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11,506 results for “Dynamics”
Model output: Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a numerical modeling study
<p>Output from model runs for Lenstra et al., “Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a modeling study”. The dataset contains two types of model output, namely (1) the default model runs and (2) the sensitity runs with waves+tides, waves only, and tides only.</p> <p>The files covering the default model runs contain:</p> <p>ModeledDays - time vector for the depth series [days]</p> <p>XT - east-west location of grid points high resolution domains [m]</p> <p>YT - north-south location of grid points high resolution domains [m]</p> <p>depth - depth series for the high resolution domains [m]</p> <p>XSea - east-west location of grid points outer sea domain [m]</p> <p>YSea - north-south location of grid points outer sea domain [m]</p> <p>depthSea - depth series for the outer sea domain [m]</p> <p>XBasin - east-west location of grid points basin domain [m]</p> <p>YBasin - north-south location of grid points basin domain [m]</p> <p>depthBasin - depth series for the basin domain [m]</p> <p>ST_Days - time vector for the sediment transport series [days]</p> <p>ST_Inlet - tidally-averaged total sediment transport through the inlet (positive seaward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Up - tidally-averaged total sediment transport through the cross-section at the updrift coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Down - tidally-averaged total sediment transport through the cross-section at the downdrif coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>The files covering the sensitivity runs contain for the high resolution domains:</p> <p>XT - east-west location of grid points [m]</p> <p>YT - north-south location of grid points [m]</p> <p>TA_STX - east-west component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_STY - north-south component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_U - east-west component of the tidally-averaged flow velocities [m/s]</p> <p>TA_V - north-south component of the tidally-averagedf low velocities [m/s]</p> <p>M2 - amplitude of the semi-diurnal tidal flow velocities (not for wave only runs) [m/s] </p>
Audio from: 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model'
<p>Audio files associated with the paper 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model', presented at the International Congress of Phonetic Sciences 2019, Melbourne, Australia.</p>
Nanoscale dipole dynamics of protein membranes studied by broadband dielectric microscopy
<p>Original data in support of our publication: Nanoscale dipole dynamics of protein membranes studied by broadband dielectric microscopy</p>
Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices
<p>The file ManscuriptDataFiles.7z contains RAW spectroscopy data, origin plot file, micromagnetic simulation MIF files at 100 mT used in the manuscript entitiled "Angle-dependent magnetization dynamics with mirror-symmetric excitations in artificial quasicrystalline nanomagnet lattices" that appeared in Physical Review B, <strong>98</strong>, 174408 (2018).</p> <p>Manuscript Abstract: We report angle-dependent spin-wave spectroscopy on aperiodic quasicrystalline magnetic lattices, i.e., Ammann, Penrose P2 and P3 lattices made of large arrays of interconnected Ni<sub>80</sub>Fe<sub>20</sub> nanobars. Spin-wave spectra obtained in the nearly saturated state contain distinct sets of resonances with characteristic angular dependencies for applied in-plane magnetic fields. Micromagnetic simulations allow us to attribute detected resonances to mode profiles with specific mirror symmetries. Spectra in the reversal regime show systematic emergence and disappearance of spin-wave modes indicating reprogrammable magnonic characteristics.</p>
Arctic Ocean freshwater dynamics: transient response to increasing river runoff and precipitation [dataset]
