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1,026 results for “kinetics”
Root multiple ion uptake kinetics data for maize NAM founders, statistical code, and RhizoFlux hardware plans
<p>This repository contains tabular data, R statistical code, protocols, and hardware plans associated with the following manuscript:</p> <p><strong>A multiple ion-uptake phenotyping platform reveals shared mechanisms that affect nutrient uptake by maize roots</strong></p> <p>Marcus Griffiths, Sonali Roy, Haichao Guo, Anand Seethepalli, David Huhman, Yaxin Ge, Robert E. Sharp, Felix B. Fritschi, Larry M. York</p> <p>Plant Physiology; doi: <a href="https://doi.org/10.1093/plphys/kiaa080">https://doi.org/10.1093/plphys/kiaa080</a></p> <p><strong>Equipment designs.zip</strong> - Contains the hardware plans, parts lists, and experimental protocol</p> <p><strong>ImageJ_macro.zip</strong> - Contains scripts to use within ImageJ to segment images to calculate leaf area</p> <p><strong>Supplementary_Data.zip</strong> - Contains the actual supplemental figures and tables for the manuscript as well as RNAseq data</p> <p><strong>R code & raw data.zip</strong> - Contains a single .R text file containing all the R code to generate all the figures and and supplemental figures from the include raw data files</p> <p>E-mail mgriffiths at danforthcenter.org or lmyork at noble.org with any questions.</p> <p>Version 1 was used for the preprint.</p> <p>Version 2 was used for the final submitted manuscript.</p> <p>Version 3 is the final published version.</p>
Reproducibility in science: calculated kinetic isotope effects for cyclopropyl carbonyl radical.
<p>Calculated kinetic isotope effects, without tunnelling corrections, for the ring opening of cyclopropylcarbinyl radical using a variety of different Hamiltonians and basis sets.</p>
Comparing photoelectrochemical water oxidation, recombination kinetics and charge trapping in the three polymorphs of TiO2
<p>Please find the TA data sets and the model calculation of rutile attached. Note that the data is normalised to the amplitude at 100 us.</p>
Transient Optoelectronic Analysis of the Impact of Material Energetics and Recombination Kinetics on the Open-Circuit Voltage of Hybrid Perovskite Solar Cells
<p>This is the data presented in the article 'Transient Optoelectronic Analysis of the Impact of Material Energetics and Recombination Kinetics on the Open-Circuit Voltage of Hybrid Perovskite Solar Cells' published in The Journal of Physical Chemistry C, DOI: 10.1021/acs.jpcc.7b02411.</p>
Ab Initio and Kinetic Modelling of β-D-xylopyranose under Fast Pyrolysis Conditions
<p>Zip file containing all the IRC connecting transition states to minima in our paper published in The Journal of Physical Chemistry A, <a title="DOI URL" href="https://doi.org/10.1021/acs.jpca.3c07063">https://doi.org/10.1021/acs.jpca.3c07063</a></p>
Data and code for figures: Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications
<p>This directory contains the datasets, code (if applicable) for measurement libraries, data processing and figure generation for the research article "Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications", Beilstein J. Nanotechnol. 2024, 15, 242-255.</p>
Dataset - Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties
<p>Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis</p> <p>1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training:</p> <ul> <li>Classification label vectors for the three analyzed metabolic concentration cases: <ul> <li>Reference case: class_vector_train_ref.mat</li> <li>Extreme1 case: class_vector_train_ex1.mat</li> <li>Extreme2 case: class_vector_train_ex2.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: training_set_ref.mat</li> <li>Extreme1 case: training_set_ex1.mat</li> <li>Extreme2 case: training_set_ex2.mat</li> </ul> </li> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ref.mat</li> <li>Extreme1 case: ccXTR_ex1.mat</li> <li>Extreme2 case: ccXTR_ex2.mat</li> </ul> </li> <li>Thermodynamics-based Flux Analysis (TFA) models for the three cases: <ul> <li>Reference case: tfa_ref.mat</li> <li>Extreme1 case: tfa_ex1.mat</li> <li>Extreme2 case: tfa_ex2.mat</li> </ul> </li> <li>Parameter names identical for the three cases <ul> <li>parameterNames.mat</li> </ul> </li> </ul> <p>2. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4).</p> <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>ccXTR_ValidNeg.mat</li> </ul> </li> <li>Parameter sets used in validation <ul> <li>validation_set_neg.mat</li> </ul> </li> </ul> <p>3. