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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cabanes, Simon; Spiga, Aymeric; Young, Roland M. B.;

    We conduct in-depth analysis of statistical flow properties from Global Circulation Model that reproduce Saturn's macroturbulence, namely large-scale zonal winds. We use a high performance Global Climate Models (GCMs), named DYNAMICO, to model the atmospheric circulation of gas giants with appropriate physical parametrizations for Saturn's atmosphere. The high-resolution model DYNAMICO solves for 3D primitive equations of motion. We ran a Saturn simulation covering 15 Saturn years using the Saturn DYNAMICO GCM. Wind fields are output every 20 Saturn days at 32 pressure levels onto 1/2° latitude-longitude grid maps. Details on this Saturn reference simulation are given in Spiga et al. (2020). In addition, to diagnose the relevant 3D dynamical mechanisms in Saturn's turbulent atmosphere, we run a set of four simulations using an idealized version of our Global Climate Model devoid of radiative transfer, with a well-defined Taylor-Green forcing and over several rotation rates (4, 1, 0.5, and 0.25 times Saturn's rotation rate). Here, we deliver a full data set, including velocity maps, at different pressure levels and time steps, from which it is possible to recompute the statistical analysis detailed in Cabanes et al. (2020). The delivered data set includes: Files of our (1) data collection and (2) numerical codes that lead to the statistical analysis: (1) Data collection: A PDF file named JUMP-zonal-jets-data-collection-Icarus.pdf that describes in depththe data set and the associated nomenclature. A netcdf file of velocity fields from our Saturn Reference Simulation (SRS) uvData-SRS-istep-312000-nstep-50-niz-12.nc StatisticalData.nc A netcdf file of velocity fields from idealized simulation at 4 times the Satrun's rotation rate, uvData-Omega-4-istep-21026.0-nstep-20-niz-8.nc A netcdf file of velocity fields from idealized simulation at 1 times the Satrun's rotation rate, uvData-Omega-1-istep-21026.0-nstep-20-niz-8.nc A netcdf file of velocity fields from idealized simulation at 0.5 times the Satrun's rotation rate, uvData-Omega-0.5-istep-20626.0-nstep-20-niz-8.nc A netcdf file of velocity fields from idealized simulation at 0.25 times the Satrun's rotation rate, uvData-Omega-0.25-istep-21026.0-nstep-20-niz-8.nc (2) Numerical codes: Codes for statistical analysis in spherical geometry are on Github. --> https://github.com/scabanes/POST Acknowledgments: The authors acknowledge exceptional computing support from Grand Équipement National de Calcul Intensif (GENCI) and Centre Informatique National de l’Enseignement Supérieur (CINES). All the simulations presented in this paper were carried out on the Occigen cluster hosted at CINES. This work was granted access to the High-Performance Computing (HPC) resources of CINES under the allocations A001-0107548, A003-0107548, A004-0110391 made by GENCI. The authors acknowledge funding from Agence Nationale de la Recherche (ANR), project HEAT ANR-14-CE23-0010 and project EMERGIANT ANR-17-CE31-0007. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 797012. Fruitful discussions with Sandrine Guerlet, Ehouarn Millour, Thomas Dubos, Frédéric Hourdin and Alexandre Boissinot from our team helped refine some discussions in the paper. {"references": ["Spiga, Aymeric, et al. \"Global climate modeling of Saturn's atmosphere. Part II: Multi-annual high-resolution dynamical simulations.\" Icarus 335 (2020): 113377.", "Cabanes, Simon et al. \"Global climate modeling of Saturn's atmosphere. Part III: Global statistical picture of zonostrophic turbulence in high-resolution 3D-turbulent simulations.\" arXiv preprint arXiv:2001.02473 (2020)."]}

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO; Sygma
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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    Authors: Vogt, Meike; O'Brien, Colleen J; Peloquin, Jill M; Schoemann, Véronique; +8 Authors

    The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1). The attached zip file contains raw data files submitted by the authors and a NetCDF file. Progressively, raw data will be imported into PANGAEA as distinct data publications related to the original sources (journal or data publications).

