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567 results for “Simulated datasets”
Semi-simulated dataset of indel calling tool evaluation
<p>The semi-simulated datasets used in Ning Wang, et al. </p> <p>100bp_5X_500_Venter_read1.fq.gz & 100bp_5X_500_Venter_read2.fq.gz: 5X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>100bp_30X_500_Venter_read1.fq.gz & 100bp_30X_500_Venter_read2.fq.gz: 30X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>250bp_30X_500_Venter_read1.fq.gz & 250bp_30X_500_Venter_read2.fq.gz: 30X coverage, 250bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>100bp_60X_500_Venter_read1.fq.gz & 100bp_60X_500_Venter_read2.fq.gz: 60X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>Haplotype_1_no_gap.fa & Haplotype_2_no_gap.fa: the semi-simulated dipoid human genome hg19 chromosome 1 and chromosome 2, including HuRef indels used in Ning Wang, et al. In order to use ART (fastq simulator) to generate simulated fastq fiels, the gaps (Ns) of genome were removed.</p> <p>chr1_chr2_variants_truthset.txt: types, position, size and genotype of HuRef indels used in Ning Wang, et al. </p>
"Genome binning of viral entities from bulk metagenomics data" - CAMISIM simulated datasets and genomes
<p><strong>Genome binning of viral entities from bulk metagenomics data</strong></p> <p> </p> <p><strong>Authors</strong></p> <p><strong>Joachim Johansen1,2, Damian R. Plichta2, Jakob Nybo Nissen1,3, Marie Louise Jespersen1,4, Shiraz A. Shah5, Ling Deng6, Jakob Stokholm5,6, Hans Bisgaard5, Dennis Sandris Nielsen6, Søren Sørensen7, Simon Rasmussen1</strong></p> <p> </p> <p><strong>Affiliations</strong></p> <p>1 Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark</p> <p>2 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA</p> <p>3 Statens Serum Institut, Viral & Microbial Special diagnostics, Copenhagen, Denmark</p> <p>4 National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark</p> <p>5 Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark</p> <p>6 Section of Food Microbiology and Fermentation, Department of Food Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark</p> <p>7 Section of Microbiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark</p> <p><strong>Methods description</strong></p> <p>We compared the viral binning performance of VAMB and MetaBAT2 using the official CAMI consortium method to create assemblies and metagenome profiles. To this end we generated 3 different metagenome compositions with up to 308 reference genomes; one mixed with bacteria, plasmids and viruses to test binning in complex samples i.e. high diversity (1), one with only crass-like viruses to test binning with highly similar viruses i.e. high relatedness (2) and a set of small-viruses (<6,000 bp) including members of the Microviridae family to address the bias of size (3). Bacterial genomes were gathered from NCBIs refseq genome repository 2021, plasmids from the PLSDB database (v. 2021_06_23) and viral genomes from the recent MGV database. </p> <p>Dataset A contained a mixture of bacteria (N=8), plasmids (N=20) and viruses (N=280) to test binning in complex samples, i.e. high diversity. Dataset B contained only crass-like viruses (N=80) to test binning with highly similar viruses i.e. high relatedness. Dataset C contained small-viruses (N=50, <6,000 bp) of the Microviridae family to address the bias of size. Bacterial genomes were sampled from the Refseq genome repository 2021, plasmids from the PLSDB database and viral genomes from the recent MGV database (Nayfach, et al. Nature Microbiology 2021).</p> <p> </p> <p> </p>
FORUM end-to-end simulator datasets
<p>The dataset contains a set of netCDF files for the cases treated in the AMT paper. Each directory contains three files:</p> <p>fe2es_fei_out_d0001.nc - the output of the FEI imager</p> <p>fe2es_fsi_out_d0001.nc - the output of the FSI spectrometer</p> <p>fe2es_sgm_ref_d0001_acq3.nc - The reference atmosphere for that case</p> <p>Any additional files are only meaningful for the FORUM E2E simulator full code.</p>
Validation of Spectral Light Simulation Tools: Dataset of Simulated and Measured Indoor Light Exposure
<p>Since the discovery of a new photoreceptor in our eye, and with the growing awareness about the related ipRGC-influenced light (IIL) responses, design applications related to these responses are flourishing. Optimizing our ocular light exposure in buildings can have beneficial effects on our health, well-being, and performance through the action of this photoreceptor. To compare different design options and optimize the lighting conditions for building occupants, lighting simulations are typically used. However, as our IIL responses depend on various aspects of the light exposure including its spectral characteristics, spectral simulations are required. The dataset shared here was originally collected to validate two spectral simulation tools, <em>ALFA</em> and <em>Lark</em>, for the study of building design in relation to occupants’ IIL responses. The validation was done by comparing the simulation outputs against actual measurements, and assessing how reliable these tools were in predicting spectral irradiance under different indoor light conditions. Data were collected in two different experimental setups, one under daylight conditions only and the other one under electric light conditions only. The experimental protocol and README files contain detailed information on how the data was collected and what data was collected.</p>
Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave models
<p>This dataset contains the skull maps and modeling results associated with the forthcoming publication "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models".</p>
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset
<p>Jets at generator and reconstruction level saved in .npy format.</p> <p>Each jet is represented as an array of jet constituents characterized by their particle momentum in Cartesian coordinates, i.e., (px, py, pz). For both generator-level and reconstruction-level jets, jet constituents are ordered by decreasing pT.</p> <p>The shape of the datasets is [N, 50, 3], where N is the total number of jets, 50 is the number of particles per jet, and 3 is the number of particle features (in order): [px, py, pz].<br> About 1.7M jets split into training, validation and testing sets at 60%, 20% and 20% respectively.</p>
Dataset on Alternaria disease on rocket under simulated climate change conditions
<p>This dataset is related to disease severity caused by the Alternaria spp. isolates tested in different temperature and CO2 combinations on cultivated rocket and published in https://doi.org/10.3920/WMJ2016.2108<em> </em>(Figure 1) and in https://doi.org/10.1007/s42161-018-0125-8,</p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 2
<p>This is dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of two files with 100k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector has a concentric cylinder geometry with 45 layers, where each layer consists of active (silicon) and passive (tungesten) material. Each layer has 144 readout cells, 9 in radial and 16 in angular direction, yielding a total of 9x16x45 = 6480 voxels.</p> <p>dataset_2_1.hdf5 should be used for training, dataset_2_2.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 3
<p>This is dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of four files with 50k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector geometry is similar to dataset 2, but has a much higher granularity. Each of the 45 layer has now 18 radial and 50 angular bins, totalling 18x50x45=40500 voxels. This dataset was produced using the <a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04 Geant4 example</a>.</p> <p>dataset_3_1.hdf5 and dataset_3_2.hdf5 should be used for training, dataset_3_3.hdf5 and dataset_3_4.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Dataset for "WUS256: An Adjoint Waveform Tomography Model of the Crust and Upper Mantle of the Western United States for Improved Waveform Simulations"
<p>This dataset contains the WUS256 seismic model and auxiliary data used in the creation of the model (Rodgers et al., 2022). WUS256 is a three-dimensional model of the seismic properties of crust and upper mantle of the western United States. The WUS256 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p>Also included are the earthquake source parameters for the 72 inversion events and 18 validation events in ASCII text format. Lastly, we include a list of all waveforms used in the creation of WUS256. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p>This effort was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-833624</p>
Hydrometeorological dataset and SWAT simulated outputs of Jaguari basin in the Cantareira reservoirs system
<p>Dataset used in the accepted paper "Hydrologic Impact of Climate Change in the Jaguari River in the Cantareira Reservoir System" (Domingues et al., 2022), from Water. A README.txt file is available within each folder with details of the dataset.</p>
Code-to-code SIMMER/FRENETIC comparison for the neutronic simulation of lead-cooled fast reactors (dataset)
<p>Dataset in support of publication "Code-to-code SIMMER/FRENETIC comparison for the neutronic simulation of lead-cooled fast reactors". README.txt has been written in compliance with the Dublin Core Standard for metadata, https://dublincore.org/.</p>
Dataset and software to analyze and simulate neuronal morphogenesis in Drosophila class IV da neurons
<p>The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell-types, as well as to branching at the subcellular and tissue levels.</p>
Dark matter flow dataset from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Dataset of simulations of SmartDeZIgn
<p>This table contains the results of simulations of SmartDeZIgn project. More analytically:</p> <p>Structural characteristics for each ZIF of our dataset the were used as descriptors to train Machine Learning models. Moreover, calculated diffusivities, solubilities and permeabilities of He, H2, O2, CO2, N2, CH4, ethylene, ethane, propylene, propane, n-butane and iso-butane in each of the ZIFs of the dataset</p>
Archive dataset for sugarcane simulations with the JULES model
