cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25781/kaust-6l345
title: Genome assembly and annotation datasets of the plant growth promoting bacterium Cellulosimicrobium sp. JZ28 isolated from the root endosphere of a pioneer desert plant in Jizan, Saudi Arabia

description: The files associated with this dataset are the Genbank, GFF and protein coding-sequence (CDSs) files of a plant growth promoting bacterium isolated from the root endosphere of a pioneer desert plant in Jizan, Saudi Arabia. Genome annotation was carried out using the Automatic Annotation of Microbial Genomes (AAMG) which is an integrated module in the in-house INDIGO-Desert v1.1 pipeline (Alam et al., 2013).
DOI: 10.25781/kaust-4ut7e
title: Genome assembly and annotation datasets of the plant growth promoting bacterium Microbacterium sp. JZ31 isolated from the root endosphere of a pioneer desert plant in Jizan, Saudi Arabia

description: The files associated with this dataset are the Genbank, GFF and protein coding-sequence (CDSs) files of a plant growth promoting bacterium isolated from the root endosphere of a pioneer desert plant in Jizan, Saudi Arabia. Genome annotation was carried out using the Automatic Annotation of Microbial Genomes (AAMG) which is an integrated module in the in-house INDIGO-Desert v1.1 pipeline (Alam et al., 2013).
DOI: 10.25781/kaust-psdx7
title: Particulate scattering and backscattering in relation to the nature of particles in the Red Sea

description: 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.
DOI: 10.25781/kaust-6mbb9
title: CM2.1 modeling framework and supporting data for "ENSO response to low-latitude volcanic eruptions"

description: 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.
DOI: 10.25781/kaust-u92e2
title: Ignition Delay Data Set

description: 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."
DOI: 10.25781/kaust-i0634
title: Acoustic Scattering from 3D Rigid Objects

description: 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.
DOI: 10.25781/kaust-027py
title: Heat Flow in Fractured Rock

description: Data set for all figures in our paper
DOI: 10.25781/kaust-1a96z
title: Crustal and upper-mantle structure below Central and Southern Mexico

description: 3D Velocity Model for Central and Southern Mexico
DOI: 10.25781/kaust-d41q0
title: Data for: "High summer temperatures amplify functional differences between coral- and algae-dominated reef communities"

description: 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 (&gt;40% and &gt;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.
DOI: 10.25781/kaust-54v14
title: How does a Pinatubo-size Volcanic Plume Reach the Middle Stratosphere?

description: This dataset includes namelist's and other files required to run WRF-Chem simulation.
DOI: 10.25781/kaust-v2ef2
title: 3D Model Construction of SARS-CoV-2 Virus

description: 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.
DOI: 10.25781/kaust-msg02
title: Stereo-Event flow dataset

description: This is a stereo event camera PTV for 3D dense flow
DOI: 10.25781/kaust-jjvcn
title: Large synthetic datasets for univariate geostatistical modeling

description: 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.
DOI: 10.25781/kaust-y4k93
title: Data for Long-Wavelength Propagation in Fractured Rock Masses (3D Stress Field)

description: The dataset includes all data that was used to generate Figures 3, 4, 5, 7, and 8.
DOI: 10.25781/kaust-8cv15
title: Scaled, unmerged X-ray diffraction data from SF2 Nef-Fyn SH3 R96I crystals

description: 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.
DOI: 10.5281/zenodo.6782464
title: Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions

description: The archive "dataset.tar.gz" contains trained models (neural networks), training-, validation- and test-data and selected structures in POSCAR format, obtained in the neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions. See README for more information on the archive content.
DOI: 10.25781/kaust-pbr81
title: Data for Aerosol Vertical Distribution and Interactions with Land/Sea Breezes over the Eastern Coast of the Red Sea from LIDAR Data and High-resolution WRF-Chem Simulations
DOI: 10.25781/kaust-19659
title: Protein-protein benchmark5 decoys balanced dataset

