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5,805 results for “Data model”
Figure_4_Model_Data
<p>The results of simulating the experiment by Chong et al. </p> <p>Use the smbl for information about the data in the lm file. The lm file can be opened with hdf5 viewer</p>
Figure_5_Model_Data
<p>The data and smbl file used for Figure 5. Use the smbl file for information pertaining to the lm file. The lm file is a hdf5. </p>
Data file for the paper "General Models for the Electrochemical Hydrogen Oxidation and Hydrogen Evolution Reactions – Theoretical Derivation and Experimental Results Under Near Mass-Transport Free Conditions", J. Phys Chem. C., 2016, DOI:10.1021/acs.jpcc.6b00011
<p>The data in this folder is supplementary information for the paper:<br /> Anthony Kucernak and Christopher Zalitis, "General Models for the Electrochemical Hydrogen Oxidation and Hydrogen Evolution Reactions – Theoretical Derivation and Experimental Results Under Near Mass-Transport Free Conditions", J. Phys Chem. C., 2016, DOI:10.1021/acs.jpcc.6b00011</p> <p>The information is © Anthony Kucernak</p> <p>A description of the models used in these files is provided in that paper. Below is a description of the files</p> <p>Experimental Data.xlsx - This file contains the experimental data used to produce figures 6 and 7 in the aforementioned paper<br /> Model.xlsx - This file contains verified versions of the Heyrovsky-Volmer, Tafel-Volmer and Heyrovsky-Tafel-Volmer mechanisms developed in the paper mentioned above. These excel spreadsheets may be used to fit experimental data user the Solver function in Excel.</p> <p>Heyrovsky-Tafel.cdf, Heyrovsky-Tafel-Volmer.cdf, Tafel-Volmer.cdf, Heyrovsky-Volmer.cdf - These are "computable document format" files produced using Wolfram Mathematica. They allow easy and quick modification of model parameters to allow real-time exploration of the effect of the parameters on current density (as linear and Tafel plots), hydrogen coverage, Effective Tafel slope, and derived parameters. The CDF viewer is availble to download from the Wolfram site - www.wolfram.com</p>
Dataset for the publication "Neutrophilic Bioleaching of Synthetic Covellite – A Model System Combining Experimental Data and Geochemical Modeling"
<p>This dataset contains data, which were used in preparation of the publication "Neutrophilic Bioleaching of Synthetic Covellite – A Model System Combining Experimental Data and Geochemical Modeling"</p>
Supplemental Data for "Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening"
<p>Data file accompanying: Haque IS, Lazarin GA, Kang HP, Evans EA, Goldberg JD, Wapner RJ. Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening. <em>JAMA. </em>2016;316(7):734-742. doi:10.1001/jama.2016.11139</p> <p>(SRC = self-reported racial/ethnic category; TG = targeted genotyping; NGS = next-generation sequencing)</p> <p>Data file includes:</p> <ul> <li><strong>Couple Data: </strong>Number of self-identified reproductive couples, separated by tandem or sequential screening status and by (mother SRC, father SRC)</li> <li><strong>Disease Severity</strong>: List of all diseases tested with severity rating as used in the manuscript.</li> <li><strong>Allele Data</strong>: Listing of all alleles considered pathogenic in manuscript's data analysis, with number of observations and number of tested chromosomes in each SRC.</li> <li><strong>Chromosome Frequencies</strong>: for each disease in each SRC: <ul> <li>Effective total chromosome count (effective sample size after integrating TG and NGS-only alleles).</li> <li>Beta posterior a,b: parameters a, b for the best-fit beta distribution approximating the probability that a random chromosome in this SRC carries a pathogenic allele (integrating both TG and NGS alleles).</li> <li># Chromosomes total/positive for TG alleles</li> <li># Chromosomes total/positive for NGS alleles</li> <li># Chromosomes total/positive for individuals tested by TG</li> <li># Chromosomes total/positive for individuals tested by NGS</li> </ul> </li> <li><strong>Disease Risks</strong>: for each disease in each pairing of SRCs <ul> <li>Father/Mother computed carrier frequency: probability that a random individual from father/mother's SRC is a carrier for the given disease</li> <li>Computed risk of affected conceptus (mean, 2.5, 97.5 percentiles): mean and CI of the posterior distribution over the probability that a random conceptus arising from the racial/ethnic pairing indicated would be homozygous or compound heterozygous for pathogenic alleles for the indicated disease.</li> <li>Computed carrier couple frequency: probability that a random couple from the given SRCs would be a carrier couple for the indicated disease (ie, that both members of the couple would be carriers for the indicated disease)</li> <li>Total couples: number of tandemly-tested couples of the indicated SRC pairing who both had the "routine carrier testing" indication for testing and were both tested for the given disease</li> <li>Number of carrier couple: from the set of "Total couples", the number of couples in which both members were carriers for the indicated disease</li> <li>Number of carrier couples expected: based on computed carrier couple frequency and number of tested couples, the expected number of carrier couples under the model described in sections 4.3.2 and 4.4 of the supplement.</li> <li>P-value: probability that the number of observed carrier couples or a more extreme count would have occurred by chance, given the posterior distribution over carrier couple counts (see section 4.4 of the supplement). One-tailed p-value.</li> </ul> </li> </ul>
