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  • American Chemical Society  (359,117)
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  • 1
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    American Chemical Society
    In:  EPIC3Environmental Science & Technology Letters, American Chemical Society
    Publication Date: 2024-01-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 2
    Publication Date: 2024-04-09
    Description: Serpentinites are polymineralic rocks distributed almost ubiquitously across the globe in active tectonic regions. Magnetite-rich serpentinites are found in the low-strain domains of serpen- tinite shear zones, which act as potential sites of nucleation of unstable slip. To assess the potential of earthquake nucleation in these materials, we investigate the link between me- chanical properties and fabric of these rocks through a suite of laboratory shear experiments. Our experiments were done at room temperature and cover a range of normal stress and slip velocity from 25 to 100 MPa and 0.3 to 300 μm s −1 , respecti vel y. We show that magnetite-rich serpentinites are ideal materials since they display strong sensitivity to the loading rate and are susceptible to nucleation of unstable slip, especially at low forcing slip velocities. We also aim at the integration of mechanical and microstructural results to describe the underlying mechanisms that produce the macroscopic behaviour. We show that mineralogical composi- tion and mineral structure dictates the coexistence of two deformation mechanisms leading to stable and unstable slip. The weakness of phyllosilicates allows for creep during the interseis- mic phase of the laboratory seismic cycle while favouring the restoration of a load-bearing granular framework, responsible of the nucleation of unstable events. During dynamic slip, fault zone shear fabric determines the mode of slip, producing either asymmetric or Gaussian slip time functions for either fast or slow events. We report rate/state friction parameters and integrate our mechanical data with microstructural observations to shed light on the mech- anisms dictating the complexity of laborator y ear thquakes. We show that mineralogical and fabric heterogeneities control fault slip behaviour.
    Description: Published
    Description: 1778–1797
    Description: OST3 Vicino alla faglia
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
    Publication Date: 2024-04-30
    Description: Current understanding of the kinetic-scale turbulence in weakly collisional plasmas still remains elusive. We employ a general framework in which the turbulent energy transfer is envisioned as a scale-to-scale Langevin process. Fluctuations in the sub-ion range show a global scale invariance, thus suggesting a homogeneous energy repartition. In this Letter, we interpret such a feature by linking the drift term of the Langevin equation to scaling properties of fluctuations. Theoretical expectations are verified on solar wind observations and numerical simulations, thus giving relevance to the proposed framework for understanding kinetic-scale turbulence in space plasmas.
    Description: Published
    Description: L042014
    Description: OSA3: Climatologia e meteorologia spaziale
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 4
    Publication Date: 2024-01-12
    Description: Mantle-derived noble gases in volcanic gases are powerful tracers of terrestrial volatile evolution, as they contain mixtures of both primordial (from Earth's accretion) and secondary (e.g., radiogenic) isotope signals that characterize the composition of deep Earth. However, volcanic gases emitted through subaerial hydrothermal systems also contain contributions from shallow reservoirs (groundwater, crust, atmosphere). Deconvolving deep and shallow source signals is critical for robust interpretations of mantle-derived signals. Here, we use a novel dynamic mass spectrometry technique to measure argon, krypton, and xenon isotopes in volcanic gas with ultrahigh precision. Data from Iceland, Germany, United States (Yellowstone, Salton Sea), Costa Rica, and Chile show that subsurface isotope fractionation within hydrothermal systems is a globally pervasive and previously unrecognized process causing substantial nonradiogenic Ar-Kr-Xe isotope variations. Quantitatively accounting for this process is vital for accurately interpreting mantle-derived volatile (e.g., noble gas and nitrogen) signals, with profound implications for our understanding of terrestrial volatile evolution.
    Description: Published
    Description: eadg2566
    Description: OSV2: Complessità dei processi vulcanici: approcci multidisciplinari e multiparametrici
    Description: JCR Journal
    Keywords: noble gases ; earth degassing
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 5
    Publication Date: 2024-01-19
    Description: We performed seismic ambient noise tomography to investigate the shallow crustal structure around the Ivrea geophysical body (IGB) in the Ivrea-Verbano Zone (IVZ). We achieved higher resolution with respect to previous tomographic works covering the Western Alps, by processing seismic data collected by both permanent and temporary seismic networks (61 broad-band seismic stations in total). This included IvreaArray, a temporary, passive seismic experiment designed to investigate the IVZ crustal structure. Starting from continuous seismic ambient noise recordings, we measured and inverted the dispersion of the group velocity of surface Rayleigh waves (fundamental mode) in the period range 4–25 s. We obtained a new, 3-D vS model of the IVZ crust via the stochastic neighbourhood algorithm (NA), with the highest resolution between 3 to 40 km depth. The fast and shallow shear wave velocity anomaly associated with the IGB presents velocities of 3.6 km s−1 directly at the surface, in remarkable agreement with the location of the exposed lower-to-middle crustal and mantle outcrops. This suggests a continuity between the surface geological observations and the subsurface geophysical anomalies. The fast IGB structure reaches vS of 4 km s−1 at 20–25 km depth, at the boundary between the European and Adriatic tectonic plates, and in correspondence with the earlier identified Moho jump in the same area. The interpretation of a very shallow reaching IGB is further supported by the comparison of our new results with recent geophysical investigations, based on receiver functions and gravity anomaly data. By combining the new geophysical constraints and the geological observations at the surface, we provide a new structural interpretation of the IGB, which features lower crustal and mantle rocks at upper crustal depths. The comparison of the obtained vS values with the physical properties from laboratory analysis of local rock samples suggests that the bulk of the IGB consists of a combination of mantle peridotite, ultramafic and lower crustal rocks, bound in a heterogeneous structure. These new findings, based on vS tomography, corroborate the recent interpretation for which the Balmuccia peridotite outcrops are continuously linked to the IGB structure beneath. The new outcomes contribute to a multidisciplinary framework for the interpretation of the forthcoming results of the scientific drilling project DIVE. DIVE aims at probing the lower continental crust and its transition to the mantle, with two ongoing and one future boreholes (down to 4 km depth) in the IVZ area, providing new, complementary information on rock structure and composition across scales. In this framework, we constrain the upper crustal IGB geometries and lithology based on new evidence for vS, connecting prior crustal knowledge to recent active seismic investigations.
    Description: Published
    Description: 1089–1105
    Description: OST1 Alla ricerca dei Motori Geodinamici
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 6
    Publication Date: 2024-01-23
    Description: On the 9th of January 2020, an Mw 6.4 strike-slip earthquake took place north of the Asian margin of the Bering Sea. The earthquake occurred within the known reverse-right-lateral active fault zone, called Khatyrka–Vyvenka, which transverses the Koryak Highland from SE to NW and is thought to be a surface manifestation of the Asian portion of either the Bering plate boundary or the northern edge of the Alaskan stream. No other strong earthquake has ever been recorded in this remote uninhabited area and the few existing seismic stations provide poor quality earthquake locations.We adopt SAR interferometry (InSAR) technique to define an improved location of the Koryak 2020 earthquake and constrain the seismic source. The analysis of the 2020 event revealed a previously unknown active fault of left-lateral kinematics that is possibly hidden and strikes NWtransversely to the Khatyrka–Vyvenka fault zone. Although several mechanisms could account for left-lateral kinematics of this fault, we propose that the structure is part of a more extended NW fault structure, that formed in pre-neotectonic times and has played a role of a pre-existing rheological discontinuity. This revived NW structure together with a similar structure located easterly, so far aseismic, make the plate/stream boundary segmented, step-like in plan view. The step-like boundary geometry may be the result of internal transform deformation of a rigid plate, but it is better explained by deflections of the Alaskan stream edge at local crustal asperities, which are pre-Cenozoic terrains.
    Description: Published
    Description: 1412–1421
    Description: OST2 Deformazione e Hazard sismico e da maremoto
    Description: JCR Journal
    Keywords: Plate motion ; Radar interferometry ; Seismic cycle ; Asia
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 7
    Publication Date: 2024-03-19
    Description: Accurate quantification of seismic activity in volcanic regions is an important asset for im- proving hazard and risk assessment. This is especially true for densely populated areas, as in the case of Etna volcano (Southern Italy). There, the volcanic hazard is amplified by the seismic risk of acti ve faults, especiall y on the eastern flank of the volcano. In such a context, it is common to rely on moment magnitude ( M W ) to characterize seismicity and monitor the energy released during an eruption. In this study, we calculate the moment-based magnitude ( M W ) for selected seismic data sets, using different approaches in distinct magnitude ranges to cover the widest possible range of magnitude that characterizes Etna’s seismicity . Specifically , we computed the M W from a data set of moment tensor solutions of earthquakes that occurred in the magnitude range 3.4 ≤M L ≤4.8 during 2005–2020; we created a data set of seismic moment and associated M W for earthquakes 1.0 ≤M L 〈 3.4 obtained by analysing source spectra; we fine-tuned two relationships, for shallow and deep earthquakes, to obtain M W from response spectra. Finally, we calibrated a specific relationship between M W and M L for the Etna area earthquakes in the range 1.0 ≤M L ≤4.8. All the empirical relationships obtained in this study can be applied in real-time analysis of the seismicity to provide fast and robust information on the released seismic energy.
    Description: INGV-DPC 2012- 2021 agreement; B2 DPC-INGV 2019-2021 project; IMPACT Department strategic project ; ‘Project PE0000005–RETURN (NRRP)
    Description: Published
    Description: 2520-2534
    Description: OST2 Deformazione e Hazard sismico e da maremoto
    Description: JCR Journal
    Keywords: Earthquak e source observations ; Earthquake hazards ; Time series analysis ; Full moment tensor
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 8
    Publication Date: 2024-03-12
    Description: This study presents a new robust statistical framework, in which to measure relative differences, or deviations from a hypothetical reference value, of Gutenberg-Richter b-value. Moreover, it applies this method to recent seismicity in Italy, to find possible changes of earthquake magnitude distribution in time and space. The method uses bootstrap techniques, which have no prior assumptions about the distribution of data, keeping their basic features. Excluding Central Italy, no significative b-value variation is found, revealing that the frequency-magnitude distribution exponent is substantially stable or that data are not able to reveal hidden variations. Considering the small size of examined magnitude samples, we cannot definitively decide if the higher b-values in Central Italy, consistently founded by all applied tests, have a physical origin or result from a statistical bias. In any case, they indicate short-lived excursions which have a temporary nature and, therefore, cannot be associated solely to spatial variations in tectonic framework. Both the methodological issues and the results of the application to seismicity in Italy show that a correct assessing of b-value changes requests appropriate statistics, that accurately quantify the low accuracy and precision of b-value estimation for small magnitude samples.
