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11,506 results for “Dynamics”
01/04 - Spatial and Amplitude Dynamics of Neurostimulation: Insights from the Acute Intrahippocampal Kainate Seizure Mouse Model
<p>Dataset #1 of 4</p>
Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations.
<p>In the present study, we describe the results from a computational microscopy perspective (also known as molecular dynamics simulation) at the atomistic resolution for the two gonadotropin hormone receptors, the follicle-stimulant hormone receptor and the luteinizing/chorionic gonadotropin hormone receptor, which are essential for reproduction in humans.</p>
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data (minimal)
<p>This is a minimal version of the DynaBench dataset, containing the first 5% of the data. The full dataset is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a></p><p><strong>Abstract:</strong></p><p>Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a>.</p><p><strong>Technical Info</strong></p><p>The dataset is split into 42 parts (6 equations x 7 combinations of resolution/structure). Each part can be downloaded separately and contains 7000 simulations of the given equation at the given resolution and structure. The simulations are grouped into chunks of 500 simulations saved in the hdf5 file format. Each chunk contains the variable "data", where the values of the simulated system are stored, as well as the variable "points", where the coordinates at which the system has been observed are stored. For more details visit the DynaBench website at <a href="https://professor-x.de/dynabench/">https://professor-x.de/dynabench/</a>. The dataset is best used as part of the dynabench python package available at <a href="https://pypi.org/project/dynabench/">https://pypi.org/project/dynabench/</a>.</p>
Data and code for: Photoacclimation dynamics in coral holobionts responding to thermal and irradiance changes correlate with photon pressure per symbiont
<p>Code and data used in the publication "Photoacclimation dynamics in coral holobionts responding to thermal and irradiance changes correlate with photon pressure per symbiont". For queries about this dataset please email robert.mason1@uq.net.au</p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (2/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>2 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (3/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>3 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (1/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>1 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Exploring the ligand binding and conformational dynamics of the substrate binding domain 1 of the ABC transporter GlnPQ
Open the record for dataset details and reuse information.
DYNAMICAL REFINEMENT WITH MULTIPOLAR ELECTRON SCATTERING FACTORS
<p>The following submission contains the data collection of datasets for the sample 1-methyluracil. Data were used in publication entitled Dynamical refinement with multipolar electron scattering factors. Many crystals were measured but only the two had sufficient quality to be included in the study. Data were collected using the precession assisted 3D electron diffraction technique. All the data sets were processed with PETS2 software. Information below summarizes the data collection parameters for the data sets.</p> <p>General information:</p> <p> </p> <p>Publication title:</p> <p>Dynamical refinement with multipolar electron scattering factors</p> <p> </p> <p>Authors:</p> <p>Barbara Olech, Petr Brázda, Lukas Palatinus, and Paulina Maria Dominiak</p> <p> </p> <p>Reference to the research article:</p> <p>Olech B, Brázda P, Palatinus L, Dominiak PM Dynamical refinement with multipolar electron scattering factors<br>IUCrJ, 2024, 11, 309-324 [DOI: 10.1107/S2052252524001763]Experimental:</p> <p> </p> <p>Reference to the research grant:</p> <p>This research was funded through the OPUS LAP program by the National Science Centre, Poland (grant No. 2020/39/I/ST4/02904 awarded to PMD) and by the Czech Science Foundation (grant No 21-44862L awarded to LP).