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1,670 results for “FORCE”
MD simulation of POPC bilayer with CHARMM36 force field, 200 mM NaCl
<p>MD simulation of POPC bilayer with CHARMM36 force field, 200 mM NaCl</p> <p>Dataset contains simulation files including centered and equilibrated trajectory Nacl200mM-45-500ns.xtc.</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: NaCl</p> <p>Concentration: 200 mM</p> <p>Number of cations: 32</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 300 K</p>
MD simulation of POPC bilayer with CHARMM36 force field, 1000 mM NaCl
<p>MD simulation of POPC bilayer with CHARMM36 force field, 1000 mM NaCl</p> <p>Dataset contains simulation files including centered and equilibrated trajectory Nacl1000mM-15-500ns.xtc.</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: NaCl</p> <p>Concentration: 1000 mM</p> <p>Number of cations: 160</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 300 K</p>
MD simulation of POPC bilayer with CHARMM36 force field, 500 mM CaCl2
<p>MD simulation of POPC bilayer with CHARMM36 force field, 500 mM CaCl<sub>2</sub></p> <p>Dataset contains simulation files including centered trajectory files. For the ease of the upload, trajectory files (.xtc) are divided to 100ns pieces (Cacl500mM_x-x.xtc)</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: CaCl<sub>2</sub></p> <p>Concentration: 500 mM</p> <p>Number of cations: 80</p> <p>Simulation time: 1000 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 300 K</p>
MD simulation of POPC bilayer with CHARMM36 force field, 500 mM NaCl
<p>MD simulation of POPC bilayer with CHARMM36 force field, 500 mM NaCl</p> <p>Dataset contains simulation files including centered and equilibrated trajectory Nacl500mM-60-500ns.xtc.</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: NaCl</p> <p>Concentration: 500 mM</p> <p>Number of cations: 80</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 300 K</p>
kinematic and force data from 11 humans interacting with a "cup of coffee"
<p>The dataset contains the kinematic and force data from 11 human participants when interacting with a virtual object that resembles a "cup of coffee". Further models that mimic human data are also provided. Codes to process and generate figures for the research paper are also available. </p> <p>Research article:<br> "Body mechanics, optimality, and sensory feedback in the human control of complex objects"</p> <p>by Reza Sharif Razavian, Mohsen Sadeghi, Salah Bazzi, Rashida Nayeem, and Dagmar Sternad</p> <p>Northeastern University</p>
MD simulation of POPC bilayer with OPLS3e force field, 1000 mM CaCl2 part 2
<p>MD simulation of POPC bilayer with OPLS3e force field, 1000 mM CaCl<sub>2</sub> part 2</p> <p>Dataset contains trajectories (_trj) for the last 500ns of the 1000ns trajectory, topology (-out.cms), and other files.</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 100ns pieces and tarred (named desmond_md_cacl1000_x-xns.tar.gz)</p> <p>Dataset also contains Gromacs converted trajectories for the last 500 ns of 1000 ns, and are as 100 ns pieces for analysis since simulation did not equilibrate during 1000 ns.</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: CaCl<sub>2</sub></p> <p>Concentration: 1000 mM</p> <p>Number of cations: 160</p> <p>Simulation time: 1000 ns (in this dataset 500-1000ns)</p> <p>Simulation engine: Desmond 2019-4</p> <p>Temperature: 300 K</p> <p>Related dataset: MD simulation of POPC bilayer with OPLS3e force field, 1000 mM CaCl<sub>2</sub> part 1</p>
MD simulation of POPC bilayer with OPLS3e force field, 100 mM NaCl part 2
<p>MD simulation of POPC bilayer with OPLS3e force field, 100 mM NaCl part 2 (500-1000ns)</p> <p>Dataset contains trajectories (_trj) for the last 500ns of the 1000ns trajectory, topology (-out.cms), and other files</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 100ns pieces and tarred (named desmond_md_nacl100_x-xns.tar.gz)</p> <p>Dataset also contains Gromacs converted trajectory (.xtc) for the last 500 ns of 1000 ns trajectory</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Concentration: 100 mM</p> <p>Number of cations: 16</p> <p>Simulation time: 1000 ns (in this dataset 500-1000ns)</p> <p>Simulation engine: Desmond 2019-4</p> <p>Temperature: 300 K</p> <p>Related dataset: MD simulation of POPC bilayer with OPLS3e force field, 100 mM NaCl part 1</p>
MD simulation of POPC bilayer with OPLS3e force field, 200 mM NaCl part 2