    Description: Published
    Description: 729–740
    Description: OST4 Descrizione in tempo reale del terremoto, del maremoto, loro predicibilità e impatto
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 9
    Publication Date: 2024-03-12
    Description: This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy. All rights reserved.
    Description: We present the results from a fully unconstrained moment tensor inversion of induced seismic events in a complex and high seismic hazard region (Val d’Agri basin, Southern Italy). The study area hosts two well-documented cases of induced microseismicity linked to (i) a wastewater injection well of a giant oilfield (the largest in onshore Europe), and (ii) severe seasonal level changes of an artificial lake. In order to gather information on the non-doublecouple components of the source and to better understand the rupture mechanisms, we analyse seismic events recorded during daily injection tests in the disposal well. The computed moment tensors have significant non-double-couple components that correlate with the well-head injection pressure. The injection parameters strongly influence the rupture mechanism that can be interpreted as due to the opening/closing of a fracture network inside a fault zone of a pre-existing thrust fault. For the case of the reservoir-induced seismicity, no direct correlations are observed with the loading/unloading of the reservoir.
    Description: Published
    Description: 1617–1627
    Description: OST3 Vicino alla faglia
    Description: JCR Journal
    Keywords: 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 10
    Publication Date: 2024-04-03
    Description: This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy. All rights reserved.
    Description: Estimation of local seismic response plays a key role in assessing local seismic hazard and particularly in the design of shaking scenarios. Modelling local seismic response involves knowing of the shear wave velocity (Vs) and quality factor (Qs) profiles for the site in question. The many techniques that have been developed to assess Vs in surface deposits produce reliable measurements of Vs , but these rarely correspond to direct measurements of Qs . The latter is often considered through damping measures from laboratory tests on small-scale soil samples, which can provide information primarily on intrinsic attenuation, neglecting the contribution of scattering effects. In this paper, using seismic recordings obtained at the surface and in boreholes at 100 m depth, we estimate an average value of Qs of some characteristic alluvial deposits of the Po Plain (northern Italy). Data come from a microseismic network which sampled an almost uniform lithology in the central Po Plain and consisted of three surface and four borehole stations with an interstation distance of about 2 km. The average value of Qs of the shallowest 100 m of the sedimentary strata, Qs100, is estimated by considering: (1) the high-frequency attenuation of seismic waves due to propagation through the corresponding stratigraphy and (2) the interference between incident and surface-reflected waves observed at borehole stations. We parametrize the first through k0_100, the difference between the values of the spectral decay parameter kappa (k) estimated at the surface and at the boreholes depth, respectively. We use the second in order to compute Vs100, the time-averaged Vs referred to the uppermost 100 m stratigraphy. We obtain: k0_100 = (11 ± 3) ms, Vs100 = (309 ± 11) m s −1 and Qs100 = 31 ± 10. At the surface, the estimated values of the site-specific kappa, k0, are found to range from 75 to 79 ms. As expected, these results are in good agreement with studies performed in other sites characterized by sandy or clayey lithologies, and can be usefully used in site response analysis at sites where the rigidity is mainly controlled by lithostatic pressure.
    Description: Comune di Minerbio (grant: “Sperimentazione ILG Minerbio”; grant number: 0913.010).
    Description: Published
    Description: 2075–2094
    Description: OST2 Deformazione e Hazard sismico e da maremoto
    Description: JCR Journal
    Keywords: Earthquake ground motions ; Seismic attenuation ; Site effects ; Wave propagation ; Wave scattering and diffraction ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 11
    Publication Date: 2024-05-09
    Description: This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy. All rights reserved.
    Description: In a recent work, we applied the every earthquake a precursor according to scale (EEPAS) probabilistic model to the pseudo-prospective forecasting of shallow earthquakes with magni- tude M 5.0 in the Italian region. We compared the forecasting performance of EEPAS with that of the epidemic type aftershock sequences (ETAS) forecasting model, using the most recent consistency tests developed within the collaboratory for the study of earthquake predictabil- ity (CSEP). The application of such models for the forecasting of Italian target earthquakes seems to show peculiar characteristics for each of them. In particular, the ETAS model showed higher performance for short-term forecasting, in contrast, the EEPAS model showed higher forecasting performance for the medium/long-term. In this work, we compare the performance of EEPAS and ETAS models with that obtained by a deterministic model based on the occur- rence of strong foreshocks (FORE model) using an alarm-based approach. We apply the two rate-based models (ETAS and EEPAS) estimating the best probability threshold above which we issue an alarm. The model parameters and probability thresholds for issuing the alarms are calibrated on a learning data set from 1990 to 2011 during which 27 target earthquakes have occurred within the analysis region. The pseudo-prospective forecasting performance is as- sessed on a validation data set from 2012 to 2021, which also comprises 27 target earthquakes. Tests to assess the forecasting capability demonstrate that, even if all models outperform a purely random method, which trivially forecast earthquake proportionally to the space–time occupied by alarms, the EEPAS model exhibits lower forecasting performance than ETAS and FORE models. In addition, the relative performance comparison of the three models demonstrates that the forecasting capability of the FORE model appears slightly better than ETAS, but the difference is not statistically significant as it remains within the uncertainty level. However, truly prospective tests are necessary to validate such results, ideally using new testing procedures allowing the analysis of alarm-based models, not yet available within the CSEP.
    Description: Published
    Description: 1541–1551
    Description: OST4 Descrizione in tempo reale del terremoto, del maremoto, loro predicibilità e impatto
    Description: JCR Journal
    Keywords: Computational seismology ; Earthquake interaction, forecasting and prediction ; Statistical seismology ; Comparison betwee earthquake forecasting methods
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 12
    Publication Date: 2024-05-09
    Description: To understand the seismic hazard of a subduction zone, it is necessary to know the geometry, location and mechanical characteristics of the interplate boundary below which an oceanic plate is thrust downward. By considering the azimuthal dependence of converted P-to-S (Ps) amplitudes in receiver functions, we have detected the interplate boundary in the Makran subduction zone, revealing significant seismic anisotropy at the base of the accretionary wedge above the slab before it bends down beneath the Jaz Murian basin. This anisotropic feature aligns with a zone of reduced seismic velocity and a high primary/secondary wave velocity ratio (Vp/Vs), as documented in previous studies. The presence of this low-velocity highly anisotropic layer at the base of the accretionary wedge, likely representing a low-strength shear zone, could possibly explain the unusually wide accretionary wedge in Makran. Additionally, it may impact the location and width of the locked zone along the interplate boundary.
    Description: Iranian National Science Foundation (INSF)
    Description: Published
    Description: 64-74
    Description: OST1 Alla ricerca dei Motori Geodinamici
    Description: JCR Journal
    Keywords: Earthquake hazards, Seismic anisotropy, Crustal structure, Subduction zone processes ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 13
    Publication Date: 2024-05-09
    Description: This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy. All rights reserved.
    Description: The Every Earthquake a Precursor According to Scale (EEPAS) forecasting model is a space– time point-process model based on the precursory scale increase (ψ ) phenomenon and associated predictive scaling relations. It has been previously applied to New Zealand, Cal- ifornia and Japan earthquakes with target magnitude thresholds varying from about 5–7. In all previous application, computations were done using the computer code implemented in Fortran language by the model authors. In this work, we applied it to Italy using a suite of computing codes completely rewritten in Matlab. We first compared the two software codes to ensure the convergence and adequate coincidence between the estimated model parameters for a simple region capable of being analysed by both software codes. Then, using the rewritten codes, we optimized the parameters for a different and more complex polygon of analysis using the Homogenized Instrumental Seismic Catalogue data from 1990 to 2011. We then perform a pseudo-prospective forecasting experiment of Italian earthquakes from 2012 to 2021 with Mw ≥ 5.0 and compare the forecasting skill of EEPAS with those obtained by other time in- dependent (Spatially Uniform Poisson, Spatially Variable Poisson and PPE: Proximity to Past Earthquakes) and time dependent [Epidemic Type Aftershock Sequence (ETAS)] forecasting models using the information gain per active cell. The preference goes to the ETAS model for short time intervals (3 months) and to the EEPAS model for longer time intervals (6 months to 10 yr).
    Description: Published
    Description: 1681–1700
    Description: OST4 Descrizione in tempo reale del terremoto, del maremoto, loro predicibilità e impatto
    Description: JCR Journal
    Keywords: Computational seismology ; Earthquake interaction ; forecasting and prediction ; Statistical seismology ; Earthquake forecasting
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 14
    Publication Date: 2024-05-27
    Description: This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy. All rights reserved.
    Description: We report on about 20 yr of relative gravity measurements, acquired on Mt. Somma–Vesuvius volcano in order to investigate the hydrological and volcano-tectonic processes controlling the present-day activity of the volcano. The retrieved long-term field of time gravity change (2003–2022) shows a pattern essentially related to the subsidence, which have affected the central part of the volcano, as detected by the permanent GNSS network and InSAR data. After reducing the observations for the effect of vertical deformation, no significant residuals are found, indicating no significant mass accumulation or loss within the volcanic system. In the north-western sector of the study area, at the border of the volcano edifice, however, significant residual positive gravity changes are detected which are associated to ground-water rebound after years of intense exploitation of the aquifers. On the seasonal timescale, we find that stations within the caldera rim are affected by the seasonal hydrological effects, while the gravity stations at the base of the Vesuvius show a less clear correlation. Furthermore, within the caldera rim a multiyear gravity transient is detected with an increase phase lasting about 4 yr followed by a slower decrease phase. Analysis of rain data seem to exclude a hydrological origin, hence, we hypothesize a deeper source related to the geothermal activity, which can be present even if the volcano is in a quiescent state. We infer the depth and volume of the source by inverting the spatial pattern of the gravity field at the peak of the transient. A volume of fluids of 9.5 × 107 m3 with density of 1000 kg m−3 at 2.3 km depth is capable to fit reasonably well the observations. To explain the gravity transient, simple synthetic models are produced, that simulate the ascent of fluids from a deep reservoir up to the depth of 2.3 km and a successive diffusion within the carbonate aquifer hosting the geothermal system. The whole process appears to not significantly affect the seismicity rate and the deformation of the volcano. This study demonstrates the importance of a 4-D gravity monitoring of a volcano to understand its complex gravity signals that cover different spatial and temporal scales. Discriminating the different contributions that mix up in the observed gravity changes, in particular those due to hydrologic/anthropogenic activities form those due to the geothermal dynamics, is fundamental for a complete and reliable evaluation of the volcano state.
