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68 results for “Trajectory modeling”
MHD Model of Ganymede's Magnetosphere: Predicted magnetic field on Juno's trajectory
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish predicted magnetic field components on Juno's trajectory that can be compared to MAG measurements and are displayed in Figure 3 of Duling et al. (2022).</p> <p>Each file contains data from one model. The dataset includes all models with parameter variations from Duling et al. (2022). These are summarized in Table 1 of Duling et al. (2022) and displayed in Figure 3 with the gray lines.</p> <p>If not varied, all models are run with the following parameters:</p> <p>Upstream Jovian background magnetic field B<sub>0 </sub>= (−15,24,−75) nT<br> Upstream plasma velocity v<sub>0</sub> = 140 km/s<br> Upstream plasma mass density <span class="math-tex">\(\rho\)</span><sub>0</sub> = 100 amu/cm<sup>3</sup><br> Upstream plasma thermal pressure p<sub>0</sub> = 2.8 nPa<br> Ionization frequency <span class="math-tex">\(\nu_{ion}\)</span> = 2.2e-8/s<br> Atmospheric surface mass density <span class="math-tex">\(n_{n,0}\)</span> = 8e6/cm<sup>3</sup><br> Dipole Gauss coefficient <span class="math-tex">\(g_1^0\)</span> = −716.8 nT</p> <p> </p> <p>The published data files correspond to the following models with each one parameter variation:</p> <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> <th scope="col">Filename Suffix</th> </tr> </thead> <tbody> <tr> <td>default model</td> <td> - </td> <td>default</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured before flyby)</td> <td>B<sub>0 </sub>= (−16,3,−70) nT</td> <td>B0before</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured after flyby)</td> <td>B<sub>0 </sub>= (−14,43,−80) nT</td> <td>B0after</td> </tr> <tr> <td>Upstream plasma velocity (min)</td> <td>v<sub>0</sub> = 120 km/s</td> <td>v-</td> </tr> <tr> <td>Upstream plasma velocity (max)</td> <td>v<sub>0</sub> = 160 km/s</td> <td>v+</td> </tr> <tr> <td>Upstream plasma mass density (min)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 10 amu/cm<sup>3</sup></td> <td>rho-</td> </tr> <tr> <td>Upstream plasma mass density (max)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 160 amu/cm<sup>3</sup></td> <td>rho+</td> </tr> <tr> <td>Upstream plasma thermal pressure (min)</td> <td>p<sub>0</sub> = 1.0 nPa</td> <td>p-</td> </tr> <tr> <td>Upstream plasma thermal pressure (max)</td> <td>p<sub>0</sub> = 5.0 nPa</td> <td>p+</td> </tr> <tr> <td>Ionization frequency (min)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 0.5e-8/s</td> <td>prod-</td> </tr> <tr> <td>Ionization frequency (max)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 10.0e-8/s</td> <td>prod+</td> </tr> <tr> <td>Atmospheric surface mass density (min)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 1.6e6/cm<sup>3</sup></td> <td>nn-</td> </tr> <tr> <td>Atmospheric surface mass density (max)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 40e6/cm<sup>3</sup></td> <td>nn+</td> </tr> <tr> <td>Dipole Gauss coefficient (min)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −702.5 nT</td> <td>dipole-</td> </tr> <tr> <td>Dipole Gauss coefficient (max)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −731.1 nT</td> <td>dipole+</td> </tr> </tbody> </table> <p>Magnetic Field components and Juno's position are in GPhiO system. GPhiO is defined by the primary direction z parallel to Jupiter’s rotation axis, the secondary direction y is pointing from Ganymede's towards Jupiter's barycenter and x completes the right-handed system approximately in direction of plasma flow.</p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Bx modeled magnetic field in GPhiO [nT]<br> By modeled magnetic field in GPhiO [nT]<br> Bz modeled magnetic field in GPhiO [nT]<br> B modeled magnetic field magnitude [nT]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p>
Air mass back-trajectory modeling output along an urban-rural transect in central Ohio, 2021
This data package contains modeled air parcel back-trajectories generated using the Stochastic Time-Inverted Lagrangian Transport model (STILT) via the R interface. The purpose of the study was to characterize the geochemical and isotopic signatures of dust in relation to different land uses, and to connect the geochemistry of deposited dust to air mass trajectories. Back-trajectories are three-dimensional paths of air parcels from a receptor site (the dust collection site) backwards in time and space for the duration of the tracking interval, calculated iteratively using wind fields from high-resolution gridded meteorological data. To calculate a probability of potential pathways, rather than a single back-trajectory, STILT introduces small random perturbations into the wind fields during each time step. For four sites along an urban-rural transect in central Ohio for June-July 2021, we generated weekly footprints of potential sources for the dust deposited at each site. These back-trajectories can be paired with geochemical data to establish a connection between land use and anthropogenic dust composition. This dataset is complete and will not be updated.