</p> <p> </p> <p>Instrument</p> <p>Transmission electron microscope FEI Tecnai G2 20</p> <p> </p> <p>Radiation source</p> <p>LaB6</p> <p> </p> <p>Accelerating voltage</p> <p>200 kV</p> <p> </p> <p>Wavelength</p> <p>0.0251 Å</p> <p> </p> <p>Probe Type</p> <p>Microdiffraction</p> <p> </p> <p>Beam Convergence</p> <p>Parallel beam, convergence <0.1mrad</p> <p> </p> <p>Precession angle</p> <p>0.7°</p> <p> </p> <p>Tilt step</p> <p>1.0°</p> <p> </p> <p>Detector</p> <p>SIS Veleta 14bit CCD camera (side-mounted)</p> <p> </p> <p>Number of pixels in the image</p> <p>2048 x 2048</p> <p> </p> <p>Effective camera length</p> <p>1200 mm</p> <p> </p> <p>Beam Diameter (crystal 1)</p> <p>1230 nm</p> <p> </p> <p>Beam Diameter (crystal 2)</p> <p>1650 nm</p> <p> </p> <p>Calibration constant</p> <p>0.001842 Å-1/pixel</p> <p> </p> <p>Compound name</p> <p>1-methyluracil</p> <p> </p> <p>Chemical composition</p> <p>C5 H6 N2 O2</p> <p> </p> <p>Sample source</p> <p>Sigma Aldrich, recrystallized from water </p> <p> </p> <p>Sample preparation</p> <p>Powder crushed in agate mortar and deposited on Cu grid with holey C film</p> <p> </p> <p>Data Type</p> <p>Electron diffraction data - 3D ED</p> <p> </p> <p>Data collection method</p> <p>Precession assisted tilt step tomography</p> <p> </p> <p>Precession angle</p> <p>0.7°</p> <p> </p> <p>Temperature (K) used during data collection</p> <p>100 K</p> <p> </p> <p>Number of crystals contributing to the data set</p> <p>2</p> <p> </p> <p>Number of experimental frames: Crystal 1</p> <p>94</p> <p> </p> <p>tilt range, tilt step: Crystal 1</p> <p>+45° to -48°, 1.00°</p> <p> </p> <p>Exposure time per frame: Crystal 1</p> <p>4000 ms</p> <p> </p> <p>Electron dose per frame: Crystal 1</p> <p>0.28 e.Å^-2</p> <p> </p> <p>Number of experimental frames: Crystal 2</p> <p>60</p> <p> </p> <p>tilt range, tilt step: Crystal 2</p> <p>-5° to +54°, 1.00°</p> <p> </p> <p>Exposure time per frame: Crystal 2</p> <p>2000 ms</p> <p> </p> <p>Electron dose per frame: Crystal 1</p> <p>0.05 e.Å^-2</p> <p> </p> <p> </p> <p>Software:</p> <p> </p> <p>Software used for the data collection</p> <p>RATS software</p> <p> </p> <p>Software used for processing</p> <p>PETS2</p> <p> </p> <p>Files and data formats</p> <p>tiff_16bit</p> <p> </p> <p>Notes:</p> <p>The software, RATS used for data collection is an in-house software developed in FZU, Prague.</p>
Data from: Molecular dynamics simulation of the interaction between palmitic acid and high pressure CO2
<p>In this study, molecular dynamics simulation was used to explore the interaction characteristics of palmitic acid and CO2, and the effects of temperature and pressure on the solubility of palmitic acid in CO2 were investigated. In the range of 293K-353K and 5MPa-30MPa, the snapshot of palmitic acid distribution in CO2 shows that the molecular chain of palmitic acid in high-density CO2 system is more straight and more dispersed than that in low-density CO2 system. The radial distribution function further clearly shows that the solubility of palmitic acid in CO2 decreases with the increase of temperature and increases with the increase of pressure, which is consistent with the fatty acid solubility data reported in the literature and the setting rules of supercritical CO2 extraction process conditions. As the temperature decreases and the pressure increases, the interaction energy between palmitic acid and CO2 increases, which is conducive to overcoming the intermolecular force of palmitic acid and promoting dissolution. The solubility parameters of palmitic acid and CO2 can better reflect the trend of palmitic acid solubility changing with temperature and pressure, which can play a guiding role in the determination of process conditions and even the development of new processes.</p>
Figure 7 in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 7. Black Sea coastal current system (http://www.ims.metu.edu.tr/cv/oguz/circulation.htm). © Temel Oguz. Reproduced by permission of Temel Oguz. Permission to reuse must be obtained from the rightsholder.
Figure 6 in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 6. Major shipping lanes in the Black Sea http://www.marinevesseltraffic.com/2013/06/black-sea-marine-traffic.html. © Marine Vessel Traffic. Reproduced by permission of Marine Vessel Traffic. Permission to reuse must be obtained from the rightsholder.
Figure 1 in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 1. (●) Previous report sites of Oithona davisae (Altukhov et al. 2014), (▲) Time series stations and (+) sampling locations.