Sampling data accompanying "An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model"
<p>Datasets recording sampling results, accompanying the manuscript <em>An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model, </em>Rothenberg, D. and Wang, C., submitted, GMD. Please see the included README for more details.</p>
Stellar model grid data for isochrones Python package
<p>These are the data files that get downloaded by the "isochrones" Python package. "mist.tgz" and "dartmouth.tgz" contain stellar model grid data for the MIST and Dartmouth stellar models (http://waps.cfa.harvard.edu/MIST/ and http://stellar.dartmouth.edu/models/). "dartmouth.tri" is the precomputed Delaunay triangulation for the Dartmouth models (the MIST models are too big to use the triangulation-based interpolation method).</p> <p>***Note***</p> <p>For the dartmouth grids here, this zenodo repository should now be used: https://zenodo.org/record/1002927.</p>
Data for Standard Codon Substitution Models Overestimate Purifying Selection for Non-Stationary Data
<p>Codon-aligned, filtered alignments for Kaehler et al. (2016) (https://peerj.com/preprints/2218/). Please refer to preprint for preparation details.</p> <p>Data obtained from Ensembl (http://www.ensembl.org/) and antbase (http://antbase.org).</p> <p> </p> <p> </p>
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Modeling exosome complexes from cross-link MS data
<p>This repository contains the modeling scripts, the ensemble of models, and the results of the analysis for the modeling of the <em>S.cerevisiae</em> exosome complex (named exo10, comprising of Rrp40, Rrp4, Csl4, Rrp45, Rrp46, Rrp42, Rrp43, Mtr3, Ski6 and Rrp44), in presence of Ski7 (exo10+Ski7 below) or Rrp6 (exo10+Rrp6 below) proteins. The modeling is performed based on cross-link Mass-Spectrometry data, crystallographic structures, and comparative models.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/exosome or the README file.</p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments" (MATLAB format)
<p>See 10.5281/zenodo.834787 for a more complete description.</p>
Physical Unclonable In-Memory Computing for Simultaneous Protecting Private Data and Deep Learning Models
Open the record for dataset details and reuse information.
Data and trained model for iPXRDnet
<p>This data set is a collection of data sets and model checkpoint in the iPXRDnet.</p> <p><br>Model checkpoint file(model.zip):<br>hmof-130T_Hydrogen: Model of adsorption prediction for H2 in the hMOF-130T database obtained by training<br>hmof-130T_CarbonDioxide: Model of adsorption prediction for CO2 in the hMOF-130T database obtained by training<br>hmof-130T_Nitrogen: Model of adsorption prediction for N2 in the hMOF-130T database obtained by training<br>hmof-130T_Methane: Model of adsorption prediction for CH4 in the hMOF-130T database obtained by training<br>hmof-300T: Adsorption prediction model in the hMOF-300T database obtained by training<br>Gas_Se: Separation selectivity prediction model obtained by training<br>Gas_SD: Self-diffusion coefficients prediction model obtained by training<br>MOD: Bulk modulus and shear modulus prediction model obtained by training<br>exAPMOF-1bar-ALM+PXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD and material ligands<br>exAPMOF-1bar-ALM: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with material ligands only<br>exAPMOF-1bar-PXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD only<br>exAPMOF-ISO:Experimental adsorption isotherm model of Anion-pillared MOFs obtained by training<br>exAPMOF-1bar-NOacvPXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD data before activation only<br>exAPMOF-1bar-acvPXRD: Experimental adsorption at 1 bar model of Anion-pillared MOFs obtained by training with PXRD data after activation only</p> <p>Checkpoint file for model without co-learning strategy(model-No-co-learning.zip):<br>hmof-130T_Hydrogen: Model of adsorption prediction for H2 in the hMOF-130T database obtained by training<br>hmof-130T_CarbonDioxide: Model of adsorption prediction for CO2 in the hMOF-130T database obtained by training<br>hmof-130T_Nitrogen: Model of adsorption prediction for N2 in the hMOF-130T database obtained by training<br>hmof-130T_Methane: Model of adsorption prediction for CH4 in the hMOF-130T database obtained by training<br>hmof-130T-str: Structural characteristics prediction model in the hMOF-130T database obtained by training<br>hMOF-130T-GradCAM: Model for GradCAM in the hMOF-130T database obtained by training<br>hmof-300T: Adsorption prediction model in the hMOF-300T database obtained by training<br>hmof-300T-str: Structural characteristics prediction model in the hMOF-300T database obtained by training<br>Gas_Se: Separation selectivity prediction model obtained by