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13,858 Research products

  • European Marine Science
  • 2014-2023
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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: Trouche, Blandine;

    Anvi'o databases of the 47 ammonia-oxidizing archaea MAGs from abyssal and hadal surface sediments presented in Trouche et al, 2023 (https://doi.org/10.1038/s43705-023-00341-6). 

    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/ ZENODOarrow_drop_down
    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/
    ZENODO
    Other ORP type . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Other ORP type . 2023
      License: CC BY
      Data sources: ZENODO
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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: Ballantyne, John;

    The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes.

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    Authors: Ballantyne, John;

    Computational fluid dynamics (CFD) is an important tool for the simulation of the cardiovascular function and dysfunction. Due to the complexity of the anatomy, the transitional regime of blood flow in the heart, and the strong mutual influence between the flow and the physical processes involved in the heart function, the development of accurate and efficient CFD solvers for cardiovascular flows is still a challenging task. In this paper we present lifex-cfd, an open-source CFD solver for cardiovascular simulations based on the lifex finite element library, written in modern C++ and exploiting distributed memory parallelism. We model blood flow in both physiological and pathological conditions via the incompressible Navier-Stokes equations, accounting for moving cardiac valves, moving domains, and transition-to-turbulence regimes. In this paper, we provide an overview of the underlying mathematical formulation, numerical discretization, implementation details and examples on how to use lifex-cfd. We verify the code through rigorous convergence analyses, and we show its almost ideal parallel speedup. We demonstrate the accuracy and reliability of the numerical methods implemented through a series of idealized and patient-specific vascular and cardiac simulations, in different physiological flow regimes. The lifex-cfd source code is available under the LGPLv3 license, to ensure its accessibility and transparency to the scientific community, and to facilitate collaboration and further developments.

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    Authors: Wang, Siyuan; Zhang, Futing; Koedooder, Coco; Qafoku, Odeta; +8 Authors

    The uploaded file contains three separate Python scripts aimed at modeling the sinking velocity of natural Trichodesmium colonies loaded with dust particles. Two scripts have been developed to calculate the dust influencing factor (K), and one script is dedicated to calculating the sinking velocity of particle-free Trichodesmium colonies (v0). All Python scripts are available on GitHub (https://github.com/Zhanzhu1110/Trichobuoyancy.git) and can be processed using the "Codespaces" function on GitHub. The file 'K given values' is a specialized script designed to simulate the dust factor (K), assuming seawater and dust densities of 1028 kg/m3 and 2500 kg/m3, respectively, with a colony radius of 1 mm.

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    ZENODO
    Model . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Model . 2023
      License: CC BY
      Data sources: ZENODO
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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: Hauck, Judith; Landschützer, Peter; Mayot, Nicolas; Jersild, Annika;

    Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, southfgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitudesfco2: Surface ocean fCO2, dimensions: time, latitude, longitudearea: Area per pixel, dimensions: latitude, longitudearea_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitudesfco2: Surface ocean fCO2, dimensions: time, latitude, longitudearea: Area per pixel, dimensions: latitude, longitude(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual. Fair data use statement:The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.Citation: Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Muller, Veleda;

    This is an apatite and zircon (U-Th)/He dataset from the Fitz Roy and Torres del Paine regions in Southern Patagonia. The AHe data were analysed in the University of Arizona and University of Berkeley, USA and the ZHe data were analysed in the GEOPS Laboratory in the Paris-Saclay University, France. This data support the study “Geodynamic and climatic forcing on late-Cenozoic exhumation of the Southern Patagonian Andes (Fitz Roy and Torres del Paine massifs)” by Muller, V.A.P. et al., 2023 (DOI: 10.22541/essoar.168332179.93378898/v1).

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    Mendeley Data
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Zhen, Yicun; Resseguier, Valentin; chapron, bertrand;

    This is the document for the code mentioned in "On a generalization of Optical Flow algorithms for geophysical applications: a differential geometry perspective". (To save time, steps 1,2,3,4 can be skipped since the data is already saved in the corresponding filefolder) 1,GENERATE ENSEMBLE MEMBERS : run getens_doublevortex_256.py 2,GENERATE THE TRUTH : run gettruth_doublevortex_256.py 3,BEFORE MORPHING : run exp/get_dataens.py and exp/get_dataobs.py 4,MORPH THE ENSEMBLE : run exp/get_morphed.py 5,BEFORE DA : run exp/get_ens_LR.py and exp/get_truth_LR.py 6,DA : run exp/EnKF_direct.py and exp/EnKF_morphed.py 7,PLOT DA RESULTS : run exp/plt_comparemean.py and exp/plt_compare_member.py 8,MORPH WITH PLAIN T#(10000 steps) : run exp/get_morphed_noDiffGeo.py 9,MORPH WITH PLAIN T#(6000 steps) : run exp/get_morphed_noDiffGeo2.py 10,PLOT MORPHED ENSEMBLE MEMBERS : run exp/ensemble_morphed/figs/plt_main.py 11,PLOT MORPHED ENSEMBLE MEMBER WITH PLAIN T# : run exp/ensemble_morphed_noDiffGEo/figs/plt_main.py and exp/ensemble_morphed_noDiffGEo2/figs/plt_main.py

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Ballantyne, John;

    Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. The adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems.

