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  • 1
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Bergen, James A; Truax III, S; de Kaenel, Eric P; Blair, Stacie A; Browning, Emily L; Lundquist, J; Boesiger, Todd; Bolivar, M; Clark, K (2019): BP Gulf of Mexico Neogene Astronomically-tuned Time Scale (BP GNATTS). Bulletin of the Geological Society of America, 131(11-12), 1871-1888, https://doi.org/10.1130/B35062.1
    Publication Date: 2024-06-05
    Description: This paper introduces an integrated Neogene microfossil biostratigraphic chart developed within post-merger BP for the Gulf of Mexico Basin and is the first published industrial framework "fully-tuned" to orbital periodicities. Astronomical-tuning was accomplished through a 15-year research program on ODP Leg 154 sediments (offshore NE Brazil) with sampling resolution for calcareous nannofossils and planktonic foraminifera about 20 k.y. and 40 k.y. (thousand year), respectively. This framework extends from the Late Oligocene (25.05 Ma) to Recent at an average Chart Horizon resolution for the Neogene of 144 k.y., approximately double that of published Gulf of Mexico biostratigraphic charts and a five-fold increase over the highest resolution global calcareous microfossil biozonation. Such resolution approximates that of 4th to 5th order parasequences and is a critical component in the verification of seismic correlations between mini-basins in the deep-water Gulf of Mexico. Its utility in global time-scale construction and correlation has been proven, in part, by application of the scheme in full to internal research for the Oligocene-Miocene boundary interval on the Global Boundary Stratotype Section and Point (GSSP) in northern Italy and offshore wells in the eastern Mediterranean Sea. This step change in Neogene resolution, now at the level of cyclostratigraphy (the orbital periodicity of eccentricity) and the magnetostratigraphic chron, demonstrates the potential for calcareous microfossil biostratigraphy to more consistently reinforce correlations of these time scale parameters. The integration of microfossil disciplines, consistent taxonomies, and rigorous analytical methodologies are all critical to obtaining and reproducing this new level of biostratigraphic resolution.
    Keywords: Ocean Drilling Program; ODP
    Type: Dataset
    Format: application/zip, 21 datasets
    Location Call Number Expected Availability
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  • 2
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Tambutté, Eric; Venn, Alexander A; Holcomb, Michael; Segonds, Natacha; Techer, Nathalie; Zoccola, Didier; Allemand, Denis; Tambutté, Sylvie (2015): Morphological plasticity of the coral skeleton under CO2-driven seawater acidification. Nature Communications, 6, 7368, https://doi.org/10.1038/ncomms8368
    Publication Date: 2024-06-05
    Description: Ocean acidification causes corals to calcify at reduced rates, but current understanding of the underlying processes is limited. Here, we conduct a mechanistic study into how seawater acidification alters skeletal growth of the coral Stylophora pistillata. Reductions in colony calcification rates are manifested as increases in skeletal porosity at lower pH, while linear extension of skeletons remains unchanged. Inspection of the microstructure of skeletons and measurements of pH at the site of calcification indicate that dissolution is not responsible for changes in skeletal porosity. Instead, changes occur by enlargement of corallite-calyxes and thinning of associated skeletal elements, constituting a modification in skeleton architecture. We also detect increases in the organic matrix protein content of skeletons formed under lower pH. Overall, our study reveals that seawater acidification not only causes decreases in calcification, but can also cause morphological change of the coral skeleton to a more porous and potentially fragile phenotype.
