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11,506 results for “Dynamics”
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 1)
<p>Replicate simulation 1/4</p> <p>Found here are all files needed to reproduce or visualize the results of molecular dynamics simulation of the SpoIVFB intramembrane protease bound to the transcription factor Pro-sigmaK. The protein complex was embedded in a POPE_POPG_DAG_CL bilayer using CHARMM-GUI and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p>Individual output (.out) and trajectory (.dcd) files are provided for each checkpoint of the simulation. A combined trajectory containing 250 ns of unrestrained simulation is also provided (combined_250ns_traj.dcd). Together with the step5_input.psf file, this combined dcd file can be used with common software such as VMD to visualize the molecular dynamics trajectory.</p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Dynamical System Multivariate Time Series
<p>The Dynamical System Multivariate Time Series (DSMTS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system under fully nominal conditions (no outliers or anomalies).</p> <p>The DSMTS Dataset exhibits a set of desirable properties that make it <strong>very suitable for benchmarking Multivariate Time Series Forecasting </strong>especially for industrial processes of complex systems:</p> <ul> <li><strong>Multivariate (17 variables)</strong> including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including: <ul> <li><strong>4 Deliberate Actuations / Control Commands sent by a simulated operator / controller</strong>, for instance, commands of an operator to turn ON/OFF some equipment.</li> <li><strong>3 Environmental Stimuli / External Forces</strong> acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.</li> <li><strong>10 Telemetry Readings representing the observable states of the complex system by means of sensors</strong>, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.</li> </ul> </li> <li><strong>5 million timestamps</strong>. Sensors readings are at 1Hz sampling frequency. </li> <li><strong>Pure signal ideal for robustness-to-noise analysis.</strong> The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.</li> <li><strong>No missing data</strong>. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.</li> </ul>
Supplementary material for "Dynamics of a goshawk population across half a century is driven by the variation of first-year survival"
<p><strong><span>Abstract</span></strong></p> <p><span>Population dynamics are driven by stochastic and density-dependent processes acting on demographic rates. Individuals differ demographically, and to capture these differences, models of population dynamics are usually structured by age and stage, rarely by sex. An effect of sex on population dynamics is expected if the dynamics of males and females differ, requiring an unequal sex ratio at birth and/or sex-specific survival probabilities. Goshawks (<em>Accipiter gentilis</em>) show large sexual size dimorphism and differential survival, but it is unknown whether males and females contribute differently to population dynamics. We studied a goshawk population in northern Germany over 47 years using brood monitoring data, collected feathers and nestling ringing data. We jointly analyzed the data using a two-sex integrated population model and performed retrospective and prospective population analyses to understand whether the demographic drivers of population change differ between the sexes. The population showed large fluctuations, during which the number of breeding pairs doubled, but the long-term trend of the population was slightly negative. Female survival exceeded male survival during the first year of life. Females started to reproduce at a younger age than males, productivity increased with female age, the sex ratio of nestlings was male biased and there was moderate male immigration. Despite these differences, temporal variation in sex ratio did not contribute to population dynamics and the contribution of temporal variation in survival was similar for both sexes. Variation in first-year survival was the strongest driver in this population, regulated by a weak density-dependent feedback acting through female first-year survival. Overall, the contributions of the two sexes to population dynamics were similar in this monogamous species with strong sexual size dimorphism.</span></p> <p><strong><span>Read me</span></strong></p> <p><span>Data files and code for carrying out all analyses and generating all figures presented in the paper. The six data files are provided in csv format. There are five code files written for R, but some of the main analyses require the NIMBLE software. The main code (IPM_Code.txt) contains a description of the data, code for loading and managing the data, and for fitting the integrated population models. The other files contain custom written functions (Functions.txt), code for performing posterior predictive tests (PPT_Code.txt), code for reporting results and performing various other analyses (Output_analyses_Code.txt), and code for generating figures (Figures_Code.txt).</span></p>
