Recent Submissions

  • Scaled, unmerged X-ray diffraction data from SF2 Nef-Fyn SH3 R96I crystals

    Arold, Stefan T.; Aldehaiman, Abdullah; Shahul Hameed, Umar F. (KAUST Research Repository, 2020-09-27) [Dataset]
    Crystals of the SF2 Nef:FynR96I SH3 complex were obtained by sitting drop vapour diffusion (details to be published). All data for SF2 Nef:FynR96I SH3 were collected at 100K at the beamline Proxima 2A at the SOLEIL Synchrotron (France), EIGER 9M detector, respectively (proposal numbers 2016 0098, 20161236, 20170193). Data from three crystals were processed, scaled and combined using XDS as implemented in the XDSme pipeline. The resulting scaled but unmerged and not anisotropically cut data are deposited here.
  • Data for Long-Wavelength Propagation in Fractured Rock Masses (3D Stress Field)

    Rached, Rached; Garcia, Adrian; Santamarina, Carlos (KAUST Research Repository, 2020-09-23) [Dataset]
    The dataset includes all data that was used to generate Figures 3, 4, 5, 7, and 8.
  • Large synthetic datasets for univariate geostatistical modeling

    Abdulah, Sameh; Ltaief, Hatem; Sun, Ying; Genton, Marc G.; Keyes, David E. (KAUST Research Repository, 2020-09-08) [Dataset]
    The enclosed datasets have been generated by the internal spatial data generator tool included in the ExaGeoStat software (https://github.com/ecrc/exageostat). The datasets are univariate 2D spatial data spanning different correlation strengths between the geospatial locations with three different smoothness levels (0.6, 1.5, and 2.3). The main purpose of these datasets is to validate any exact or approximation geospatial modeling algorithm by providing the truth parameters used for generating each dataset. For certain data configurations that are not covered by the provided datasets, ExaGeoStat software can be used directly to generate geospatial data with a prescribed number of locations and prescribed parameter set.
  • 3D Model Construction of SARS-CoV-2 Virus

    Mesri, Youssef; Bader, Wael; Alomairy, Rabab; Ltaief, Hatem; Keyes, David E. (KAUST Research Repository, 2020-09-06) [Dataset]
    Description: The dataset contains SARS-COV-2 virus 3D geometry extracted from the Protein Data Bank (PDB) codenamed PDBID 6VXX available at (https://www.rcsb.org/structure/6VXX). In 3D model construction, we first generate a tetrahedral mesh of the volume surrounding the molecular structure of the Spike glycoprotein of the SARS-CoV-2 virus, a.k.a. the S protein from PDB. Codenamed PDBID 6VXX at the Protein Data Bank (PDB)2, the S protein has been generated with a 2.8Å resolution, i.e., 0.28nm. Once the mesh of the S protein has been constructed, We stitch several S proteins, approximately evenly distributed around the spheric geometry of the virus main body. Based on real geometrical description of the virus structure, we set the virus diameter to 140nm and attach 80 S proteins. We build a CAD model which represents the building block for the simulation pipeline. The CAD model is then triangulated to generate a 3D surface mesh of the overall SARS-COV-2 virus geometry. Software or equipment used to create the data: The mesh is generated using OxyGen Suite with third party tools: TetGen and VTK. OxyGen Suite is a collection of mesh generation, adaptation and deformation algorithms and tools that produces high quality static and dynamic simplex meshes.
  • How does a Pinatubo-size Volcanic Plume Reach the Middle Stratosphere?

    Stenchikov, Georgiy L.; Ukhov, Alexander; Osipov, Sergey; Ahmadov, Ravan; Grell, Georg; Cady-Pereira, Karen; Mlawer, Eli; Iacono, Michael (KAUST Research Repository, 2020-09-01) [Dataset]
    This dataset includes namelist's and other files required to run WRF-Chem simulation.
  • Data for: "Regional Geoengineering to Increase Rainfall over the Red Sea Arabian Coastal Plains by Utilizing Sea Breezes"

    Mostamandi, Suleiman; Predybaylo, Evgeniya; Osipov, Sergey; Zolina, Olga; Gulev, Sergey K.; Parajuli, Sagar P.; Stenchikov, Georgiy L. (KAUST Research Repository, 2020-08-28) [Dataset]
  • Data for: "High summer temperatures amplify functional differences between coral- and algae-dominated reef communities"