    Keywords: Acid-base regulation; Alkalinity, total; Alkalinity, total, standard deviation; Animalia; Aragonite saturation state; Aragonite saturation state, standard deviation; Area, standard error; Area in square milimeter; Benthic animals; Benthos; Bicarbonate ion; Bicarbonate ion, standard deviation; Biomass/Abundance/Elemental composition; Calcification/Dissolution; Calcification rate, standard error; Calcification rate of calcium carbonate; Calcifying fluid, pH; Calcifying fluid, pH, standard error; Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Chlorophyll a; Chlorophyll a, per protein; Chlorophyll a, standard error; Chlorophyll a per cell; Chlorophyll c2; Chlorophyll c2, per protein; Chlorophyll c2, standard error; Chlorophyll c2 per cell; Cnidaria; Coast and continental shelf; Corallite, per skeleton surface area; Corallite, per skeleton surface area, standard error; Density, skeletal bulk; Density, skeletal bulk, standard error; Figure; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Laboratory experiment; Linear extension; Linear extension, standard error; Mediterranean Sea; OA-ICC; Ocean Acidification International Coordination Centre; Organic matrix protein, per skeleton; Organic matrix protein, per skeleton, standard error; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); pH; pH, standard deviation; pH, standard error; Photosynthesis rate, oxygen, per protein; Photosynthesis rate of oxygen; Photosynthesis rate of oxygen, per symbiont cell; Photosynthesis rate of oxygen, standard error; Porosity; Porosity, standard error; Potentiometric; Potentiometric titration; Primary production/Photosynthesis; Protein per surface area; Proteins, standard error; Respiration; Respiration rate, oxygen; Respiration rate, oxygen, per protein; Respiration rate, oxygen, standard error; Salinity; Single species; Species; Stylophora pistillata; Symbiont cell density; Symbiont cell density, standard error; Table; Temperature, water; Treatment; Tropical
    Type: Dataset
    Format: text/tab-separated-values, 464 data points
    Location Call Number Expected Availability
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  • 3
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Mendoza, Irene; Peres, Carlos Augusto; Morellato, Leonor Patricia C (2016): Continental-scale patterns and climatic drivers of fruiting phenology: A quantitative Neotropical review. Global and Planetary Change, https://doi.org/10.1016/j.gloplacha.2016.12.001
    Publication Date: 2024-06-05
    Description: Changes in the life cycle of organisms (i.e. phenology) are one of the most widely used early-warning indicators of climate change, yet this remains poorly understood throughout the tropics. We exhaustively reviewed any published and unpublished study on fruiting phenology carried out at the community level in the American tropics and subtropics (latitudinal range: 26°N?26°S) to (1) provide a comprehensive overview of the current status of fruiting phenology research throughout the Neotropics; (2) unravel the climatic factors that have been widely reported as drivers of fruiting phenology; and (3) provide a preliminary assessment of the potential phenological responses of plants under future climatic scenarios. Despite the large number of phenological datasets uncovered (218), our review shows that their geographic distribution is very uneven and insufficient for the large surface of the Neotropics (~ 1 dataset per ~ 78,000 km2). Phenological research is concentrated in few areas with many studies (state of São Paulo, Brazil, and Costa Rica), whereas vast regions elsewhere are entirely unstudied. Sampling effort in fruiting phenology studies was generally low: the majority of datasets targeted fewer than 100 plant species (71%), lasted 2 years or less (72%), and only 10.4% monitored 〉 15 individuals per species. We uncovered only 10 sites with ten or more years of phenological monitoring. The ratio of numbers of species sampled to overall estimates of plant species richness was wholly insufficient for highly diverse vegetation types such as tropical rainforests, seasonal forest and cerrado, and only slightly more robust for less diverse vegetation types, such as deserts, arid shrublands and open grassy savannas. Most plausible drivers of phenology extracted from these datasets were environmental (78.5%), whereas biotic drivers were rare (6%). Among climatic factors, rainfall was explicitly included in 73.4% of cases, followed by air temperature (19.3%). Other environmental cues such as water level (6%), solar radiation or photoperiod (3.2%), and ENSO events (1.4%) were rarely addressed. In addition, drivers were analyzed statistically in only 38% of datasets and techniques were basically correlative, with only 4.8% of studies including any consideration of the inherently autocorrelated character of phenological time series. Fruiting peaks were significantly more often reported during the rainy season both in rainforests and cerrado woodlands, which is at odds with the relatively aseasonal character of the former vegetation type. Given that climatic models predict harsh future conditions for the tropics, we urgently need to determine the magnitude of changes in plant reproductive phenology and distinguish those from cyclical oscillations. Long-term monitoring and herbarium data are therefore key for detecting these trends. Our review shows that the unevenness in geographic distribution of studies, and diversity of sampling methods, vegetation types, and research motivation hinder the emergence of clear general phenological patterns and drivers for the Neotropics. We therefore call for prioritizing research in unexplored areas, and improving the quantitative component and statistical design of reproductive phenology studies to enhance our predictions of climate change impacts on tropical plants and animals.