training<br>Gas_SD: Self-diffusion coefficients prediction model obtained by training<br>MOD: Bulk modulus and shear modulus prediction model obtained by training</p> <p><br>Data sets file (data.zip):<br>hmof-xrd+str+ad :PXRD and gas adsorption and structural feature of hmof-300T database<br>hMOF-130T_ad_list_mof :Gas adsorption data of hmof-130T database <br>hMOF-130T_GAS_DICT :Gas descriptors data of hmof-130T database<br>hMOF-130T_STR_DICT :Structural feature data of hmof-130T database<br>hMOF-130T_PXRD_DICT :PXRD data of hmof-130T database<br>MOD_data :Bulk modulus and shear modulus data of Moghadam's MOFs<br>MOD_PXRD_dict : PXRD data of Moghadam's MOFs<br>GAS_SD-data : self-diffusion coefficients data in CoREMOF database<br>SE-CO2,N2_data:Separation selectivity ,PXRD and structural feature of CO2/N2 selectivity database<br>Sa_sp:Data set partitioning results of CO2/N2 selectivity database<br>gas_dict : gas descriptors data used in the self-diffusion coefficients database<br>PXRD_DICT : PXRD data after activation of MOFs in Anion-pillared MOFs' experimental database<br>xrd_noacv : PXRD data before activation of MOFs in Anion-pillared MOFs' experimental database<br>Smiles_ads : Smiles data of gas in Anion-pillared MOFs' experimental database<br>all_exAPMOF-1bar : Anion-pillared MOFs' experimental adsorption data under 298K and 1 bar.<br>all_exAPMOF-1bar-NOacv : Experimental adsorption data for anion-pillared MOFs with PXRD before activation under 298K and 1 bar.<br>exAPMOF_DICT : Anion-pillared MOFs' Smiles data of MOFs' ligands and descriptors of metal centers in the experimental database<br>all_exAPMOF-iso : Key library of MOF and gas combinations in Anion-pillared MOFs' experimental isotherm database.<br>exAPMOF_ISOdata: Anion-pillared MOFs' experimental adsorption isotherm data under 298K.</p> <p> </p> <p>New Data sets file (data_new.zip):</p> <p>Files starting with ‘4gas_’: Files are named in the form of ‘4gas_Gas_Pressure’, recording the adsorption amounts of 20,000 randomly selected structures from the hMOF-130T database at different pressures at 298K.<br>all_adinfo_list_robustness: Gas adsorption data file used for study of model robustness .<br>hmof-130T_Xnm: PXRD data of structures in the hmof-130T database at different crystal sizes.<br>hMOF-130T_ad_list_mof: Corrected Gas adsorption data of hmof-130T database . There are problems with the data in the data.zip file.<br>X_PXRD,AD: Adsorption amount and PXRD data of ionic MOFs (iMOFs), zeolitic imidazolate frameworks (ZIF) and Uio-66 series from experimental literature. Among them, the Uio-66 series uses CO2 as the gas, and other files contain adsorption data of different gases.</p> <p> </p>
Accompanying simulated data for "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
The model for new data mapping to human endoderm-derived organoids cell atlas (HEOCA)
<p><strong>The model for new data mapping to human endoderm-derived organoids cell atlas (HEOCA).</strong></p>
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Data and code for "Large language models identify causal genes in complex trait GWAS"
<p><span>This file contains the data and scripts for the preprint "Large language models identify causal genes in complex trait GWAS"</span></p>
CFD Modeling Results and Related Data and Codes for Plotting of "A Mesoscale-to-LES Modeling of Tornado-like Vortex and Associated Local Strong Winds in Urban Area"
<p>The CFD modeling outputs, derived maximum wind fields in the analysis area, the topography data, the Python codes used to produce the figures, as we as the namelist of WRF simulation are available. The CFD modeling outputs are in binary format. The ctl. files of corresponding binary data (or dataset if ordered chronologically) are available in each directory (named after each experiment in our study).</p>
Supplementary data and microkinetic model for 'Key Role of CO Coverage for Chain Growth in Co-Based Fischer-Tropsch Synthesis'
<p>This repository contains:</p> <ol> <li>The DFT data (energies, frequencies and structures) of all important intermediates of the microkinetic model constructed for the publication ‘Key Role of CO Coverage for Chain Growth in Co-Based Fischer-Tropsch Synthesis’.</li> <li>Input files for the high CO coverage microkinetic model in Chemkin.</li> <li>(update 2025-06-19) Input files for the high CO coverage microkinetic model in Chemkin with CO2 activation (sim2.zip).</li> </ol> <p>DOI: <u>10.1021/acscatal.5c03024</u> and <u>10.1021/acscatal.3c04844</u></p>
Products developed through the "What About Model Data?, Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network" project
This dataset includes products developed through the "What About Model Data? Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network (RCN)" project. Products include: 1) a rubric worksheet to assist researchers in deciding what simulation output needs to be preserved in a trusted, community repository to communicate knowledge and satisfy publisher and funder requirements, 2) instructions on how to use the rubric worksheet, which include reference use cases, and 3) outputs and presentations from the three project workshops.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.