Find research datasets worth reusing

Simulation of a POPE bilayer, lipid model based on OPLS-aa by Rog et al.

A 500 ns-long simulation of a bilayer consisting of 144 POPE lipids and 40 water molecules per lipid. All GROMACS-compatible input and output files are required. Topologies are provided by their original authors. If you use the topologies, please cite the papers indicated in the POPE.itp file.

Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model

This dataset is for the plots in the article titled "Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model", which was published by Journal of Geophysical Research: Atmospheres. There are totally nine files in the ".mat" format for Matlab. The nine data files are corresponding to nine Figures in the article.

Mean transit times simulations with the EcH2O-iso model

Input, forcing and and output files, as well as running and plotting scripts used in the numerical experiments with the EcH2O-iso critical zone model described in Kuppel et al. manuscript "Catchment storage control on the transit times of ecohydrological fluxes" submitted for publication to Geophysical Research Letters. ------------------------------------------------------------------------------ Ensemble simulations NetCDF maps and time series used in the analysis can be found in the Output_w30Yspin directory. Time series covers the whole simulations (02/2013 - 08/2016 + 30-yr spinup) while maps are output The ensemble simulations are launch by executing the jobRuns shellscript. It calls the Multitool_* python scripts , which uses (see also options in the header of Multitool_Main.py): - a global definition file located in the root directory, here defRuns_w30Yspin.py - a EcH2O-iso executable; here the ech2o_iso file has been compiled for a linux environment. The source code of EcH2O-iso is available <a href="https://bitbucket.org/sylka/ech2o_iso/">here</a> (branch master_2.0 used in this study), and the associated documentation <a href="https://ech2o-iso.readthedocs.io/en/latest/">here</a>. - Input_Configs contains template configurations files for EcH2O-iso: one for general simulations (config_w30Yspin.ini), one for options regarding the "tracking" (i.e. water isotopes and ages) mode (configTrck_w30Yspin.ini) - Input_Forcings contains the meteorological forcing files needed for simulations - Input_Params contains the ensemble parameter file used for the simulations - Input_Maps_100m contains the maps defining the simulated domain at the 100-m resolution used in the study, as well as the vegetation-specifics files, some of which are modified using the values contained in the ensemble parameter file. -------------------------------------------------------------------------- Plots The R plotting scripts found in the Plotting_scripts directory generate the basic plots used in the submitted manuscript (see examples in Plots directory), with some post-editing using a vector graphics editor.

opencc-by-4.0Jan 2020View details →

High-CAPE summer convection in large-domain large-eddy simulations with ICON - model and observational data sets

Data sets including all observational and ICON model data for publication in Atmosperic Chemistry and Physics Journal (ACP) - "High-CAPE summer convection in large-domain large-eddy simulations with ICON"

opencc-by-4.0Feb 2020View details →

Simulation data for Modelling DNA-strand displacement reactions in the presence of base-pair mismatches

Raw and processed simulation data from the paper Modelling DNA-strand displacement reactions in the presence of base-pair mismatches (J. Am. Chem. Soc.)

GEOS-Chem v9-02 with simple SOA scheme for ATAL simulations & model output

This code/data repository includes (1) GEOS-Chem (v9-02, <a href="http://www.geos-chem.org/">http://www.geos-chem.org/</a>) code modified to use the simple SOA scheme in the simulation of Asian Tropopause Aerosol Layer (ATAL), and (2) model ATAL simulation output. Please see README.txt and this paper for details: Citation: Fairlie, T.D., H. Liu, J.-P. Vernier, P. Campuzano-Jost, J. L. Jimenez, D.S. Jo, B. Zhang, M. Natarajan, M.A. Avery, and G. Huey, Estimates of regional source contributions to the Asian Tropopause Aerosol Layer using a chemical transport model, Journal of Geophysical Research-Atmospheres, in press, Jan. 2020.

opencc-by-4.0Feb 2020View details →

Model outputs: Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations

This dataset contains six model outputs of vegetation biomass on plant functional type level and total vegetation biomass on grid cell with and without bias-correction in different sensitivity experiments, as described in Readme.txt.

