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1,670 results for “FORCE”
The bite force-gape relationship as an avenue of biomechanical adaptation to trophic niche in two salmonid fishes
<p>All skeletal muscles produce their largest forces at a single optimal length, losing force when stretched or shortened. In vertebrate feeding systems, this fundamental force-length relationship translates to variation in bite force across gape, which affects the food types that can be eaten effectively. We measured the bite force-gape curves of two sympatric species: king salmon (<em>Oncorhynchus tshawytscha</em>) and pink salmon (<em>O. gorbuscha</em>). Cranial anatomical measurements are not significantly different between species, however, peak bite forces are produced at significantly different gapes. Maximum bite force is achieved at 67% of maximum gape for king salmon and 43% of maximum gape for pink salmon. This may allow king salmon to use greater force when eating large or elusive prey. In contrast, pink salmon do not require high forces at extreme gapes for filter feeding. Our results illustrate that the bite force-gape relationship is an important ecophysiological axis of variation.</p>
Supplementary datasets for: Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution
<p>Microalgae are integral primary producers for global ecosystems whose genomes can be mined for ecological insights, but representative genome sequences are lacking for many phyla. We cultured and sequenced 107 microalgae species from 11 different phyla indigenous to varied geographies and climates. This genome collection was used to resolve genomic differences between saltwater and freshwater microalgae. Freshwater species showed domain-centric ontology enrichment for nuclear and nuclear membrane functions, while saltwater species were enriched in organellar and cellular membrane functions. Marine species contained significantly more viral families in their genomes (<span>p-value = 8 x 10(-4))</span>. Viral sequences were identified from Chlorovirus, Coccolithovirus, Pandoravirus, Marseillevirus, Tupanvirus, and others integrated into algal genomes. Algal, viral-origin sequences were found to be expressed and to code for a wide variety of functions. Our results clarify the poorly characterized occurrences of viral elements in algal genomes and define a unified adaptive strategy for algal halotolerance.</p>
Raw data accompanying: Ground reaction forces in monitor lizards (Varanidae) and the scaling of locomotion in sprawling tetrapods
<p>Geometric scaling predicts a major challenge for legged, terrestrial locomotion.<b> </b>Locomotor support requirements scale identically with body mass (α M<sup>1</sup>), while force generation capacity should scale α M<sup>2/3</sup> as it depends on muscle cross-sectional area. Mammals compensate with more upright limb postures at larger sizes, but it remains unknown how sprawling tetrapods deal with this challenge. Varanid lizards are an ideal group to address this question because they cover an enormous body size range while maintaining a similar bent-limb posture and body proportions. This study reports the scaling of ground reaction forces and duty factor for varanid lizards ranging from 7 g 37 kg. Impulses (force x time) scaled roughly as predicted by the inverted pendulum model (α M<sup>0.99-1.34</sup>) while peak forces (α M<sup>0.73-1.00</sup>) scaled higher than expected. Duty factor scaled α M<sup>0.04 </sup>and was higher for the hindlimb than the forelimb. The proportion of vertical impulse to total impulse increased with body size, and impulses decreased while peak forces increased with speed. These results provide valuable data into how locomotor forces vary with body size and suggest how other, extinct, sprawling tetrapods may have dealt with the biomechanical challenges associated with generating sufficient locomotor forces at larger body sizes.</p>
Data from: Climate warming prolongs the time interval between leaf-out and flowering in temperate trees: effects of chilling, forcing and photoperiod
<p><span>1. Leaf-out and flowering are two key phenological events of plants, denoting the respective onsets of visible vegetative growth and reproduction during the year. For each species, the schedule of vegetative growth and reproduction is crucial to the maximization of its fitness. Warming-induced advances of leaf-out and flowering have been reported frequently, however, it is unclear whether the responses of the two events are equal for any given species. </span></p> <p><span>2. Using long-term phenological records in Europe, we examined simultaneously the responses of both leaf-out and flowering of four common temperate tree species to climate warming and further examined the effects of winter chilling, spring forcing and photoperiod on the responses of the two events. </span></p> <p><span>3. We found that regardless whether flowering or leaf-out occurred first, the first event advanced more than the second during 1950 – 2013, resulting in a prolonged time interval between the two events. The temporal changes were also supported by a similar geographical trend that the time interval between the two events increased from cold to warm sites. Due to the warming-induced reduction in chilling, the spring forcing accumulated until the second event was increased more than the forcing accumulated until the first event, and that reduced the temperature sensitivity of the second event. In addition to the effect of chilling, the shorter photoperiod, associated with the advanced spring phenology, was also likely to substantially increase the spring forcing accumulated until the second event, which thus slowed down its advance, compared to the advance of the first event. The relative contributions of chilling and photoperiod to the increased forcing varied between species and events, with chilling mostly outweighing photoperiod. </span></p> <p><span>4. Synthesis. This study provides the large-scale empirical evidence of prolonged time interval between leaf-out and flowering with climate warming. The unequal advances of the two events may alter the partition of resources between vegetative growth and reproduction and cause different changes of spring frost damage to vegetative and reproductive tissues, which may alter species fitness and further affect ecosystem structure and function.</span></p>
Molecular geometries and energies from quantum mechanical calculations and small molecule force field evaluations.