    Description: Published
    Description: 1565–1580
    Description: OSV2: Complessità dei processi vulcanici: approcci multidisciplinari e multiparametrici
    Description: JCR Journal
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  • 15
    Publication Date: 2024-06-17
    Description: Thermobarometry provides a critical means of assessing locations of magma storage and dynamics in the lead-up to volcanic eruptions and crustal growth. A common approach is to utilise minerals that have compositions sensitive to changes in pressure and/or temperature, such as clinopyroxene, which is ubiquitous in mafic to intermediate magmas. However, clinopyroxene thermobarometry may carry significant uncertainty and require an appropriate equilibrium melt composition. In addition, the degree of magma undercooling (ΔT) affects clinopyroxene composition and zoning, with common sector zoning potentially obfuscating thermobarometry results. Here, we use a set of crystallisation experiments on a primitive trachybasalt from Mt. Etna (Italy) at ΔT = 25–233 °C, P = 400–800 MPa, H2O = 0–4 wt % and fO2 = NNO + 2, with clinopyroxene crystals defined by Al-rich zones (prisms and skeletons) and Al-poor zones (hourglass and overgrowths) to assess common equilibrium models and thermobarometric approaches. Under the studied conditions, our data suggest that the commonly applied Fe–Mg exchange (cpx-meltKdFe–Mg) is insensitive to increasing ΔT and may not be a reliable indicator of equilibrium. The combined use of DiHd (CaMgSi2O6 + CaFeSi2O6) and EnFs (Mg2Si2O6 + Fe2Si2O6) models indicate the attainment of equilibrium in both Al-rich and Al-poor zones for almost all investigated ΔT. In contrast, CaTs (CaAl2SiO6) and CaTi (CaTiAl2O6) models reveal substantial deviations from equilibrium with increasing ΔT, particularly in Al-rich zones. We postulate that this reflects slower diffusion of Al and Ti in the melt compared with Ca and Mg and recommend the concurrent application of these four models to evaluate equilibrium between clinopyroxene and melt, particularly for sector-zoned crystals. Thermobarometers calibrated with only isothermal–isobaric experiments closely reproduce experimental P–T at low ΔT, equivalent to natural phenocrysts cores and sector-zoned mantles. Models that also consider decompression experiments are most accurate at high ΔT and are therefore suitable for outermost phenocryst rims and groundmass microlites. Recent machine learning approaches reproduce P–T conditions across all ΔT conditions. Applying our experimental constraints to sector-zoned microphenocrysts and groundmass microlites erupted during the 1974 eccentric eruption at Mt. Etna, we highlight that both hourglass and prism sectors are suitable for thermobarometry, given that equilibrium is sufficiently tested for. The combination of DiHd, EnFs, CaTs and CaTi models identifies compositions closest to equilibrium with the bulk melt composition, and results in smaller differences in P–T calculated for hourglass and prism sectors compared with applying only DiHd and EnFs equilibrium models. This provides a framework to assess crystallisation conditions recorded by sector-zoned clinopyroxene crystals in mafic alkaline settings.
    Description: Published
    Description: egad074
    Description: OSV2: Complessità dei processi vulcanici: approcci multidisciplinari e multiparametrici
    Description: JCR Journal
    Keywords: Experimental Petrology ; Petrology ; Clinopyroxene ; Thermobarometry ; Experimental Petrology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 16
    Publication Date: 2024-07-02
    Description: The joint ESA/NASA Mass-change And Geosciences International Constellation (MAGIC) has the objective to extend time-series from previous gravity missions, including an improvement of accuracy and spatio-temporal resolution. The long-term monitoring of Earth’s gravity field carries information on mass change induced by water cycle, climate change and mass transport processes between atmosphere, cryosphere, oceans and solid Earth. MAGIC will be composed of two satellite pairs flying in different orbit planes. The NASA/DLR-led first pair (P1) is expected to be in a near-polar orbit around 500 km of altitude; while the second ESA-led pair (P2) is expected to be in an inclined orbit of 65°–70° at approximately 400 km altitude. The ESA-led pair P2 Next Generation Gravity Mission shall be launched after P1 in a staggered manner to form the MAGIC constellation. The addition of an inclined pair shall lead to reduction of temporal aliasing effects and consequently of reliance on de-aliasing models and post-processing. The main novelty of the MAGIC constellation is the delivery of mass-change products at higher spatial resolution, temporal (i.e. subweekly) resolution, shorter latency and higher accuracy than the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). This will pave the way to new science applications and operational services. In this paper, an overview of various fields of science and service applications for hydrology, cryosphere, oceanography, solid Earth, climate change and geodesy is provided. These thematic fields and newly enabled applications and services were analysed in the frame of the initial ESA Science Support activities for MAGIC. The analyses of MAGIC scenarios for different application areas in the field of geosciences confirmed that the double-pair configuration will significantly enlarge the number of observable mass-change phenomena by resolving smaller spatial scales with an uncertainty that satisfies evolved user requirements expressed by international bodies such as IUGG. The required uncertainty levels of dedicated thematic fields met by MAGIC unfiltered Level-2 products will benefit hydrological applications by recovering more than 90 per cent of the major river basins worldwide at 260 km spatial resolution, cryosphere applications by enabling mass change signal separation in the interior of Greenland from those in the coastal zones and by resolving small-scale mass variability in challenging regions such as the Antarctic Peninsula, oceanography applications by monitoring meridional overturning circulation changes on timescales of years and decades, climate applications by detecting amplitude and phase changes of Terrestrial Water Storage after 30 yr in 64 and 56 per cent of the global land areas and solid Earth applications by lowering the Earthquake detection threshold from magnitude 8.8 to magnitude 7.4 with spatial resolution increased to 333 km.
    Description: Published
    Description: 1288–1308
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 17
    Publication Date: 2024-01-03
    Keywords: AGE; Antarctic Circumpolar Current; Clay; DEPTH, sediment/rock; Diatoms; Giant piston corer (Calypso); GPC-C; Grain size, Mastersizer S, Malvern Instrument Inc.; magnetic parameters; Marion Dufresne (1995); MD12-3401; MD128; mineralogic parameters; Silt; Summer sea surface temperature; SWAF
    Type: Dataset
    Format: text/tab-separated-values, 498 data points
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  • 18
    Publication Date: 2024-01-03
    Keywords: AGE; Anhysteretic susceptibility/magnetic susceptibility; Antarctic Circumpolar Current; Cryogenic magnetometer, 2G Enterprises; DEPTH, sediment/rock; Giant piston corer (Calypso); GPC-C; magnetic parameters; Magnetic susceptibility; Marion Dufresne (1995); MD12-3401; MD128; mineralogic parameters; Summer sea surface temperature; SWAF
    Type: Dataset
    Format: text/tab-separated-values, 320 data points
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  • 19
    Publication Date: 2024-01-03
    Keywords: AGE; Antarctic Circumpolar Current; calculated, 1 sigma; DEPTH, sediment/rock; Giant piston corer (Calypso); GPC-C; magnetic parameters; Marion Dufresne (1995); MD12-3401; MD128; mineralogic parameters; Reconstructed from the percentage of Neogloboquadrina pachyderma sinistral; Reconstructed from the percentage of planktic foraminifera; Sea surface temperature, summer; Sea surface temperature, summer, standard deviation; Summer sea surface temperature; SWAF
    Type: Dataset
    Format: text/tab-separated-values, 186 data points
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  • 20
    Publication Date: 2024-02-02
    Description: The onset of the first sustained Antarctic glaciation at the Eocene-Oligocene Transition (~34 Ma; EOT) was marked by several changes in calcareous nannofossils coinciding with long-term cooling and modifications in the sea-surface water structure. Here, we combined a high-resolution calcareous nannofossil assemblage data (%) with bulk geochemical data from IODP Site U1509 (New Caledonia Trough, Tasman Sea) in order to give an overview of the paleoclimatic and palaeoceanographic evolution of the study area.
    Keywords: 371-U1509A; Calcareous nannofossils; Calcium carbonate; DEPTH, sediment/rock; DSDP/ODP/IODP sample designation; Eocene-Oligocene Transition.; Exp371; Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP; IODP Depth Scale Terminology; Isotope ratio mass spectrometry; Joides Resolution; Sample code/label; Tasman Frontier Subduction Initiation and Paleogene Climate; Tasman Sea; δ13C, carbonate; δ18O, carbonate
    Type: Dataset
    Format: text/tab-separated-values, 732 data points
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  • 21
    Publication Date: 2024-02-01
    Description: Marine heat flow data from RV Meteor cruise M186. The GEOMAR project name is Azores Hot Vents. We used the 6 m Bremen heat probe with 21 channels @ 0.26 m spacing.
    Keywords: Azores; Center for Marine Environmental Sciences; heatflow; MARUM
    Type: Dataset
    Format: application/zip, 10 datasets
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  • 22
    Publication Date: 2024-03-01
    Description: This data set contains airborne gravity data across central Dronning Maud Land, East Antarctica, acquired during the austral summer of 2009/2010 and within the project 'West-East Gondwana Amalgamation and its Separation' (WEGAS). The data span the offshore Astrid Ridge, and parts of the Nivl and Lazarev ice shelves. The survey was conducted using a ZLS Ultrasys Lacoste & Romberg Air/Sea gravimeter S56 installed into - and operated with - the research aircraft Polar 5. Base readings were performed with a handheld gravity meter at the base station Novolazarevskaja and in Cape Town. A ground speed of 130 knots and a time-domain filter of 220 s leads to a spatial resolution of around 7 km. The average crossover error after bias adjustment is 4.2 mGal. When citing this data set, please also cite the associated manuscript: Eisermann, H., Eagles, G. & Jokat, W. Coastal bathymetry in central Dronning Maud Land controls ice shelf stability. Sci Rep 14, 1367 (2024). https://doi.org/10.1038/s41598-024-51882-2.