Data-driven physics-based modeling of pedestrian dynamics - dataset: Pedestrian trajectories at Eindhoven train station
<p>Pedestrian trajectories measured at train station Eindhoven Centraal (the Netherlands) on platform 2 with acces to tracks 3 and 4.</p> <p>The dataset is partitioned in files containing 10 consecutive days each, recording 4 data fields:</p> <ul> <li><strong>time_ms:</strong> Passed time since start of the measurements. Unit: milliseconds.</li> <li><strong>object_identifier:</strong> unique id identifying an object.</li> <li><strong>x_position_mm: </strong>coordinates of the object along the x-axis at the given time. Unit: millimeters.</li> <li><strong>y_position_mm:</strong> coordinates of the object along the y-axis at the given time. Unit: millimeters.</li> </ul> <p>Each object resembles a pedestrian on the train platform recorded with 10 frames per second. We deliberately removed exact date and time information for privacy reasons (see additional note). The data set consists of 60 consecutive days starting at an unkown time between 00:00 AM and 01:00 AM of a random date between April 1st and May 1st 2022. An overhead image of the platform is included showing train track 3 in the bottom and train track 4 in the top of the image.</p> <p>The data set is supplemented to the paper <a title="Data-driven physics-based modeling of pedestrian dynamics" href="https://doi.org/10.48550/arXiv.2407.20794" target="_blank" rel="noopener">Data-driven physics-based modeling of pedestrian dynamics</a> and can be processed by the associated <a title="Software: Data-driven physics-based modeling of pedestrian dynamics" href="https://github.com/c-pouw/physics-based-pedestrian-modeling" target="_blank" rel="noopener">Python implementation</a> to create pedestrian models. </p>
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I
<p>Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation.</p> <p>There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite. </p> <p> </p> <p><strong>1. File naming convention</strong></p> <p>"< Model simulation label >" + _ + "deformation" + _ + "< year >" </p> <p> </p> <p><strong>2. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Position of the cells' corners (Lagrangian trajectories) - (meters);</li> <li><em>A</em>: Cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets.</p> <p> </p> <p><strong>3. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> -------------------<strong> o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p>Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j)</p> <p> </p> <p><strong>4. Recommended citation usage</strong></p> <p>If <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022). If only <em>selected </em>simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).</p>
VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic grids for interpolation
<p>VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic interpolation grids in ASCII format. The generation of these grids is described in http://doi.org/10.1007/s00190-015-0871-8</p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
WV-TTL: water vapor mixing ratio from GEOSCCM and trajectory model simulations in tropical tropopause layer
<p>This dataset includes 100 hPa water vapor mixing ratio simulated from a trajectory transport model and a climate-chemistry model in the tropical tropopause layer from 2005 to 2016 in the format of netCDF. The data are monthly and have three dimensions as lon/lat/time in the unit of parts per million by volume.</p> <p>Also included the tropical average time series of indices for Brewer-Bobson circulation (BDC), tropospheric temperature and/or Quasi-biennial Oscillation (QBO) from ERAi/MERRA-2/GEOSCCM. These indices are used in a multivariate regression.</p>
FESOM-REcoM model data: Lagrangian particle trajectories