description: This is the most complete and diverse protein docking decoys set derived from the Benchmark5. We used three different rigid-body docking programs to generate the decoys. We analyzed these docking decoys with more than 150 different scoring functions from different sources ( CCharppi, FreeSASA, CIPS, CONSRANK). This version of the dataset is balanced with the raw values from the scoring functions. This data is intended for the training of Machine learning algorithms.
DOI: 10.25781/kaust-dn0kr
title: Protein-protein interactions decoys datasets for machine learning algorithm development

description: This is the most complete and diverse protein docking decoys set derived from the Benchmark5, Scorers_set. We used three different rigid-body docking programs to generate the decoys for the Bechmark5. We analyzed all docking decoys with more than 150 different scoring functions from different sources ( CCharppi, FreeSASA, CIPS, CONSRANK). We provide a balanced and unbalanced version of the data. This balanced data is intended for the training and test of machine learning algorithms. the unbalanced data is provided to simulated the real-world scenario. We also provide a set of rigid-body docking decoys from Interactome3D that spans 1391 interactions. We obtained the labels for this set using a weakly-supervised approach we called hAIkal. We used this data to augment the train data and improve machine learning classifiers.
DOI: 10.25781/kaust-sw314
title: Datasets for "Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally ReducedEcho State Networks"

description: The datasets for "Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally ReducedEcho State Networks"
DOI: 10.25781/kaust-7b4gu
title: Simulation data for the Toba supervolcano eruption caused severe tropical ozone depletion

description: NASA GISS ModelE simulations used in the Osipov et al., Toba supervolcano eruption caused severe tropical ozone depletion, 2021
DOI: 10.25781/kaust-9yhx6
title: Data for "Immiscible fluid displacement in fractured media"
DOI: 10.25781/kaust-8vp2v
title: Data for "Competition on Spatial Statistics for Large Datasets"

description: The enclosed datasets have been generated by the internal spatial data generator tool included in the ExaGeoStat software (https://github.com/ecrc/exageostat). It was used for the 2021 KAUST Competition on Spatial Statistics for Large Datasets (https://cemse.kaust.edu.sa/stsds/kaust-competition-spatial-statistics-large-datasets). The competition had four parts (Sub-competition 1a, Sub-competition 1b, Sub-competition 2a, and Sub-competition 2b). The main purpose of the competition was to reassess existing approximation methods on large spatial datasets in a uniform way that guarantees a fair comparison. The true parameters used for generating each dataset are provided in the PDF file attached with the datasets.
DOI: 10.6084/m9.figshare.20195573
title: PRISMA-P-checklist - Artificial Intelligence to Improve Antimicrobial Prescribing- A Protocol for a Systematic Review

description: PRISMA-P-checklist - Artificial Intelligence to Improve Antimicrobial Prescribing- A Protocol for a Systematic Review.
DOI: 10.6084/m9.figshare.20195573.v1
title: PRISMA-P-checklist - Artificial Intelligence to Improve Antimicrobial Prescribing- A Protocol for a Systematic Review

description: PRISMA-P-checklist - Artificial Intelligence to Improve Antimicrobial Prescribing- A Protocol for a Systematic Review.
DOI: 10.25781/kaust-6930v
title: Dataset for multispectral illumination estimation using deep unrolling network
DOI: 10.25781/kaust-xyb8z
title: Droplet lift-off from hydrophobic surfaces from impact with soft-hydrogel spheres
DOI: 10.6084/m9.figshare.20195282.v1
title: Subject 3 Data

description: Data for Subject 3 is provided.
DOI: 10.6084/m9.figshare.16910488
title: Subject 1 Data

description: This dataset is the high gamma band amplitude estimate for Subject 1 (NYU). NYU_cell_normalized.mat is comprised of cells containing continuous free behavior. Data is z-scored in each cell block. NYU_cell_unnormalized.mat is comprised of cells containing continuous free behavior. Data is NOT z-scored in each cell block. NYU_epoch_normalized.mat is comprised of 30-second epochs of different types of behavior. Data is NOT z-scored in each cell block. X are the epochs and Y are the behavioral labels.
DOI: 10.6084/m9.figshare.20195282
title: Subject 3 Data

