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1,670 results for “FORCE”
Forced Oscillations On The Orocopia Schist Under Dry Conditions
<p>Forced Oscillations on Orocopia Schist under Dry Conditions</p> <p>Test #187<br> Type of Experiment: Forced Oscillations</p> <p>Sample: Orocopia Schist (#4)</p> <p>Experimental Conditions: </p> <ul> <li>Confining pressure: 10 MPa</li> <li>Pore Pressure: 0 MPa (Dry)</li> <li>Seating Differential Stress: 10 MPa</li> <li>Amplitude of oscillating Stress: 1 MPa (Peak-to-peak)</li> <li>LVDTs: 2 high-resolution axial LVDTs but no radial LVDTs</li> </ul> <p>More details are in the Excel file, including the initial processing of the data before calibration and a list of experiments.</p> <p> </p>
Pure POPC Membrane with 1000mM NaCl simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 115 NaCl, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 450mM NaCl simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 51 NaCl, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 650mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 76 CaCl2, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p>The initial structures have been obtained from CHARMM-GUI.</p> <p> </p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPE membrane simulations with the CHARMM-Drude force field (OpenMM 7.5.0)
<p>MD simulation data of a pure POPE membrane with the CHARMM-Drude force field generated with the OpenMM 7.5.0 simulation engine.</p> <p>All the input parameters are available in the *inp file. The initial structures have been obtained from CHARMM-GUI.</p> <p>Total simulation duration is 300 ns (100 ns x 3, continuing from the last frame, combined with the mdconvert). First 50 ns is discarded as equilibration. This data set contains 300 ns data with 3000 frames (saving frequency is 100 ps).</p> <p>In total, 72 POPC lipids in each leaflet (144 in total), 5040 SWM4-NPD water molecules.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "unwrapped_all_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 100 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 3 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p> </p> <p> </p>
Pure POPC Membrane with 350mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 41 CaCl2, and 6400 SWM4 water molecules.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC membrane simulations with 1000 mM CaCl2 with the CHARMM-Drude force field (OpenMM)
<p>400 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 114 CaCl2, and 6400 SWM4 water molecules.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset. Total simulation time is 500 ns, included data is 397.5 ns.</p> <p> </p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps and there are 39750 frames.</strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 4 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p><strong>Centering of the trajectories has been done via below MDAnalysis script</strong></p> <p><strong> ...: u = mda.Universe('../step3_charmm2omm.psf', 'step5.dcd')<br> ...: prot = u.select_atoms("resname POPC")<br> ...: ag = u.atoms<br> ...: workflow = (transformations.unwrap(ag),<br> ...: transformations.center_in_box(prot, center='mass'),<br> ...: transformations.wrap(ag, compound='fragments'))<br> ...: u.trajectory.add_transformations(*workflow)</strong></p> <p> </p>
Pylake tutorial dataset: Force extension curves
<p>Force extension curves of various constructs acquired on a LUMICKS C-Trap.</p> <p>The constructs are</p> <ul> <li>a DNA Hairpin</li> <li>Adenylate Kinase with DNA handles</li> <li>DNA in the presence and absence of RecA</li> </ul>
Band Excitation Piezoresponse Force Microscopy of PbZrTiO3
<p>400 nm thick (001)-oriented PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub>/30 nm Ba<sub>0.5</sub>Sr<sub>0.5</sub>RuO<sub>3</sub>/NdScO<sub>3</sub> epitaxial thin films were grown using pulsed laser deposition. The films have been the subject of prior studies (<em>9</em>, <em>15</em>, <em>22</em>). These films exist in a typical tetragonal ferroelectric phase. NdScO<sub>3</sub> imposes a large tensile strain that drives a strain-induced spinodal instability driving a hierarchical <em>c/a/c/a</em> and <em>a<sub>1</sub>/a<sub>2</sub>/a<sub>1</sub>/a<sub>2</sub></em> domain structure (<em>29</em>). These domains have large variations in piezoresponse and switching mechanisms. Within the primary out-of-plane polarized <em>c/a/c/a,</em> there is a large vertical piezoresponse and classical ferroelectric switching mechanisms. Conversely, there is suppressed vertical piezoresponse in the <em>a<sub>1</sub>/a<sub>2</sub>/a<sub>1</sub>/a<sub>2</sub></em> domains. Since this material is at an energetic degeneracy between the <em>c</em>/<em>a</em>/<em>c</em>/<em>a</em> and <em>a<sub>1</sub></em>/<em>a<sub>2</sub></em>/<em>a<sub>1</sub></em>/<em>a<sub>2,</sub></em><sub> </sub>applying bipolar-triangular switching waveforms results in a two-step, three-state ferroelastic switching process. The films topography has a sawtooth-like structure with an