<p>This dataset contains the underlying data for the manuscript Brown et al., Arctic Ocean freshwater dynamics: transient response to increasing river runoff and precipitation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Descriptors in the filenames, shown below as *, correspond to the various simulations, as follows:</p> <p>Simulations forced with JRA-25 reanalysis data:<br> AR: Unperturbed control simulation<br> B1: Simulation involving a step change in river runoff of -30% <br> C1: Simulation involving a step change in river runoff of +30% <br> B7: Simulation involving a step change in precipitation of -30% <br> C8: Simulation involving a step change in precipitation of +30% </p> <p>Simulations forced with the CORE-II climatology:<br> AR_CORE: Unperturbed control simulation <br> P-30_CORE: Simulation involving a step change in precipitation of -30% <br> P+30_CORE: Simulation involving a step change in precipitation of +30% </p> <p>-----------------------------------<br> The files named as fwvolume_Sref35_*.nc contain the variables:</p> <p>freshwater: horizontally-integrated liquid freshwater volume in m^3<br> freshwater_pos: as before, but including only positive values in the integration<br> h_f: basin-mean freshwater height in m<br> h_f_pos: as before, but including only positive values in the integration</p> <p>The reference salinity used in each case is 35.</p> <p>-----------------------------------</p> <p>The files named as freshwater_h_f_*.nc contain horizontally-gridded, depth-integrated liquid freshwater heights in m. The integration is made to a depth of 276.68m.</p> <p>h_f_34p8: freshwater height, using a reference salinity of 34.8 <br> h_f_34p8_pos: as before, but including only positive values in the integration<br> h_f_35: freshwater height, using a reference salinity of 35 <br> h_f_35_pos: as before, but including only positive values in the integration</p> <p>The grid for these files is grid.nc</p> <p>-----------------------------------</p> <p>The files named as fluxes_ed3_*.nc contain strait-integrated volume fluxes (in m^3 s^-1) into and out of the Arctic domain: </p> <p>f: liquid volume flux<br> fw: freshwater, with variables numbered 1 using a reference salinity of 34.8 and those numbered 2 using a reference salinity of 35<br> ice: sea ice volume flux</p> <p>The first dimension, ngate, of the variables indicates the strait:</p> <p>1) Fram Strait<br> 2) Barents Sea Opening<br> 3) Bering Strait<br> 4) Amundsen Gulf<br> 5) McClure Strait<br> 6) Canadian Arctic Archipelago<br> 7) Nares Strait</p> <p>-----------------------------------</p> <p>Sea ice volumes (in m^3) for the JRA-25 forced simulations are contained in the files named area_int_*.nc with variable name "ivol"</p> <p>-----------------------------------</p> <p>Basin-integrated liquid freshwater volumes for a further series of JRA-forced runoff perturbation experiments are contained within the file FWLvol_AO_Sref35_runoff.mat:</p> <p>R_10: a step increase in runoff of 10%<br> R_60: a step increase in runoff of 60%<br> R_100: a step increase in runoff of 100%</p> <p>The reference salinity is 35 and depth of integration 276.68m</p>
Experimental data and benchmarks used in the paper "Nucleosome Dynamics: A new tool for the dynamic analysis of nucleosome positioning"
<p>Experimental data used to illustrate the analysis with Nucleosome Dynamics pipeline. Three publicly available data sets were used:</p> <ol> <li> <p>Yeast metabolic cycle MNase-seq data downloaded from GEO under accession number GSE77631 corresponding to time points 9 and 12<br> Nocetti, N., and Whitehouse, I. (2016). Nucleosome repositioning underlies dynamic gene expression. Genes & Development 30, 660–672.</p> </li> <li> <p>MNase-seq data for S. cerevisiae cells synchronized in G1 and S phase, as described by Deniz (2016). Raw data available under accession number SAMEA2698380<br> Deniz, Ö., Flores, O., Aldea, M., Soler-López, M., and Orozco, M. (2016). Nucleosome architecture throughout the cell cycle. Scientific Reports 6, 19729.</p> </li> <li> <p>MNase-seq data for S. cerevisiae grown in different media: YPD, Gal, and EtOH. Data aligned to sacCer1 downloaded from GEO using accession numbers GSM351492, GSM351493, and GSM351494.</p> Kaplan N, Moore IK, Fondufe-Mittendorf Y, Gossett AJ et al. The DNA-encoded nucleosome organization of a eukaryotic genome. <em>Nature</em> 2009 Mar 19;458(7236):362-6. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/19092803">19092803</a></li> </ol> <p>Each tar file contains two folders:</p> <ul> <li>inputs: bam/RData files can be used to run Nucleosome Dynamics analyses. bigWig files contain nucleosome coverage and can be used to visualise in a genome browser. </li> <li>outputs: results from all analyses (nucleR, NFR, TSS, Periodicity, Stiffness, NucDyn)</li> </ul> <p> </p> <p>Simulated data used to benchmark nucleosome positioning by nucleR, and nucleosome dynamics by NucDyn, DANPOS and Dimnp.</p> <p><strong>Figure </strong><strong>2B: </strong>synthetic data simulated for comparison of nucleR and Danpos to detect a second family of nucleosomes. Each folder pX contains simulations when the second nucleosome is present in X% of the families.