<p>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction.<br> The quality of these studies relies on the quality of the force fields used to represent the systems. <br> Focusing on small molecules of fewer than 50 heavy atoms, this data compares nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2.<br> On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data.<br> <br> The data was created using scripts of the <a href="https://github.com/MobleyLab/benchmarkff/commit/fa45247aa9867f504c02eb6f62d8459a94a0a936">benchmarkff github repository</a>.</p> <p>A corresponding manuscript is submitted, a preprint is available on ChemRxiv:<br> <a href="https://doi.org/10.26434/chemrxiv.12551867.v2 ">Lim, Victoria T.; Hahn, David F.; Tresadern, Gary; Bayly, Christopher I.; Mobley, David (2020): Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields. ChemRxiv. Preprint</a></p> <p>Read below or the file README.md for further information and description of the content:</p> <pre><code class="language-markdown"># README Version: 04 Nov 2020 For Python scripts that are NOT found in these directories, please check the [BenchmarkFF Github repo](https://github.com/MobleyLab/benchmarkff/tree/master/tools). ## Procedure 1. Prep OPLS3e file for analysis: standardize format by OpenEye in case of differences and convert from kJ/mol to kcal/mol. ``` cd prep python convert_extension.py -i opls3e_minimized.sd -o opls3e.sdf ``` 2. Remove mols that couldn't parameterize by ALL FFs. ``` python get_by_tag.py -i opls3e.sdf -s "SMILES QCArchive" -list trim3.txt -o trim3_full_opls3e.sdf ``` 3. Run analysis. ``` conda activate parsley # calc ddE, RMSD, and TFD distributions python compare_ffs.py -i match.in -t 'SMILES QCArchive' --plot > metrics.out # match_minima, only in 01_analysis_all and 02_analysis_all_smaller_cutoff python match_minima.py -i match.in --plot --cutoff 1.0 --readpickle # look at specific subsets, only in 01_analysis_all python color_by_moiety.py -i match.in -p metrics.pickle -s N-N.dat azetidine.dat octahydrotetracene.dat -o scatter_tfd_3_ # look at outliers,only in 01_analysis_all and 02_analysis_all_smaller_cutoff python tailed_parameters.py -i refdata_trim_overlap_full_openff_unconstrained-1.2.0.sdf -f <offxml file> --metric 'TFD' --cutoff 0.12 --tag "TFD to trim_overlap_full_qcarchive.sdf" --tag_smiles "SMILES QCArchive" > output_tfd.dat ``` ## Brief description of contents * High level: ``` . ├── 00_prep │ ├── convert_extension.py │ ├── opls3e_minimized.sd OPLS3e minimized structures from Schrodinger Maestro │ ├── opls3e.sdf standardized through OpenEye tools │ ├── opt_openff*.sdf OpenFF minimized conformations ├── 01_analysis_all compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) ├── 02_analysis_all_smaller_cutoff compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) with a smaller cutoff of .3 for match_minima ├── 03_analysis_latest_ffs compare only the latest versions of ffs (qm, GAFF2, MMFF94S, OpenFF-1.2, OPLS3e) ├── 04_analysis_openff_only compare only OpenFF ffs (qm, Smirnoff, OpenFF-X.X) └── README.md ``` * Inside an output directory: ``` YY_analysis_* various output files of above mentioned scripts, some are listed and described below: ├── bar*.png parameter coverage bar plots ├── ddE.dat relative energies data ├── fig_density_*.png scatter plots of ddE vs (RMSD or TFD) for each force field ├── match.in input file for compare_ffs.py ├── metrics.out output file for compare_ffs.py ├── metrics.pickle pickle file for compare_ffs.py -- you can read this into compare_ffs instead of rerunning the full analysis ├── refdata_*.sdf output SDF files with stored RMSD / TFD scores with reference to QM for each structure ├── relene_*.dat relative energies of matched conformers ├── ridge_dde.png compared energies plot ├── ridge_rmsd.svg compared rmsds plot ├── ridge_tfd.svg compared tfds plot ├── fig_scatter_*.png scatter plots of ddE vs (RMSD or TFD). these are noisy; I don't use these ├── trim3_*.sdf input SDF files for compare_ffs.py listed in match.in file ├── violin*.* violin plot showing ddE distributions ``` </code></pre>