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    PANGAEA
    Dataset . 2012
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2012
      Data sources: B2FIND
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    Authors: Mathissen, Marcel;

    This dataset is brake wear cycle that was used as a reference cycle in the EU co-funded project LOWBRASYS which aims at particle reduction from automotive brakes. One task within the LOWBRASYS project was to define a real-drive braking schedule. This cycle is a short version of an existing brake procedure (Los Angeles City Traffic (LACT)) that was generated from actual on-road driving data and addresses typical urban, extra-urban and highway drive conditions. It is noteworthy that another real-world cycle has been developed [Mathissen et al. 2018]. The main difference between the two newly developed cycles is that the present LOWBRASYS cycle is based on a limited dataset from a specific region, while the other cycle is based on the WLTP database, which covers much more use-case and driving conditions. The PMP IWG has decided to use the present cycle as a backup cycle for future brake wear emissions evaluation.

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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    Authors: Bajard, Manon;

    This data set concerns the analysis performed on Lake Gers sediment sequence and includes its age-depth model, data from the short-lived radionuclides, data of the grain-size of the sediments, loss-on-ignition, magnetic susceptibility, XRF geochemistry, the list of the instantaneous deposits, the erosion, the flood-frequency and the data of the DNA of mammals on trees. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Mendeley Data
    Dataset . 2019
    License: CC BY NC
    Data sources: Mendeley Data
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    Mendeley Data
    Dataset . 2019
    License: CC BY NC
    Data sources: Datacite
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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    Mendeley Data; NARCIS
    Dataset . 2019
    License: CC BY NC
    Data sources: Datacite; NARCIS
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    Mendeley Data
    Dataset . 2019
    License: CC BY NC
    Data sources: Mendeley Data
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      Mendeley Data
      Dataset . 2019
      License: CC BY NC
      Data sources: Mendeley Data
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      Mendeley Data
      Dataset . 2019
      License: CC BY NC
      Data sources: Datacite
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      Mendeley Data; NARCIS
      Dataset . 2019
      License: CC BY NC
      Data sources: Datacite; NARCIS
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      Mendeley Data
      Dataset . 2019
      License: CC BY NC
      Data sources: Mendeley Data
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    Authors: Holding, Thomas; Ashton, Ian G C; Shutler, Jamie D;

    The Surface Ocean CO₂ Atlas (SOCAT) version 2019 dataset (Bakker et al., 2016) is a quality-controlled dataset containing 25.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models. Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values, Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system. Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in (Goddijn-Murphy et al., 2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes. This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2019 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Shutler et al., 2016; Holding et al., in-review). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Reynolds et al., 2007) was used to provide the climate quality and depth consistent temperature data. The original OISST data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2019 dataset. The original SOCAT version 2019 data are included in full, with four additional columns containing the reanalysed data: * T_reynolds - The temperature (in degrees C) taken from the consistent OISST temperature field for the corresponding time and location. * fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. * pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature. * pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al. 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file. Notes: 1. Due to the temporal range of the OISST dataset the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns. 2. The download for this submission is provided as a single .zip file (1.1 GB, uncompressed: 10.7GB) containing two files: SOCATv2019_reanalysed_subskin.tsv (containing every data point, ungridded) and SOCATv2019_reanalysed_subskin.nc (the gridded monthly mean data). How to cite these data: Please cite this PANGAEA submission, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Shutler et al., 2016, Holding et al. in review) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013). Acknowledgements: The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. These data were provided by two Integrated Carbon Observing System (ICOS) European Union (EU) readiness projects, Ringo (grant no. 730944) and BONUS Integral (grant no. 03FO773A).