    Keywords: AC; airborne gravity; Aircraft; Antarctica; Antarctica, East; Astrid Ridge; DATE/TIME; Event label; Free-air gravity anomaly; Gravity; Height; LATITUDE; Lazarev Ice Shelf; Line; LONGITUDE; Nivl Ice Shelf; PGM17 (NGA's Preliminary Geopotential Model 2017); POLAR 5; WEGAS_2009/10; WEGAS_2009/10_02; WEGAS_2009/10_03; WEGAS_2009/10_04; WEGAS_2009/10_05; WEGAS_2009/10_06; WEGAS_2009/10_07; WEGAS_2009/10_08; WEGAS_2009/10_09; WEGAS_2009/10_10; WEGAS_2009/10_11; WEGAS_2009/10_12; WEGAS_2009/10_13; WEGAS_2009/10_14; WEGAS_2009/10_16; WEGAS_2009/10_17; WEGAS_2009/10_18; WEGAS_2009/10_19; WEGAS_2009/10_20; WEGAS_2009/10_21; WEGAS offshore
    Type: Dataset
    Format: text/tab-separated-values, 128088 data points
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  • 23
    Publication Date: 2024-03-01
    Description: Attached data comprise a bathymetric model of central Dronning Maud Land, including the seabed beneath the Nivl Ice Shelf and the Lazarev Ice Shelf, as well as the offshore Astrid Ridge and adjacent parts of the Riiser-Larsen Sea. Here, this model is embedded within the larger Antarctic-wide bathymetric compilation IBCSOV2 (Dorschel et al., 2022). This is an addition to the stand-alone bathymetric model here: https://doi.org/10.1594/PANGAEA.961492. The embedded model gives seabed depths relative to WGS84 at a resolution of 2.5 km. It is generated by complementing existing topographic data sets - such as seismic data, ice penetrating radar data, and shipborne hydroacoustic data - with the inversion of airborne gravity data towards bathymetry. The airborne gravity data used for the inversion consist of data acquired during aerogeophysical campaigns VISA from the early 2000s and WEGAS from the austral summer of 2009/2010. When citing this model, please also cite the associated manuscript: Eisermann, H., Eagles, G. & Jokat, W. Coastal bathymetry in central Dronning Maud Land controls ice shelf stability. Sci Rep 14, 1367 (2024). https://doi.org/10.1038/s41598-024-51882-2.
    Keywords: Antarctica; Bathymetry; BathymetryModel_cDronningMaudLan; Bed elevation; Coordinate, x, relative; Coordinate, y, relative; Dronning Maud Land; Dronning Maud Land, Antarctica; gravity inversion; LATITUDE; Lazarev Ice Shelf; LONGITUDE; Model; Nivl Ice Shelf; water column
    Type: Dataset
    Format: text/tab-separated-values, 206742 data points
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  • 24
    Publication Date: 2024-03-05
    Keywords: 138-846B; 138-846C; AGE; Alkenone; Aluminium oxide; Barium sulfate; Calcium carbonate; Calibrated after Weltje & Tjallingi (2008); Date/Time of event; Depth, composite; DRILL; Drilling/drill rig; Eastern Equatorial Pacific; Event label; Iron oxide, Fe2O3; Joides Resolution; Latitude of event; Leg138; Longitude of event; Manganese oxide; ODP Site 846; Sample code/label; Sea surface temperature; Silicon dioxide; South Pacific Ocean; Titanium dioxide
    Type: Dataset
    Format: text/tab-separated-values, 75384 data points
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  • 25
    Publication Date: 2024-03-05
    Keywords: 138-846; According to Herbert et al. (2021); Accumulation rate, alkenone C37; AGE; Alkenone; Alkenone, C37, logarithm; Calculated; COMPCORE; Composite Core; Eastern Equatorial Pacific; Joides Resolution; Leg138; ODP Site 846; Sea surface temperature; South Pacific Ocean
    Type: Dataset
    Format: text/tab-separated-values, 1056 data points
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  • 26
    Publication Date: 2024-03-05
    Keywords: 138-849; According to Herbert et al. (2021); Accumulation rate, alkenone C37; AGE; Alkenone; Alkenone, C37, logarithm; Calculated; COMPCORE; Composite Core; Eastern Equatorial Pacific; Joides Resolution; Leg138; North Pacific Ocean; ODP Site 846; Sea surface temperature
    Type: Dataset
    Format: text/tab-separated-values, 388 data points
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  • 27
    Publication Date: 2024-03-05
    Keywords: 321-U1338; According to Herbert et al. (2021); Accumulation rate, alkenone C37; AGE; Alkenone; Alkenone, C37, logarithm; Calculated; COMPCORE; Composite Core; Eastern Equatorial Pacific; Exp321; Joides Resolution; ODP Site 846; Pacific Equatorial Age Transect II / Juan de Fuca; Sea surface temperature
    Type: Dataset
    Format: text/tab-separated-values, 422 data points
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  • 28
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 22236 data points
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  • 29
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 257782 data points
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  • 30
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 25033 data points
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  • 31
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 23254 data points
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  • 32
    Publication Date: 2024-02-29
    Description: Dinoflagellate cysts have been determined in sediments of core DP30PC on a resolution of 1 sample per 2.5 mm core depth (representing approximately 3 year) and 119.65 - 180.4 cm core depth. These data form the basis of high temporal resolution temperature and precipitation reconstructions for Roman times between about 200 BCE and 600 CE (ca. 205 BCE - 605 CE).
    Keywords: 64PE297; Age; Ataxiodinium choane; Bitectatodinium tepikiense; Center for Marine Environmental Sciences; Counting, dinoflagellate cysts; DEPTH, sediment/rock; Dinoflagellate cyst, other; Dinoflagellate cyst, warm water/cold water, ratio; Dinoflagellate cyst reworked; Discharge index; DP30PC; elements; Impagidinium aculeatum; Impagidinium paradoxum; Impagidinium patulum; Impagidinium plicatum; Impagidinium sphaericum; Impagidinium strialatum; Lingulodinium polyedrum; MARUM; Mediterranean; Nematosphaeropsis labyrinthus; Operculodinium israelianum; PC; Pelagia; Piston corer; Polysphaeridium zoharyi; Pseudoschizea spp.; Pyxidinopsis reticulata; Roman Climate Optimum; Spiniferites elongatus; Spiniferites mirabilis; Spiniferites ramosus; Tectatodinium pellitum; Temperature, water; Tuberculodinium vancampoae; volcanic glass shards
    Type: Dataset
    Format: text/tab-separated-values, 6092 data points
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  • 33
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 27270 data points
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  • 34
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 29012 data points
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  • 35
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 267840 data points
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  • 36
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 24245 data points
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  • 37
    Publication Date: 2024-03-12
    Keywords: Aluminium oxide; Ardmore; Area/locality; Barium oxide; Barr_Creek; Calcium oxide; Cerium; Chromium(III) oxide; Depth, description; DEPTH, sediment/rock; DTREE; Duchess; Dysprosium; Erbium; Europium; Event label; Gadolinium; Georgina Basin; Hole; Holmium; Iron oxide, Fe2O3; Lanthanum; Laser Ablation; LATITUDE; Lily_Creek; LONGITUDE; Loss on ignition; Lutetium; Magnesium oxide; Manganese oxide; Neodymium; Paradise_North; Paradise_South; Phosphate_Hill; Phosphorite; Phosphorus pentoxide; Potassium oxide; Praseodymium; Rare-earth elements; ROCK; Rock sample; Samarium; Sample code/label; Sherrin_Creek; Silicon dioxide; Sodium oxide; Strontium oxide; Terbium; Thorium; Thulium; Titanium dioxide; Total; Uranium; Whole rock geochemistry; Ytterbium; Yttrium; Zirconium
    Type: Dataset
    Format: text/tab-separated-values, 1327 data points
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  • 38
    Publication Date: 2024-03-12
    Keywords: Aluminium; Aluminium oxide; Barium; Barium oxide; Calcium; Calcium oxide; Cerium; Cerium oxid; Chlorine; Date of determination; Electron micro probe analyser (EMPA); Fluorine; Gadolinium; Gadolinium oxide; Georgina Basin; Iron; Iron oxide, Fe2O3; Lanthanum; Lanthanum oxide; Laser Ablation; LATITUDE; LONGITUDE; Magnesium; Magnesium oxide; Manganese; Manganese oxide; Mineral name; Neodymium; Neodymium oxid; Oxygen; Phosphorite; Phosphorus; Phosphorus pentoxide; Sample ID; Silicon; Silicon dioxide; Site; Sodium; Sodium oxide; Strontium; Strontium oxide; Sulfur; Sulfur trioxide; Total; Whole rock geochemistry; Yttrium; Yttrium oxide
    Type: Dataset
    Format: text/tab-separated-values, 1764 data points
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  • 39
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    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2024-03-12
    Description: We continuously measured aerosol size distributions in the range between 18 nm and 820 nm in 64 bit per decade resolution by means of a Scanning Mobility Particle Sizer (SMPS, TSI, i.e. a Series 3080 Electrostatic Classifier equipped with a Differential Mobility Analyzer DMA 3081). The measurements were conducted at the Air Chemistry Observatory (SPUSO) at Neumayer III Station (Antarctica) between 4 August 2023 and 31 December 2023. The data are based on an original 10-minute temporal resolution, submitted as 60-minute averages. Aerosol size distribution measurements are part of the air chemistry long-term observations at Neumayer III. Details about the instrument can be found under "resources" of the corresponding metadata link: https://hdl.handle.net/10013/sensor.81ece554-068a-4c6e-8de5-1ef1944c0156
    Keywords: aerosol; Air chemistry observatory; Air Chemistry Observatory; Atmospheric Chemistry @ AWI; AWI_AC; AWI_Glac; DATE/TIME; Date/time end; Dronning Maud Land, Antarctica; Glaciology @ AWI; HEIGHT above ground; Log-normal particle size distribution, normalized concentration at particle diameter 101.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 105.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 109.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 113.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 117.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 121.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 126.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 131 nm; Log-normal particle size distribution, normalized concentration at particle diameter 135.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 140.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 145.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 151.2 nm; Log-normal particle size distribution, normalized concentration at particle diameter 156.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 162.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 168.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 174.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 18.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 18.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 181.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 187.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 19.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 194.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 20.2 nm; Log-normal particle size distribution, normalized concentration at particle diameter 20.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 201.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 209.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 21.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 216.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 22.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 224.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 23.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 232.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 24.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 241.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 25.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 250.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 259.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 25 nm; Log-normal particle size distribution, normalized concentration at particle diameter 26.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 269 nm; Log-normal particle size distribution, normalized concentration at particle diameter 27.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 278.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 28.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 289 nm; Log-normal particle size distribution, normalized concentration at particle diameter 299.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 30 nm; Log-normal particle size distribution, normalized concentration at particle diameter 31.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 310.