Data package for "Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation"
<p>This dataset contains supporting data for the publication:</p> <p><em>Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation</em></p> <p>A. Castagnède, L. Filion, and F. Smallenburg, J. Chem. Phys. 160 (2024), doi:10.1063/5.0209178, arXiv:2403:12755</p> <p> </p> <p><strong>Contents:</strong></p> <p>The main folder <em>data_package</em> contains three subfolders: <em>figures</em>, <em>SLNN</em>, and <em>snapshots</em>. The <em>figures</em> subfolder contains supporting data for each of the figures found in the publication, accompanied by details on statepoints and methods in individual README files. The <em>SLNN</em> subfolder contains the trained neural network classifier used in this work for crystalline phase identification, alongside usage instructions and an exemple system to analyze. Finally, the <em>snapshots</em> subfolder contains supplementary snapshots of the crystalline clusters obtained in simulations. </p> <p> </p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Data for: Nonlinear dynamics of the near-shore boundary layer of a large lake (Lake Geneva)
<p>We examined near-shore and pelagic current variability in Lake Geneva, a large and deep lake in western Europe, using observations from several measurement locations and a three dimensional numerical model, based on the period 2014–2016. The data include measurements from Acoustic Doppler Current Profiler (ADCP) campaigns and vertical temperature profiles along with the corresponding modelling results. The model used in this study is based on the MIT General Circulation Model (MITgcm, <a href="http://mitgcm.org/">http://mitgcm.org/</a>, <a href="https://doi.org/10.1029/96JC02775">https://doi.org/10.1029/96JC02775</a>).</p>
Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% POPS with Na+ counterions using ff99 ions
<p><strong>System: </strong>Symmetric bilayer of anionic POPS (palmitoyl-oleoyl-phosphatidylserine 100 mol-%) lipids with sodium (Na<sup>+</sup>) counter ions.</p> <p><strong>Number of POPS:</strong> 128.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 128.<br> <strong>Number of waters:</strong> 4480.</p> <p><strong>Lipid model:</strong> Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)].</p> <p><strong>Ion model:</strong> Amber ff99 [J Åqvist <em>J. Phys. Chem.</em> <strong>94</strong> 8021 (1990)].</p> <p><strong>Water model:</strong> TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein <em>J. Chem. Phys.</em> <strong>79</strong> 926 (1983)].</p> <p><strong>Simulation engine:</strong> Amber16 [DA Case et al. <em>AMBER 2017</em> UCSF (2017)].</p> <p><strong>Number of independent repeats per setup: </strong>2.<br> <strong>Trajectory lengths per repeat:</strong> 400 ns + 100 ns.<br> <strong>Previously equilibrated for:</strong> 100 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Langevin' at T = 298 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984); <em>J. Chem. Phys.</em> <strong>103</strong> 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993);<em> J. Chem. Theory Comput.</em> <strong>9</strong> 3878 (2013)].<br> <strong>Van der Waals:</strong> Turned off between 1.0 nm and 1.5 nm.</p> <p><strong>Constraints: </strong>Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [<em>J. Comput. Phys.</em> <strong>23</strong> 327 (1977)], in water using SETTLE [<em>J. Comput. Chem. </em><strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).</p>
Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% DOPS with Na+ counterions using Joung-Cheetham Ions