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    PANGAEA
    Dataset . 2019
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2019
      Data sources: B2FIND
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    Authors: Kortenhaus, Andreas; Streicher, Maximilian; Gruwez, Vincent; Altomare, Corrado; +21 Authors

    Coasts of low lying countries are often comprised of a gentle foreshore and shallow waters, followed by a dike and a promenade. At the end of the promenade buildings or storm walls are constructed. This setting makes it possible for waves to overtop the dike and impact on the storm wall or building. Especially during storm season the overtopping waves induce large loads on these structures. New scenarios for climate change and sea level rise make it worthwhile to invest in research regarding overtopping wave loads. Within the European project 'Wave Loads on Walls' (WaLoWa) model tests in the Delta flume (The Netherlands) were conducted. It is the aim to study overtopping wave loads on storm walls and buildings. The project is coordinated by Ghent University (Belgium), in cooperation with TU Delft (The Netherlands), RWTH Aachen (Germany), University of Bari, University of L'Aquila, University of Calabria and University of Florence (Italy) and Flanders Hydraulics Research (Belgium). The project is financed by a grant by Hydralab+ in the framework of the EC Horizon 2020 program. A model geometry comprised of a sandy beach, a sloping dike, promenade and wall structure was built into the Delta flume. The beach alone consists of 1000m³ sand material and was an essential part of the structure, to obtain the broken wave conditions similar to reality. Waves representing a storm with a 1000 year return period and an additional water level to account for sea level rise result in the tested superstorm conditions. Measurements of the water surface elevation were taken close to the paddle, along the mildly sloping foreshore and at the dike toe location by resistance type wave gauges mounted to the flume side wall. The bathymetry of the sandy foreshore was measured by a mechanical profiler before and after the test. The overtopping flow properties thickness and velocity were measured by resistance type wave gauges, ultra-sonic distance sensors, paddle wheels and an electro-magnetic current meter installed along the promenade. Finally, the impact forces and pressures on the wall were measured by compression load cells and pressure sensors respectively. The data-set was complemented by a number of synoptic measurements, such as laser scan profiles, GoPro images, High-speed camera images, Digital camera images. Due to its large storage size, these data are provided on request.

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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: ZENODO; Sygma
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    Authors: Fernandez-Turiel, J. L.; Perez-Torrado, F. J.; Rodriguez-Gonzalez, A.; Saavedra, J.; +8 Authors

    This dataset compiles SEM images, modelled isopach map and topographic profiles, and data of radiocarbon ages, parameters of Tephra2 and AshCalc codes of Holocene volcanic ashes of of Southern Puna and neighbouring areas (NW Argentina). SEM images detail differences among the Bolsón de Fiambalá, Cerro Blanco and Cueros de Purulla fallout ash deposits. Tephra2 code was used to simulate the ash fallout, and the AshCalc code to compare different methods for ash volume estimates associated with the 4.2 ka cal BP eruption of the Cerro Blanco Volcanic Complex. Topographic profiles are used to explain the secondary thickening of fallout ash deposits. Material suplementario (Figuras S1-S4 y Tablas S1-S4 del artículo Fernandez-Turiel, J.-L.; Perez-Torrado, F. J.; Rodriguez-Gonzalez, A.; Saavedra, J.; Carracedo, J. C., Rejas, M.; Lobo, A.; Osterrieth, M.; Carrizo, J. I.; Esteban, G.; Gallardo, J.; Ratto, N. (2019). The large eruption 4.2 ka cal BP in Cerro Blanco, Central Volcanic Zone, Andes: Insights to the Holocene eruptive deposits in the southern Puna and adjacent regions. Estudios Geológicos 75(1): e088. https://doi.org/10.3989/egeol.43438.515 MINECO, CGL2011-23307, Proyecto QUECA Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2019
    Data sources: Datacite; Sygma
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      DIGITAL.CSIC
      Dataset . 2019
      Data sources: Datacite; Sygma
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    Authors: Cabrerizo, Marco J.; Álvarez-Manzaneda, M. Inmaculada; León-Palmero, Elizabeth; Guerrero-Jiménez, Gerardo; +3 Authors