<p>MD simulation of POPC bilayer with OPLS3e force field, 200 mM NaCl part 2 (500-1000ns)</p> <p>Dataset contains trajectories (_trj) for the last 500ns of the 1000ns trajectory and topology (-out.cms).</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 100ns pieces and tarred (named desmond_md_nacl200_x-xns.tar.gz)</p> <p>Dataset also contains Gromacs converted trajectory (.xtc) for the last 500 ns of 1000 ns trajectory</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Concentration: 200 mM</p> <p>Number of cations: 32</p> <p>Simulation time: 1000 ns (in this dataset 500-1000ns)</p> <p>Simulation engine: Desmond 2019-4</p> <p>Temperature: 300 K</p> <p>Related dataset: MD simulation of POPC bilayer with OPLS3e force field, 200 mM NaCl part 1</p>
MD simulation of POPC bilayer with OPLS3e force field, 500 mM CaCl2 part 1
<p>MD simulation of POPC bilayer with OPLS3e force field, 500 mM CaCl<sub>2</sub> part 1</p> <p>Dataset contains trajectories (_trj) for the first 500ns of the 1000ns trajectory, topology (-out.cms) and input files (.cfg, .msj, .cms).</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 100ns pieces and tarred (named desmond_md_cacl500_x-xns.tar.gz)</p> <p>Dataset also contains Gromacs converted files (.xtc, .gro and .top). Converted trajectories are as 100 ns pieces for analysis since simulation did not equilibrate during 1000 ns.</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: CaCl<sub>2</sub></p> <p>Concentration: 500 mM</p> <p>Number of cations: 80</p> <p>Simulation time: 1000 ns (in this dataset 0-500ns)</p> <p>Simulation engine: Desmond 2019-4</p> <p>Temperature: 300 K</p> <p>Related dataset: MD simulation of POPC bilayer with OPLS3e force field, 500 mM CaCl<sub>2</sub> part 2</p>
MD simulation of POPC bilayer with OPLS3e force field, 200 mM CaCl2 part 1
<p>MD simulation of POPC bilayer with OPLS3e force field, 200 mM CaCl<sub>2</sub> part 1 (0-500ns)</p> <p>Dataset contains trajectories (_trj) for the first 500ns of the 1000ns trajectory, topology (-out.cms), input files (.cfg, .msj, .cms), and other files</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 100ns pieces and tarred (named desmond_md_cacl200_x-xns.tar.gz)</p> <p>Dataset also contains Gromacs converted files (.xtc, .gro and .top). Converted trajectories are as 100 ns pieces for analysis since simulation did not equilibrate during 1000 ns.</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 8880</p> <p>Salt: CaCl<sub>2</sub></p> <p>Concentration: 200 mM</p> <p>Number of cations: 32</p> <p>Simulation time: 1000 ns (in this dataset 0-500ns)</p> <p>Simulation engine: Desmond 2019-4</p> <p>Temperature: 300 K</p> <p>Related dataset: MD simulation of POPC bilayer with OPLS3e force field, 200 mM CaCl<sub>2</sub> part 2</p>
The influence of the choice of force field on the conformational landscape of monomeric rat and human islet amyloid polypeptide
<p>Dataset of the BEMD data used in the paper "The influence of the choice of force field on the conformational landscape of monomeric rat and human islet amyloid polypeptide"</p>
Bite force in the strictly subterranean rodent family of African mole-rats (Bathyergidae): the role of digging mode, social organisation, and ecology
<p>Bite force is an ecologically relevant performance trait that has been measured to better understand the adaptations to diet and habitat use. Moreover, bite force is relevant in understanding reproductive success, as well as inter- and intraspecific competition. African mole-rats (Bathyergidae, Rodentia) are a unique clade of mammals that use different digging strategies, show different types of social organisation, and occur in ecologically diverse savannah habitats in Sub-Saharan Africa. Whereas previous studies have suggested these animals have exceptionally high bite forces, the ecological and other proximate and ultimate drivers of variation in bite force in the group remain unstudied. In the present study we measured in vivo bite force of 394 adult specimens from ten African mole-rat species including all genera within the family. Our results show that in African mole-rats digging mode is a major driver of variation in bite force, with chisel-tooth diggers being stronger biters than scratch-diggers. Moreover, species living in habitats characterised by low and irregular precipitation patterns and in soils with a high content of coarse particles have a higher bite force than species occupying habitats with a regular rainfall pattern and fine soil types. This suggests that bite force in bathyergids has evolved in concert with rainfall and soil characteristics of different savannah habitats, which have contributed to the successful radiation of these subterranean mammals across sub-Saharan Africa.</p>