MOSTWAS models, TWAS summary statistics, and simulation results for Bhattacharya and Love, 2020

This compressed folder contains three sub-folders that all pertain to models and results generated with Multi-Omic Strategies for Transcriptome-Wide Association Studies (MOSTWAS): <ol> <li>MOSTWAS_Models contains compressed folders for MOSTWAS models trains on TCGA breast cancer and ROS/MAP pre-frontal cortex multi-omic data.</li> <li>Simulations contains Simulation_Results_MOSTWAS.xlsx that provides full simulation results outlined in Bhattacharya and Love, 2020 (paper accompanying MOSTWAS).</li> <li>TWAS_associations contains four Excel files that provide MOSTWAS and local-only TWAS associations for breast cancer-specific survival (using iCOGs GWAS summary statistics), late-onset Alzheimer's disease risk (using IGAP GWAS summary statistics), and major depressive disorder risk (using PGC GWAS and UK Biobank GWAX summary statistics).</li> </ol>

Forearm pro-supination motion videos, 3D models, and simulation scenes

This dataset contains 3D models for a patient-specific forearm bones (radius and ulna) with its interosseous membrane (IOM) ligament it composed of several heterogeneous parts, comprising the central band (CB), accessory band (AB), distal oblique accessory cord (DOAC), distal oblique bundle (DOB), and proximal oblique cord (POC). - simulation_scenes_iom_multi_different_res.zip contains the models with the simulation scenes written in SOFA framework. The IOM is modeled as 7 components. Different resolutions have been experimented. - simulation_scenes_iom_single_cbp.zip contains the models with the simulation scenes written in SOFA framework. The IOM is modeled as a single wide band ligament. Different resolutions have been experimented. - prosup_simulation_videos.zip A folder containing videos of the simulations. - Prosup_Tech_report A technical report describing the approach of the numerical simulations

Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)

opencc-by-4.0Jul 2020View details →

The simulated dataset associated with the paper "Mesoscale modelling of optical turbulence in the atmosphere: The need for ultrahigh vertical grid resolution"

The WRF model-generated meteorological profiles are available in netcdf format. More information will be provided shortly.

opencc-by-4.0Jun 2020View details →

Dataset for molecular simulations of human IRE1 tetramer models.

Dataset from SymmDock protein-protein docking of different conformers of IRE1 tetramers (dimer of back-to-back dimers); input files scripts and trajectories from subsequent MD simulations using Gromacs. Videos showing PC1 and PC2 of tetramers yIRE14 (S1, S2), hIRE14(R) (S3, S4), hIRE14(L) (S5,S6) and hIRE14(S) (S7,S8), in avi format.

opencc-by-4.0Aug 2020View details →

Model simulation data used in "Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54" (Beer et al., Geosci. Model Dev., 2020)

This dataset contains the output and the namelist setups of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. (Geosci. Model Dev., 2020).

opencc-zeroAug 2020View details →

Data from: Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning

How the brain representation of conceptual knowledge vary as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. Here we asked how the brain representation of semantic categories is shaped by the depth of processing during mental simulation. Participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models - associated with the image referents of the words - and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

opencc-zeroSep 2020View details →

How to build a dinosaur: musculoskeletal modelling and simulation of locomotor biomechanics in extinct animals

The intersection of paleontology and biomechanics can be reciprocally illuminating, helping to improve paleobiological knowledge of extinct species and furthering our understanding of the generality of biomechanical principles derived from study of extant species. However, working with data gleaned primarily from the fossil record has its challenges. Building on decades of prior research, we outline and critically discuss a complete workflow for biomechanical analysis of extinct species, using locomotor biomechanics in the Triassic theropod dinosaur Coelophysis as a case study. We progress from the digital capture of fossil bone morphology to creating rigged skeletal models, to reconstructing musculature and soft tissue volumes, to the development of computational musculoskeletal models, and finally to the execution of biomechanical simulations. Using a three-dimensional musculoskeletal model comprising 33 muscles, a static inverse simulation of the mid-stance of running shows that Coelophysis probably used more upright (extended) hindlimb postures, and was likely capable of withstanding a vertical ground reaction force of magnitude more than 2.5 times body weight. We identify muscle force-generating capacity as a key source of uncertainty in the simulations, highlighting the need for more refined methods of estimating intrinsic muscle parameters such as fibre length. Our approach emphasizes the explicit application of quantitative techniques and physics-based principles, which helps maximize results robustness and reproducibility. Although we focus on one specific taxon and question, many of the techniques and philosophies explored here have much generality to them, so they can be applied in biomechanical investigation of other extinct organisms.