description: Data for Subject 3 is provided.
DOI: 10.6084/m9.figshare.16910488.v1
title: Subject 1 Data

description: This dataset is the high gamma band amplitude estimate for Subject 1 (NYU). NYU_cell_normalized.mat is comprised of cells containing continuous free behavior. Data is z-scored in each cell block. NYU_cell_unnormalized.mat is comprised of cells containing continuous free behavior. Data is NOT z-scored in each cell block. NYU_epoch_normalized.mat is comprised of 30-second epochs of different types of behavior. Data is NOT z-scored in each cell block. X are the epochs and Y are the behavioral labels.
DOI: 10.6084/m9.figshare.20195120
title: Subject 2 Data

description: This dataset is the high gamma band amplitude estimate for Subject 2 (RCH1). <br> RCH1_cell_normalized.mat is comprised of cells containing continuous free behavior. Data is z-scored in each cell block. <br> RCH1_cell_unnormalized.mat is comprised of cells containing continuous free behavior. Data is NOT z-scored in each cell block. <br> RCH1_epoch_normalized.mat is comprised of 30-second epochs of different types of behavior. Data is NOT z-scored in each cell block. X are the epochs and Y are the behavioral labels. <br>
DOI: 10.17028/rd.lboro.20195342
title: Supplementary information files for Using invertebrate functional traits to improve flow variability assessment within European rivers

description: Supplementary files for article Using invertebrate functional traits to improve flow variability assessment within European rivers. Rivers are among the most threatened ecosystems worldwide and are experiencing rapid biodiversity loss. Flow alteration due to climate change, water abstraction and augmentation is a severe stressor on many aquatic communities. Macroinvertebrates are widely used for biomonitoring river ecosystems although current taxonomic approaches used to characterise ecological responses to flow have limitations in terms of generalisation across biogeographical regions. A new macroinvertebrate trait-based index, Flow-T, derived from ecological functional information (flow velocity preferences) currently available for almost 500 invertebrate taxa at the European scale is presented. The index was tested using data from rivers spanning different biogeographic and hydro-climatic regions from the UK, Cyprus and Italy. The performance of Flow-T at different spatial scales and its relationship with an established UK flow assessment tool, the Lotic-invertebrate Index for Flow Evaluation (LIFE), was assessed to determine the transferability of the approach internationally. Flow-T was strongly correlated with the LIFE index using both presence-absence and abundance weighted data from all study areas (r varying from 0.46 to 0.96). When applied at the river reach scale, Flow-T was effective in identifying communities associated with distinct mesohabitats characterised by their hydraulic characteristics (e.g., pools, riffles, glides). Flow-T can be derived using both presence/absence and abundance data and can be easily adapted to varying taxonomic resolutions. The trait-based approach facilitates research using the entire European invertebrate fauna and can potentially be applied in regions where information on taxa-specific flow velocity preferences is not currently available. The inter-regional and continental scale transferability of Flow-T may help water resource managers gauge the effects of changes in flow regime on instream communities at varying spatial scales.
DOI: 10.17028/rd.lboro.20195342.v1
title: Supplementary information files for Using invertebrate functional traits to improve flow variability assessment within European rivers

