total hydrocarbons (C27+C29+C31)/(C23+C25+C27+C29+C31); Late Holocene; macrofossil; n-Alkane, average chain length; n-Alkane, C23/(C27+C31) ratio; n-Alkane, C23/C25 ratio; n-Alkane C19; n-Alkane C20; n-Alkane C21; n-Alkane C22; n-Alkane C23; n-Alkane C24; n-Alkane C26; n-Alkane C27; n-Alkane C28; n-Alkane C29; n-Alkane C30; n-Alkane C31; n-Alkane C32; n-Alkane C33; n-Alkanol average chain length; n-Alkanol C15; n-Alkanol C16; n-Alkanol C17; n-Alkanol C18; n-Alkanol C19; n-Alkanol C20; n-Alkanol C21; n-Alkanol C22; n-Alkanol C23; n-Alkanol C24; n-Alkanol C25; n-Alkanol C26; n-Alkanol C27; n-Alkanol C28; n-fatty acid C14; n-fatty acid C15; n-fatty acid C16; n-fatty acid C17; n-fatty acid C18; n-fatty acid C19; n-fatty acid C20; n-fatty acid C21; n-fatty acid C22; n-fatty acid C23; n-fatty acid C24; n-fatty acid C25; n-fatty acid C26; n-fatty acid C27; n-fatty acid C28; n-fatty acid C29; n-fatty acid C30; n-fatty acid C31; n-fatty acid C32; n-fatty acids, average chain length; peat; PEATC; Peat corer; Pollen; radiocarbon; Sample code/label; Thuringia, Germany
    Type: Dataset
    Format: text/tab-separated-values, 5185 data points
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  • 93
    Publication Date: 2024-04-23
    Keywords: Accumulation rate, n-Alkanes C27-C33; AGE; Alkenone; Average chain length, n-Alkanes, C27-C33; Biomarkers; brGDGT; Carbon Preference Index, n-Alkanes (C26-C34); Chile; Density, dry bulk; DEPTH, sediment/rock; Helmholtz-Verbund Regionale Klimaänderungen = Helmholtz Climate Initiative (Regional Climate Change); Mirai; MR16-09; MR16-09_PC03; MR16-09 PC03; n-alkane; n-alkanes, C27-C33; Patagonia; Patagonian Ice Sheet; PC; Piston corer; REKLIM; Sedimentation rate; Sediment core; SST; UK'37; UK37
    Type: Dataset
    Format: text/tab-separated-values, 996 data points
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  • 94
    Publication Date: 2024-04-23
    Description: Se caracterizó y evaluó el estado de conservación de los pastos marinos en áreas de interés conservacionista del Archipiélago Sabana-Camagüey en el periodo 2001-2003, considerando variables descriptivas de las angiospermas marinas, de la estructura del macrofitobentos y abióticas para conocer las causas de su afectación. Las áreas estudiadas fueron las bahías de Cárdenas, Santa Clara, Buena Vista, de Perros, Jigüey, La Gloria y Nuevitas, así como algunas lagunas arrecifales. Los pastos marinos mejor conservados se encontraron en las zonas con mayor intercambio con el océano, destacándose las lagunas arrecifales. El deterioro de los pastos marinos se debió principalmente al aumento de la turbidez por contaminación orgánica cerca de la isla principal en sectores costeros de las bahías de Cárdenas, Santa Clara y Buena Vista, y a este factor, junto con el incremento de la salinidad, en las bahías de Perros y Jigüey. Las condiciones más propicias para el desarrollo de los pastos parecen ser: la visibilidad submarina 〉 1 m, la salinidad 〈 43 ups, la variabilidad de salinidad 〈 10 ups, la DQO 〈 5,6 mgO2 L-1 y el nitrógeno total 〈 173 μM en el agua. La especie de angiosperma dominante fue Thalassia testudinum, seguida por Syringodium filiforme y Halodule wrightii, que dominaron donde disminuyó la luz y aumentaron los nutrientes. El inventario del macrofitobentos en el ASC acumuló 227 especies (100 Rhodophyta, 26 Ochrophyta, 96 Chlorophyta y cinco Magnoliophyta), con 66 nuevos registros para la zona y 16, para Cuba. Las macroalgas más frecuentes fueron de los órdenes Bryopsidales (Chlorophyta) y Ceramiales (Rhodophyta). La estructura del macrofitobentos estuvo modulada por los mismos factores que afectan el desarrollo de las angiospermas, con una riqueza específica menor donde está deteriorado el hábitat.
    Description: PhD
    Repository Name: AquaDocs
    Type: Thesis/Dissertation
    Format: 103pp.