    Keywords: Area/locality; Biome; Code; Country; Duration; Feces; Frequency; Herbarium; Herbs; Identification; Individuals; Latin_America; LATITUDE; Liana; LONGITUDE; Number of species; Number of trap; Observation; Peak of fruiting; Plant, others; Reference/source; Shrubs; Surface of trap; Trees; Uniform resource locator/link to reference; Vegetation type
    Type: Dataset
    Format: text/tab-separated-values, 4889 data points
    Location Call Number Expected Availability
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  • 4
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Böger, Horst; Kowalczyk, Gotthard (1993): Stratigraphische, sedimentologische und paläoökologische Untersuchungen im Mesozoikum der Depressão Periférica in Rio Grande do Sul, Brasilien. Berichte-Reports, Geologisch-Paläontologisches Institut der Universität Kiel, 63, 72 pp, https://doi.org/10.2312/reports-gpi.1993.63
    Publication Date: 2024-06-04
    Description: Stratigraphy, sedimentology and paleoecology of Mesozoic continental sequences in the Depressao periferica, Rio Grande do Sui, Brazil, are subject of a DFG (German Research Foundation) research project. Results of the first two years period of activities in which the Geologicai-Paleontological Institutes of the Universities of Kiel and Frankfurt/M. in collaboration with the Departamento de Geociencias, University of Santa Maria in Camobi, RS, were involved are reported here. A second phase of field activities is planned for the time period from fall 1993 to the spring of 1995. The stratigraphic boundaries of the investigation are the underlying sediments of the Permian Passa Dais-Series and the overlying basalts of the Serra Geral Formation, covering the time span of 235 Ma to 133 Ma. A subordinate, chronostratigraphic system encompassing the sediments of this time period has yet to be established and extensive hiatuses are to be expected. Correlations with the lschigualasto Formation in NW-Argentina support the assumption that the upper Santa Maria Formation (Aiemoa member) falls in the mid Carnian. This is the only reasonable certain chronostratigraphic date from the Mesozoic of the Depressao periferica established at the present time. The classical tetrapod sites of the Triassic Santa Maria Formation all fall within the Alemoa-Member, the sediments of which were deposited under in part evaporitic conditions on playa mud flats. Evidence points to isochronic sedimentation and discounts the possibility of a diachronic genesis. The Santa Maria Formation and the underlying Sanga do Cabral Formation are placed together in the Rio do Rasta Subgroup as a genetic unit in accordance with the original definition, which conflicts with present day usage of the names. The Rio do Rasto Subgroup pinches out west of Sao Francisco do Assis and east of the Taquarl river. The entire Rio do Rasto Formation is enclosed in eolic sediments, indicating an extensive sedimentation complex arising from a persistently subsiding playa areal within the Botucatu desert. Beyond the range of the Rio do Rasto Subgroup, it is difficult or impossible to distinguish between the eolic sediments of the older, underlying Rosario do Sui Formation and the overlying, younger Botucatu Sandstone Member. As such, the entire paleogeographically and genetically uniform sedimentation complex is compiled together under the term Botucatu Group. The Sanga do Cabral Formation is characterized by an abundance of detritic micas (muscovite and biotite). K/ Ar dating have indicated a preliminary age for muscovite of 418 ± 8 Ma and 423.5 ± 9.7 Ma. Presumably, they originated from volcanites, subvolcanites and pyroclastics of the Camaqua Group (Brasiliano molasse). As such, the Precambrian/ lower Paleozoic Escudo Sui in Rio Grande do Sui was exposed and eroded to the level found today at the time of deposition of the Sanga do Cabral Formation.
    Keywords: Area/locality; GIK/IfG; Institute for Geosciences, Christian Albrechts University, Kiel; LATITUDE; LONGITUDE; Outcrop ID; Stratigraphy
    Type: Dataset
    Format: text/tab-separated-values, 841 data points
    Location Call Number Expected Availability
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  • 5
    facet.materialart.
    Unknown
    PANGAEA
    In:  Research School of Earth Sciences, The Australian National University, Canberra | Supplement to: Willmes, Malte; McMorrow, Linda; Kinsley, Les; Armstrong, R; Aubert, Maxime; Eggins, Stephen M; Falguères, Christophe; Maureille, Bruno; Moffat, Ian; Grün, R (2014): The IRHUM (Isotopic Reconstruction of Human Migration) database - bioavailable strontium isotope ratios for geochemical fingerprinting in France. Earth System Science Data, 6(1), 117-122, https://doi.org/10.5194/essd-6-117-2014
    Publication Date: 2024-06-04
    Description: The dataset consists of 87Sr/86Sr isotope ratios of plant samples and soil leachates covering the major geologic regions of France. In addition to the isotope data it provides the spatial context for each sample, including background geology, field observations and soil descriptions. The dataset can be used to create Sr isoscapes for France, which can be applied in a wide range of fields including archaeology, ecology, soil, food, and forensic sciences.