    Eutrophication, global warming, and rising carbon dioxide (CO2) levels are the three most prevalent pressures impacting the biosphere. Despite their individual effects are well-known, it remains untested how oligotrophication (i.e. nutrients reduction) can alter the planktonic community responses to warming and elevated CO2 levels. Here, we performed an indoor mesocosm experiment to investigate the warming × CO2 interaction under a nutrient reduction scenario (40%) mediated by an in-lake management strategy (i.e. addition of a commercial solid-phase phosphorus sorbent -Phoslock®) on a natural freshwater plankton community. Biomass production increased under warming × CO2 relative to present-day conditions; however, a Phoslock®-mediated oligotrophication reduced such values by 30–70%. Conversely, the warming × CO2 × oligotrophication interaction stimulated the photosynthesis by 20% compared to ambient nutrient conditions, and matched with higher resource use efficiency (RUE) and nutrient demand. Surprisingly, at a group level, we found that the multi-stressors scenario increased the photosynthesis in eukaryotes by 25%, but greatly impaired in cyanobacteria (ca. −25%). This higher cyanobacterial sensitivity was coupled with a reduced light harvesting efficiency and compensation point. Since Phoslock®-induced oligotrophication unmasked a strong negative warming × CO2 effect on cyanobacteria, it becomes crucial to understand how the interplay between climate change and nutrient abatement actions may alter the, ecosystems functioning. With an integrative understanding of these processes, policy makers will design more appropriate management strategies to improve the ecological status of aquatic ecosystems without compromising their ecological attributes and functioning.

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    KNAW Pure
    Dataset
    Data sources: KNAW Pure
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      KNAW Pure
      Dataset
      Data sources: KNAW Pure
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  • Authors: Compo, G. P.; Whitaker, J. S.; Sardeshmukh, P. D.; Matsui, N.; +23 Authors

    The International Surface Pressure Databank (ISPD; Cram et al. 2015) [http://reanalyses.org/observations/international-surface-pressure-databank] is the world's largest collection of pressure observations. It has been gathered through international cooperation with data recovery facilitated by the ACRE Initiative and the other contributing organizations and assembled under the auspices of the GCOS Working Group on Surface Pressure and the WCRP/GCOS Working Group on Observational Data Sets for Reanalysis by NOAA Earth System Research Laboratory (ESRL), NOAA's National Climatic Data Center (NCDC), and the Climate Diagnostics Center (CDC) of the University of Colorado's Cooperative Institute for Research in Environmental Sciences (CIRES). The ISPDv2 consists of three components: station, marine, and tropical cyclone best track pressure observations. The station component is a blend of many national and international collections. NOTE: A newer version of this dataset, the International Surface Pressure Databank version 3, is available in RDA dataset ds132.1 [http://rda.ucar.edu/datasets/ds132.1/]. Users are recommended to access this updated dataset. The Twentieth Century Reanalysis Project used resources of the National Energy Research Scientific Computing Center [http://www.nersc.gov/] and of the Oak Ridge Leadership Computing Facility [http://www.olcf.ornl.gov/] at Oak Ridge National Laboratory, which are supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and Contract No. DE-AC05-00OR22725, respectively.

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    Authors: Urgeles, Roger; Camerlenghi, Angelo; Rüther, Denise C.; Fantoni, L.; +2 Authors