<p>This dataset contains supporting information to simulate sugarcane growth and development with the JULES model. It provides a collection of csv files with crop responses to CO2, Temperature and Soil moisture conditions (CTW-response), comparison against field and regional observations (performance), and climate change projections (projections). The parameters' values and model configuration are provided in sub-folders "sim_db" and "jules_run". Model runs were carried out with <a href="https://github.com/Murilodsv/wpy-jules">wpy-jules</a>, whereas the scientific documentation is described in Vianna et al. (2022). </p> <p>This work was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). We acknowledge the use of the Monsoon HPC system, maintained through a strategic partnership between the Met Office and the Natural Environment Research Council.</p> <p><strong>Summary</strong>:</p> <p>- CTW-response: CSV files initiated with "C", "T" and "W"<br> - performance: CSV files with suffix "perf" as well as "yield_var"<br> - projections: CSV files with suffix "future"<br> - input: Folder "sim_db"<br> - configuration: Folder "jules_run"</p> <p><strong>References</strong></p> <p>Vianna et al. (2022). Improving the representation of sugarcane crop in the JULES model for climate impact assessment. Global Change Biology Bioenergy (<a href="https://doi.org/10.1111/gcbb.12989">https://doi.org/10.1111/gcbb.12989</a>).</p>
Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset
<p><strong>Abstract</strong>:<br> (from [1])</p> <blockquote> <p>The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed<br> by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic<br> samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all<br> constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by<br> employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer<br> composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy.<br> Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately. <br> The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the<br> obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented<br> transition from a fine-scale particle model to a coarse and computationally efficient continuum description adds to the body of knowledge of molecular science<br> as well as the engineering community.<br> </p> </blockquote> <p><br> <strong>Contact</strong>:<br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><br> <strong>Software</strong>:<br> All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029<br> Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11<br> Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p><strong>Installed packages:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p><br> <strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context</strong>:<br> Data set supplementing journal paper:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.</p> <p><br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content</strong>:<br> The files to reproduce our simulations and their results are structured as follows:</p> <ul> <li>01_potentials<br> tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table<br> angular interactions</li> <li>Bond_table<br> bond interactions</li> <li>Nonbond_table<br> pair interactions</li> </ul> </li> <li>02_sample<br> Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations<br> The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional<br> time-proportional simulation data</li> <li>02_time-periodic<br> time-periodic simulation data</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> <p><br> <strong>Naming convention</strong>:<br> Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate]<br> ● [deformation]: uniaxial tension (UT), simple shear (SS)<br> ● [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS)<br> ● [deformation function]: time-proportional (strain), time-periodic (strain_ampl)<br> ● [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless<br> ● [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns</p> <p><br> <strong>Output quantities</strong> (columns of *.Dat files):<br> ● Step: time step<br> ● Time: time in fs<br> ● TotEng: total energy in kcal/mol<br> ● PotEng: potential energy in kcal/mol<br> ● KinEng: kinetic energy in kcal/mol<br> ● E_pair: pair energy in kcal/mol<br> ● E_bond: bond energy in kcal/mol<br> ● E_angle: angle energy in kcal/mol<br> ● E_dihed: dihedral energy in kcal/mol<br> ● Temp: temperature in K<br> ● Press: hydrostatic pressure in atm<br> ● Pxx: xx component of pressure tensor in atm<br> ● Pyy: yy component of pressure tensor in atm<br> ● Pzz: zz component of pressure tensor in atm<br> ● Pxy: xy component of pressure tensor in atm<br> ● Pxz: xz component of pressure tensor in atm<br> ● Pyz: yz component of pressure tensor in atm<br> ● Volume: volume of simulation box in (Angstroms)^3<br> ● Lx: box length in x direction in Angstroms<br> ● Ly: box length in y direction in Angstroms<br> ● Lz: box length in z direction in Angstroms<br> ● Density: density in g/(cm^3)<br> ● c_RG: radius of gyration in Angstroms<br> ● c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2<br> ● c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2<br> ● c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2<br> ● c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2<br> ● c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2<br> ● c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2<br> ● c_bondave[1]: bond energy averaged over all atoms in kcal/mol<br> ● c_bondave[2]: bond distance averaged over all atoms in Angstroms<br> ● c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2<br> ● c_angleave[1]: angle energy averaged over all atoms in kcal/mol<br> ● c_angleave[2]: angle averaged over all atoms degree<br> ● c_angleave[3]: cosine of angle (unitless)<br> ● c_angleave[4]: squared cosine of angle (unitless)<br> ● c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2<br> ● c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2<br> ● c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2<br> ● c_MSD[4]: total mean squared displacement in (Angstroms)^2<br> ● c_COM[1]: x coordinate of center of mass in Angstroms<br> ● c_COM[2]: y coordinate of center of mass in Angstroms<br> ● c_COM[3]: z coordinate of center of mass in Angstroms<br> ● v_strain_xx: xx component of engineering strain tensor (unitless) <br> ● v_strain_yy: yy component of engineering strain tensor (unitless) <br> ● v_strain_zz: zz component of engineering strain tensor (unitless) <br> ● v_vMisesequivstress: von Mises equivalent stress in MPa<br> ● v_Cauchy_xx: xx component of stress tensor in MPa <br> ● v_Cauchy_yy: yy component of stress tensor in MPa<br> ● v_Cauchy_zz: zz component of stress tensor in MPa<br> ● v_Cauchy_xy: xy component of stress tensor in MPa<br> ● v_Cauchy_xz: xz component of stress tensor in MPa<br> ● v_Cauchy_yz: yz component of stress tensor in MPa<br> ● v_strain_xy: xy component of engineering strain tensor (unitless) <br> ● v_strain_xz: xz component of engineering strain tensor (unitless) <br> ● v_strain_yz: yz component of engineering strain tensor (unitless) </p> <p><strong>References</strong>:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.<br> [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene<br> Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584.<br> [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p> </p>
Supercoiling-mediated feedback simulation dataset
<p><strong>Supercoiling-mediated feedback simulation dataset</strong></p> <p><strong>Background</strong><br> These files represent simulation datasets generated for the publication<br> "Supercoiling-mediated feedback rapidly couples and tunes transcription"<br> by Christopher Johnstone and Kate E. Galloway.</p> <p>All figures in the paper can be replicated by using the code available at <a href="https://github.com/GallowayLabMIT/tangles_model">https://github.com/GallowayLabMIT/tangles_model</a> (<a href="https://doi.org/10.5281/zenodo.7054394">permalink</a>) and these datasets.</p> <p><strong>File summary</strong></p> <ul> <li>`unprocessed_datasets.zip` contains the merged Julia simulation files.</li> <li>`preprocessed_datasets.zip` contains the smaller, preprocessed datasets used for actuall plotting of data figures.</li> </ul> <p><strong>File format</strong></p> <ul> <li>The preprocessed datasets are serialized Pandas dataframes (gzipped Parquet files).</li> <li>The unprocessed datasets are self-describing HDF/H5 files.</li> </ul> <p><strong>Usage</strong><br> The main figure-plotting notebook, `notebooks/modeling_paper_figures.ipynb`, contained in the code repository mentioned above can use either the unprocessed or preprocessed datasets. If the preprocessed datasets are present, it will load them directly. If the preprocessed datasets are not present, that Python notebook will preprocess the data.</p> <p><br> <strong>License</strong><br> This data is available under a CC-BY 4.0 International License.<br> Please attribute:</p> <ul> <li>Christopher Johnstone (cjohnsto@mit.edu)</li> <li>Kate E. Galloway (katiegal@mit.edu)</li> </ul>
Global sea surface dimethyl sulfide dataset simulated by artificial neural network
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 2005 to 2014 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes; (3) the simulated global monthly sea surface concentrations of DMS ranging from 2005 to 2100 by ANN model and CMIP6 ensemble and the calculated Kt and sea-to-air fluxes; (4) the yearly mean DMS concentration of each grid in different sensitivity experiments exploring the roles different variables play in driving DMS future changes. The input variables of this ANN model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The future projections (2015-2100) are subjected into two Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L<sup>–1</sup>, m s<sup>–1</sup>, and μmol S m<sup>–2</sup> d <sup>–1</sup>, respectively.</p> <p>Compared with the previous version (v1.0), this version is based on an updated ANN model after adjusting the data match-up between satellite and in-situ chlorophyll <em>a</em> for ANN training. In addition, the historical simulation based on CMIP6 only covers the time period from 2005 to 2014, which was from 1850 to 2014 for v1.0.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.25% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.25% contaminant spike-in.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.