<p><strong>System: </strong>Symmetric bilayer of anionic DOPS (1,2-Dioleoyl-<em>sn</em>-glycero-3-phosphoserine 100 mol-%) lipids with sodium (Na<sup>+</sup>) counter ions.</p> <p><strong>Number of DOPS:</strong> 128.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 128.<br> <strong>Number of waters:</strong> 4480.</p> <p><strong>Lipid model:</strong> Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)].</p> <p><strong>Ion models: </strong>Joung–Cheatham [IS Joung, TE Cheatham III <em>J. Phys. Chem. B </em><strong>112</strong> 9020 (2008)].</p> <p><strong>Water model:</strong> TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein <em>J. Chem. Phys.</em> <strong>79</strong> 926 (1983)].</p> <p><strong>Simulation engine:</strong> Amber16 [DA Case et al. <em>AMBER 2017</em> UCSF (2017)].</p> <p><strong>Number of independent repeats per setup: </strong>2.<br> <strong>Trajectory lengths per repeat:</strong> 400 ns + 100 ns.<br> <strong>Previously equilibrated for:</strong> 100 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Langevin' at T = 303 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984); <em>J. Chem. Phys.</em> <strong>103</strong> 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993);<em> J. Chem. Theory Comput.</em> <strong>9</strong> 3878 (2013)].<br> <strong>Van der Waals:</strong> Turned off between 1.0 nm and 1.5 nm.</p> <p><strong>Constraints: </strong>Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [<em>J. Comput. Phys.</em> <strong>23</strong> 327 (1977)], in water using SETTLE [<em>J. Comput. Chem. </em><strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).</p>
A molecular dynamics study of adenylyl cyclase: the impact of ATP and G-protein binding
<p>Adenylyl cyclases (ACs) catalyze the biosynthesis of cyclic adenosine monophosphate (cAMP) from adenosine triphosphate (ATP) and play an important role in many signal transduction pathways. The enzymatic activity of ACs is carefully controlled by a variety of molecules, including G-protein subunits that can both stimulate and inhibit cAMP production. Using homology models developed from existing structural data, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase and on its complexes with ATP and with the stimulatory G-protein subunit Gsα. The results show that both ATP and Gsα binding have significant effects on the structure and flexibility of adenylyl cyclase. New data on ATP bound to AC5 in the absence of Gsα notably help to explain how Gsα binding enhances enzyme activity and could aid product release. Simulations also suggest a possible coupling between ATP binding and interactions with the inhibitory G-protein subunit Gαi.</p> <p>All-atom molecular dynamics simulations were performed with the GROMACS 5 package. The simulations were carried out in an NTP ensemble at a temperature of 310 K and a pressure of 1 bar using a Bussi velocity-rescaling thermostat (t<sub>T</sub> = 1 ps) and a Parrinello-Rahman barostat (t<sub>P</sub> = 1 ps). We provide the atomistic trajectories of the following 6 systems after 400 ns of equilibration:</p> <ul> <li>AC5</li> <li>AC5+ATP</li> <li>AC5+Gsα</li> <li>AC5+ATP+Gsα</li> <li>AC5+FOK</li> <li>AC5+ATP+FOK</li> </ul> <p>In each trajectory, the frames are saved each 20 ps.</p>
Deciphering hot- and multi-exciton dynamics in core–shell QDs by 2D electronic spectroscopies
<p>2D spectroscopy datasets from PCCP 20 (2018) 18176, DOI: 10.1039/c8cp02574f</p> <p>Dasets are in the Matlab format .mat, each one containing:</p> <p>R(or N or T).X = 3-dimensional matrix containing 3d signal. dimensions=(w1,w3,t2)<br> R.t= t2 axis<br> R.f= w1=w3 axis</p> <p> </p> <p>R=rephasing; N=non-rephasing; T=total signal</p> <p>2D-BC=2D photon echo in BOXCARS configuration; 2D-PP= 2D pump-probe in quasi-collinear configuration.</p>
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
FLAVIC DYNAMICS - 703519
<p>Labeling Flavivirus and its dynamics during infection by fluorescence microscopy</p> <p>The major goal of this project was to understand the delivery of the <em>flavivirus</em> genome to its translation site at the cytoplasmic side of the ER membrane; the cellular components, the pH and machineries that intervene during this process.</p> <p>This dataset includes microscopy acquisitions from Yellow fever virus. Titration of virus stocks. And flow cytometry files of yellow fever infected cells. Control MOCK samples are also included in the dataset.</p>
Groundwater dynamics indices
<p>Data set to reproduce results from Heudorfer et al., 2019 (submitted) containing:</p> <ul> <li>45 index values of 751 daily groundwater level time series from Central Europe, raw and scaled.</li> <li>Controls used in chapter 5 and Figure 8</li> </ul> <p>Data is a R data.frame object saved in an uncompressed RDS file. Can be easily read into R with readRDS in <em>base</em> R or read_rds with the packages <em>readr</em>.</p> <p>(to be updated, when paper is published)</p>
Supporting data for "Quantifying the Strength of a Salt Bridge by Neutron Scattering and Molecular Dynamics"
<p>Supporting data for the following published paper: Mason, Jungwirth, Duboué-Dijon, 2019, JPhysChemLett, 10, 3254-3259</p> <p>Contains both data from neutron scattering measurements and input simulation files necessary for reproduction of the work.</p>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</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.