D-JRP14-WP0.3 Data sharing in Full Force - recorded webinar
<p>A recorded presentation on the data sharing platform OwnCloud, used during the full force project. </p>
FORCE 2020 Well well log and lithofacies dataset for machine learning competition
<p>This well log dataset from 118 wells in the Norwegian Sea that has been used in the FORCE 2020 machine learning competition with seismic and wells to predict the lithofacies using machine learning models. </p> <p>The well logs have been slightly cleaned up and partially despiked.</p> <p>The lithofacies and lithology interpretation has been hand crafted using skilled geoscientists (Thanks to Explocrowd for excellent work). For citation in addition to the DOI please also refer to the github repository where the documentation and trained models reside</p> <p><a href="https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition">https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition </a></p> <p> </p> <p>The original well log data comes form the Norwegian government and is provided by a NOLD 2.0 license</p> <p> </p>
Pure POPC membrane simulations using Amber Lipid 14 Force Field
<p>Pure POPC membrane simulations using the Amber Lipid 14 force field.</p> <pre>@article{dickson2014lipid14, title={Lipid14: the amber lipid force field}, author={Dickson, Callum J and Madej, Benjamin D and Skjevik, {\AA}ge A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C}, journal={Journal of chemical theory and computation}, volume={10}, number={2}, pages={865--879}, year={2014}, publisher={ACS Publications} }</pre> <p>The trajectories are centered such that the center of mass of the lipid tails are at the origin. <strong>Please check the imaging again to make sure that there are no problems. </strong></p> <p><strong>The trajectories do not contain water molecules.</strong> </p> <p>Simulation Details:</p> <p>Lipids : 72 POPC lipids, 36 per leaflet</p> <p>Water: 9560 TIP3P water molecules (<strong>water coordinates are not saved</strong>)</p> <p>Temperature: 303 K</p> <p>Pressure: 1 bar</p> <p>Thermostat: Langevin</p> <p>Barostat: Berendsen</p> <p>Pressure coupling: Semi-isotropic</p> <p>Trajectory Length: 100 ns (after 100 ns pre-equilibration)</p> <p>Saving frequency: 100 ps</p> <p>Further details are available at the 04_Run.in file</p> <p>All trajectories started from the same structure but equilibriated for 100 ns independently (using 03_Hold.in)</p>
SAXS calculations for Refinement of 𝛼-synuclein ensembles against SAXS data: Comparison of force fields and methods
<p>SAXS curves calculated from MD simulations used as input to reweighting as described in preprint Refinement of 𝛼-synuclein ensembles against SAXS data: Comparison of force fields and methods</p>
A Laboratory Forced-Oscillation Apparatus
<p>In this file, we present supplementary materials for the paper titled </p> <p><em><strong>"A Laboratory Forced-Oscillation Apparatus for Measurements of Elastic and Anelastic Properties of Rocks at Seismic Frequencies" </strong></em></p> <p>by <strong>Vassily Mikhaltsevitch, Maxim Lebedev, Rafael Chavez, </strong><strong>Euripedes</strong> <strong>A. </strong><strong>Vargas Jr. and </strong><strong>Guilherme F. </strong><strong>Vasquez</strong></p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as Simulated by MOSAIC Module in CESM2-MAM4
<p>Netcdf outputs from CESM2 simulations for publication: Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as Simulated by MOSAIC Module in CESM2-MAM4</p>
Data from: Effects of a titin mutation on force enhancement and force depression in mouse soleus muscles