</p> <p><strong>Figure 2C</strong>: Distance between the dyads identified by nucleR and DANPOS to the dyad position in the true synthetic nucleosome map for fuzzy and well positioned nucleosomes.</p> <p><strong>Figure 2D</strong>: Synthetic data used to compute sensitivity of the EVICTION prediction for NucDyn, DANPOS and Dimnp. Evictions were simulated removing reads from a given percentage of families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with point_log2FC < -1 and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software), and with default parameters for Dimnp </p> <p><strong>Figure 2E</strong>: Synthetic data used to compute sensitivity of the SHIFT prediction. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%). </p> <p>For each simulated data:</p> <ul> <li><em> *.RData</em> files can be used to run nucleR or NucDyn (*mod* corresponds to the modified reads: eviction or shift introduced)</li> <li><em>*.bed </em>files can be used to run DANPOS or Dimnp (*mod* corresponds to the modified reads: eviction introduced)</li> <li>results/ folder contains results from DANPOS</li> <li><em>NR.gff</em> contains the results from nucleR (*mod* corresponds to the results for modified reads: eviction or shift introduced)</li> <li><em>ND.gff</em> contains the results from NucDyn</li> <li><em>res_dimnp_*</em> contains the results from Dimnp</li> </ul> <p><strong>FigSupDanposShift:</strong> Synthetic data used to compute sensitivity of the SHIFT prediction for DANPOS. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with treat2control_dis-10 larger than the given displacement and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software).</p> <p>For each simulated data:</p> <ul> <li><em> *.bed</em> files contain the modified nucleosome positions</li> <li><em>results</em> folder contains output from DANPOS</li> </ul>
SeisSol dynamic rupture model setup of the Mw 7.5 Palu earthquake scenario published in Ulrich et al. (2019)
<p>All data required to run the dynamic rupture model of the Palu earthquake presented in:</p> <p>Ulrich, T., Vater, S., Madden, E. H., Behrens, J., van Dinther, Y., van Zelst, I., Fielding, E. J., Liang, C. & Gabriel, A. A. (2019). Coupled, Physics-based Modeling Reveals Earthquake Displacements are Critical to the 2018 Palu, Sulawesi Tsunami. doi: 10.31223/osf.io/3bwqa.</p> <p>A detailed readme file summarizing the data and data formats is also provided.</p>
Revisiting the allosteric regulation of sodium cation on the binding of adenosine at the human A2A adenosine receptors: insights from Supervised Molecular Dynamics (SuMD) simulations.
<p><strong>SuMD trajectories Videos </strong></p> <p> </p> <p><strong>Video 1: </strong>Sodium binding pathway on the antagonist-bound state of A<sub>2A</sub>R.</p> <p>The video is composed of four synchronized and animated panels that depict the molecular trajectory obtained by the SuMD simulation considering different aspects of the simulation. The time evolution is reported in a nanosecond. In the first panel (upper-left), the molecular representation of the macromolecular system is shown. The A<sub>2A</sub>R antagonist-bound state backbone is represented by the ribbon style (cyan colour) and the residues within 4 Å of sodium ion during the entire simulation are dynamically shown. Na<sup>+</sup> is rendered showing its VdW volume in yellow. In the second panel (upper-right), the dynamic distance of sodium center of mass (CM) from the A<sub>2A</sub>R allosteric binding site during the trajectory is reported. In the third panel (lower-left), the MMGBSA energy profile is reported. The animated red circle highlights the value of the corresponding frame. The trend is depicted by a continuous black line obtained by smoothing the raw data (grey circles) using a Bezier curve procedure. In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 A<sub>2A</sub>R residues most contacted by sodium during the whole simulation.</p> <p> </p> <p><strong>Video 2: </strong>Adenosine different binding pathways collection on the two relevant states of A<sub>2A</sub>R</p> <p>The video is composed of two panels, which summarizes the recognition process of the adenosine agonist, sampled by means of the supervised molecular dynamics methodology, in the two pharmacologically relevant states of the receptor. In particular, on the right side are shown simultaneously all ten replicas collected starting from the agonist-bound conformation of the A<sub>2A</sub>R (pink ribbon). The meta-binding site located at the level of the ECL2 and the orthosteric binding site were highlighted. On the left side are represented simultaneously all ten replicas collected starting from the antagonist-bound conformation of the A<sub>2A</sub>R (cyan ribbon). The meta-binding site located at the level of the ECL2 and the extracellular receptor vestibule were highlighted.