    Keywords: Age, comment; Age, maximum/old; Age, minimum/young; Area/locality; Comment; ELEVATION; Environment; Event label; F06(F)-001; F06(F)-002; F06(F)-004; F06(F)-006; F06(F)-008; F06(F)-012; F06(F)-016; F06(F)-018; F06(F)-021; F06(F)-023; F06(F)-024; F06(F)-025; F06(F)-027; F06(F)-033; F06(F)-034; F06(F)-035; F06(F)-037; F06(F)-038; F06(F)-040; F06-002; F06-003; F06-005; F06-007; F06-008; F06-009; F06-010; F06-011; F06-012; F06-014; F06-015; F06-016; F06-017; F06-019; F06-020; F06-023; F06-024; F06-026; F06-027; F06-029; F06-030; F06-031; F06-032; F06-033; F06-036; F06-037; F06-038; F06-040; F06-042; F06-043; F06-044; F06-045; F06-046; F06-047; F06-050; F06-051; F06-054; F06-055; F06-056; F06-057; F06-058; F06-059; F06-061; F06-062; F09-001; F09-002; F09-003; F09-004; F09-005; F09-006; F09-007; F09-008; F09-009; F09-010; F09-011; F09-012; F09-013; F09-014; F09-015; F09-016; F09-017; F09-018; F09-019; F09-020; F09-021; F09-022; F09-023; F09-024; F09-025; F09-026; F09-027; F09-028; F09-029; F09-030; F09-031; F09-032; F09-033; F09-034; F09-035; F09-036; F09-037; F09-038; F09-039; F09-040; F09-041; F09-042; F09-043; F09-044; F09-045; F09-046; F09-047; F09-048; F09-049; F09-050; F09-051; F09-052; F09-053; F09-054; F09-055; F09-056; F09-057; F09-058; F09-060; F09-061; F09-062; F09-063; F09-064; F09-065; F09-066; F09-067; F09-068; F09-069; F09-070; F09-071; F09-072; F09-073; F09-074; F09-075; F09-076; F09-077; F09-078; F09-079; F09-080; F09-081; F09-082; F09-083; F09-084; F09-085; F09-086; F09-087; F09-088; F09-089; F09-090; F09-091; F09-092; F09-093; F09-094; F09-095; F09-096; F09-097; F09-098; F09-099; F09-100; F09-101; F09-102; F09-103; F09-104; F09-105; F09-106; F09-107; F09-108; F09-109; F09-110; F09-111; F09-112; F09-113; F09-114; F09-115; F09-116; F09-117; F09-118; F11-001; F11-002; F11-003; F11-004; F11-005; F11-006; F11-007; F11-008; F11-009; F11-010; F11-011; F11-012; F11-013; F11-014; F11-015; F11-016; F11-017; F11-018; F11-019; F11-020; F11-021; F11-022; F11-023; F11-024; F11-025; F11-026; F11-027; F11-028; F11-029; F11-030; F11-031; F11-032; F11-033; F11-034; F11-035; F11-036; F11-037; F11-038; F11-039; F11-040; F11-041; F11-042; F11-043; F11-044; F11-045; F11-046; F11-047; F11-048; F11-049; F11-050; F11-051; F11-052; F11-053; F11-054; F11-055; F11-056; F11-057; F11-058; F11-059; F11-060; F11-061; F11-062; F11-063; F11-064; F11-065; F11-066; F11-067; F11-068; F11-069; F11-070; F11-071; F11-072; F11-073; F11-074; F11-075; F11-076; F11-077; F11-078; F11-079; F11-080; F11-081; F11-082; F11-083; F11-084; F11-085; F11-086; F11-087; F11-088; F11-089; F11-090; F11-091; F11-092; F11-093; F11-094; F11-095; F11-096; F11-097; F11-099; F11-100; F11-101; F11-102; F11-103; F11-104; F11-105; F11-106; F11-107; F11-108; F11-109; F11-110; F11-111; F11-112; F11-113; F11-114; F11-115; F11-116; F11-117; F11-118; F11-119; F11-120; F11-121; F11-122; F11-123; F11-124; F11-125; F11-126; F11-127; F11-128; F11-129; F11-130; F11-131; F11-132; F11-133; F11-134; F11-135; F11-136; F11-137; F11-138; F11-139; F11-140; F11-141; F11-142; F11-143; F11-144; F11-145; F11-146; F11-147; F11-148; F11-149; F11-150; F11-151