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150 results for “Use of Force”
Input files for simulation of potassium channels using the AMOEBA polarizable force field
<p>This dataset contains input Tinker xyz and key files for the simulation of KcsA potassium channels in DOPC bilayer, a simple script for converting CHARMM pdb file to Tinker xyz file, and modified Tinker source code to support one-dimensional position restraints.<br> "params.tar.gz" contains a description of the force field modifications.<br> <br> To use "mod2", add the following lines to the key file.</p> <pre><code>#compatible with amoebabio18.prm polarize 5 1.4500 0.3900 3 polarize 11 1.4500 0.3900 9 polarize 3 1.7500 0.3900 1 5 7 50 225 227 polarize 9 1.7500 0.3900 1 7 11 50 225 227</code></pre> <p> </p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept
<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08 ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within the developed Digital Twin concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at https://gitlab.com/ptb/dcc.</p>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
Sensitivity maps of the Amundsen Sea Embayment to changes in external forcings using Automatic Differentiation
<p>Sensitivity maps of the final volume above flotation after 20 years to the basal friction coefficient, rheology factor, surface mass balance, and ocean-induced melting. These results were computed from STREAMICE and ISSM using automatic differentiation. See manuscript for complete description</p>
Illustrative dataset for Ozone radiative forcing calculations using SOCRATES-RF
<p>This dataset provides to the reader/user with two netCDF files which illustrate the structure and properties of the input datasets (used directly by the software SOCRATES-RF) in support of the publication: "<strong>Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database</strong>". It comprises two examples of January (pre-industrial decade, 1850s): one based on CMIP5 ozone concentrations and other based on the recently available CMIP6 ozone dataset. Both were created with the procedure described on the supplementary information of the publication "Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database", Checa-Garcia, R et al.</p> <p>The sources of information for these datasets are the CMIP5 / CMIP6 ozone dataset, the ERA-Interim reanalysis dataset (2000-01 to 2009-12) and the solar irradiance from SORCE and TIM projects. Please see the references:</p> <ul> <li>Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu, F., Bodeker, G. E., Shepherd, T. G., Shindell, D. T., and Waugh, D. W.: Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing, Atmos. Chem. Phys., 11, 11267-11292, https://doi.org/10.5194/acp-11-11267-2011, 2011.</li> <li>Hegglin, M. I., D. Kinnison, D. Plummer, R.Checa-Garcia et al., Historical and future ozone database (1850-2100) in support of CMIP6, GMD, in preparation.</li> <li>Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi: 10.1002/qj.828</li> <li>Kopp G., Heuerman K., Lawrence G. (2005) The Total Irradiance Monitor (TIM): Instrument Calibration. In: Rottman G., Woods T., George V. (eds) The Solar Radiation and Climate Experiment (SORCE). Springer, New York, NY</li> </ul>
Pure POPC Membrane Simulation Using Charmm-Drude Force Field with OpenMM
<p>400 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 72 POPC lipids and 2809 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>This dataset does not contain the water molecules.</p> <p><strong>Please note that</strong> the trajectories might need to be realigned.</p>
Climate Forced Hydropower Simulations Using NASA NEX-GDDP
<p>* This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset. </p> <p>This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099"). </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B. "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".</p>
Quantifying local stiffness and forces in soft biological tissues using droplet optical microcavities
<p>Dataset for publication Quantifying local stiffness and forces in soft biological tissues using droplet optical microcavities</p>
Simulations used in the Ocean Science Journal submission titled "Internal and forced ocean variability in the Mediterranean Sea " by Benincasa et al., 2024
<p>Temperature (votemper) and current speed datasets from the EAS5 (Clementi et al., <em>Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents, EAS5 system),</em> 2019; Coppini et al., <em>The Mediterranean forecasting system. Part I: evolution and performance</em>, EGUsphere, pp. 1–50, 2023) simulations used in the manuscript titled "<em>Internal and forced ocean variability in the Mediterranean Sea"</em> and submitted to the journal Ocean Science by Benincasa et al. </p> <p>The daily fields are at 2 depth levels ( 0 = 0 m, 2 = 30 m) and in the 2 seasons (JFMA = winter, JASO = summer) for the entire Mediterranean Sea. The vertical profile of the temperature field up to about 950 m depth is available at 8 locations distributed over the basin. The depth levels are found in <em>depth.pkl</em>: the first column represents the depth of the various levels, whereas the second is the increments between 2 consecutive depth levels. </p>
MD Simulation of AtALMT9 TMD Using Martini3 and charmm36 Force Field