<p>This data set includes the results of Lagrangian particle tracking experiments with FESOM1.4-REcoM2. Particles were seeded at 596 positions near the Filchner Ice Shelf front at 78°S between Berkner Island and Coats Land and tracked forwards and backwards using daily mean model output. Particles were seeded every 10th day in 1990 and 1991 (historical, forward), 2009 and 2008 (historical, backward), 2080 and 2081 (future/SSP5-8.5 scenario, forward), and 2099 and 2098 (future/SSP5-8.5 scenario, backward). Particles were tracked outside of ice-shelf cavities for 20 (19) years or until the the particle left the domain of interest in the north (62°S), west (65°W), or east (2°E). </p> <p>The following information for each particle is stored twice a day: longitude (blon in files), latitude (blat), time (bday and time), depth (bdepth), temperature (btemp), salinity (bsalt), density (bsigma0; potential density anomaly referenced to 0dbar) dissolved inorganic carbon (bdic), and total carbon (btotc).</p> <p>Each *tar.gz archive contains one experiment, i.e., all trajectories for e.g., forward/1990-2009 (seeding days 1,11,...,361 in 1990). For each seeding day, there are 12 *nc files, which together constitute the 596 particles seeded on a given day.</p> <p> </p> <p><strong>Naming convention / *tar.gz files: </strong></p> <p>Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_<em>EXPERIMENT</em>_<em>START_END_YEAR</em>.tar.gz</p> <p><em>EXPERIMENT</em>: bw (backward) or fw (forward)</p> <p><em>START_END_YEAR</em>: 2009_1990, 2008_1990, 2098_2080, or 2099_2080 for backward experiments; 1990_2009, 1990_2008, 2080_2099, or 2081_2099 for forward experiments</p> <p>(EXAMPLE: Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_fw_1990_2009.tar.gz)</p> <p><strong>Naming convention / *nc files: </strong></p> <p>drifter_start_Filchner_ice_shelf_day<em>SEEDING_DAY</em>_<em>START_END_YEAR</em>_<em>NUM_FILE</em>_reduced.nc</p> <p><em>SEEDING_DAY: </em>1, 11, ..., 361</p> <p><em>START_END_YEAR: </em>same as above</p> <p><em>NUM_FILE: </em>1, ..., 12</p> <p>(EXAMPLE: drifter_start_Filchner_ice_shelf_day1_1990_2009_1_reduced.nc)</p> <p> </p> <p><strong>NOTE:</strong> The following files are duplicates and also contained in the larger *tar archives described above (disregard them if all tar archives starting with "Nissen2023_FESOM1.4_REcoM2_Trajectories_FT_" have been downloaded):</p> <p>Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_1991_2009.tar.gz</p> <p>Nissen2023_FESOM1.4_REcoM2_LagrangianExperiments_FT_fw_day91_2080_2099.tar.gz</p> <p> </p> <p><strong>The Lagrangian particle trajectories have been analyzed here:</strong> </p> <p>Nissen, C., Timmermann, R., van Caspel, M., and Wekerle, C.: Altered Weddell Sea warm- and dense-water pathways in response to 21st-century climate change, Ocean Sci., 20, 85–101, <a href="https://doi.org/10.5194/os-20-85-2024">https://doi.org/10.5194/os-20-85-2024</a>, 2024</p> <p><strong>The Eulerian fields underlying the Lagrangian experiments are described in more detail here: </strong></p> <p>Nissen, C., Timmermann, R., Hoppema, M. <em>et al.</em> Abruptly attenuated carbon sequestration with Weddell Sea dense waters by 2100. <em>Nat Commun</em> <strong>13</strong>, 3402 (2022). <a href="https://doi.org/10.1038/s41467-022-30671-3">https://doi.org/10.1038/s41467-022-30671-3</a> </p> <p>Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. <em>J. Climate</em>, <strong>36</strong>, 6613–6630, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>. </p> <p>Original model output is available at the World Data Center for Climate (WDCC) under the following DOIs:</p> <ul> <li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li> <li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li> </ul>
MD simulation trajectory and related files for POPC bilayer in low hydration (Berger model delivered by Tieleman, Gromacs 4.5)
<p>Equilibrated POPC lipid bilayer simulation in low hydration (7 water per lipid molecule) ran with Gromacs 4.5, Berger force field delivered by Peter Tieleman (http://wcm.ucalgary.ca/tieleman/downloads) with fixed double bond dihedrals, 60ns, T=298K, 128 POPC molecules, 896 water molecules. This data is used in the nmrlipids.blospot.fi project. More details from nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi. If data is used, please cite the nmrlipids.blogspot.fi project and the original publications related to the force field.</p>