amplitude of ~4 nm and a periodicity of ~900 nm because of the tetragonality and large difference in crystallographic orientation of domain variants. Furthermore, the highly asymmetric elastic modulus tensor results in elastic modulus variations by up to 23%. Further details regarding the structure, properties, and switching mechanisms of this material can be obtained in prior reports (<em>9</em>, <em>15</em>, <em>22</em>). The hierarchical PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> provides a model system to stress test Band-excitation piezoresponse force microscopy (BE-PFM) due to the highly variable piezoresponse, switching mechanisms, and elastic modulus. The dataset used is an original creation of the authors, has been the subject of prior reports, and publicly released under the open-source creative commons attribution 4.0 License (<em>30</em>).</p>
Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies
<p>Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below</p> <p>Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at <a href="https://arxiv.org/abs/2301.00496">https://arxiv.org/abs/2301.00496</a>.</p> <p>Code found at https://doi.org/10.5281/zenodo.7796266.</p> <p> </p>
Optimized OPEP Force Field for Simulation of Crowded Protein Solutions
<p>Initial structures (in the PDB format) and LBMD trajectories (in the GROMACS XTC format) of crowded protein solutions simulated using the OPEPv7 force field and presented in the article entitled <em>Optimized OPEP Force Field for Simulation of Crowded Protein Solutions </em>(<a href="https://doi.org/10.1021/acs.jpcb.3c00253">https://doi.org/10.1021/acs.jpcb.3c00253</a>).</p>
Spatio-temporal dynamics of the proton motive force on single bacteria - dataset
<p>Data set used in our manuscript "Spatio-temporal dynamics of the proton motive force on single bacteria" [<a href="https://www.biorxiv.org/content/10.1101/2023.04.03.535353v1">Biorxiv</a>].</p> <p> </p> <p>To produce fig1 and fig2, unzip file in bash:</p> <pre><code class="language-bash">$ 7z e data_fig1_fig2.7z</code></pre> <p>Open fig1 data in python:</p> <pre><code class="language-python">> b = pickle.load(open('fig1.p', 'rb')) > b {'speed_Hz': array([-34.01139986, 13.07744575, 79.66060694, ..., -4.79440373, -4.88539275, -0.1366687 ]), 'speed_Hz_f': array([-11.11722186, 15.74953016, 32.24685265, ..., 20.11726694, -3.54263861, -36.89432049]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 5000.0}</code></pre> <p>where</p> <p>b['speed_Hz'] : speed trace in Hz</p> <p>b['speed_Hz_f'] : speed trace in Hz, savgol filtered (5th order, 41 points)</p> <p>b['laser'] : laser trace in arbitrary units</p> <p>b['FramesPerSecond'] : camera frame acquisition rate</p> <p> </p> <p>Open fig2 data:</p> <pre><code class="language-python">> a = pickle.load(open('fig2.p','rb')) > a {11: {'speed_Hz': array([ 16.2828179 , 38.42508915, 94.68772452, ..., -12.24519659, 160.68335892, 114.39589274]), 'speed_Hz_f': array([ 25.8833788 , 31.87792607, 37.33062434, ..., 66.97264585, 91.38908923, 122.73732123]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 12: {'speed_Hz': array([ 57.41541418, 24.4936895 , 248.68571544, ..., 86.08667522, -46.11733599, 18.16993041]), 'speed_Hz_f': array([102.04557049, 91.82376088, 84.51288552, ..., 33.63965888, 16.31075159, -5.36834171]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 21: {'speed_Hz': array([ -57.50587524, 74.55546084, 65.87878605, ..., -197.26554361, 140.95754077, 52.72452236]), 'speed_Hz_f': array([-24.36465769, 22.9146126 , 49.48957346, ..., 47.67536653, 43.44702708, 36.80127664]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 22: {'speed_Hz': array([ 56.78413066, -147.32742392, -14.28783413, ..., -29.51455248, 15.66125098, 41.38001828]), 'speed_Hz_f': array([-23.57073686, -18.61154574, -15.85582681, ..., 7.05157621, 18.45809539, 37.52083946]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}} </code></pre> <p>where</p> <p>a[11] : dictionary for motor 1 trace, laser on motor 1, composed as above.</p> <p>a[12] : dictionary for motor 1 trace, laser on motor 2.</p> <p>a[21] : dictionary for motor 2 trace, laser on motor 1.</p> <p>a[22] : dictionary for motor 2 trace, laser on motor 2.</p>
Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021
<p>data for Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021</p>
Data from: Global change in brain state during spontaneous and forced walk in Drosophila is composed of combined activity patterns of different neuron classes
<p><span>Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole-brain lightfield imaging in adult <em>Drosophila </em>to investigate the relationship between walk and brain-wide neuronal activity. We observed a global change in activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual candidate neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to global activity of all major neuronal classes.</span></p>
Top-of-the-atmosphere radiative forcing by aerosol due to continuous OCS injection near the tropical tropopause simulated by EMAC