<p>This dataset contains the MD simulation data associated with the article:</p> <p>"Structural basis for malate-driven, pore lipid-regulated activation of the Arabidopsis vacuolar anion channel ALMT9"</p> <p><em>(Not published yet)</em></p> <p> </p> <p>Folder</p> <p>AA : All-atom simulation files.</p> <p>CG : Coarse-grained simulation files.</p> <p>toppar : parameter files.</p> <p> </p> <p>File Description</p> <p>conf.pdb : Initial structure of the simulation.</p> <p>all.fit.10ns.now.zen.xtc : trajectory file without water. </p> <p>now.pdb : coordinate file of corresponding trajectory.</p> <p>topol.top : GROMACS topology file.</p> <p> </p> <p> </p>
Photo-thermal expansion of a PMMA nanosphere using mid-IR photo-induced force microscopy (PiF-IR)
<p>This dataset contains the raw data associated with our manuscript, <em>'Photo-thermal expansion of nanostructures in photo-induced force microscopy</em><strong>'</strong></p> <p>by Shohely Tasnim Anindo,1,2 Daniela Täuber,3,4 and Christin David*1,5</p> <div> <div> <div> <p>1 Institute of Condensed Matter Theory and Optics, Friedrich-Schiller-Universität Jena, Max-Wien-Platz 1, 07743 Jena, Germany<br>2 Abbe Center of Photonics, Albert-Einstein-Straße 6, 07745 Jena, Germany<br>3 Institute of Physical Chemistry, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany <br>4 Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany <br>5 University of Applied Sciences Landshut, Am Lurzenhof 1, 84036 Landshut, Germany</p> </div> </div> </div> <p>The raw data were acquired using a VistaScope (Molecular Vista, US) operated in the side-band mode of mid-infrared photo-induced force microscopy (PiF-IR). These data are associated with the experimental part in this manuscript. Details of the data acquisition and processing are described in the Methods section of the manuscript.</p> <p>The dataset is structured in the following:</p> <ul> <li>PiF-IR scans of a spherical PMMA nanoparticle with radius R = 50 nm (PMMA NP) at varied illumination power in the resonant condition using the illumination frequency: 1150 cm^-1</li> <li>PiF-IR scans of the same PMMA NP at varied illumination power in the non-resonant condition using the illumination frequency: 1300 cm^-1</li> <li>PiF-IR hyperspectral scan of the same PMMA NP over the spectral range 989 - 1349 cm^-1</li> </ul>
Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces
<p>Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.</p>
Extracting Session Keys From the Main Memory Using Brute-force and Machine Learning
<p>This dataset contains:</p> <ol> <li>Heap dump of three version of OpenSSH (V_7_9_P1, V_8_0_P1 and V_8_1_P1)</li> <li>Heap dump of two applications that uses TLS (lynx and curl)</li> <li>Network recording in format of pcap</li> <li>JSON file that contains the keys' information</li> </ol> <p>The source code is available at: https://github.com/smartvmi/SSH-TLS-key-extraction</p>
Micro-wear data from robotic use-wear experiments on force
<p>This data is the result of highly controlled experiments investigating the influence of force and duration on lithic micro-wear using a robot arm. Its targeted application is in archaeology and anthropology on the study of human tool use in the prehistory. The data consists of three zip files of html reports containing experimental data together with the microscopic images, and MATLAB scripts of the analysis methods. The images were collected at different stages of the experiment using a focus variation microscope, which produces true-color as well as topographic images. </p>
METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
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Land use and volcanic forcing files for past2k CESM simulation
<p>Data associated with the manuscript entitled "Asymmetric Cooling of the Atlantic and Pacific Arctic during the Past Two Millennia: A Dual Observation-Modeling Study".</p> <p> Transient simulations for the past 2,000 years (past2k) have been proposed for the Paleoclimate Model Intercomparison Phase 4 (PMIP4) contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Jungclaus et al., 2017). Forcing data for the past2k experiments was compiled by PMIP4. However, there was no land-cover change available for prior to 850 CE when this past2k CESM simulation was started. We therefore constructed a land cover forcing (Dataset1.tar) for this past2k simulation from 1 CE to 849 CE as a superposition of HYDE3.1 cropland changes (Goldewijk et al. 2010, 2011) onto the land cover forcing for the CESM Last Millennium Ensemble runs (LME, Otto-Blisner et al. 2016) for 850 CE. Land cover forcing for this past2k CESM simulation from 850 CE to 2005 CE is taken from LME, which implements grassland changes in addition to cropland changes as compiled by PMIP3.</p> <p>PMIP4 Easy Volcanic Aerosol data (EVA, Toohey et al. 2016) was used to generate volcanic aerosol forcing file for the past2k simulation with CESM. EVA provides space-time distribution of volcanic aerosol mass, effective radius, and optical depth. CESM reads in space-time distribution of volcanic aerosol mass, but assumes constant effective radius while computing optical depth. In order for CESM to match EVA optical depth, EVA aerosol mass needed to be scaled up by a factor of 1.67 based on a set of sensitivity experiments for the Tambora eruption in 1815. The forcing file (Dataset2.nc) is included for others to use.</p> <p>Data citation: Zhong, Y., Jahn, A., Miller, G. H., Geirsdottir, A. (2018). Asymmetric Cooling of the Atlantic and Pacific Arctic during the Past Two Millennia: A Dual Observation-Modeling Study. <em>Submitted to GRL</em>.</p>
Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'
<p>Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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