    The Euro-Mediterranean Submarine landSlide database (EMSS21) is a catalogue of submarine landslides of the Mediterranean Sea and the European continental margins of the Atlantic and Arctic Oceans. The catalogue is compiled from data available in the literature as well as information collected from geophysical data and not published in the scientific literature. The data set includes polygons and polylines for the landslide deposits and landslide scars as well as information relative to age, volume, area, runout, thickness, typology, scar elevation, geological setting, depth, slope, inferred trigger mechanism, relationship to fluid flow features as well as the relevant metadata. The catalogue includes submarine landslides that span the Miocene to Present day. Nevertheless a clear bias exist towards submarine landslides of younger age, particularly for the smaller events, as these events are difficult to map/identify on lower resolution geophysical data sets typically collected in deep-water and large sub-surface depths. The catalogue aims to offer improved understanding of mass-wasting processes, the potentially resulting tsunamis and derived geohazard. The EMSS21 displays information collected from both the literature and geophysical records. Data from the literature is collected from georeferenced map figures, from which the outline of the landslide deposit and the landslide scar have been digitized. Most information such as age, volume, typology, inferred trigger mechanism and relationship to fluid flow features and metadata is extracted from the relevant article. Area of the deposit and landslide runout is determined from the digitized polygons and polylines, volume (if not available) is determined from area and mean thickness. If the latter is not available, the volume is determined from the Area-Volume relationship for landslides where volume has already been determined. When the data originates directly from geophysical records the outline and scars have been determined from geomorphological interpretation of swath bathymetric maps and interpretation of submarine landslides on a variety of seismic records. In such instances, age information originates from a variety of methods presenting a wide range of accuracies, from radiometric dating to well-seismic correlation as well as ages inferred from mean sedimentation rates. In many instances, landslides identified in the literature are dated with an age epoch only. The Euro-Mediterranean Submarine landSlide database (EMSS21) displays information collected from both the literature and geophysical records. Data from the literature is collected from georeferenced map figures, from which the outline of the landslide deposit and the landslide scar have been digitized. Most information such as age, volume, typology, inferred trigger mechanism and relationship to fluid flow features and metadata is extracted from the relevant article. Area of the deposit and landslide runout is determined from the digitized polygons and polylines, volume (if not available) is determined from area and mean thickness. If the latter is not available, the volume is determined from the Area-Volume relationship for landslides where volume has already been determined. When the data originates directly from geophysical records the outline and scars have been determined from geomorphological interpretation of swath bathymetric maps and interpretation of submarine landslides on a variety of seismic records. In such instances, age information originates from a variety of methods presenting a wide range of accuracies, from radiometric dating to well-seismic correlation as well as ages inferred from mean sedimentation rates. In many instances, landslides identified in the literature are dated with an age epoch only. The Euro-Mediterranean Submarine landSlide database (EMSS21) is a catalogue of submarine landslides of the Mediterranean Sea and the European continental margins of the Atlantic and Arctic Oceans. The catalogue is compiled from data available in the literature as well as information collected from geophysical data and not published in the scientific literature. The data set includes polygons and polylines for the landslide deposits and landslide scars as well as information relative to age, volume, area, runout, thickness, typology, scar elevation, geological setting, depth, slope, inferred trigger mechanism, relationship to fluid flow features as well as the relevant metadata. The catalogue includes submarine landslides that span the Miocene to Present day. Nevertheless a clear bias exist towards submarine landslides of younger age, particularly for the smaller events, as theseevents are difficult to map/identify on lower resolution geophysical data sets typically collected in deep-water and large sub-surface depths. The catalogue aims to offer improved understanding of mass-wasting processes, the potentially resulting tsunamis and derived geohazard. Disclaimer: The EMSS21 is open to later additions and improvements. For these reasons and due to its intrinsic nature, the EMSS21 cannot be guaranteed to be complete, accurate and updated in any part, and will be subjected to successive revisions. Although the Institut de Ciències del Mar of CSIC makes every effort to supply the best available information, no warranty, expressed or implied, is provided as to the accuracy and reliability of all the data supplied in the EMSS21. Users are cautioned to consider carefully the nature of the data and information before using it for decisions that concern personal or public safety. Conclusions drawn from the EMSS21, or actions undertaken on the basis of its contents, are the sole responsibility of the user. Data compilation has been possible thanks to projects: Submarine landslides and Their impact on European continental margins (SLATE) - H2020-MSCA-ITN-2016-721403. ImagiNg large SeismogenIc and tsunamiGenic structures of the Gulf of Cadiz with ultra-High resolution Technologies (INSIGHT). - CTM2015-70155-R. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Cabanes, Simon; Spiga, Aymeric; Young, Roland M. B.;