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 32.2 nm; Log-normal particle size distribution, normalized concentration at particle diameter 322 nm; Log-normal particle size distribution, normalized concentration at particle diameter 33.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 333.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 34.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 346 nm; Log-normal particle size distribution, normalized concentration at particle diameter 35.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 358.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 37.2 nm; Log-normal particle size distribution, normalized concentration at particle diameter 371.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 38.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 385.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 399.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 40 nm; Log-normal particle size distribution, normalized concentration at particle diameter 41.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 414.2 nm; Log-normal particle size distribution, normalized concentration at particle diameter 42.9 nm; Log-normal particle size distribution, normalized concentration at particle diameter 429.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 44.5 nm; Log-normal particle size distribution, normalized concentration at particle diameter 445.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 46.1 nm; Log-normal particle size distribution, normalized concentration at particle diameter 461.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 47.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 478.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 49.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 495.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 51.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 514 nm; Log-normal particle size distribution, normalized concentration at particle diameter 53.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 532.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 55.2 nm; Log-normal particle size distribution, normalized concentration at particle diameter 552.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 57.3 nm; Log-normal particle size distribution, normalized concentration at particle diameter 572.5 nm; Log-normal particle size distribution, normalized
    Type: Dataset
    Format: text/tab-separated-values, 385097 data points
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  • 40
    Publication Date: 2024-03-12
    Keywords: Aluminium; Aluminium oxide; Antimony; Ardmore; Area/locality; Arsenic; Barium; Barr_Creek; Beryllium; Bismuth; Boron; Cadmium; Caesium; Calcium; Calcium oxide; Cerium; Chromium; Cobalt; Copper; DTREE; Duchess; Dysprosium; Erbium; Europium; Gadolinium; Georgina Basin; Hafnium; Holmium; Inductively coupled plasma - mass spectrometry (ICP-MS); Iron; Iron oxide, Fe2O3; Lanthanum; Laser Ablation; LATITUDE; Lead-208; Lily_Creek; Lithium; Lithium borate fusion; acid digestion; LONGITUDE; Lutetium; Magnesium; Magnesium oxide; Manganese; Manganese oxide; Molybdenum; Neodymium; Nickel; Niobium; Paradise_North; Paradise_South; Phosphate_Hill; Phosphorite; Phosphorus; Phosphorus pentoxide; Potassium; Potassium oxide; Praseodymium; Rhenium; ROCK; Rock sample; Rock type; Rubidium; Samarium; Sample ID; Scandium; Sherrin_Creek; Silicon; Silicon dioxide; Sodium; Sodium oxide; Strontium; Tantalum; Tellurium; Terbium; Thallium; Thorium; Thulium; Tin; Titanium; Titanium dioxide; Total; Uranium; Vanadium; Whole rock geochemistry; Ytterbium; Yttrium; Zinc; Zirconium
    Type: Dataset
    Format: text/tab-separated-values, 837 data points
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  • 41
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    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2024-03-09
    Description: We operate a multi angle absorption photometer MAAP (Model 5012, Thermo Electron Corp.). which is in operation since March 2006 ongoing. This instrument measures atmospheric light absorption by aerosol (mainly caused by black carbon, BC). To this end, ambient aerosol was sampled on a glass filter tape. The measured absorption coefficients abs(637) refer to a wavelength of 637 nm. Raw data were originally sampled in one-minute resolution. Finally, hourly averaged MAAP data are presented here. We also provide BC concentrations (ng/m³) derived from the absorption coefficients using the specific BC attenuation cross section (QBC) of 6.6 m²/g.
    Keywords: aerosol; Aerosol absorption at 637 nm; AIRCHEM; Air chemistry observatory; Atmospheric chemistry; Atmospheric Chemistry @ AWI; AWI_AC; Black carbon, aerosol; DATE/TIME; Dronning Maud Land, Antarctica; Duration; HEIGHT above ground; Multi angle absorption spectrometer MAAP5012; Neumayer_based; Neumayer_SPUSO; NEUMAYER III; Spuso; SPUSO
    Type: Dataset
    Format: text/tab-separated-values, 26274 data points
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  • 42
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    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2024-03-09
    Description: We continuously measured aerosol size distributions in the range between 90 nm and 5000 nm in 64 bit resolution with an optical particle sizer (TSI LAS3340). The measurements were conducted at the Air Chemistry Observatory (SPUSO) at Neumayer III Station (Antarctica) between 1 January 2023 and 10 July 2023. The data rely on an original 10-minute temporal resolution and are finally submitted as 60-minute averages. Aerosol size distribution measurements are part of the air chemistry long-term observations at Neumayer III. Details about the instrument can be found under "resources" of the corresponding metadata link: https://hdl.handle.net/10013/sensor.5d9a9253-e118-4744-be3a-05f31551314a.
    Keywords: aerosol; Air chemistry observatory; Air Chemistry Observatory; Atmospheric Chemistry @ AWI; AWI_AC; AWI_Glac; DATE/TIME; Date/time end; Dronning Maud Land, Antarctica; Glaciology @ AWI; HEIGHT above ground; las3340; Laser Aerosol Spectrometer TSI LAS3340; Log-normal particle size distribution, normalized concentration at particle diameter 1008.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 105.29 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1074.15 nm; Log-normal particle size distribution, normalized concentration at particle diameter 112.11 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1143.74 nm; Log-normal particle size distribution, normalized concentration at particle diameter 119.38 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1217.84 nm; Log-normal particle size distribution, normalized concentration at particle diameter 127.11 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1296.74 nm; Log-normal particle size distribution, normalized concentration at particle diameter 135.34 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1380.74 nm; Log-normal particle size distribution, normalized concentration at particle diameter 144.11 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1470.19 nm; Log-normal particle size distribution, normalized concentration at particle diameter 153.45 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1565.43 nm; Log-normal particle size distribution, normalized concentration at particle diameter 163.39 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1666.85 nm; Log-normal particle size distribution, normalized concentration at particle diameter 173.97 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1774.83 nm; Log-normal particle size distribution, normalized concentration at particle diameter 185.24 nm; Log-normal particle size distribution, normalized concentration at particle diameter 1889.81 nm; Log-normal particle size distribution, normalized concentration at particle diameter 197.25 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2012.24 nm; Log-normal particle size distribution, normalized concentration at particle diameter 210.03 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2142.6 nm; Log-normal particle size distribution, normalized concentration at particle diameter 223.63 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2281.41 nm; Log-normal particle size distribution, normalized concentration at particle diameter 238.12 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2429.21 nm; Log-normal particle size distribution, normalized concentration at particle diameter 253.55 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2586.58 nm; Log-normal particle size distribution, normalized concentration at particle diameter 269.97 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2754.15 nm; Log-normal particle size distribution, normalized concentration at particle diameter 287.46 nm; Log-normal particle size distribution, normalized concentration at particle diameter 2932.57 nm; Log-normal particle size distribution, normalized concentration at particle diameter 306.08 nm; Log-normal particle size distribution, normalized concentration at particle diameter 3122.55 nm; Log-normal particle size distribution, normalized concentration at particle diameter 325.91 nm; Log-normal particle size distribution, normalized concentration at particle diameter 3324.84 nm; Log-normal particle size distribution, normalized concentration at particle diameter 347.02 nm; Log-normal particle size distribution, normalized concentration at particle diameter 3540.24 nm; Log-normal particle size distribution, normalized concentration at particle diameter 369.51 nm; Log-normal particle size distribution, normalized concentration at particle diameter 3769.59 nm; Log-normal particle size distribution, normalized concentration at particle diameter 393.45 nm; Log-normal particle size distribution, normalized concentration at particle diameter 4013.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 418.93 nm; Log-normal particle size distribution, normalized concentration at particle diameter 4273.82 nm; Log-normal particle size distribution, normalized concentration at particle diameter 446.08 nm; Log-normal particle size distribution, normalized concentration at particle diameter 4550.7 nm; Log-normal particle size distribution, normalized concentration at particle diameter 474.98 nm; Log-normal particle size distribution, normalized concentration at particle diameter 4845.51 nm; Log-normal particle size distribution, normalized concentration at particle diameter 505.75 nm; Log-normal particle size distribution, normalized concentration at particle diameter 538.51 nm; Log-normal particle size distribution, normalized concentration at particle diameter 573.4 nm; Log-normal particle size distribution, normalized concentration at particle diameter 610.54 nm; Log-normal particle size distribution, normalized concentration at particle diameter 650.09 nm; Log-normal particle size distribution, normalized concentration at particle diameter 692.21 nm; Log-normal particle size distribution, normalized concentration at particle diameter 737.05 nm; Log-normal particle size distribution, normalized concentration at particle diameter 784.8 nm; Log-normal particle size distribution, normalized concentration at particle diameter 835.64 nm; Log-normal particle size distribution, normalized concentration at particle diameter 889.78 nm; Log-normal particle size distribution, normalized concentration at particle diameter 92.87 nm; Log-normal particle size distribution, normalized concentration at particle diameter 947.42 nm; Log-normal particle size distribution, normalized concentration at particle diameter 98.89 nm; Neumayer; Neumayer_based; Neumayer_SPUSO; NEUMAYER III; size distribution; Spuso; SPUSO; Time in minutes
    Type: Dataset
    Format: text/tab-separated-values, 300234 data points
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  • 43
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    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2024-03-12
    Description: We operate a 7-wavelength aethalometer (Model AE33, Magee Scientific) which is in operation since 23 January 2019 ongoing. The Aethalometer model AE33 collects aerosol particles continuously by drawing the aerosol-laden air stream through a spot on the filter tape. It analyzes the aerosol by measuring the transmission of light through one portion of the filter tape containing the sample, versus the transmission through an unloaded portion of the filter tape acting as a reference area. This analysis is done at seven optical wavelengths spanning the range from the near-infrared to the near-ultraviolet. The Aethalometer calculates the instantaneous concentration of optically-absorbing aerosols from the rate of change of the attenuation of light transmitted through the particle-laden filter.