; F11-152; F11-153; F11-154; F11-155; F11-156; F11-157; F11-158; F11-159; F11-160; F11-161; F11-162; F11-163; F11-164; F11-165; F11-166; F11-167; F11-168; F11-169; F11-170; F11-171; F11-172; F11-173; F11-174; F11-175; F11-176; F11-178; F11-179; F11-180; F11-181; F11-182; F11-183; F11-184; F11-185; F11-186; F11-187; F11-188; F11-189; F11-190; F11-191; F11-192; F11-193; F11-194; F11-195; F11-196; F11-197; F11-198; F12-001; F12-002; F12-003; F12-004; F12-005; F12-006; F12-007; F12-008; F12-009; F12-010; F12-011; F12-012; F12-013; F12-014; F12-015; F12-016; F12-017; F12-018; F12-019; F12-020; F12-021; F12-022; F12-023; F12-024; F12-025; F12-026; F12-027; F12-028; F12-029; F12-030; F12-031; F12-032; F12-033; F12-034; F12-035; F12-036; F12-037; F12-038; F12-039; F12-040; F12-041; F12-042; F12-044; F12-045; F12-046; F12-047; F12-048; F12-049; F12-050; F12-051; F12-052; F12-053; F12-054; F12-055; F12-056; F12-057; F12-058; F12-060; F12-061; F12-062; F12-063; F12-064; F12-065; F12-066; F12-067; F12-068; F12-069; F12-070; F12-071; F12-072; F12-073; F12-074; F12-075; F12-076; F12-077; F12-078; F12-079; F12-080; F12-081; F12-082; F12-083; F12-084; F12-085; F12-086; F12-087; F12-088; F12-089; F12-090; F12-091; F12-092; F12-093; F12-094; F12-095; F12-096; F12-097; F12-098; F12-099; F12-100; F12-101; F12-102; F12-103; F12-104; F12-105; F12-106; F12-107; F12-108; F12-109; F12-110; F12-111; F12-112; F12-113; F12-114; F12-115; F12-116; F12-117; F12-118; F12-119; F12-120; F12-121; F12-122; F12-123; F12-124; F12-125; F12-126; F12-127; F12-128; F12-129; F12-130; F12-131; F12-132; F12-133; F12-134; F12-135; F12-136; F12-137; F12-138; F12-139; F12-140; F12-141; F12-142; F12-143; F12-144; F12-145; F12-146; F12-147; F12-148; F12-149; F12-150; F12-151; F12-153; F12-154; F12-155; F12-156; F12-157; F12-158; F12-159; F12-160; F12-161; F12-162; F12-163; F12-164; F12-165; F12-166; F12-167; F12-168; F12-169; F12-170; F12-171; F12-172; F12-173; F12-174; F12-175; F12-176; F12-177; F12-178; F12-179; F12-180; F12-181; F12-182; F12-183; F12-184; F12-185; F12-186; F12-187; F12-188; F12-189; F12-190; F12-191; F12-192; F12-193; F12-194; F12-195; F12-196; F12-197; F12-198; F12-199; F12-200; F12-201; F12-202; F12-203; F12-204; F12-205; F12-206; F12-207; F12-208; F12-209; F12-210; F12-211; F12-212; F12-213; F12-214; F12-215; F12-216; F12-217; F12-218; F12-219; F12-220; F12-221; F12-222; F12-223; F12-224; F12-225; F12-226; F12-227; F12-228; F12-229; F12-230; F12-231; F12-232; F12-233; F12-234; F12-235; F12-236; F12-237; F12-238; F13-001; F13-002; F13-003; F13-004; F13-005; F13-006; F13-007; F13-008; F13-009; F13-010; F13-011; F13-012; F13-013; F13-014; F13-015; F13-016; F13-017; F13-018; F13-019; F13-020; F13-021; F13-022; F13-023; F13-024; F13-025; F13-026; F13-027; F13-028; F13-029; F13-030; F13-031; F13-032; F13-033; F13-034; F13-035; F13-036; F13-037; F13-038; F13-039; F13-040; F13-042; F13-043; F13-044; F13-045; F13-046; F13-047; F13-048; F13-049; F13-051; F13-052; F13-053; F13-054; F13-055; F13-056; F13-057; F13-058; F13-059; F13-060; F13-061; F13-062; F13-063; F13-064; F13-065; F13-066; F13-067; F13-068; F13-069; F13-070; F13-071; F13-072; F13-073; F13-074; F13-075; F13-076; F13-077; F13-078; F13-079; F13-080; F13-081; F13-082; F13-084; F13-085; F13-086; F13-087; F13-088; F13-089; F13-090; F13-092; F13-093; F13-094; F13-095; F13-096; F13-097; F13-098; F13-099; F13-100; F13-101; F13-102; F13-103; F13-104; F13-105; F13-106; F13-107; F13-108; F13-109; F13-110; F13-111; F13-112; F13-113; F13-114; F13-115; F13-116; F13-117; F13-118; F13-119; F13-120; F13-121; F13-122; F13-123; F13-124; F13-125; F13-126; F13-127; F13-129; F13-130; F13-131; F13-132; F13-133; F13-134; F13-135; F13-136; F13-137; F13-138; F13-139; F13-140; F13-141; F13-142; F13-143; F13-144; F13-145; F13-146; F13-147; F13-148; F13-149; F13-150; F13-151; F13-152; F13-153; F13-154; F13-155; F13-156; F13-157; F13-158; F13-159; F13-160; F13-161; F13-162; F13-163; F13-164; F13-165; F13-166; F13-167; F13-168; F13-169; F13-170; F13-171; F13-172; F13-173; F13-174; F13-175; F13-176; F13-177; F13-178; F13-179; F13-180; F13-181; F13-182; F13-183; F13-184; F13-185; F13-186; F13-187; F13-188; F13-189; F13-190; F13-191; F13-192; F13-193; F13-194; F13-195; F13-196; F13-197; F13-198; F13-199; F13-200; F13-201; F13-202; F13-203; F13-204; F13-205; F13-206; F13-207; F13-208; F13-209; F13-210; F13-211; F13-212; F13-213; F13-214; F13-215; F13-216; F13-217; F13-218; F13-219; F13-220; F13-221; F13-222; F13-223; F13-224; F13-225; F13-226; F13-227; F13-228; France; HAND; Latitude of event; Lithologic unit/sequence; Longitude of event; Name; Observation; Outcrop ID; Rock type; Sample comment; Sample type; Sampling by hand; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, error
    Type: Dataset
    Format: text/tab-separated-values, 15675 data points
    Location Call Number Expected Availability
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  • 6
    Publication Date: 2024-06-04
    Keywords: Alkalinity, total; Bottle number; Carbon, inorganic, dissolved; Chlorophyll a; Chlorophyll b; Chlorophyll c; Conductivity; CTD/Rosette; CTD-RO; Date/Time of event; Density, sigma-theta (0); DEPTH, water; Discovery (2013); DY081; DY081_1; DY081_12; DY081_19; DY081_21; DY081_22; DY081_23; DY081_24; DY081_25; DY081_26; DY081_27; DY081_28; DY081_29; DY081_33; DY081_39; DY081_40; DY081_43; DY081_46; DY081_54; DY081_55; DY081_56; DY081_59; DY081_CTD01; DY081_CTD02; DY081_CTD04; DY081_CTD05; DY081_CTD06; DY081_CTD07; DY081_CTD08; DY081_CTD09; DY081_CTD10; DY081_CTD11; DY081_CTD12; DY081_CTD13; DY081_CTD14; DY081_CTD15; DY081_CTD16; DY081_CTD17; DY081_CTD18; DY081_CTD20; DY081_CTD21; DY081_CTD22; DY081_CTD24; Event label; Fluorescence, chlorophyll; ICY-LAB; Identification; Isotope CYcling in the LABrador Sea; LATITUDE; LONGITUDE; Nitrate and Nitrite; Nitrite; Oxygen; Phosphate; Pressure, water; Radiance, downward, photosynthetically active; Radiance, upward, photosynthetically active; Salinity; Sensor reading; Silicic acid; Temperature, water; Temperature, water, potential; Turbidity; δ18O, water
    Type: Dataset
    Format: text/tab-separated-values, 11649 data points
    Location Call Number Expected Availability
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  • 7
    facet.materialart.