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_neg_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_neg_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_neg_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_neg_agg.mat</li> <li>Extreme1 case: validation_set_ref_neg_agg.mat</li> <li>Extreme2 case: tvalidation_set_ref_neg_agg.mat</li> </ul> </li> </ul> </li> </ul> <ul> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_pos_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_pos_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_pos_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_pos_agg.mat</li> <li>Extreme1 case: validation_set_ex1_pos_agg.mat</li> <li>Extreme2 case: validation_set_ex2_pos_agg.mat</li> </ul> </li> </ul> </li> </ul> <p>4. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_neg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_neg_reassignment.mat</li> </ul> </li> </ul> </li> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_pos.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_pos_reassignment.mat</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <p><strong>Datasets:</strong></p> <ul> <li> <strong>models.zip </strong>- Datasets parameterizing kinetic nonlinear models of a wild-type <em>E. coli </em>strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2: thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3: steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels) (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li><strong>vanilla_learning_training.zip:</strong> contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-<em>{n} </em>( N-10, N-50, N-100, N-500, N-1000, N-72000), where <em>{n} </em>represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li><strong>transfer_learning_training.zip</strong> - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>best_generators.zip</strong> <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>vanilla_ODE_sample_parameters.zip</strong> - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li><strong>ode_solutions_physiology1.zip (available at </strong><a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiology1_ORACLE.zip (available at </strong><a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiologies2-4.zip -</strong> contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li><strong>transfer_learning_ODE_solutions.zip - </strong>contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using <em>{n}</em> samples from physiology 2 and so on (where <em>{n}</em>=10 and 50 respectively). Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul> <p> </p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1_ORACLE.zip</strong> - contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1.zip - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Analytical kinetic model of native tandem promoters in E. coli
<p><span>Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in <i>E. coli</i>. We also studied the sequence (distance between transcription start sites '<i>d<sub>TSS</sub>'</i>,<i> </i>pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and <i>d<sub>TSS</sub> </i>are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when <i>d<sub>TSS</sub> </i>≤ 35 bp) and RNAP occupancy of the downstream promoter (when <i>d<sub>TSS</sub> </i>> 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of <i>d<sub>TSS</sub> i</i>s implemented by a continuous step function, fit to <i>in vivo</i> data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of <i>d<sub>TSS</sub></i>. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.</span></p>
Dataset 2 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution
<p><strong>Dataset</strong></p> <p>Molecular dynamics simulation trajectories of TTR peptide aggregation kinetics:</p> <ul> <li>multi-eGO-XXmM-Y: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration replicate Y.</li> </ul>
TERMINUS WP5: Chain Extension Kinetics for Solvent-Free Adhesive Components
<p>In two-component polyurethane (PUR) adhesives, polyols and isocyanates are reacted to produce binding media.In order to obtain a Solvent-Free prepolymer as one of PUR components, an excess of macrodiols can be reacted with diisocyanates.The addition reaction between OH and NCO functional groups is frequently called “Chain-Extension”.PUR polymerization rarely follows classical kinetic theories, therefore, experimental study was carried out to monitor the rate of polymerization.Small specimens of the reaction mixture were periodically withdrawn during the course of Chain Extension.The amount of isocyanates was measured by titration.Four different macrodiols and two diisocyanates were tested at several temperatures and concentrations.Some data on two reactions from previous Dataset 5-1 “Adhesive_Chain_Extension_WP5” is also included to broaden the range of studied temperatures. Collected data can be used for future kinetic studies of both Solvent-Based and Solvent-Free PUR adhesives.</p>
Datasets for the publication "Sedimentation Kinetics of Hydrous Ferric Oxides in Ferruginous, Circumneutral Mine Water"