    Keywords: aerosol; Aerosol absorption at 370 nm; Aerosol absorption at 470 nm; Aerosol absorption at 520 nm; Aerosol absorption at 590 nm; Aerosol absorption at 660 nm; Aerosol absorption at 880 nm; Aerosol absorption at 950 nm; aerosol absorption coefficient; Aethalometer, AE33, Magee Scientific; Air chemistry observatory; Air Chemistry Observatory; Atmospheric Chemistry @ AWI; AWI_AC; DATE/TIME; Dronning Maud Land, Antarctica; Duration; HEIGHT above ground; Neumayer_based; Neumayer_SPUSO; NEUMAYER III; Neumayer Station; Spuso; SPUSO
    Type: Dataset
    Format: text/tab-separated-values, 131400 data points
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  • 44
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    PANGAEA
    In:  Meteorologisches Observatorium Potsdam
    Publication Date: 2024-03-02
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Code; DATE/TIME; Dew/frost point; Germany; HYGRO; Hygrometer; LIN; Lindenberg; Monitoring station; MONS; Past weather1; Past weather2; Present weather; Pressure, atmospheric; Station pressure; Temperature, air; Thermometer; Total cloud amount; Visual observation; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 6265 data points
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  • 45
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    PANGAEA
    In:  Meteorologisches Observatorium Potsdam
    Publication Date: 2024-03-02
    Keywords: Baseline Surface Radiation Network; BSRN; Cloud base height; DATE/TIME; Germany; LIN; Lindenberg; Monitoring station; MONS
    Type: Dataset
    Format: text/tab-separated-values, 3422 data points
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  • 46
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    PANGAEA
    In:  Meteorologisches Observatorium Potsdam
    Publication Date: 2024-03-02
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; DATE/TIME; Dew/frost point; Germany; LIN; Lindenberg; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Vaisala, RS41; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 782989 data points
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  • 47
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    PANGAEA
    In:  Izaña Atmospheric Research Center, Meteorological State Agency of Spain
    Publication Date: 2024-03-08
    Keywords: Baseline Surface Radiation Network; BSRN; DATE/TIME; IZA; Izaña; Monitoring station; MONS; Ozone total; Tenerife, Spain
    Type: Dataset
    Format: text/tab-separated-values, 1663 data points
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  • 48
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    PANGAEA
    In:  Izaña Atmospheric Research Center, Meteorological State Agency of Spain
    Publication Date: 2024-03-08
    Keywords: Baseline Surface Radiation Network; BSRN; DATE/TIME; HEIGHT above ground; IZA; Izaña; Monitoring station; MONS; Tenerife, Spain; Ultraviolet-a global; Ultraviolet-a global, maximum; Ultraviolet-a global, minimum; Ultraviolet-a global, standard deviation; Ultraviolet-b global; Ultraviolet-b global, maximum; Ultraviolet-b global, minimum; Ultraviolet-b global, standard deviation; UV-Radiometer, Kipp & Zonen, UVB1, SN 970839, WRMC No. 61007; UV-Radiometer, Kipp & Zonen, UV-S-A-T, SN 080005, WRMC No. 61006
    Type: Dataset
    Format: text/tab-separated-values, 356432 data points
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  • 49
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    PANGAEA
    In:  Izaña Atmospheric Research Center, Meteorological State Agency of Spain
    Publication Date: 2024-03-08
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; DATE/TIME; Dew/frost point; IZA; Izaña; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Vaisala, RS92; Temperature, air; Tenerife, Spain; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 1318328 data points
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  • 50
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    PANGAEA
    In:  Izaña Atmospheric Research Center, Meteorological State Agency of Spain
    Publication Date: 2024-03-08
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Code; DATE/TIME; Dew/frost point; Geopotential of a standard isobaric surface; High cloud; HYGRO; Hygrometer; IZA; Izaña; Low/middle cloud amount; Low cloud; Middle cloud; Monitoring station; MONS; Past weather1; Past weather2; Present weather; Station pressure; Temperature, air; Tenerife, Spain; Thermometer; Total cloud amount; Visual observation; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 996 data points
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  • 51
    Publication Date: 2024-02-27
    Description: This data set includes biogeochemical proxy data analysed on sediment core TAN1106-28 in 2021 at the University of Tasmania, The Australian Nuclear Science and Technology Organisation, National Taiwan University, and the University of Southampton. This sediment core was collected in 2011 by the RV Tangaroa, in the Solander Trough, south of New Zealand (-48.372°S, 165.659°E). The data spans from 71–0 ka, and is comprised of sea surface temperatures, thorium normalised fluxes of iron, lithogenic material, total organic carbon, chlorins, CaCO3, and excess barium, as well as formanifera-bound nitrogen isotopic compositions, authigenic uranium and excess manganese concentrations, and Benthic-Planktic 14C offsets (yrs).
    Keywords: biogeochemistry; dust; Export Production; iron fertilization; Millennial scale variability; Southern Ocean; SST
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 52
    Publication Date: 2024-02-27
    Description: Current velocities of the upper water column along the cruise track of R/V Maria S. Merian cruise MSM112/1 were collected by a vessel-mounted 75 kHz RDI Ocean Surveyor ADCP. The ADCP transducer was located at 6.0 m below the water line. The instrument was operated in narrowband mode (WM10) with a bin size of 8.00 m, a blanking distance of 8.00 m, and a total of 100 bins, covering the depth range between 22.0 m and 814.0 m. Heading, pitch and roll data from the ship's motion reference unit and the navigation data from the Global Positioning systems were used by the data acquisition software VmDAS internally to convert ADCP velocities into earth coordinates. Single-ping data were screened for bottom signals and, where appropriate, a bottom mask was manually processed. The ship's velocity was calculated from position fixes obtained by the Global Positioning System (GPS). Accuracy of the ADCP velocities mainly depends on the quality of the position fixes and the ship's heading data. Further errors stem from a misalignment of the transducer with the ship's centerline. Data post-processing included water track calibration of the misalignment angle (-47.4056° +/- 0.6284°) and scale factor (1.0037 +/- 0.0102) of the Ocean Surveyor signal. The velocity data were averaged in time using an average interval of 60 s. Velocity quality flagging is based on different threshold criteria: Depth cells with ensemble-averaged percent-good values below 25% are marked as 'bad data'. Depth cells with velocities above 2.0 m/s are flagged as 'bad data'. Depth cells with a root-mean-square deviation between the measured ensemble-average velocity and a cell-wise running-mean velocity above 0.5 m/s are flagged as 'probably bad data'.
    Keywords: Current velocity, east-west; Current velocity, north-south; DAM_Underway; DAM Underway Research Data; DATE/TIME; DEPTH, water; Echo intensity, relative; LATITUDE; LONGITUDE; Maria S. Merian; MSM112; MSM112_0_Underway-3; Pings, averaged to a double ensemble value; Quality flag, current velocity; RM ROFI; Seadatanet flag: Data quality control procedures according to SeaDataNet (2010); Vessel mounted Acoustic Doppler Current Profiler [75 kHz]; VMADCP-75
    Type: Dataset
    Format: text/tab-separated-values, 4385480 data points
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  • 53
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    PANGAEA
    In:  Instituto de Geofísica, Universidad Nacional Autónoma De México
    Publication Date: 2024-02-27
    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; 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; Mexico; Monitoring station; MONS; Pyranometer, Kipp & Zonen, CMP22, SN 160484, WRMC No. 83003; Pyranometer, Kipp & Zonen, CMP22, SN 160486, WRMC No. 83001; Pyrgeometer, Kipp & Zonen, CGR4, SN 140084, WRMC No. 83004; Pyrheliometer, Kipp & Zonen, CHP 1, SN 140189, WRMC No. 83002; SEL; Selegua, Mexico Solarimetric Station; 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; Thermometer; UV-Biometer, Solar Light 501A, SN 19489, WRMC No. 83007
    Type: Dataset
    Format: text/tab-separated-values, 1202633 data points
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  • 54
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    PANGAEA
    In:  Aerological Observatory, Japan Meteorological Agency
    Publication Date: 2024-02-21
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; DATE/TIME; Dew/frost point; Japan; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; TAT; Tateno; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 32168 data points
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  • 55
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    PANGAEA
    In:  GeoSphere Austria
    Publication Date: 2024-02-23
    Keywords: Amount of cloud layer 1; Amount of cloud layer 2; Amount of cloud layer 3; Anemometer; Austria; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cloud base height code, layer 1; Cloud base height code, layer 2; Cloud layer 1; Cloud layer 2; Cloud layer 3; Code; DATE/TIME; Dew/frost point; High cloud; HYGRO; Hygrometer; Low/middle cloud amount; Low cloud; Middle cloud; Monitoring station; MONS; Past weather1; Past weather2; Present weather; SON; Sonnblick; Station pressure; Temperature, air; Thermometer; Total cloud amount; Visibility sensor; Visual observation; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 5511 data points
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  • 56
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    PANGAEA
    In:  GeoSphere Austria
    Publication Date: 2024-02-23
    Keywords: Amount of cloud layer 1; Amount of cloud layer 2; Amount of cloud layer 3; Anemometer; Austria; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cloud base height code, layer 1; Cloud base height code, layer 2; Cloud base height code, layer 3; Cloud layer 1; Cloud layer 2; Cloud layer 3; Code; DATE/TIME; Dew/frost point; High cloud; HYGRO; Hygrometer; Low/middle cloud amount; Low cloud; Middle cloud; Monitoring station; MONS; Past weather1; Past weather2; Present weather; SON; Sonnblick; Station pressure; Temperature, air; Thermometer; Total cloud amount; Visibility sensor; Visual observation; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 5214 data points
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  • 57
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    PANGAEA
    In:  GeoSphere Austria
    Publication Date: 2024-02-23
    Keywords: Air temperature at 2 m height; Austria; 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; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave downward radiation, standard deviation; Monitoring station; MONS; Pyranometer, Hukseflux, SR30, SN 2302, WRMC No. 75040; Pyranometer, Hukseflux, SR30, SN 6355, WRMC No. 75039; Pyrgeometer, Kipp & Zonen, CGR4, SN 100175, WRMC No. 75041; Pyrheliometer, Hukseflux, DR02-T2-10, SN 9119, WRMC No. 75038; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave downward (GLOBAL) radiation, standard deviation; SON; Sonnblick; Station pressure; Thermometer
    Type: Dataset
    Format: text/tab-separated-values, 832913 data points
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  • 58
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    PANGAEA
    In:  GeoSphere Austria
    Publication Date: 2024-02-23
    Keywords: Amount of cloud layer 1; Amount of cloud layer 2; Amount of cloud layer 3; Anemometer; Austria; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cloud base height code, layer 1; Cloud base height code, layer 2; Cloud base height code, layer 3; Cloud layer 1; Cloud layer 2; Cloud layer 3; Code; DATE/TIME; Dew/frost point; High cloud; HYGRO; Hygrometer; Low/middle cloud amount; Low cloud; Middle cloud; Monitoring station; MONS; Past weather1; Past weather2; Present weather; SON; Sonnblick; Station pressure; Temperature, air; Thermometer; Total cloud amount; Visibility sensor; Visual observation; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 5666 data points
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  • 59
    Publication Date: 2024-02-29
    Description: Concentrations of volcanic glass shards of the sediment size fraction between 45 - 100 micrometer of sediments of core DP30PC (southeastern Gulf of Taranto) have been determined by polar light microscopy. Based on these results the positions of cryptotephras can be determined. This forms the basis for the establishment of the tephra-chronologic age model of core DP30PC that is based on the elemental analysis of selected shards from these crypto-tephra.