Hydrated DPPC, MD simulation trajectory and related files for UA charmm36 model by Lee et al 2014
<p>MD simulation files</p> <p>72 hydrated DPPC + 2189 water TIP3P </p> <p>NPgT</p> <p>P=1atm, gamma=0, T=323K (liquid crystalline phase)</p> <p>20 ns equilibration (not here)</p> <p>50 ns trajectory (dcd file)</p> <p>Model : Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB. CHARMM36 United Atom Chain Model for Lipids and Surfactants. <em>J Phys Chem B</em>. 2014;118(2):547-556. doi:10.1021/jp410344g.</p> <p>----<br /> * bilayer-72DPPC-c36-AU.psf : NAMD2.10 structure file Obtained with psfgen utility, using</p> <p>1) topology from Lee et al. 2014</p> <p>2) positions from J. Klauda.</p> <p>http://terpconnect.umd.edu/~jbklauda/research/download.html</p> <p>----</p> <p>* dppc_c36_AU.equil.2.dcd : trajectory file of 2635 frames every 20 ps.</p> <p>---<br /> * measure_SCD_heads.tcl : file used to measure order parameters for the head hydrogens using vmd-1.9</p> <p>---</p> <p>*namd_input.tar files usefull to launch the simulations using NAMD(2.10).</p>
Hydrated DPPC, MD simulation trajectory and related files for UA charmm36 model by Lee et al 2014
<p>MD simulation files</p> <p>72 hydrated DPPC + 2189 water TIP3P </p> <p>NPgT</p> <p>P=1atm, gamma=0, T=323K (liquid crystalline phase)</p> <p>20 ns equilibration (not here)</p> <p>50 ns trajectory (dcd file)</p> <p>Model : Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB. CHARMM36 United Atom Chain Model for Lipids and Surfactants. <em>J Phys Chem B</em>. 2014;118(2):547-556. doi:10.1021/jp410344g.</p> <p>----<br /> * bilayer-72DPPC-c36-AU.psf : NAMD2.10 structure file Obtained with psfgen utility, using</p> <p>1) topology from Lee et al. 2014</p> <p>2) positions from J. Klauda.</p> <p>http://terpconnect.umd.edu/~jbklauda/research/download.html</p> <p>----</p> <p>* dppc_c36_AU.equil.2.dcd : trajectory file of 2635 frames every 20 ps.</p> <p>---<br /> * measure_SCD_heads.tcl : file used to measure order parameters for the head hydrogens using vmd-1.9</p> <p>---</p> <p>*namd_input.tar files usefull to launch the simulations using NAMD(2.10).<br /> </p>
Postprocessed trajectory output for ice cloud microphysics - ICON and CLaMS-Ice models
<p>Postprocessed output from a trajectory module implemented in ICON v 2.3.0. The trajectories track density, temperature, pressure, specific humidity, cloud ice mass and number mixing ratios, cloud liquid mass and number mixing ratios, graupel mass and number mixing ratios, and ice sedimentation mass and number mixing ratios both into and out of the parcel. They are initiated over the Sichuan basin and allowed to flow for 51 hours westward during which the cross India into the Arabian Sea. This trajectory output is also used to run an offline microphysics box model, CLaMS-Ice. qih-Nih* files contain histograms of ice mass mixing ratio (qi) and ice crystal number concentration (Ni); het-hom-pre* files contain process tendencies from heterogeneous nucleation, homogeneous nucleation, and preexisting ice in CLaMS-Ice; qippmvNi-TRHi* files contain qi and Ni versus a range of cirrus temperatures and a range of supersaturations with respect to ice; qi_ppmv_abs* and Ni_abs* files contain probability distributions of qi and Ni differences over absolute time; and qi_ppmv_norm* and Ni_norm* files contain probability distributions of qi and Ni differences over normalized time. Suffixes in all cases indicate the cloud microphysical setup that the trajectory values were used to run with 1M = one-moment scheme, 2M = two-moment scheme, Tf = temperature fluctuation parameterization in CLaMS-Ice simulations, and noSHflux = no pseudo-mixing tendency included in CLaMS-Ice simulations.</p>
Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - trajectories employed in Markov State Models and water analyses
<p>The following trajectories were employed in the generation of MSM models and water analyses:</p> <p>SaFabI_60us_align.zip</p> <p>EcFabI_60us_align.zip</p> <p>waters_SaFabI.tar.gz</p> <p>waters_EcFabI.tar.gz</p>