    Unknown
    In:  Utrecht Studies in Earth Sciences vol. 64
    Publication Date: 2024-06-04
    Description: Foraminifera are unicellular eukaryotic organisms that live individually autonomous in the sea (Hottinger, 2005). They form mechanically resistant tests, either by gluing material found in the environment or by secreting organic or calcareous shells. Along with the test, main characteristic of foraminifera are their pseudopodia emerging from the cell body through multiple apertures. Foraminifera are extremely abundant in marine sediments, which makes them useful in recent and fossil paleoenvironmental studies. The first simple forms of foraminifera appeared in Cambrian and since provide a long and well recorded evolutionary record throughout Paleozoic, Mesozoic and Cenozoic (BouDagher‐ Fadel, 2008). Based on life strategy, foraminifera are divided in two groups: benthic and planktonic foraminifera. Planktonic foraminifera passively float through the waters of open oceans moved by currents. Benthic foraminifera live on the sea floor; on the surface, buried into the sediment, or attached to plants, rocks or sediment particles. Based on their size and internal morphological structure benthic foraminifera can be divided into two groups; smaller and larger benthic foraminifera. The main criteria for identifying LBF is the complex internal structure which evolved to efficiently host photosymbionts, the key elements in the ecology of LBF. The symbiotic algae utilize the waste product of the foraminifera, allowing them to efficiently recycle of nutrients and to facilitate calcification (Ross, 1974; Leutenegger, 1984). This life strategy, LBF as a greenhouse, limits their occurrences to photic zone since algal symbionts are dependent on light for photosynthesis (Leutenegger, 1984). Besides light levels, the distribution and abundance of LBF is determined by relatively well‐known parameters, including hydrodynamic energy, water temperature, salinity, food availability and substrate type (Hottinger, 1983; Hohenegger, 1994; Renema, 2006). Therefore, the assemblage composition of fossil LBF can provide important and valuable data for paleoenvironmental reconstructions (Hallock and Glenn, 1986; Renema and Troelstra, 2001). Present day Southeast Asia represents the region that supports the most diverse marine ecosystems on Earth. The origin of this biodiversity is still unresolved, but it is proposed to be present at least since the Early Miocene (Renema et al., 2008). Therefore, the data acquired from the fossil assemblages may contribute to our understanding of this biodiversity hotspot. In this thesis Miocene LBF were investigated in order to provide new insights regarding their biostratigraphy and depositional paleonvironments of Indonesia. The focus of the research includes mixed carbonate‐siliciclastic (MCS) systems of the Kutai Basin in East Kalimantan. However, to provide a comparative model with the blue‐water systems (Wilson, 2012), the study also included localities from Bulu Formation with carbonate platform deposits in Central Java. Until recently, MCS systems were considered to be environments inhospitable for carbonate producers compared to the blue‐water marine systems, and hence were often neglected in biodiversity studies (Friedman, 1988). However, recent studies reveal high biodiversity in these turbid water settings, including corals (Santodomingo et al., in press), LBF (Novak and Renema, in press), algae (Rosler et al., in press), and bryozoans (Di Martino and Taylor, 2014). The Kutai Basin was a host for the development of numerous MCS systems, with a peak of their deposition during the Miocene (Wilson and Rosen, 1998; Wilson, 2005). Herein MCS systems are defined as in situ mixing (Mount, 1984) with the carbonate fraction consisting of autochthonous or parautochthonous death assemblages of calcareous organisms accumulated on or within siliciclastic substrates. In these systems LBF are important contributors to carbonate production, and combined with their high tolerance of terrigenous input, individually they are the most suitable taxa for paleoenvironmental reconstruction and interpretation in MCS systems (Lokier et al., 2009; Novak et al., 2013). By investigating LBF assemblages of Miocene MCS systems of the Kutai Basin by updating their biostratigraphy, providing environmental reconstructions, and comparing them with contemporaneous carbonate platform deposits, this research helps in untangling the origins of the Indo‐Pacific biodiversity hotspot.
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/doctoralThesis
    Format: application/pdf
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  • 8
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    Unknown
    PANGAEA
    In:  Aerological Observatory, Japan Meteorological Agency
    Publication Date: 2024-06-03
    Keywords: Air temperature at 2 m height; BARO; Barometer; Baseline Surface Radiation Network; BSRN; DATE/TIME; Diffuse radiation; Diffuse radiation, maximum; Diffuse radiation, minimum; Diffuse radiation, standard deviation; Direct radiation; Direct radiation, maximum; Direct radiation, minimum; Direct radiation, standard deviation; HEIGHT above ground; Humidity, relative; HYGRO; Hygrometer; Japan; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave downward radiation, standard deviation; Long-wave upward radiation; Long-wave upward radiation, maximum; Long-wave upward radiation, minimum; Long-wave upward radiation, standard deviation; Monitoring station; MONS; Pyranometer, Kipp & Zonen, CM21, SN 960330, WRMC No. 16013; Pyranometer, Kipp & Zonen, CM21, SN 960332, WRMC No. 16015; Pyranometer, Kipp & Zonen, CM21, SN 970423, WRMC No. 16019; Pyrgeometer, Kipp & Zonen, CG4, SN 010582, WRMC No. 16026; Pyrgeometer, Kipp & Zonen, CG4, SN 030641, WRMC No. 16032; Pyrheliometer, Kipp & Zonen, CH1, SN 950093, WRMC No. 16011; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave downward (GLOBAL) radiation, standard deviation; Short-wave upward (REFLEX) radiation; Short-wave upward (REFLEX) radiation, maximum; Short-wave upward (REFLEX) radiation, minimum; Short-wave upward (REFLEX) radiation, standard deviation; Station pressure; TAT; Tateno; Thermometer
    Type: Dataset
    Format: text/tab-separated-values, 1038960 data points
    Location Call Number Expected Availability
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  • 9
    facet.materialart.