opencc-zeroSep 2020View details →

VTFT_Demography: global ageclass simulation data from the LPJ-wsl v2.0 Dynamic Global Vegetation Model

Forest ecosystem processes follow classic responses with age, peaking production around canopy closure and declining thereafter. Although age dynamics might be more dominant in certain regions over others, demographic effects on net primary production (NPP) and heterotrophic respiration (Rh) are bound to exist. Yet, explicit representation of ecosystem demography is notably absent in most global ecosystem models. This is concerning because the global community relies on these models to regularly update our collective understanding of the global carbon cycle. This paper aims to fill this gap in understanding by presenting the technical developments of a computationally-efficient approach for representing age-class dynamics within a global ecosystem model, the LPJ-wsl v2.0 Dynamic Global Vegetation Model. The modeled age-classes are initially created by fire feedbacks, wood harvesting, and abandonment of managed land, otherwise aging naturally until a stand-clearing disturbance is simulated or prescribed. In this paper, we show that the age-module can capture classic demographic patterns in stem density and tree height compared to inventory data, and that patterns of ecosystem function follow classic responses with age. We also present a few scientific applications of the model to assess the modeled age-class distribution over time and to determine the demographic effect on ecosystem fluxes relative to climate. Simulations show that, between 1860 and 2016, zonal age distribution on Earth was driven predominately by fire, causing a ~45-year difference in ages between boreal (50N-90N) and tropical (23S-23N) latitudes. Land use change and land management was responsible for an additional decrease in zonal age by -6 years in boreal and by -21 years in temperate (23N-50N) and tropical latitudes, with the anthropogenic effect on zonal age distribution increasing over time. A statistical model helped reduced LPJ-wsl complexity by predicting per-grid-cell annual NPP and Rh fluxes by three terms: precipitation, temperature and age-class; at global scales, R2 was between 0.95 and 0.98. As determined by the statistical model, the demographic effect on ecosystem function was often less than 0.10 kg C m-2 yr-1 but as high as 0.60 kg C m-2 yr-1 where the effect was greatest. In eastern forests of North America, the demographic effect was of similar magnitude, or greater than, the effects of climate; demographic effects were similarly important in large regions of every vegetated continent. Spatial datasets are provided for global ecosystem ages and the estimated coefficients for effects of precipitation, temperature and demography on ecosystem function. The discussion focuses on our finding of an increasing role of demography in the global carbon cycle, the effect of demography on relaxation times (resilience) following a disturbance event and its implications at global scales, and a finding of a 40-Pg C increase in turnover from age dynamics at global scales. Whereas time is the only mechanism that increases ecosystem age, any additional disturbance not explicitly modeled will decrease age. This LPJ-based age-module therefore simulates the upper limit of age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.

Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)

This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit. Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34). data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols) data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols) data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols) The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated. The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper. The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b). The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).

opencc-by-4.0Sep 2020View details →

Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"

Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder): - scenario 1 - pdemax.rich - scenario 2 - pdemax.sparse - scenario 3 - pdhillhigh.rich - scenario 4 - pdhillhigh.sparse The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.

opencc-zeroSep 2020View details →

Dataset for METAPOPGEN 2.0: a multi-locus genetic simulator to model populations of large size

Multi-locus genetic processes in subdivided populations can be complex and difficult to interpret using theoretical population genetics models. Genetic simulators offer a valid alternative to study multi-locus genetic processes in arbitrarily complex scenarios. However, the use of forward-in-time simulators in realistic scenarios involving high numbers of individuals distributed in multiple local populations is limited by computation time and memory requirements. These limitations increase with the number of simulated individuals. We developed a genetic simulator, MetaPopGen 2.0, to model multi-locus population genetic processes in subdivided populations of arbitrarily large size. It allows for spatial and temporal variation in demographic parameters, age structure, adult and propagule dispersal, variable mutation rates and selection on survival and fecundity. We developed MetaPopGen 2.0 in the R environment to facilitate its use by non-modeler ecologists and evolutionary biologists. We illustrate the capabilities of MetaPopGen 2.0 for studying adaptation to water salinity in the striped red mullet Mullus surmuletus.