Forcing data, evaluation data, model output and analysis scripts used in LPJ-GUESS/LSM description paper
<p>This archive contains:</p> <p>- model_output.tar.gz: Model output<br> - extracted_fluxes.tar.gz: Sensible heat, latent heat and CO2 fluxes extracted from the FLUXNET2015 dataset, used to evaluate the model output<br> - extracted_climate.tar.gz: Climate data extracted from the FLUXNET2015 dataset, used to force the simulations<br> - scripts.tar.gz: Python scripts used to analyze the data and produce the results reported in the model description paper</p> <p>The climate forcing data has been extracted from the FLUXNET2015 dataset [1]. Input and output data is stored in netCDF files. Evaluation data is stored in python serialized files (pickle).</p> <p>[1] Pastorello, G., Trotta, C., Canfora, E. <em>et al.</em> The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. <em>Sci Data</em> <strong>7, </strong>225 (2020). https://doi.org/10.1038/s41597-020-0534-3</p>
Pleistocene dynamics of the Eurasian steppe as a driving force of evolution: Phylogenetic history of the genus Capsella (Brassicaceae)
<p><i>Capsella</i> is a model plant genus of the Brassicaceae, closely related to <i>Arabidopsis</i>. To disentangle its biogeographical history and intrageneric phylogenetic relationships, 282 individuals of all five currently recognised <i>Capsella</i> species were genotyped, using a restriction digest-based next generation sequencing method. Our analysis retrieved two main lineages within <i>Capsella</i> that split <i>c.</i> one million years ago, with western <i>C. grandiflora</i> and <i>C. rubella</i> forming a sister lineage to the eastern clade consisting of <i>C. orientalis</i>. The split was attributed to continuous latitudinal displacements of the Eurasian steppe belt to the south during early Pleistocene glacial cycles. During the interglacial cycles of the Late Pleistocene, hybridisation of the two lineages took place in the southwestern East European Plain, leading to the allotetraploid <i>C. bursa-pastoris</i>. Extant genetic variation within <i>Capsella orientalis</i> postdated any extensive glacial events. Ecological niche modelling showed that suitable habitat for <i>C. orientalis</i> existed during the Last Glacial Maximum around the north coast of the Black Sea and in southern Kazakhstan. Such a scenario is also supported by population genomic data that uncovered the highest genetic diversity in the south Kazakhstan cluster, suggesting that <i>C. orientalis</i> originated in continental Asia and migrated north- and possibly eastwards after the last ice age. Post-glacial hybridisation events between <i>C. bursa-pastoris</i> and <i>C. grandiflora/rubella</i> in the southwestern East European Plain and the Mediterranean gave rise to <i>C. thracica</i>. Introgression of <i>C. grandiflora</i>/<i>rubella</i> into <i>C. bursa-pastoris</i> resulted in a new Mediterranean cluster within the already existing Eurasian <i>C. bursa-pastoris</i> cluster. This study shows that the continuous displacement and disruption of the Eurasian steppe belt during the Pleistocene was the driving force in the evolution of <i>Capsella</i>.</p>
LPF Calibrated Force Data
<p>This is calibrated force data recorded from the LISA(Laser Interferometer Space Antenna) Pathfinder. The data is taken from the adjustments made by the control systems on the LPF to stay centered on its 2 free falling mass.</p> <p>The data was obtained from the Nasa Goddard Space Flight Center</p>
Data for "Estimation of microtubule-generated forces using a DNA origami nanospring"
<p>Data in support of the publication "Estimation of microtubule-generated forces using a DNA origami nanospring"</p>
Evaluating Uncertainty in Aerosol Forcing of Tropical Precipitation Shifts
<p>This dataset contains simplified data and code to reproduce the main figures in "Evaluating Uncertainty in Aerosol Forcing of Tropical Precipitation Shifts" accepted for publication in Earth System Dynamics, 2022, same authors. </p> <p>A Jupyter notebook and csv data files are provided in each folder to reproduce the respective figure.</p>
Foraging strategies, craniodental traits and interaction in the bite force of Neotropical frugivorous bats (Phyllostomidae: Stenodermatinae)