    Unknown
    PANGAEA
    In:  Aerological Observatory, Japan Meteorological Agency
    Publication Date: 2024-06-03
    Keywords: Air temperature at 2 m height; BARO; Barometer; Baseline Surface Radiation Network; BSRN; DATE/TIME; Diffuse radiation; Diffuse radiation, maximum; Diffuse radiation, minimum; Diffuse radiation, standard deviation; Direct radiation; Direct radiation, maximum; Direct radiation, minimum; Direct radiation, standard deviation; HEIGHT above ground; Humidity, relative; HYGRO; Hygrometer; Japan; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave downward radiation, standard deviation; Long-wave upward radiation; Long-wave upward radiation, maximum; Long-wave upward radiation, minimum; Long-wave upward radiation, standard deviation; Monitoring station; MONS; Pyranometer, Kipp & Zonen, CM21, SN 960330, WRMC No. 16013; Pyranometer, Kipp & Zonen, CM21, SN 960332, WRMC No. 16015; Pyranometer, Kipp & Zonen, CM21, SN 970423, WRMC No. 16019; Pyrgeometer, Kipp & Zonen, CG4, SN 010582, WRMC No. 16026; Pyrgeometer, Kipp & Zonen, CG4, SN 030641, WRMC No. 16032; Pyrheliometer, Kipp & Zonen, CH1, SN 950093, WRMC No. 16011; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave downward (GLOBAL) radiation, standard deviation; Short-wave upward (REFLEX) radiation; Short-wave upward (REFLEX) radiation, maximum; Short-wave upward (REFLEX) radiation, minimum; Short-wave upward (REFLEX) radiation, standard deviation; Station pressure; TAT; Tateno; Thermometer
    Type: Dataset
    Format: text/tab-separated-values, 1073592 data points
    Location Call Number Expected Availability
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  • 10
    facet.materialart.
    Unknown
    PANGAEA
    In:  Aerological Observatory, Japan Meteorological Agency
    Publication Date: 2024-06-03
    Keywords: Air temperature at 2 m height; BARO; Barometer; Baseline Surface Radiation Network; BSRN; DATE/TIME; Diffuse radiation; Diffuse radiation, maximum; Diffuse radiation, minimum; Diffuse radiation, standard deviation; Direct radiation; Direct radiation, maximum; Direct radiation, minimum; Direct radiation, standard deviation; HEIGHT above ground; Humidity, relative; HYGRO; Hygrometer; Japan; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave downward radiation, standard deviation; Long-wave upward radiation; Long-wave upward radiation, maximum; Long-wave upward radiation, minimum; Long-wave upward radiation, standard deviation; Monitoring station; MONS; Net radiation; Net radiation, maximum; Net radiation, minimum; Net radiation, standard deviation; Pyranometer, Kipp & Zonen, CM21, SN 950236, WRMC No. 16017; Pyranometer, Kipp & Zonen, CM21, SN 960332, WRMC No. 16015; Pyranometer, Kipp & Zonen, CM21, SN 970423, WRMC No. 16019; Pyrgeometer, Eppley, PIR, SN 29460F3, WRMC No. 16009; Pyrgeometer, Eppley, PIR, SN 31714F3, WRMC No. 16021; Pyrheliometer, Kipp & Zonen, CH1, SN 950093, WRMC No. 16011; Radiometer, EKO, CN-11, SN 87057, WRMC No. 16020; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave downward (GLOBAL) radiation, standard deviation; Short-wave upward (REFLEX) radiation; Short-wave upward (REFLEX) radiation, maximum; Short-wave upward (REFLEX) radiation, minimum; Short-wave upward (REFLEX) radiation, standard deviation; Station pressure; TAT; Tateno; Thermometer
    Type: Dataset
    Format: text/tab-separated-values, 1241096 data points
    Location Call Number Expected Availability
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