</p> <p> </p> <p><strong>Video 3: </strong>Adenosine binding pathway on the agonist-bound state of A<sub>2A</sub>R.</p> <p>The video is composed of four synchronized and animated panels that depict the molecular trajectory obtained by the SuMD simulation considering different aspects of the simulation. The time evolution is reported in a nanosecond. In the first panel (upper-left), the molecular representation of the macromolecular system is shown. The A<sub>2A</sub>R agonist-bound state backbone is represented by the ribbon style (pink colour) and the residues within 4 Å of sodium ion during the entire simulation are dynamically shown. Adenosine molecule is rendered by orange carbon atoms and by a transparent surface. In the second panel (upper-right), the dynamic distance of agonist center of mass (CM) from the A<sub>2A</sub>R allosteric binding site during the trajectory is reported. In the third panel (lower-left), the MMGBSA energy profile is reported. The animated red circle highlights the value of the corresponding frame. The trend is depicted by a continuous black line obtained by smoothing the raw data (grey circles) using a Bezier curve procedure. In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 A<sub>2A</sub>R residues most contacted by adenosine during the whole simulation.</p>
Movie files: Actin dynamics and the Bmp pathway drive apical extrusion of proepicardial cells.
<p>The epicardium, the outer mesothelial layer enclosing the myocardium, plays key roles in heart development and regeneration. During embryogenesis it arises from the proepicardium (PE), a cell cluster that appears in the dorsal pericardium (DP) close to the venous pole of the heart. Little is known about how the PE emerges from the pericardial mesothelium. Using the zebrafish model and a combination of genetic tools, pharmacological agents and quantitative <em>in vivo</em> imaging, we reveal that a coordinated collective movement of DP cells drives PE formation. We found that BMP signaling and the actomyosin cytoskeleton promote constriction of the DP, which enabled PE cells to extrude apically. We provide evidence that cell extrusion, which has been described in the elimination of unfit cells from epithelia and the emergence of hematopoietic stem cells, is also a mechanism for PE cells to exit an organized mesothelium and fulfil the developmental fate to form a new tissue layer, the epicardium.</p>
Fine litter dynamics
<b>Description: </b><p>Fine leaf litter data from litter traps in the vegetation plots across the SAFE landscape during 2011 and 2012 </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/64"><b>Fine litter dynamics</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby (Standard grant )</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence na)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265722">here</a></p><p><b>Files: </b>This consists of 1 file: Litter_Collection_Data.xlsx</p><p><b>Litter_Collection_Data.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Litter_Collection_Data</b> (described in worksheet Litter_Collection_Data)</p><p>Description: Litter collection data 2011- 2012</p><p>Number of fields: 24</p><p>Number of data rows: 386</p><p>Fields: </p><ul><li><b>Location</b>: Location of little trap (Field type: Location)</li><li><b>Plot</b>: Second order point within the safe landscape (Field type: ID)</li><li><b>Block</b>: Fragment number (Field type: ID)</li><li><b>SurveyNum</b>: Survey number (Field type: Replicate)</li><li><b>DateSet</b>: Date trap set (Field type: Date)</li><li><b>TimeSet</b>: Time trap set (Field type: Time)</li><li><b>DateCollected</b>: Data trap collected (Field type: Date)</li><li><b>TimeCollected</b>: Time trap collected (Field type: Time)</li><li><b>SampleTime</b>: Number of days litter was accumulated (Field type: Numeric)</li><li><b>WW.litter</b>: Total wet weight of litter (Field type: Numeric)</li><li><b>DW.litter</b>: Total dry weight of Litter (Field type: Numeric)</li><li><b>DW.leaves.photo</b>: Dry weight of leaves with photos (Field type: Numeric)</li><li><b>DW.leaves.other</b>: Dry weight of leaves without photos (Field type: Numeric)</li><li><b>DW.wood</b>: Wood dry weight <2cm (Field type: Numeric)</li><li><b>DW.flower</b>: Dry weight of flowers (Field type: Numeric)</li><li><b>DW.fruit</b>: Dry weight of fruit (Field type: Numeric)</li><li><b>DW.seed</b>: Dry weight of seeds (Field type: Numeric)</li><li><b>DW.other</b>: Dry weight of other matter (Field type: Numeric)</li><li><b>DW.wood>2cm</b>: Dry weight of wood >2cm (Field type: Numeric)</li><li><b>WoodDiameter1</b>: Wood diameter (Field type: Numeric)</li><li><b>WoodDiameter2</b>: Wood diameter (Field type: Numeric)</li><li><b>WoodLength</b>: Wood length (Field type: Numeric)</li><li><b>Herbivory</b>: Are there signs of herbivory? (Field type: Categorical)</li><li><b>PhotoFile</b>: Photo file number (Field type: Comments)</li></ul></li></ol><p><b>Date range: </b>2011-03-24 to 2012-10-05</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Dataset of predicted dynamic specific yield under periodic water table oscillations
<p>This dataset accompanies the submitted paper in the Water Resources Research: An approximate model for predicting the specific yield under periodic water table oscillations.</p> <p>The dataset includes 299 numerical experiments on predicting dynamic specific yield with different soil properties and different amplitudes of groundwater level oscillations. With this dataset, we proposed an approximate model for predicting the specific yield under diurnal and seasonal water level fluctuations.</p>
Impact of chronic and massive resuspension mechanisms on the microphytobenthos dynamics in a temperate intertidal mudflat
<p><em>In situ</em> data along with input/output files and code of the model used in the manuscript by Savelli et al. submitted to Journal of Geophysical Research: Biogeosciences</p> <p>Savelli, R., Bertin, X., Orvain, F., Gernez, P., Dalle, A., Coulombier, T., Pineau, P., Lachaussée, N., Polsenaere, P., Dupuy, D., Le Fouest, V., submitted. Impact of chronic and massive resuspension mechanisms on the microphytobenthos dynamics in a temperate intertidal mudflat. <em>Journal of Geophysical Research: Biogeosciences</em></p> <p>See the readme.txt file for explicit details on the repository.</p>
Codes and datasets associated with the paper "Dynamic LES modeling of a diurnal cycle"
<p>Here, you will find some of the codes, images, and datasets utilized in the article: </p> <p>Basu et al. (2008). "Dynamic LES modeling of a diurnal cycle", Journal of Applied Meteorology and Climatology, 47, 1156-1174. (<a href="https://journals.ametsoc.org/doi/pdf/10.1175/2007JAMC1677.1">https://journals.ametsoc.org/doi/pdf/10.1175/2007JAMC1677.1</a>)</p> <p>Matlab codes: MatlabCodes.tar.gz (Contains all the analyses and visualization codes used in the paper)</p> <p>Observational data: Wangara_Observations.tar.gz (These data were manually typed directly from the original Wangara document)</p> <p>LES data (binary): LES_160x160x160.tar.gz and LES_80x80x80.tar.gz</p>
ROS-Specific Huntingtin Interactions: Huntingtin chromatin retention dynamics by FRAP with veliparib
<p>Measurement of huntingtin chromatin recruitment dynamics by fluorescence recovery after photobleaching (FRAP) of the YFP-tagged huntingtin-specific intrabody, nucHCB2, under conditions of oxidative stress and PARP inhibition</p>
Supervised Molecular Dynamics Movies from: Deciphering the molecular recognition mechanism of multidrug resistance Staphylococcus aureus NorA efflux pump using a Supervised Molecular Dynamics approach
<p>Molecular Recognition pathway of Supervised molecular dynamic simulations of MdfA-CLM NorA-CPX and NorA-CPX.</p>
Research data supporting "Investigating the accumulation and translocation of titanium dioxide nanoparticles with different surface modifications in static and dynamic human placental transfer models"
<p>Research data supporting the publication: Aengenheister, L. et al., 2019, "Investigating the accumulation and translocation of titanium dioxide nanoparticles with different surface modifications in static and dynamic human placental transfer models", Eur J Pharm Biopharm. https://doi.org/10.1016/j.ejpb.2019.07.018</p>
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
Water Vapor Sorption Properties of Illinois Shales under Dynamic Water Vapor Conditions: Experimentation and Modeling
<p>Data sets for the Publication 'Water Vapor Sorption Properties of Illinois Shales under Dynamic Water Vapor Conditions: Experimentation and Modeling' in Water Resources Research.</p>
Deciphering the molecular recognition mechanism of multidrug resistance Staphylococcus aureus NorA efflux pump using a Supervised Molecular Dynamics approach.