<p>1. Bats in the family Phyllostomidae exhibit great diversity in skull size and morphology that reflects the degree of resource division and ecological overlap in the group. In particular, the subfamily Stenodermatinae has high morphological diversification associated with cranial and mandibular traits that is associated with the ability to consume the full range of available fruits (soft and hard).</p> <p>2. Was analyzed craniodental traits and their relationship to the bite force in 343 specimens distributed in seven species of stenodermatine bats with two foraging strategies: nomadic and sedentary frugivory. We evaluated 19 traits related to feeding and bite force in live animals by correcting bite force with body size.</p> <p>3. We used a generalized linear model (GLM) and post hoc tests to determine possible relationships and differences between cranial traits, species, and sex. We also used Blomberg's K to measure the phylogenetic signal and Phylogenetic generalized least-squares (PGLS) to ensure the phylogenetic independence of the traits.</p> <p>4. We found that smaller nomadic, <i>A. anderseni</i> and <i>A. phaeotis</i> have a similar bite force to the large species <i>A. planirostris</i> and <u>A. lituratus</u>; furthermore, <i>P. helleri</i> registered a bite force similar to that of the sedentary bat, <i>S. giannae</i>. Our study determined that all the features of the mandible and most of the traits of the skull have a low phylogenetic signal. Through the PGLS we found that the diet and several cranial features (mandibular toothrow length, dentary length, braincase breadth, mastoid breadth, greatest length of skull, condyloincisive length and condylocanine length) determined bite force performance among Stenodermatiane.</p> <p>5. Our results reinforce that skull size is a determining factor in the bite force, but also emphasize the importance of its relationships with morphology, ecology, and phylogeny of the species, which gives us a better understanding of the evolutionary adaptions of this highly diverse Neotropical bat group.</p>
Data from: A brief history and popularity of methods and tools used to estimate micro-evolutionary forces
<p class="western">Population genetics is a field of research that predates the current generations of sequencing technology. Those approaches, that were established before massively parallel sequencing methods, have been adapted to these new marker systems (in some cases involving the development of new methods) that allow genome-wide estimates of the four major micro-evolutionary forces – mutation, gene flow, genetic drift and selection. Nevertheless, classic population genetic markers are still commonly used and a plethora of analysis methods and programs is available for these and High Throughput Sequencing (HTS) data. These methods employ various and diverse theoretical and statistical frameworks, to varying degrees of success, to estimate similar evolutionary parameters making it difficult to get a concise overview across the available approaches. Presently, reviews on this topic generally focus on a particular class of methods to estimate one or two evolutionary parameters. Here, we provide a brief history of methods and a comprehensive list of available programs for estimating micro-evolutionary forces. We furthermore analyzed their usage within the research community based on popularity (citation bias) and discuss the implications of this bias for the software community. We found that a few programs received the majority of citations, with program success being independent of both the parameters estimated and the computing platform. The only deviation from a model of exponential growth in the number of citations was found for the presence of a graphical user interface (GUI). Interestingly, no relationship was found for the impact factor of the journals, when the tools were published, suggesting accessibility might be more important than visibility.</p>
Data set containing the energy landscapes for GPO and GPP tropocollagen models under pulling forces
<p>Energy landscapes (databases of minima and transition states) for GPO and GPP repeat collagen models under constant pulling forces as explored with OPTIM and PATHSAMPLE with an AMBER force field.</p> <p>The systems are seven GPO or GPP per chain capped with ACE and NME.</p> <p> The forces applied are 0 pN (F0), 10 pN (F1), 50 pN (F2), 100 pN (F3), 250 pN (F4), 500 pN (F5) and 750 pN (F6).</p> <p>The folders contains numerous analysis scripts and graphs. Most of these assume python with numpy and pandas, as well as cpptraj from AMBERTools.</p>
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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.