<p>This file contains datasets that were generated during laboratory-based column sedimentation experiments. The article was published in Environmental Science & Technology (accepted April 8, 2022).</p>
Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes
<p>This dataset represents the raw data that gave rise to the study by Baleisyte et al., Cell Reports 2022 (DOI: 10.1016/j.celrep.2022.110850), previously published as a preprint at bioRxiv (DOI: 10.1101/2021.06.30.450543). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis. Below we supply information on the provided metadata files which, in turn, refer to individual raw data files.</p> <p><strong>General repository structure:</strong></p> <ul> <li>the raw data is organized in 11 datasets related to the Figures 1, S1, 2, S2, 3A-H, 3I-K, 3L-N, S3A-I, S3J-P, 4A;C-G;H_top;I-J;S4, 4B;C-G;H_bottom;I-J;S4;</li> <li>the metadata listing individual data filenames from the individual dataset are stored in separate “.csv” files, one per dataset. Field separator: comma;</li> <li>the custom script for the reconstruction of the optic fiber placement is described in a separate metadata file “script_metadata.csv”. Field separator: comma;</li> <li>all individual metadata files are summarized in a master metadata file “metadata_master.csv”. Field separator: comma.</li> <li>the data files related to Figures 2, S2, S3A-I are continued in a separate linked repository accessible by the following doi: 10.5281/zenodo.6489354</li> </ul> <p> </p> <p><strong>Description of the data formats:</strong></p> <ul> <li>video recordings of resident-intruder test experiments (Figures 1, 3, S1) are provided as unmodified “.mpg” files created by the acquisition software EthoVision (Noldus Information Technology). The files were, however, renamed for convenience. Video stream parameters: MPEG-4 (DIVX) codec, color space yuv420p, 1280x512 pixels, 30 fps. Along with each video file, there is an associated text file (“.txt”) containing the metadata of video recording and the timestamps of hardware state changes. In these files, unmodified after creation by the EthoVision software, the status of hardware TTL inputs was logged whenever a change of state of these inputs was detected. Typically, “input 2” was sampling the gating signal from the Master-8 pulse generator, with the "high" signal level indicating the application of train of light pulses. This hardware state, signaling the presence of the light train, is noted in the individual metadata files; </li> <li>widefield fluorescent images of single coronal sections containing the MeApd (Figures 2, S2) were converted from the proprietary format of Olympus slide scanning microscope into composite TIFF format, readable by FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> or <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>). The information on pixel resolution and inter-section distance is embedded in the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata files.</li> <li>confocal fluorescent image stacks acquired from single coronal sections containing MeApd (Figure S3) are provided in composite TIFF format after stitching the tiles (originally stored as “lsm” format; Carl Zeiss) using a stitching plugin (Preibisch et al., Bioinformatics 2009) in FIJI. The information on pixel resolution is embedded inside the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata file. For each stack, a region of interest (ROI) highlighting the borders of the MeApd is provided as a separate file in a “.roi“ format (FIJI).</li> <li>patch clamp recordings (Figures 4, S4) are provided as “.dat” files, unmodified from the original version created by the acquisition software PatchMaster (HEKA Elektronik, Germany). Besides by the original PatchMaster software, these files can be imported using one of the following methods: I) via Igor Pro extension bpc_ReadHeka.xop (for 32-bit Igor Pro versions 5.xx - 6.37) by Holger Taschenberger (<a href="https://www.wavemetrics.com/project/bpc_ReadHeka">https://www.wavemetrics.com/project/bpc_ReadHeka</a>); II) via Python script by Luke Campagnola (<a href="https://github.com/campagnola/heka_reader">https://github.com/campagnola/heka_reader</a>); III) via Matlab script HEKA PatchMaster Importer by Christian Keine (<a href="https://github.com/ChristianKeine/HEKA_Patchmaster_Importer">https://github.com/ChristianKeine/HEKA_Patchmaster_Importer</a>).</li> </ul>
Generic solving of a multi-compartment physiologically-based kinetic model
<p>This repository makes available supplementary material related to a scientific paper in progress.</p>
Raw data for "Interplay of Kinetic and Thermodynamic Reaction Control Explains Incorporation of Dimethylammonium Iodide into CsPbI3"