    Keywords: 64PE297; Age; Age, tephra-chronostratigraphy; Center for Marine Environmental Sciences; DEPTH, sediment/rock; DP30PC; elements; MARUM; Mediterranean; PC; Pelagia; Piston corer; Polarisation microscopy; Roman Climate Optimum; Volcanic glass; volcanic glass shards
    Type: Dataset
    Format: text/tab-separated-values, 699 data points
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  • 60
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    PANGAEA
    In:  National Meteorological Office of Algeria
    Publication Date: 2024-02-29
    Keywords: Algeria; ALTITUDE; Baseline Surface Radiation Network; BSRN; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Vaisala, RS92; TAM; Tamanrasset; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 3650 data points
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  • 61
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 259082 data points
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  • 62
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 253409 data points
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  • 63
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 264341 data points
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  • 64
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 24497 data points
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  • 65
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 23263 data points
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  • 66
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 22391 data points
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  • 67
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    PANGAEA
    In:  National Meteorological Office of Algeria
    Publication Date: 2024-02-29
    Keywords: Algeria; Amount of cloud layer 1; Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cloud base height code, layer 1; Cloud layer 1; Code; DATE/TIME; Dew/frost point; High cloud; HYGRO; Hygrometer; Low/middle cloud amount; Low cloud; Middle cloud; Monitoring station; MONS; Past weather1; Past weather2; Present weather; Station pressure; TAM; Tamanrasset; Temperature, air; Thermometer; Total cloud amount; Visibility sensor; Visual observation; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 3943 data points
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  • 68
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 263041 data points
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  • 69
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    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 23112 data points
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  • 70
    Publication Date: 2024-02-29
    Description: Here we present annual linear extension rates of modern Porites corals recovered at Tahiti that grew between 1996 and 2008.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; Extension rate; IODP; MARUM; Sampling by diver; Sr/Ca; Tahiti; TIAREI-A1
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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  • 71
    Publication Date: 2024-02-29
    Description: Here we present annual linear extension rates of modern Porites corals recovered at Tahiti that grew between 1996 and 2008.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; Extension rate; IODP; MARAA-7; MARUM; Sampling by diver; Sr/Ca; Tahiti
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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  • 72
    Publication Date: 2024-02-29
    Description: Here we present annual linear extension rates of modern Porites corals recovered at Tahiti that grew between 1996 and 2008.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; Extension rate; IODP; MARAA-8; MARUM; Sampling by diver; Sr/Ca; Tahiti
    Type: Dataset
    Format: text/tab-separated-values, 4 data points
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  • 73
    Publication Date: 2024-02-29
    Description: Here we present annual linear extension rates of modern Porites corals recovered at Tahiti that grew between 1996 and 2008.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; Extension rate; IODP; MARUM; Sampling by diver; Sr/Ca; Tahiti; TIAREI-3B
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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  • 74
    Publication Date: 2024-02-29
    Description: Here we present annual linear extension rates of modern Porites corals recovered at Tahiti that grew between 1996 and 2008.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; Extension rate; IODP; MARUM; Sampling by diver; Sr/Ca; Tahiti; TIAREI-4
    Type: Dataset
    Format: text/tab-separated-values, 12 data points
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  • 75
    Publication Date: 2024-02-29
    Description: Here we present monthly resolved geochemical proxy data (Sr/Ca, Mg/Ca, δ18O, δ13C) extracted along the growth axes of Porites modern corals ecovered at Tahiti that grew between 1996 and 2008 and serve as a modern-day benchmark.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; ICP-OES (Agilent 720); IODP; MARUM; Mass spectrometer Finnigan MAT 251; Porites sp., Magnesium/Calcium ratio; Porites sp., Strontium/Calcium ratio; Porites sp., δ13C; Porites sp., δ18O; Sampling by diver; Sr/Ca; Tahiti; TIAREI-A1
    Type: Dataset
    Format: text/tab-separated-values, 480 data points
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  • 76
    Publication Date: 2024-02-29
    Description: Here we present monthly resolved geochemical proxy data (Sr/Ca, Mg/Ca, δ18O, δ13C) extracted along the growth axes of Porites modern corals ecovered at Tahiti that grew between 1996 and 2008 and serve as a modern-day benchmark.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; ICP-OES (Agilent 720); IODP; MARUM; Mass spectrometer Finnigan MAT 251; Porites sp., Magnesium/Calcium ratio; Porites sp., Strontium/Calcium ratio; Porites sp., δ13C; Porites sp., δ18O; Sampling by diver; Sr/Ca; Tahiti; TIAREI-3B
    Type: Dataset
    Format: text/tab-separated-values, 455 data points
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  • 77
    Publication Date: 2024-02-29
    Description: Here we present monthly resolved geochemical proxy data (Sr/Ca, Mg/Ca, δ18O, δ13C) extracted along the growth axes of Porites modern corals ecovered at Tahiti that grew between 1996 and 2008 and serve as a modern-day benchmark.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; ICP-OES (Agilent 720); IODP; MARAA-8; MARUM; Mass spectrometer Finnigan MAT 251; Porites sp., Magnesium/Calcium ratio; Porites sp., Strontium/Calcium ratio; Porites sp., δ13C; Porites sp., δ18O; Sampling by diver; Sr/Ca; Tahiti
    Type: Dataset
    Format: text/tab-separated-values, 155 data points
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  • 78
    Publication Date: 2024-02-29
    Description: Here we present monthly resolved geochemical proxy data (Sr/Ca, Mg/Ca, δ18O, δ13C) extracted along the growth axes of Porites modern corals ecovered at Tahiti that grew between 1996 and 2008 and serve as a modern-day benchmark.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; ICP-OES (Agilent 720); IODP; MARAA-7; MARUM; Mass spectrometer Finnigan MAT 251; Porites sp., Magnesium/Calcium ratio; Porites sp., Strontium/Calcium ratio; Porites sp., δ13C; Porites sp., δ18O; Sampling by diver; Sr/Ca; Tahiti
    Type: Dataset
    Format: text/tab-separated-values, 480 data points
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  • 79
    Publication Date: 2024-02-29
    Description: Here we present monthly resolved geochemical proxy data (Sr/Ca, Mg/Ca, δ18O, δ13C) extracted along the growth axes of Porites modern corals ecovered at Tahiti that grew between 1996 and 2008 and serve as a modern-day benchmark.
    Keywords: Age; AGE; Center for Marine Environmental Sciences; Coral; d13C; d18O; DIVER; ICP-OES (Agilent 720); IODP; MARUM; Mass spectrometer Finnigan MAT 251; Porites sp., Magnesium/Calcium ratio; Porites sp., Strontium/Calcium ratio; Porites sp., δ13C; Porites sp., δ18O; Sampling by diver; Sr/Ca; Tahiti; TIAREI-4
    Type: Dataset
    Format: text/tab-separated-values, 395 data points
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  • 80
    Publication Date: 2024-02-27
    Description: We conducted a study on the Late Miocene-Early Pliocene Biogenic Bloom, a phase of increased marine biological productivity that occurred approximately between 9 and 3.5 Ma. This phenomenon was observed worldwide in various ocean locations and was attributed to either a rise in nutrient input or a significant reorganization of nutrients within the oceans. Our focus was on the Biogenic Bloom at Ocean Drilling Program (ODP) Site 1085 in the Atlantic Ocean. We provided new biostratigraphic data to develop an updated age model and subsequently examined the variations observed in benthic foraminiferal assemblages throughout the Biogenic Bloom. At Site 1085, the Biogenic Bloom occurred between 8.1 and 3.0 Ma and we studied it employing various proxies, including linear sedimentation rates, carbonate mass accumulation rates, benthic foraminifera assemblage data and associated indexes (such as benthic foraminifera accumulation rate and diversity). We identified four distinct intervals within the Biogenic Bloom based on paleoenvironmental changes inferred from the benthic foraminiferal assemblages. Two intervals within the Biogenic Bloom, spanning from 8.1 to 5.2 Ma and from 3.8 to 3.0 Ma, indicated oligotrophic conditions with highly seasonal phytoplankton blooms. Between 5.2 and 4.8 Ma, we observed short-term fluctuations between well-oxygenated oligotrophic conditions and phases of low-oxygen eutrophic conditions. From 4.8 to 3.8 Ma, we noted a transition to a higher input of food supply to the seafloor. Our findings indicate that the onset of the Biogenic Bloom coincided with well-dated global records and its conclusion aligned with other records from the Atlantic Ocean. Furthermore, our research supports the hypothesis that the Biogenic Bloom should not be viewed as a single productivity event but rather as a complex occurrence comprising several shorter-lived high-productivity periods driven by different factors.