    Roth, Florian; Rädecker, Nils; Carvalho, Susana; Duarte, Carlos M.; Saderne, Vincent; Anton, Andrea; Silva, Luis; Calleja, Maria; Voolstra, Christian R.; Kürten, Benjamin; Jones, Burton; Wild, Christian (KAUST Research Repository, 2020-08-26) [Dataset]
    This is the data to "High summer temperatures amplify functional differences between coral- and algae-dominated reef communities". All information on how the dataset was collected can be found in the manuscript.  Abstract of the manuscript: Shifts from coral to algal dominance are expected to increase in tropical coral reefs as a result of anthropogenic disturbances. The consequences for key ecosystem functions such as primary productivity, calcification, and nutrient recycling are poorly understood, particularly under changing environmental conditions. We used a novel in situ incubation approach to compare functions of coral- and algae-dominated communities in the central Red Sea bi-monthly over an entire year. In situ gross and net community primary productivity, calcification, dissolved organic carbon fluxes, dissolved inorganic nitrogen fluxes, and their respective activation energies were quantified to describe the effects of seasonal changes. Overall, coral-dominated communities exhibited 30% lower net productivity and 10 times higher calcification than algae-dominated communities. Estimated activation energies indicated a higher thermal sensitivity of coral-dominated communities. In these communities, net productivity and calcification were negatively correlated with temperature (>40% and >65% reduction, respectively, with +5°C increase from winter to summer), while carbon losses via respiration and dissolved organic carbon release were more than doubled at higher temperatures. In contrast, algae-dominated communities doubled net productivity in summer, while calcification and dissolved organic carbon fluxes were unaffected. These results suggest pronounced changes in community functioning associated with phase shifts. Algae-dominated communities may outcompete coral-dominated communities due to their higher productivity and carbon retention to support fast biomass accumulation while compromising the formation of important reef framework structures. Higher temperatures likely amplify these functional differences, indicating a high vulnerability of ecosystem functions of coral-dominated communities to temperatures even below coral bleaching thresholds. Our results suggest that ocean warming may not only cause but also amplify coral-algal phase shifts in coral reefs. Usage information: The data Excel file contains two data sheets: Sheet 1 (Community composition): This sheet gives the community composition of the assessed benthic communities in % cover of functional groups. Sheet 2 (Metabolism): This sheet contains the metabolic data of the benthic communities. All abbreviations and units are in the related publication.
  • Crustal and upper-mantle structure below Central and Southern Mexico

    Espindola Carmona, Armando; Peter, Daniel; Ortiz Aleman, Carlos (KAUST Research Repository, 2020-08-25) [Dataset]
    3D Velocity Model for Central and Southern Mexico
  • Heat Flow in Fractured Rock

    Garcia, Adrian; Santamarina, Carlos (KAUST Research Repository, 2020-08-16) [Dataset]
    Data set for all figures in our paper
  • Data for article "Tracking Charge Transfer to Residual Metal Clusters in Conjugated Polymers for Photocatalytic Hydrogen Evolution"

    Sachs, Michael; Cha, Hyojung; Kosco, Jan; Aitchison, Catherine M.; Francàs, Laia; Corby, Sacha; Chiang, Chao-Lung; Wilson, Anna A.; Godin, Robert; Fahey-Williams, Alexander; Cooper, Andrew I.; Sprick, Reiner Sebastian; McCulloch, Iain; Durrant, James R. (Zenodo, 2020-07-28) [Dataset]
    Data presented in the publication "Tracking Charge Transfer to Residual Metal Clusters in Conjugated Polymers for Photocatalytic Hydrogen Evolution", published in the Journal of the American Chemical Society. The published article is available at https://doi.org/10.1021/jacs.0c06104
  • Ignition Delay Data Set

    Almohammadi, S.M.; Knio, Omar; Le Maître, O.P. (KAUST Research Repository, 2020-07-22) [Dataset]
    This data file has 38,311 lines. Each line contains 3812 entries. The first 3811 entries on each line correspond to the coordinates of a random vector, whose components are independent and uniformly distributed in [-1,1]. The last entry on each line is the quantity of interest (QoI), namely the ignition delay time (s). The simulations are described in S.M. Almohammadi, O.P. Le Maitre, O.M. Knio (2020) "Computational Challenges in Sampling and Representation of Uncertain Kinetic Systems in Large Dimensions."
  • Particulate scattering and backscattering in relation to the nature of particles in the Red Sea

    Kheireddine, Malika; Ouhssain, Mustapha (KAUST Research Repository, 2020-07-17) [Dataset]
    Dataset of particulate scattering and backscattering coefficients, chlorophyll a, temperature, salinity and density from surface to 200 m depth for different stations in the Red Sea during different cruises. Particulate scattering and backscattering coefficient were obtained using a wetlabs ac-s sensor and a wetlabs BB9 sensor respectively. Temperature, salinity and density were obtained using a seabird CTD sensor. Chlorophyll a concentration was obtained by HPLC.
  • CM2.1 modeling framework and supporting data for "ENSO response to low-latitude volcanic eruptions"

    Predybaylo, Evgeniya; Stenchikov, Georgiy L.; Wittenberg, Andrew; Osipov, Sergey (KAUST Research Repository, 2020-07-16) [Dataset]
    This is the supporting information for the article "El Niño/Southern Oscillation response to low-latitude volcanic eruptions depends on ocean pre-conditions and eruption timing". The archive includes all necessary files needed to reproduce the results - the modified source code of the CM2.1 climate model, input data, SATO1.8 volcanic dataset, and other data necessary to reproduce the simulations described in the paper.
  • Dataset for Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study