<p>Raw solid-state NMR and XRD data, and input files for DFT and MD calculations shown in https://doi.org/10.1021/acsenergylett.2c00877</p>
Trajectories used for Tailoring Charge Transfer Kinetics in Organic Radical Batteries
<p>Trajectories, which are discussed in our work "Tailored Charge Transfer Kinetics in Organic Radical Batteries - A Joint Synthetic-Theoretical Approach"</p> <p>The trajectories were obtained by linearly interpolating in internal coordinates (LIICs) of the relaxed ground state species of molecules A to F, where the charge is either localized on the thiophene backbone (B) or the TEMPO moiety (T1 and T2). Thereby, the program suite pysisyphus (Steinmetzer <em>et. al.</em> 2021) was used to obtain the LIICs. Endpoints represent the fully optimized redox species. The trajectories were used to calculate the intramolecular charge transfer reactions.</p> <p>Trajectories of the following charge transfer reactions are uploaded:</p> <p>1. A<sub>B</sub>→A<sub>T1</sub> (ABtAT1.trj)</p> <p>2. A<sub>B</sub>→A<sub>T2 </sub>(ABtAT2.trj)</p> <p>3. B<sub>B</sub>→B<sub>T1 </sub>(BBtBT1.trj)</p> <p>4. B<sub>B</sub>→B<sub>T2 </sub>(BBtBT2.trj)</p> <p>5. C<sub>B</sub>→C<sub>T1 </sub>(CBtCT1.trj)</p> <p>6. C<sub>B</sub>→C<sub>T2 </sub>(CBtCT2.trj)</p> <p>7. D<sub>B</sub>→D<sub>T1 </sub>(DBtDT1.trj)</p> <p>8. D<sub>B</sub>→D<sub>T2 </sub>(DBtDT2.trj)</p> <p>9. E<sub>B</sub>→E<sub>T </sub>(EBtET.trj)</p> <p>10. F<sub>B</sub>→F<sub>T </sub>(FBtFT.trj)</p>
Supplemental materials for "Benchmarking magnetized three-wave coupling for laser backscattering: Analytic solutions and kinetic simulations"
<p>Place the unzipped Data and Programs folders in the same directory. The contents of these folders are as follows:</p> <ul> <li>Data<br> Post processed data underlying each figure in the paper. The data files are .txt files with self-contained explanations. The files are organized in subfolders according to their purposes.<br> </li> <li>Programs <ul> <li>./PlotFigures<br> Contains python scripts for reading and plotting Data</li> <li>input.deck<br> Example input for EPOCH PIC code that generates raw data </li> <li>setup_batch.csh<br> Linux/Unix shell script for setting up batch simulations</li> <li>submit_batch<br> Slurm script for submitting jobs on computing clusters</li> </ul> </li> </ul>
Syngas kinetics inside steady Perfectly Stirred Reactor
<p>We present the dataset and python scripts used in our autoencoder (AE) neural network (NN)-based reduced chemistry work (<a href="https://www.dl.begellhouse.com/journals/558048804a15188a,22e553d25a1b5ff0,65e7b0537eb64be1.html">Zhang and Sankaran, 2022</a>).</p> <p>1. To train the AE NN, run "<strong>python Train_PSR_AE_PCA.py</strong> " with Keras </p> <p>2. The dataset is about syngas combustion inside 0-D steady perfectly stirred reactor (PSR) at a wide range of parameter conditions.</p> <ol> <li>The fuel is CO, H2, N2 with a volume ratio 5:1:4. The oxidizer is O2 and N2 mixed in 1:3 by volume. The inflow temperature is 500 K and combustion occurs at atmospheric pressure.</li> <li>In total, there are 1.63 million samples with equivalence ratio varying from 0.09 to 20.0 and residence time scale varying to cover the entire S-curve.</li> <li>The dataset is in hdf5 format and can be loaded with the python script, <strong>load_data_h5.py</strong>. Inside the dataset, there 12 entries.</li> </ol> <ul> <li>1. asciiListtmp = h5f['vars_name'][()] ##name of the 12 thermochemical state variables</li> <li>2. para_Phi = h5f['parameters_Phi'][()] ##equivalence ratio, varying from 0.09 to 20.0</li> <li>3. para_Tin = h5f['parameters_Tin'][()] ##inflow temperature, constant=500 [K]</li> <li>4. para_inv_tau_res = h5f['parameters_inv_tau_res'][()] ##inverse of residence time, varying from 4.53e-09 to 1.54e+04 [1/s]</li> <li>5. x_train_min = h5f['trainset_min'][()] ##minimum value of training set</li> <li>6. x_train_max = h5f['trainset_max'][()] ##maximum value of training set</li> <li>7. x_data = h5f['dataset'][()] ##Thermochemical state variables (temperature, mass fractions of chemical species), normalized with x_train_min and x_train_max to be [-1,1]</li> <li>8. x_src_data = h5f['dataset_src'][()] ##source term * 2/(x_train_max-x_train_min)</li> <li>9. x_del_data = h5f['dataset_del'][()] ##(xinflow-x)* 2/(x_train_max-x_train_min)</li> <li>10. train_ind = h5f['train_dataset_indices'][()] #0.7, sample index of training set </li> <li>11. test_ind = h5f['test_dataset_indices'][()] #0.3*0.5, sample index of test set</li> <li>12. vali_ind = h5f['valid_dataset_indices'][()] #0.3*0.5, sample index of validation set</li> <li>The training/test/validation splitting is used in our reduced chemistry work. </li> </ul> <p> </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)
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.