<p><strong>Legend of Movie-S1</strong></p> <p>The Movie is composed by four synchronized and animated panels that show different aspects of the SuMD simulation. The time evolution is reported in nanosecond. In the first panel (upper left), the molecular representation of the system is shown. The MdfA backbone is represented by the new cartoon style (cyan). The CLM is shown in yellow and by a transparent surface. The protein residues within 3 Å from the ligand are made explicit by a stick representation.</p> <p>In the second panel (upper-right), the CM-distance between the protein and the ligand centers of mass is reported.</p> <p>In the third panel (lower left), the MMGBSA energy profile is reported.</p> <p>In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 MdfA residues most contacted by CLM during the whole simulation.</p> <p> </p> <p><strong>Legend of Video-S2</strong></p> <p>The Movie shows the SuMD trajectory of CLM on MdfA compared to the CLM crystallographic pose. MdfA is represented in cyan new cartoon transparency. The crystallographic pose is showed in yellow while the experimental one in light green. At 16.69 ns a RMSD value of 1.77 Å is highlighted.</p> <p> </p> <p><strong>Legend of Video-S3</strong></p> <p>The Movie is composed by four synchronized and animated panels that show different aspects of the SuMD simulation. The time evolution is reported in nanosecond. In the first panel (upper left), the system is shown. The NorA backbone is represented by the new cartoon style (red) and the protein residues within 3 Å of CPX are showed in stick. CPX is rendered by a green stick.</p> <p>In the second panel (upper-right), the distance between the centre of mass of the ligand and the protein during the trajectory is reported.</p> <p>In the third panel (lower left), the MMGBSA energy profile is reported. In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 NorA residues most contacted by CPX during the whole SuMD trajectory.</p> <p>It is important to note that the following video has a duration that is half of the simulation of SuMD. However, this straid does not alter the description of the trajectory performed by the ligand.</p> <p> </p> <p><strong>Legend of Video-S4</strong></p> <p>The Movie depicts the clustering analysis of CPX during the whole SuMD simulation. The NorA protein is shown in red new cartoon transparency. CPX is rendered by a light-green stick and by a transparent surface. The spheres are shown in 7 different colours, according to the different clusters. Each sphere dimension is in according to the cluster dimensions. After a first recognition site, the ligand conformations are clustered in different sites of the NorA channel. It is important to note that the following video has a duration that is half of the simulation of SuMD. However, this straid does not alter the description of the trajectory performed by the ligand.</p>
Data and code for figures: Dynamics of soliton crystals in optical microresonators
<p>This dataset contains the data presented in the figures of the paper Dynamics of soliton crystals in optical microresonators.</p> <p>The data for figure X (X = 1, 2, 3, 4, S1, S2, S3, S4, S5) is gathered in one matlab dataset file Fig_X.mat, which contains structure variable figX whose fields are the panels of the figure in the manuscript (a,b,c,...). In each of the panel field, you find subfields containing the data arrays for all the lines presented in the panel.</p> <p><br> A minimal script znd_FigX_plot.m is provided for each figure in order to plot all the panels.</p> <p>The datasets and scripts were generated and tested using Matlab 2018b.</p>
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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.