description: Supplementary files for article Using invertebrate functional traits to improve flow variability assessment within European rivers. Rivers are among the most threatened ecosystems worldwide and are experiencing rapid biodiversity loss. Flow alteration due to climate change, water abstraction and augmentation is a severe stressor on many aquatic communities. Macroinvertebrates are widely used for biomonitoring river ecosystems although current taxonomic approaches used to characterise ecological responses to flow have limitations in terms of generalisation across biogeographical regions. A new macroinvertebrate trait-based index, Flow-T, derived from ecological functional information (flow velocity preferences) currently available for almost 500 invertebrate taxa at the European scale is presented. The index was tested using data from rivers spanning different biogeographic and hydro-climatic regions from the UK, Cyprus and Italy. The performance of Flow-T at different spatial scales and its relationship with an established UK flow assessment tool, the Lotic-invertebrate Index for Flow Evaluation (LIFE), was assessed to determine the transferability of the approach internationally. Flow-T was strongly correlated with the LIFE index using both presence-absence and abundance weighted data from all study areas (r varying from 0.46 to 0.96). When applied at the river reach scale, Flow-T was effective in identifying communities associated with distinct mesohabitats characterised by their hydraulic characteristics (e.g., pools, riffles, glides). Flow-T can be derived using both presence/absence and abundance data and can be easily adapted to varying taxonomic resolutions. The trait-based approach facilitates research using the entire European invertebrate fauna and can potentially be applied in regions where information on taxa-specific flow velocity preferences is not currently available. The inter-regional and continental scale transferability of Flow-T may help water resource managers gauge the effects of changes in flow regime on instream communities at varying spatial scales.
DOI: 10.25781/kaust-3o88t
title: Samples of wind power and electricity prices used in the manuscript "Risk-averse stochastic programming vs. adaptive robust optimization: a virtual power plant application"

description: Description of the files with the wind power and electricity prices scenarios used in the manuscript: "Risk-averse stochastic programming vs. adaptive robust optimization: a virtual power plant application" by Ricardo M. Lima, Antonio J. Conejo, Loic Giraldi, Olivier Le Maitre, Ibrahim Hoteit, and Omar Knio, submitted to the INFORMS Journal on Computing. This file describes the wind power and electricity prices for two weeks used in the manuscript above. Please refer to the manuscripts above for a detailed description of the data sources, sampling process, and its characteristics. The sample average approximation methodology involves two stages: 1) optimization stage; 2) bound estimation stage. Data for the the stochastic programming approach The optimization stage uses sample sizes of N=10, 50, 100, 500, and 5000; and for each sample size, M = 30 optimization replications are performed. This data is available in the following files: wp_week{X}_N{Y}_M{Z}.csv pp_week{X}_N{Y}_M{Z}.csv where X:={1,2} is the week number Y:={10,50,100,500,5000} is the sample size Z:={1,...,30} is the replication number The bound estimation stage, the lower bound on the true optimal objective function value is estimated in two steps: 1) for each distinct first-stage solution obtained from the optimization replications, a lower bound is estimated using T=30 samples of size N=25,000, and 2) the first-stage solution with the best lower bound from the previous step is selected and a new lower bound is estimated using S=30 samples of size N=25,000. The data is available in the following files wp_week{X}_N{Y}_T{Z}.csv pp_week{X}_N{Y}_T{Z}.csv wp_week{X}_N{Y}_S{Z}.csv pp_week{X}_N{Y}_S{Z}.csv where X:={1,2} is the week number Y:={25000} is the sample size Z:={1,...,30} is the replication number Data for the adaptive robust optimization approach The optimization stage uses uncertainty sets defined in these files: pp_week{X}_pf.csv pp_week{X}_95ci.csv wp_week{X}_ensemble.csv where X:={1,2} is the week number The lower bound estimation stage requires only the second step above, using S = 30 samples of size N=25,000. wp_week{X}_N{Y}_S{Z}.csv pp_week{X}_N{Y}_S{Z}.csv where X:={1,2} is the week number Y:={25000} is the sample size Z:={1,...,30} is the replication number
DOI: 10.25781/kaust-v2644
title: Horizontal and vertical components of the GNSS-derived velocity field

description: This dataset contains two tables with the horizontal and vertical components of the GNSS-derived velocity field used in the study by Viltres et al., 2022 "Present-day motion of the Arabian plate"
DOI: 10.6084/m9.figshare.20195723
title: Search Strategy - Artificial Intelligence to Improve Antimicrobial Prescribing- A Protocol for a Systematic Review

description: Search Strategy - Artificial Intelligence to Improve Antimicrobial Prescribing- A Protocol for a Systematic Review