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  • PANGAEA
  • 2010-2014  (151)
  • 1980-1984
  • 1940-1944
  • 2012  (151)
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  • 2010-2014  (151)
  • 1980-1984
  • 1940-1944
Year
  • 1
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    PANGAEA
    In:  Supplement to: Daniau, Anne-Laure; Bartlein, Patrick J; Harrison, S P; Prentice, Iain Colin; Brewer, Simon; Friedlingstein, Pierre; Harrison-Prentice, T I; Inoue, J; Izumi, K; Marlon, Jennifer R; Mooney, Scott D; Power, Mitchell J; Stevenson, J; Tinner, Willy; Andric, M; Atanassova, J; Behling, Hermann; Black, M; Blarquez, O; Brown, K J; Carcaillet, C; Colhoun, Eric A; Colombaroli, Daniele; Davis, Basil A S; D'Costa, D; Dodson, John; Dupont, Lydie M; Eshetu, Z; Gavin, D G; Genries, A; Haberle, Simon G; Hallett, D J; Hope, Geoffrey; Horn, S P; Kassa, T G; Katamura, F; Kennedy, L M; Kershaw, A Peter; Krivonogov, S; Long, C; Magri, Donatella; Marinova, E; McKenzie, G Merna; Moreno, P I; Moss, Patrick T; Neumann, F H; Norstrom, E; Paitre, C; Rius, D; Roberts, Neil; Robinson, G S; Sasaki, N; Scott, Louis; Takahara, H; Terwilliger, V; Thevenon, Florian; Turner, R; Valsecchi, V G; Vannière, Boris; Walsh, M; Williams, N; Zhang, Yancheng (2012): Predictability of biomass burning in response to climate changes. Global Biogeochemical Cycles, 26(4), https://doi.org/10.1029/2011GB004249
    Publication Date: 2024-01-13
    Description: We analyze sedimentary charcoal records to show that the changes in fire regime over the past 21,000 yrs are predictable from changes in regional climates. Analyses of paleo- fire data show that fire increases monotonically with changes in temperature and peaks at intermediate moisture levels, and that temperature is quantitatively the most important driver of changes in biomass burning over the past 21,000 yrs. Given that a similar relationship between climate drivers and fire emerges from analyses of the interannual variability in biomass burning shown by remote-sensing observations of month-by-month burnt area between 1996 and 2008, our results signal a serious cause for concern in the face of continuing global warming.
    Keywords: Center for Marine Environmental Sciences; MARUM
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 2
    Publication Date: 2023-03-27
    Description: The smallest marine phytoplankton, collectively termed picophytoplankton, have been routinely enumerated by flow cytometry since the late 1980s, during cruises throughout most of the world ocean. We compiled a database of 40,946 data points, with separate abundance entries for Prochlorococcus, Synechococcus and picoeukaryotes. We use average conversion factors for each of the three groups to convert the abundance data to carbon biomass. After gridding with 1° spacing, the database covers 2.4% of the ocean surface area, with the best data coverage in the North Atlantic, the South Pacific and North Indian basins. The average picophytoplankton biomass is 12 ± 22 µg C L-1 or 1.9 g C m-2. We estimate a total global picophytoplankton biomass, excluding N2-fixers, of 0.53 - 0.74 Pg C (17 - 39 % Prochlorococcus, 12 - 15 % Synechococcus and 49 - 69 % picoeukaryotes). Future efforts in this area of research should focus on reporting calibrated cell size, and collecting data in undersampled regions.
    Keywords: MAREMIP; MARine Ecosystem Model Intercomparison Project
    Type: Dataset
    Format: application/zip, 6.6 MBytes
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  • 3
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    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2023-03-27
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.