The force produced by muscles varies with muscle length. Compared to isometric contractions at the same final length, force increases after lengthening, known as force enhancement, and decreases after shortening, known as force depression. The mechanisms of force enhancement remain unexplained by the sliding-filament and cross-bridge theories of muscle contraction. Although cross-bridge mechanisms have been proposed to account for force depression, so too have other mechanisms. In addition to cross bridges, titin has been suggested to contribute to both force enhancement and depression. Although titin is too compliant in passive muscles to contribute to active stiffness, recent evidence suggests that calcium activation increases titin stiffness. To test the hypothesis that titin plays a role in force enhancement and depression, we investigated muscle force in active and passive wild type and mdm soleus muscles after isovelocity stretching and shortening at three activation levels. Muscles from mdm mice (muscular dystrophy with myositis) have a predicted 83 amino acid deletion in the N2A region of titin and show no increase in stiffness when activated. We found that: 1) force enhancement and depression were reduced in mdm soleus compared to wild type muscles; 2) force enhancement increased with the amplitude of stretch across all activation levels in wild type and mdm muscles, while force depression increased with amplitude in wild type but not in mdm muscles; and 3) maximal shortening velocity of wild type and mdm muscles was similar, although active stress was reduced in mdm compared to wild type muscles. The results of this study suggest a role for titin in force enhancement and depression, which contribute importantly to regulation of muscle force during natural movements.
Forces
João Bargas PUC minas digital games technology college Modeling Discipline Forces low poly medieval style used as scenery composition in a fantasy world. With shades of brown and gray color. Source: Objaverse 1.0 / Sketchfab
DLPC@ 323K, CHARMM36UA force field
<p>Files required for a simulation of a DLPC bilayer in LAMMPS (<strong>lammps</strong>.sandia.gov/). Force field is taken from http://pubs.acs.org/doi/abs/10.1021/jp410344g</p> <p>This data is used in the project "Matching lipid force fields with NMR data", see: http://nmrlipids.blogspot.fi </p>
Underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes underlying data for the publication "Evaluating<br /> parameterization protocols for hydration free energy calculations with the<br /> AMOEBA polarizable force field"</p> <p>Contents:<br /> Modified valence parameters for the Poltype software (valence.py). This can be<br /> substituted for the existing valence.py module packaged with Poltype to make the<br /> parameter assignment changes detailed in the article supplementary information. </p> <p>Results files for each parameter set (*.txt). Each consists of a 4 x 47 array of<br /> numbers. Rows correspond to entries for each sequential ligand. The first column<br /> in each row is the experimental hydration free energy. The following three rows<br /> are computational hydration free energy predictions from three independent<br /> repeat simulations.</p> <p>Script for analysis of results files (analyse_hfe.py). Short script to produce<br /> descriptive statistics for packaged datasets. Expects input files in the syntax<br /> of *.txt (i.e. 4 x 47 arrays)</p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for openMM simulation engine v7
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for for openMM simulation engine v7</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration).</p> <p>Note that the provided trajectories are in Gromacs XTC format, whereas NAMD DCD format was generated by openMM. This required trajectory conversion using Gromacs package (v5.1.2) with binary topology file from https://doi.org/10.5281/zenodo.153944 </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</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.