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    Authors: Martínez Moreno, Josué;

    This dataset includes the NEMO 4.0.2 configuration used and analysed in the paper titled "Eddy-driven heterogeneity in sea ice during the ice-growth season". The output data is approximately 4TB for the 3 idealised configuration used in the manuscript, thus we opted to distribute the configuration. Note: The initial conditions for each simulation are compressed into the file `init_cond.zip` The configuration for one of the simulations is compressed in the file `config.zip` In order to reproduce all the runs, it's only required to change the initial conditions in the file namelist_cfg and namelist_ice_cfg. Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/Ice_formation

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    ZENODO
    Model . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Model . 2023
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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: Ballantyne, John;

    PARSIFAL (PARametrized SImulation) is a fast and reliable software tool that reproduces the complete response of a triple-GEM detector to the passage of a charged particle, taking into account the main physical effects. Starting from the detector configuration and the particle information, PARSIFAL reproduces ionization, spatial and temporal diffusion, effect of magnetic field, if present, and GEM amplification to provide the dependable triple-GEM detector response. In the design and optimization stages of this kind of detectors, simulations play an important role. Accurate and robust software programs, such as GARFIELD++, can simulate the transport of electrons and ions in a gas medium and their interaction with the electric field, but they are CPU-time consuming. The necessity to reduce the processing time while maintaining the precision of a full simulation is the main driver of this work. For a given set of geometrical and electrical settings, GARFIELD++ is run once-and-for-all to provide the input parameters for PARSIFAL. Once PARSIFAL is initialized and run, it produces the detector output, including the signal induction and the output of the electronics. The results of the analysis of the simulated data obtained with PARSIFAL are compared with the results of the experimental data collected during a testbeam: some tuning factors are applied to the simulation to improve the agreement. This paper describes the structure of the code and the methodology used to match the output to the experimental data.

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13,858 Research products
  • 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: Trouche, Blandine;

    Anvi'o databases of the 47 ammonia-oxidizing archaea MAGs from abyssal and hadal surface sediments presented in Trouche et al, 2023 (https://doi.org/10.1038/s43705-023-00341-6). 

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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/
    ZENODO
    Other ORP type . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Other ORP type . 2023
      License: CC BY
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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: Ballantyne, John;

    The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes.

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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: Ballantyne, John;

    Computational fluid dynamics (CFD) is an important tool for the simulation of the cardiovascular function and dysfunction. Due to the complexity of the anatomy, the transitional regime of blood flow in the heart, and the strong mutual influence between the flow and the physical processes involved in the heart function, the development of accurate and efficient CFD solvers for cardiovascular flows is still a challenging task. In this paper we present lifex-cfd, an open-source CFD solver for cardiovascular simulations based on the lifex finite element library, written in modern C++ and exploiting distributed memory parallelism. We model blood flow in both physiological and pathological conditions via the incompressible Navier-Stokes equations, accounting for moving cardiac valves, moving domains, and transition-to-turbulence regimes. In this paper, we provide an overview of the underlying mathematical formulation, numerical discretization, implementation details and examples on how to use lifex-cfd. We verify the code through rigorous convergence analyses, and we show its almost ideal parallel speedup. We demonstrate the accuracy and reliability of the numerical methods implemented through a series of idealized and patient-specific vascular and cardiac simulations, in different physiological flow regimes. The lifex-cfd source code is available under the LGPLv3 license, to ensure its accessibility and transparency to the scientific community, and to facilitate collaboration and further developments.

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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: Wang, Siyuan; Zhang, Futing; Koedooder, Coco; Qafoku, Odeta; +8 Authors

    The uploaded file contains three separate Python scripts aimed at modeling the sinking velocity of natural Trichodesmium colonies loaded with dust particles. Two scripts have been developed to calculate the dust influencing factor (K), and one script is dedicated to calculating the sinking velocity of particle-free Trichodesmium colonies (v0). All Python scripts are available on GitHub (https://github.com/Zhanzhu1110/Trichobuoyancy.git) and can be processed using the "Codespaces" function on GitHub. The file 'K given values' is a specialized script designed to simulate the dust factor (K), assuming seawater and dust densities of 1028 kg/m3 and 2500 kg/m3, respectively, with a colony radius of 1 mm.

    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/ ZENODOarrow_drop_down
    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/
    ZENODO
    Model . 2023
    License: CC BY
    Data sources: ZENODO
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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/ ZENODOarrow_drop_down
      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/
      ZENODO
      Model . 2023
      License: CC BY
      Data sources: ZENODO
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.