    Luo, Jessica Y.; Condon, Robert H.; Stock, Charles A.; Duarte, Carlos M.; Lucas, Cathy H.; Pitt, Kylie A.; Cowen, Robert K. (Zenodo, 2020-06-30) [Dataset]
    Gridded dataset of gelatinous zooplankton (GZ) biomass (mg C m$^{-3}$) and numeric density (individuals m$^{-3}$), time-averaged, in a 1-degree grid. Data are separated by phyla: Cnidaria, Ctenophora, and Chordata (pelagic tunicates). Original data compiled as part of the Jellyfish Database Initiative Project (JeDI; Condon et al. 2015, doi:10.1575/1912/7191) and converted to carbon biomass units for Lucas et al. 2014. Cnidarian additions to this dataset include records from the northern California Current (Brodeur et al., 2014) and Gulf of Mexico (Robinson et al., 2015). Chordata additions include salps from the Bermuda Atlantic Time Series (BATS; Stone & Steinberg, 2014), Western Antarctic Peninsula (WAP; Steinberg et al., 2015), and Southern Ocean, from KRILLBASE (Atkinson et al., 2017). Note that we excluded the KRILLBASE records from the WAP region that to prevent double-counting. See Methods in Luo et al. (2020) for details on biometric conversions to carbon biomass. Data were averaged by time (season, then year), and then within each 1-degree grid cell. Code for the model using this dataset is available at: https://github.com/jessluo/gz_biogeochem_pub Luo, Jessica Y., Condon, R. H., Stock, C. A., Duarte, C. M., Lucas, C. H., Pitt, K. A., & Cowen, R. K. (2020). Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study. Global Biogeochemical Cycles, 34, e2020GB006704. https://doi.org/10.1029/2020GB006704
  • Acoustic Scattering from 3D Rigid Objects

    Al-Harthi, Noha; Alomairy, Rabab; Akbudak, Kadir; Chen, Rui; Ltaief, Hatem; Bagci, Hakan; Keyes, David E. (KAUST Research Repository, 2020-06-15) [Dataset]
    The dataset contains meshes of a unit sphere and a simplified submarine model generated using I-DEAS software. There are three important parameters: edge length, order of curvilinear elements (both are inputs to I-DEAS), and number of elements. Element order 2 for all meshes in the dataset.
  • Supplementary data to the article: Impact of fracture geometry and topology on the connectivity and flow properties of stochastic fracture networks

    Zhu, Weiwei; Khirevich, Siarhei; Patzek, Tadeusz (KAUST Research Repository, 2020-05-26) [Dataset]
    From the shared data, you can: 1. Find the data to reproduce Fig. 7 and 8 in the paper. Those data are averaged by 6000 random realizations for each case; 2. Find the data to plot 2D fracture networks and its graph representation; 3. Find the data to plot 3D fracture networks and its graph representation; 4. Find the data to plot the pressure distribution in 2D fracture networks; 5. Find the data to plot the pressure distribution in 3D fracture networks; 6. Find the corresponding Matlab code to help you visualize the result; To have a better visualization, the system size of 2D ad 3D fracture networks are modified. Those modifications are only for visualization purpose and will not change the results we have in the paper. 
  • SARS-COV-2 Virus Mesoscale Model

    Nguyen, Ngan; Strnad, Ondrej; Klein, Tobias; Luo, Deng; Alharbi, Ruwayda; Wonka, Peter; Maritan, Martina; Mindek, Peter; Autin, Ludovic; Goodsell, David; Viola, Ivan (KAUST Research Repository, 2020-05-06) [Dataset]
    The dataset contains SARS-COV-2 virus mesoscale atomistic model created as a part of "Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models" paper.
  • CCDC 1997794: Experimental Crystal Structure Determination

    Alhaddad, Maha; Chakraborty, Priyanka; Hu, Jinsong; Huang, Kuo-Wei (Cambridge Crystallographic Data Centre, 2020-04-19) [Dataset]
  • CCDC 1997809: Experimental Crystal Structure Determination

    Alhaddad, Maha; Chakraborty, Priyanka; Hu, Jinsong; Huang, Kuo-Wei (Cambridge Crystallographic Data Centre, 2020-04-19) [Dataset]
  • Dynamic fault interaction during a fluid-injection induced earthquake: The 2017 Mw 5.5 Pohang event

    Palgunadi, Kadek Hendrawan; Gabriel, Alice-Agnes; Ulrich, Thomas; Lopéz-Comino, José Ángel; Mai, Paul Martin (Zenodo, 2020-03-02) [Dataset]
    All files required to run the dynamic rupture modeling of The 2017 Mw 5.5 Pohang Earthquake represented in Palgunadi, K. H., A.-A. Gabriel, T. Ulrich, J. A. Lopez-Comino, P. M. Mai (2020). Dynamic fault interaction during a fluid-injection induced earthquake: The 2017 Mw 5.5 Pohang event. The detailed description of each file and SeisSol version used is described in README.txt, and parameter choice is explained in each ".yaml" and parameter file.

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