Dynamical Visualisations of Pendulum Motion
<p>This submission includes two animated GIFs showcasing simulations of pendulum motion in three distinct scenarios: a massless pendulum, a damped pendulum, and a forced damped pendulum. The animations are generated using Python with the matplotlib library.</p>
Molecular dynamics trajectory for CsPbI3
<p>This record provides a molecular dynamics trajectories for CsPbI3 generated via GPUMD using the input files included in this record. This trajectories are used in one of the examples that demonstrate the functionality of the <a href="https://dynasor.materialsmodeling.org">dynasor package</a>.</p>
Fitacf data for "Ionospheric Flow Vortex Induced by the Sudden Decrease in the Solar Wind Dynamic Pressure"
<p>This is the SuperDARN dataset in fitacf format (2.5) from the Hankasalmi radar. The data is used in the publication: Jin, Y., Moen, J. I., Spicher, A., Liu, J., Clausen, L. B. N., & Miloch, W. J. (2023). Ionospheric flow vortex induced by the sudden decrease in the solar wind dynamic pressure. Journal of Geophysical Research: Space Physics, 128, e2023JA031690. https://doi.org/10.1029/2023JA031690</p>
Single-Molecule FRET-Resolved Protein Dynamics - from Plasmid to Data in Six Steps
<p>SmFRET trace files of 58 yeast Hsp90 molecules (in presence of AMP-PNP) containing raw intensity values through time of acceptor emission after acceptor excitation (r_r_*), donor emission after donor excitation (o_g_*), and acceptor emission after donor excitation (r_g_*).</p>
Hydrologic Connectivity Regulates Riverine N2O Sources and Dynamics [Dataset]
<p>This repository contains summarized N2O concentration, isotope, watershed attribute, and stream hydraulics data for the Cannon River Watershed. More details about the data can be found in the enclosed metadata file.</p>
Data files for «Wood warbler population dynamics in response to mast seeding regimes in Europe», Journal: Ecology
<p><strong>Abstract</strong></p><p>Mast seeding is the episodic, massive production of plant seeds synchronized over large areas. The resulting superabundance of seeds represents a resource pulse that can profoundly affect animal populations across trophic levels. Following years of high seed production, abundances of both seed consumers and their predators increase. Higher predator abundance leads to increased predation pressure across the trophic web, impacting non-seed consumers such as the wood warbler <i>Phylloscopus sibilatrix</i> through increased nest predation after tree mast years. Over the past 30 years, the frequency of tree seed masts has increased while wood warbler populations have declined in several regions of Europe. We hypothesised that increasing mast frequencies may have contributed to the observed population declines by creating suboptimal breeding conditions in years after masting. We measured reproductive output in four study areas in central Europe, which was between 0.61 and 1.24 fledglings lower in years following masting than non-masting. For each study area, we used matrix population models to predict population trends based on the estimated reproductive output and the local mast frequencies. We then compared the predicted with the observed population trends to assess if the frequency of mast years contributed to the population dynamics. In Wielkopolska National Park (PL) and Hessen (DE), masting occurred on average only every 4 years and populations were stable or nearly so, whereas in Jura (CH) and Białowieża National Park (PL), masting occurred every 2 and 2.5 years, respectively, and populations were declining. The simple matrix population models predicted the relative difference among local population trends over the past 10-20 years well, suggesting that the masting frequency may partly explain regional variation in population trends. Simulations suggest that further increases in mast frequency will lead to further declines in wood warbler populations. We show that changes in a natural process, such as mast seeding, may contribute to the declines of animal populations through cascading effects.</p>
Dataset for the paper "Wedge Plasticity and a Minimalist Dynamic Rupture Model for the 2011 Mw 9.1 Tohoku-Oki Earthquake and Tsunami" in Tectonophysics
<p>This dataset contains the Fortran source codes and input files to reproduce the results in the paper "Wedge Plasticity and a Minimalist Dynamic Rupture Model for the 2011 Mw 9.1 Tohoku-Oki Earthquake and Tsunami". Sample Matlab scripts are included to visualize the results. Model results, such as final fault slip, rupture times, stress changes on fault, final surface displacements, and moment density tensors in the wedge associated with inelastic deformation, are also included as ascii files. See readme for more details.</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)
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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.