    We conduct in-depth analysis of statistical flow properties from Global Circulation Model that reproduce Saturn's macroturbulence, namely large-scale zonal winds. We use a high performance Global Climate Models (GCMs), named DYNAMICO, to model the atmospheric circulation of gas giants with appropriate physical parametrizations for Saturn's atmosphere. The high-resolution model DYNAMICO solves for 3D primitive equations of motion. We ran a Saturn simulation covering 15 Saturn years using the Saturn DYNAMICO GCM. Wind fields are output every 20 Saturn days at 32 pressure levels onto 1/2° latitude-longitude grid maps. Details on this Saturn reference simulation are given in Spiga et al. (2020). In addition, to diagnose the relevant 3D dynamical mechanisms in Saturn's turbulent atmosphere, we run a set of four simulations using an idealized version of our Global Climate Model devoid of radiative transfer, with a well-defined Taylor-Green forcing and over several rotation rates (4, 1, 0.5, and 0.25 times Saturn's rotation rate). Here, we deliver a full data set, including velocity maps, at different pressure levels and time steps, from which it is possible to recompute the statistical analysis detailed in Cabanes et al. (2020). The delivered data set includes: Files of our (1) data collection and (2) numerical codes that lead to the statistical analysis: (1) Data collection: A PDF file named JUMP-zonal-jets-data-collection-Icarus.pdf that describes in depththe data set and the associated nomenclature. A netcdf file of velocity fields from our Saturn Reference Simulation (SRS) uvData-SRS-istep-312000-nstep-50-niz-12.nc StatisticalData.nc A netcdf file of velocity fields from idealized simulation at 4 times the Satrun's rotation rate, uvData-Omega-4-istep-21026.0-nstep-20-niz-8.nc A netcdf file of velocity fields from idealized simulation at 1 times the Satrun's rotation rate, uvData-Omega-1-istep-21026.0-nstep-20-niz-8.nc A netcdf file of velocity fields from idealized simulation at 0.5 times the Satrun's rotation rate, uvData-Omega-0.5-istep-20626.0-nstep-20-niz-8.nc A netcdf file of velocity fields from idealized simulation at 0.25 times the Satrun's rotation rate, uvData-Omega-0.25-istep-21026.0-nstep-20-niz-8.nc (2) Numerical codes: Codes for statistical analysis in spherical geometry are on Github. --> https://github.com/scabanes/POST Acknowledgments: The authors acknowledge exceptional computing support from Grand Équipement National de Calcul Intensif (GENCI) and Centre Informatique National de l’Enseignement Supérieur (CINES). All the simulations presented in this paper were carried out on the Occigen cluster hosted at CINES. This work was granted access to the High-Performance Computing (HPC) resources of CINES under the allocations A001-0107548, A003-0107548, A004-0110391 made by GENCI. The authors acknowledge funding from Agence Nationale de la Recherche (ANR), project HEAT ANR-14-CE23-0010 and project EMERGIANT ANR-17-CE31-0007. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 797012. Fruitful discussions with Sandrine Guerlet, Ehouarn Millour, Thomas Dubos, Frédéric Hourdin and Alexandre Boissinot from our team helped refine some discussions in the paper. {"references": ["Spiga, Aymeric, et al. \"Global climate modeling of Saturn's atmosphere. Part II: Multi-annual high-resolution dynamical simulations.\" Icarus 335 (2020): 113377.", "Cabanes, Simon et al. \"Global climate modeling of Saturn's atmosphere. Part III: Global statistical picture of zonostrophic turbulence in high-resolution 3D-turbulent simulations.\" arXiv preprint arXiv:2001.02473 (2020)."]}

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO; Sygma
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2020
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    Authors: Vogt, Meike; O'Brien, Colleen J; Peloquin, Jill M; Schoemann, Véronique; +8 Authors

    The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1). The attached zip file contains raw data files submitted by the authors and a NetCDF file. Progressively, raw data will be imported into PANGAEA as distinct data publications related to the original sources (journal or data publications).

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    PANGAEA
    Dataset . 2012
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2012
      Data sources: B2FIND
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    Authors: Mathissen, Marcel;

    This dataset is brake wear cycle that was used as a reference cycle in the EU co-funded project LOWBRASYS which aims at particle reduction from automotive brakes. One task within the LOWBRASYS project was to define a real-drive braking schedule. This cycle is a short version of an existing brake procedure (Los Angeles City Traffic (LACT)) that was generated from actual on-road driving data and addresses typical urban, extra-urban and highway drive conditions. It is noteworthy that another real-world cycle has been developed [Mathissen et al. 2018]. The main difference between the two newly developed cycles is that the present LOWBRASYS cycle is based on a limited dataset from a specific region, while the other cycle is based on the WLTP database, which covers much more use-case and driving conditions. The PMP IWG has decided to use the present cycle as a backup cycle for future brake wear emissions evaluation.

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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