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  • 95
    Publication Date: 2024-04-23
    Description: Upscaling plant biomass distribution and dynamics is essential for estimating carbon stocks and carbon balance. In this respect, the Russian Far East is among the least investigated sub-Arctic regions despite its known vegetation sensitivity to ongoing warming. We representatively harvested above-ground biomass (AGB; separated by dominant taxa) at 40 sampling plots in central Chukotka. We used ordination to relate field-based taxa projective cover and Landsat-derived vegetation indices. A general additive model was used to link the ordination scores to AGB. We then mapped AGB for paired Landsat-derived time slices (i.e. 2000/2001/2002 and 2016/2017), in four study regions covering a wide vegetation gradient from closed-canopy larch forests to barren alpine tundra. We provide AGB estimates and changes in AGB that were previously lacking for central Chukotka at a high spatial resolution and a detailed description of taxonomical contributions. Generally, AGB in the study region ranges from 0 to 16 kg m−2, with Cajander larch providing the highest contribution. Comparison of changes in AGB within the investigated period shows that the greatest changes (up to 1.25 kg m−2 yr−1) occurred in the northern taiga and in areas where land cover changed to larch closed-canopy forest. As well as the notable changes, increases in AGB also occur within the land-cover classes. Our estimations indicate a general increase in total AGB throughout the investigated tundra–taiga and northern taiga, whereas the tundra showed no evidence of change in AGB.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
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  • 96
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    In:  EPIC3Polar Biology, 26(4), pp. 276-278, ISSN: 0722-4060
    Publication Date: 2024-04-23
    Description: With two exceptions, no general patterns of patchiness of the megabenthos were found on the Antarctic shelf and off northeast Greenland. Underwater videos were used as a sampling method and Morisita's Index of Dispersion for statistical analysis. A gradient from randomness to patchiness occurred for most taxa, whereas the pattern of asteroids could not be distinguished from randomness. In the Antarctic, the totals of other mobile animals were less aggregated than for sessile taxa. The findings are interpreted as a result of ecological complexity within species assemblages.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 97
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    Alfred Wegener Institute
    In:  EPIC3Reports on Polar and Marine Research - Russian-German Cooperation: Expeditions to Siberia in 2019, Bremerhaven, Alfred Wegener Institute, pp. 141-149, ISBN: 1866-3192
    Publication Date: 2024-04-23
    Description: The aim of the expedition CACOON Sea was to investigate the transition from fresh water to salt water and its impact on fate and quality on dissolved and particulate organic and inorganic carbon and nitrogen. This is in accordance with the main Changing Arctic Carbon cycle in the cOastal Ocean Near-shore (CACOON, https://www.changing-arctic-ocean.ac.uk/project/cacoon/) project goal to investigate the changing freshwater export and impact of terrestrial permafrost thaw into the near-shore zone of the Laptev Sea.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Inbook , peerRev
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  • 98
    Publication Date: 2024-04-23
    Description: With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross- cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge.The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.
    Repository Name: EPIC Alfred Wegener Institut
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  • 99
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    American Geophysical Union (AGU)
    In:  EPIC3Geophysical Research Letters, American Geophysical Union (AGU), 49(22), ISSN: 0094-8276
    Publication Date: 2024-04-23
    Description: Future precipitation levels remain uncertain because climate models have struggled to reproduce observed variations in temperature-precipitation correlations. Our analyses of Holocene proxy-based temperature-precipitation correlations and hydrological sensitivities from 2,237 Northern Hemisphere extratropical pollen records reveal a significant latitudinal dependence and temporal variations among the early, middle, and late Holocene. These proxy-based variations are largely consistent with patterns obtained from transient climate simulations (TraCE21k). While high latitudes and subtropical monsoon areas show mainly stable positive correlations throughout the Holocene, the mid-latitude pattern is temporally and spatially more variable. In particular, we identified a reversal from positive to negative temperature-precipitation correlations in the eastern North American and European mid-latitudes from the early to mid-Holocene that mainly related to slowed down westerlies and a switch to moisture-limited convection under a warm climate. Our palaeoevidence of past temperature-precipitation correlation shifts identifies those regions where simulating past and future precipitation levels might be particularly challenging.
    Repository Name: EPIC Alfred Wegener Institut
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  • 100
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    Alfred Wegener Institute
    In:  EPIC3Reports on Polar and Marine Research - Russian-German Cooperation: Expeditions to Siberia in 2019, Bremerhaven, Alfred Wegener Institute, pp. 14-24, ISBN: 1866-3192
    Publication Date: 2024-04-23
    Description: With the CACOON project, we aim to quantify the effect of changing freshwater export and terrestrial permafrost thaw on the type and fate of river-borne organic matter (OM) delivered to Arctic coastal waters, and resultant changes on ecosystem functioning in the coastal Arctic Ocean. The CACOON ice expedition was the first step to set the observational basis for the projects combined observational, experimental and modelling approach. With the gained sample material, we will conduct laboratory experiments to parameterise the susceptibility of terrigenous carbon to abiotic and biotic transformation and losses, and then use the results from these to deliver a marine ecosystem model capable of representing the major biogeochemical cycles of carbon, nutrients and OM cycling in these regions. We will apply this model to assess how future changes to freshwater runoff and terrigenous carbon fluxes alter the biogeochemical structure and function of shelf ecosystems. Our aims for the project are the following: • generate novel seasonally-explicit datasets of OM source and transformation across the Lena River nearshore environments • identify and parameterise key abiotic and biotic processes affecting terrestrial organic matter fluxes from land-to-ocean • deliver projections of how future changes to freshwater runoff and terrestrial organic matter fluxes will alter the biogeochemical structure and function of shelf ecosystems.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Inbook , peerRev
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