    Keywords: MAREMIP; MARine Ecosystem Model Intercomparison Project
    Type: Dataset
    Format: application/zip, 1.7 MBytes
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  • 4
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    PANGAEA
    In:  Supplement to: McPherren, Eric D; Kuiper, Yvette D (2013): The effects of Dissolution–Precipitation Creep on quartz fabrics within the Purgatory Conglomerate, Rhode Island. Journal of Structural Geology, 51, 105-117, https://doi.org/10.1016/j.jsg.2013.03.002
    Publication Date: 2023-01-13
    Description: Quartz Crystallographic Preferred Orientation (CPO) patterns are most commonly a result of deformation by dislocation creep. We investigated whether Dissolution-Precipitation Creep (DPC) a process that occur at lower differential stresses and temperatures, may result in CPO in quartz. Within the Purgatory Conglomerate, DPC led to quartz dissolution along cobble surfaces perpendicular to the shortening direction, and quartz precipitation in overgrowths at the ends of the cobbles (strain shadows), parallel to the maximum extension direction. The Purgatory Conglomerate is part of the SE Narragansett basin where strain intensity increases from west to east and is associated with top-to-the-west transport and folding during the Alleghanian orogeny. Quartz c-axis orientations as revealed by Electron Backscatter Diffraction (EBSD) methods, were random in all analyzed domains within the cobbles and strain shadows irrespective of the intensity of strain or metamorphic grade of the sample. Quartz dissolution probably occurred exclusively along the cobbles' margins, leaving the remaining grains unaffected by DPC. The fact that quartz precipitated in random orientations may indicate that the strain shadows were regions of little or no differential stress.
    Keywords: Black Point, Rhode Island, USA; Geological sample; GEOS; Hill Point, Rhode Island, USA; Purgatory Chasm, Newport, Rhode Island, USA; Site_1; Site_2; Site_3
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 5
    Publication Date: 2023-01-13
    Keywords: Black Point, Rhode Island, USA; Event label; File format; File size; Geological sample; GEOS; Hill Point, Rhode Island, USA; Purgatory Chasm, Newport, Rhode Island, USA; Site_1; Site_2; Site_3; Uniform resource locator/link to image
    Type: Dataset
    Format: text/tab-separated-values, 9 data points
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  • 6
    Publication Date: 2023-01-13
    Keywords: -; Black Point, Rhode Island, USA; Event label; Geological sample; GEOS; Hill Point, Rhode Island, USA; Plunge; Purgatory Chasm, Newport, Rhode Island, USA; Sample code/label; Site_1; Site_2; Site_3; Trend; x/y ratio; X Axis; y/z ratio; Y Axis; Z Axis
    Type: Dataset
    Format: text/tab-separated-values, 733 data points
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  • 7
    Publication Date: 2023-01-13
    Keywords: -; average; Black Point, Rhode Island, USA; Calculated; Change; delta; Event label; Geological sample; GEOS; Hill Point, Rhode Island, USA; Purgatory Chasm, Newport, Rhode Island, USA; Site_1; Site_2; Site_3; Volume; x/y ratio; X Axis; y/z ratio; Y Axis; Z Axis
    Type: Dataset
    Format: text/tab-separated-values, 45 data points
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  • 8
    Publication Date: 2023-09-30
    Keywords: 93-603B; Aluminium; Deep Sea Drilling Project; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP; DSDP/ODP/IODP sample designation; Glomar Challenger; Iron; Iron/Aluminium ratio; Leg93; Molybdenum; Replicates; Sample code/label; δ56Fe; δ56Fe, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 306 data points
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  • 9
    Publication Date: 2023-09-30
    Keywords: 302-M0002A; 302-M0004A; 302-M0004B; ACEX-M2A; ACEX-M4A; ACEX-M4B; AGE; Arctic Coring Expedition, ACEX; Arctic Ocean; CCGS Captain Molly Kool (Vidar Viking); Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Depth comment; Event label; Exp302; ICP-MS, see further details; Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP; Neodymium-143/Neodymium-144 ratio; Neodymium-143/Neodymium-144 ratio, error; Rubidium-87/Strontium-86 ratio; Samarium-147/Neodymium-144 ratio; Sample code/label; Sample comment; Sample type; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, error; ε-Neodymium, standard deviation; ε-Neodymium (0)
    Type: Dataset
    Format: text/tab-separated-values, 290 data points
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  • 10
    Publication Date: 2023-09-30
    Keywords: Aluminium; HEIGHT above ground; Iron; Iron/Aluminium ratio; Molybdenum; Replicates; S75; Sample code/label; δ56Fe; δ56Fe, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 161 data points
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