    Type: Dataset
    Format: application/zip, 7 datasets
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  • 81
    Publication Date: 2024-02-27
    Keywords: AGE; Barium excess; Barium excess, flux; biogeochemistry; Calcium carbonate; Calcium carbonate, flux; Carbon, organic, total; Carbon, organic, total, flux; Chlorins; Chlorins, flux; DEPTH, sediment/rock; dust; Export Production; Iron; Iron, flux; iron fertilization; Lithogenic, flux; Lithogenic material; Manganese excess; Millennial scale variability; Sea surface temperature, annual mean; Southern Ocean; SST; TAN1106; TAN1106/28; Tangaroa; Thorium-230, activity; Thorium-230, activity, standard deviation; Thorium-232, activity; Thorium-232, activity, standard deviation; Uranium, authigenic; Uranium-238, activity; Uranium-238, activity, standard deviation; δ15N, foraminifera-bound organic matter
    Type: Dataset
    Format: text/tab-separated-values, 1755 data points
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  • 82
    Publication Date: 2024-02-27
    Keywords: AGE; Benthic-Planktic 14C offset; Benthic-Planktic 14C offset, standard deviation; biogeochemistry; DEPTH, sediment/rock; dust; Export Production; iron fertilization; Millennial scale variability; Southern Ocean; SST; TAN1106; TAN1106/28; Tangaroa
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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  • 83
    Publication Date: 2024-02-27
    Keywords: 175-1085A; 175-1085B; Benguela Current, South Atlantic Ocean; Date/Time of event; Depth, composite revised; Depth, logging; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Event label; IODP Depth Scale Terminology; Joides Resolution; Latitude of event; Leg175; Longitude of event; Reference/source; Sample code/label; Sample comment
    Type: Dataset
    Format: text/tab-separated-values, 921 data points
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  • 84
    Publication Date: 2024-02-27
    Keywords: 175-1085A; 175-1085B; Amaurolithus delicatus; Amaurolithus primus; Benguela Current, South Atlantic Ocean; Calcidiscus macintyrei; Ceratolithus acutus; Ceratolithus atlanticus; Ceratolithus cristatus; Counting; Date/Time of event; Depth, composite revised; DEPTH, sediment/rock; Discoaster asymmetricus; Discoaster berggrenii; Discoaster brouweri; Discoaster hamatus; Discoaster pentaradiatus; Discoaster quinqueramus; Discoaster tamalis; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Event label; IODP Depth Scale Terminology; Joides Resolution; Latitude of event; Leg175; Longitude of event; Nickilithus amplificus; Orthorhabdus rugosus; Reticulofenestra pseudoumbilicus; Sample code/label; Sphenolithus moriformis
    Type: Dataset
    Format: text/tab-separated-values, 1710 data points
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  • 85
    Publication Date: 2024-02-27
    Keywords: 175-1085A; 175-1085B; Abditodentrix pseudothalmanni; Age; Alabaminella weddellensis; Anomalinoides globulosus; Anomalinoides spp.; Benguela Current, South Atlantic Ocean; Bolivina spathulata; Bolivina spp.; Bolivina subaenariensis; Bulimina elongata; Bulimina spp.; Bulimina striata; Bulimina truncana; Cassidulina carapitana; Cassidulina carinata; Cassidulina havanensis; Cassidulina laevigata; Cassidulina minuta; Cassidulina sp.; Cassidulina spp.; Cibicides lobatulus; Cibicidoides bradyi; Cibicidoides cf. alazanensis; Cibicidoides cf. grimsdalei; Cibicidoides cf. pachyderma; Cibicidoides cicatricosus; Cibicidoides micrus; Cibicidoides mundulus; Cibicidoides robertsonianus; Cibicidoides spp.; Cibicidoides wuellerstorfi; Counting; Date/Time of event; Depth, composite revised; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Eggerella bradyi; Ehrenbergina carinata; Ehrenbergina glabra; Elevation of event; Epistominella exigua; Event label; Fissurina spp.; Foraminifera, benthic agglutinated; Frondicularia sp.; Gaudryina sp.; Gavelinopsis lobatulus; Globobulimina spp.; Globocassidulina crassa; Globocassidulina subglobosa; Gyroidinoides orbicularis; Gyroidinoides planulatus; Gyroidinoides soldanii; Gyroidinoides spp.; Hanzawaia ammophila; Hoeglundina elegans; IODP Depth Scale Terminology; Joides Resolution; Karreriella bradyi; Lagena spp.; Laticarinina altocamerata; Laticarinina pauperata; Latitude of event; Leg175; Lenticulina spp.; Longitude of event; Melonis barleeanus; Melonis pompilioides; Melonis spp.; Miliolidae; Nodosariidae; Nonionella spp.; Nonion spp.; Nuttallides umbonifera; Nuttallinella sp.; Obesopleurostomella bierigi; Oridorsalis umbonatus; Osangularia culter; Osangularia spp.; Pleurostomella spp.; Polymorphinidae; Pullenia bulloides; Pullenia quinqueloba; Pyrgo lucernula; Pyrgo murrhina; Quinqueloculina sp.; Rectuvigerina multicostata; Sample code/label; Sigmoilina schlumbergeri; Sphaeroidina bulloides; Spiroplectammina spectabilis; Spirosigmoilina spp.; Stilostomellidae; Textularia lythostrota; Textularia spp.; Uvigerina occidentalis; Uvigerina peregrina; Uvigerina proboscidea; Vulvulina sp.
    Type: Dataset
    Format: text/tab-separated-values, 2871 data points
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  • 86
    Publication Date: 2024-02-27
    Keywords: 175-1085A; 175-1085B; Abditodentrix pseudothalmanni; Age; Alabaminella weddellensis; Benguela Current, South Atlantic Ocean; Bolivina spathulata; Bolivina subaenariensis; Bulimina elongata; Bulimina striata; Bulimina truncana; Calculated; Cassidulina carapitana; Cibicidoides bradyi; Cibicidoides mundulus; Cibicidoides wuellerstorfi; Cluster; Cluster analysis; Date/Time of event; Depth, composite revised; DEPTH, sediment/rock; Diversity; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Epistominella exigua; Event label; Fishers alpha index of diversity; Foraminifera, benthic, dysoxic/suboxic group; Foraminifera, benthic, epifaunal; Foraminifera, benthic, infaunal; Foraminifera, benthic, oxic fauna; Foraminifera, benthic agglutinated; Foraminifera, benthic calcareous; Globocassidulina crassa; Globocassidulina subglobosa; Hoeglundina elegans; IODP Depth Scale Terminology; Joides Resolution; Latitude of event; Leg175; Longitude of event; Melonis barleeanus; Nuttallides umbonifera; Oridorsalis umbonatus; Relative abundance (counted and calculated); Sample code/label; Uvigerina occidentalis; Uvigerina peregrina; Uvigerina proboscidea
    Type: Dataset
    Format: text/tab-separated-values, 1221 data points
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  • 87
    Publication Date: 2024-02-27
    Description: We used the positions of the calcareous nannofossil events from this study, combined with shipboard data, as tie-points to develop the age model between 526.87 and 182.00 m. Between two successive tie-points, the sedimentation rate was assumed constant. The time scale used is the Geological Time Scale 2020 (GTS2020; Raffi et al. (2020, doi:10.1016/B978-0-12-824360-2.00029-2)) and is based on calcareous nannofossil biochronology provided in GTS2020.
    Keywords: 175-1085A; 175-1085B; Age; Age, error; Benguela Current, South Atlantic Ocean; Date/Time of event; Depth, composite revised; Depth, error; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Event label; Event layer type; IODP Depth Scale Terminology; Joides Resolution; Latitude of event; Leg175; Longitude of event; Provenance/source; Reference/source; Sample code/label; Sample code/label 2; see description in data abstract
    Type: Dataset
    Format: text/tab-separated-values, 153 data points
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  • 88
    Publication Date: 2024-02-27
    Description: Carbonate mass accumulation rates (MARCarb) were calculated for Site 1085 following successive steps: first, we derived the carbonate content of our samples from Diester-Haass et al. (2004, doi:10.1029/2003PA000933) data using linear interpolation between 542.45 and 149.00 m. Secondly, we calculated the carbonate mass accumulation rates (MARCarb, expressed as g/m²/ka) for each sample, using the LSR (cm/ka) obtained, and the calcium carbonate content (wt% CaCO3) and the dry bulk density (DBD, g/cm3) interpolated from Diester-Haass et al. (2004). We applied the formula: MARCarb = %wtCaCO₃ * LSR * DBD. MARCarb from this study has been plotted with MARCarb from Diester-Haass et al. (2004), recalibrated using our age model.
    Keywords: 175-1085A; 175-1085B; Accumulation rate, calcium carbonate; Age; AGE; Benguela Current, South Atlantic Ocean; Calcium carbonate; Date/Time of event; Density, dry bulk; Depth, composite revised; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Event label; IODP Depth Scale Terminology; Joides Resolution; Latitude of event; Leg175; Linear sedimentation rate; Longitude of event; Sample code/label; see description in data abstract
    Type: Dataset
    Format: text/tab-separated-values, 231 data points
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  • 89
    Publication Date: 2024-02-27
    Keywords: 175-1085A; 175-1085B; Accumulation rate, benthic foraminifera by number; Accumulation rate, calcareous benthic foraminifera by number; Age; Benguela Current, South Atlantic Ocean; Calculated (Herguera and Berger, 1991); Counted; Counting 〉63 µm fraction; Date/Time of event; Density, dry bulk; Depth, composite revised; DEPTH, sediment/rock; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Elevation of event; Event label; Foraminifera, benthic; Foraminifera, benthic, wet mass; IODP Depth Scale Terminology; Joides Resolution; Latitude of event; Leg175; Linear sedimentation rate; Longitude of event; Sample code/label; Weighted
    Type: Dataset
    Format: text/tab-separated-values, 396 data points
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  • 90
    Publication Date: 2024-02-29
    Description: Volcanic glass shards have been selected from core DP30PC section 8, collected in the southeast Gulf of Taranto to establish a robust tephra chronology. In the core 4 Cryptotephras are observed. Selected glass shards from these Tephras contain elemental signals typical for Lipari outbreaks as well as of the Somma-Vesuvius.
    Keywords: 64PE297; Aluminium oxide; Calcium oxide; Center for Marine Environmental Sciences; Date; DEPTH, sediment/rock; DP30PC; elements; Elements, total; Iron oxide, FeO; Magnesium oxide; Manganese oxide; MARUM; Mediterranean; Normalized to VG-568 standard and to 100 wt% volatile-free; PC; Pelagia; Phosphorus pentoxide; Piston corer; Potassium oxide; Roman Climate Optimum; Sample comment; Sample ID; Silicon dioxide; Sodium and Potassium oxide; Sodium oxide; Titanium dioxide; volcanic glass shards
    Type: Dataset
    Format: text/tab-separated-values, 1170 data points
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  • 91
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    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 259050 data points
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  • 92
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    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 23888 data points
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  • 93
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 266730 data points
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  • 94
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    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 26411 data points
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  • 95
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 267750 data points
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  • 96
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 28633 data points
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  • 97
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 24424 data points
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  • 98
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 265543 data points
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  • 99
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    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 266217 data points
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  • 100
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-02-29
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, MEISEI, RS11G; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 28352 data points
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