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 4.</p> <p>The dataset contains additional stratospheric aerosol forcing for injections of 6 Tg S a<sup>-1</sup> OCS for several years over 5 tropical cities at the tropopause (97 hPa) calculated with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) CCM (e.g. Brühl et al., 2018; Schallock et al., 2023). Data are given for a four year time series starting in January 2017.</p> <p>- - - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> time, 10 hourly, as 'day since 1997-01-01' (note that the two variables named time4 and time6 are identical)</p> <p>Parameters:</p> <p> SOLFORCCSO: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> TOTFORCCSO: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> SOLFORCCSO_FREE: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p> TOTFORCCSO_FREE: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p>- - - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Brühl, C., Schallock, J., Klingmüller, K., Robert, C., Bingen, C., Clarisse, L., Heckel, A., North, P., and Rieger, L.: Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using Aerosol CCI satellite data, Atmos. Chem. Phys., 18, 12845-12857, 10.5194/acp-18-12845-2018, 2018</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Schallock, J., Brühl, C., Bingen, C., Höpfner, M., Rieger, L., and Lelieveld, J.: Reconstructing volcanic radiative forcing since 1990, using a comprehensive emission inventory and spatially resolved sulfur injections from satellite data in a chemistry climate model, Atmos. Chem. Phys., 23, 1169-1207, 10.5194/acp-23-1169-2023, 2023.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
charmm2gmx: An Automated Method to Port the CHARMM Additive Force Field to GROMACS
<p>Validation dataset for the paper "charmm2gmx: An Automated Method to Port the CHARMM Additive Force Field to GROMACS". The dataset includes molecular dynamics input and output files, as well as scripts for running the simulations and analyzing the results, used for validating the ported CHARMM parameters.</p> <p>CHARMM is one of the most widely used biomolecular force fields. Although developed in close connection with a dedicated molecular simulation engine of the same name, it is also usable with other codes. GROMACS is a well-established, highly optimized and multi-purpose software for molecular dynamics, versatile enough to accommodate many different force field potential functions and the associated algorithms. Due to conceptional differences related to software design and the large amount of numeric data inherent to residue topologies and parameter sets, conversion from one software format to another is not straightforward. Here, we present an automated and validated means to port the CHARMM force field to a format read by the GROMACS engine, harmonizing the different capabilities of the two codes in a self-documenting and reproducible way, with a bare minimum of user interaction required. Being based entirely on the upstream data files, the presented approach does not involve any hard-wired/boilerplate code, in contrast with previous attempts to solve the same problem. The heuristic approach used for perceiving local internal geometry is directly applicable for analogous transformations of other force fields.</p> <p> </p>
CICE gx3 JRA55do Forcing Data - 2023.07.03
<p>This file contains the gx3 JRA55do forcing data for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1).</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
An axisymmetric impinging jet under two-frequency external forcing: animations of low-order reconstruction from DMD modes of PIV data
<p>Animations of the low-order reconstruction of vortex structures dynamics in an impinging turbulent jet under external periodic forcing. The reconstruction is obtained from DMD modes of time-resolved PIV data. The flow cases correspond to the unmodulated (main case) and modulated amplitude of the forcing frequency. The modulation frequencies are set to as 1/2, 1/3, 1/4, 1/5 and 1/8 of the main one, respectively.</p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
Dataset to the article "Omnidirectional endpoint force control through Functional Electrical Stimulation" by Marek Sierotowicz and Claudio Castellini
<p>The present .zip files contains the anonimized datasets of a static force output control test facilitated through FES.</p> <p>In addition, the DataExtraction MATLAB code is meant to provide the synchronisation offsets for the logs.</p> <p>The data is sampled from 3 anonymized participants, who all signed informed consent forms.</p> <p>The experiment was carried out in compliance with the standards of the Declaration of Helsinki, and was approved by the ethical committee of the Friedrich-Alexander Universität Erlangen-Nürnberg (approval ID 22-304-S).</p> <p> </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
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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
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