Trajectories of backtracked passive particles for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the trajectories of bactracked passive particles using Ocean Parcels v2.0 (The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Delandmeter, P and E van Sebille (2019), <em>Geoscientific Model Development</em>, <em>12</em>, 3571–3584) and ocean surface velocity fields from the output of: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". </p> <p>trajectories_2009.tar: trajectories for particles released between 2009-01-01 and 2009-12-31</p> <p>trajectories_2010.tar: trajectories for particles released between 2010-01-01 and 2010-12-31 (as shown in "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea")</p> <p>trajectories_2011.tar: trajectories for particles released between 2011-01-01 and 2011-12-31</p> <p>release_sites.csv: release sites for each release date.</p> <p>ocean_parcels_backtrack_VIB_carib12.py : python script to run ocean parcels to generate the trajectories published here.</p>
Initial model and Molecular Dynamics trajectory of WGR domain of PARP2 with ZN ions
<div>Files:</div> <div>1. Initial model of PARP2 WGR domain (based on PDB entry: 6F5B) with two sites potentially capable of binding zinc ions: one formed by residues E97, C98, H160 and another - by residues H106, C109, E138</div> <div>2. PARP2 WGR domain with Zn ions bound after energy minimization.</div> <div>3. 400 ns MD trajectory of PARP2 WGR domain with Zn ions.</div>
Improving Volcanic SO2 Cloud Modeling Through Data Fusion and Trajectory Analysis: A Case Study of 2022 Hunga Tonga Eruption
<p><strong>Dataset Overview</strong>: This dataset comprises approximately 500 clusters of aggregated observational data collected from January 16 to 20 during the ascending (ASC) and descending (DES) periods. We grouped a large number of observation points into these clusters and calculated trajectories from the center of each cluster. The choice of 500 clusters was driven by pragmatic considerations, aiming for a balance between computational feasibility and the level of detail needed for our analysis.</p> <p><strong>Data Unit Description</strong>: The "mass" values in this dataset for each cluster are calculated by multiplying the mass per unit area (<span><span>g/m2</span></span>) of individual data points by the area covered by each point, thus providing the total mass in grams (g). The "heights" are presented in units of kilometers (km), representing the observed top heights of each cluster.</p>
DC-FSSH trajectory for 10D SBH model
<p>It contains H5MD files for 2000 DC-FSSH trajectories for 10 Dimensional Spin-Boson Hamiltonian with energy based SDM decoherence correction scheme with timestep 0.5 fs. The simulations are performed using Newton-X NS.</p>
Progeny Project Gromacs input and trajectories of a C12E6 surfactant model at the vacuum-water interface
<p>This is gromacs 2021.2 input and output for an all-atom C12E6 surfactant molecule at the water-vacuum<br> interface with TIP4P-ew and SPC/E water models.</p>
Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic
<p><span>Studies examining factors responsible for COVID-19 incidence </span><span>are</span><span> mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses. </span><span>Univariate analysis identified</span> <span>eleven variables as independently associated with COVID-19 infections at a global level (p<1e-05). Multivariable analysis identified a 4-variable model as optimal for explaining global variations in COVID-19 (p<0.01). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country.</span> <span>Globally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results </span><span>provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks.</span></p>
Deep Clinical Trajectory Modeling to Optimize Accrual to Cancer Clinical Trials
ClinicalTrials.gov study NCT06888089. IPD Sharing: YES. Countries: 1. Publications: 1.
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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.