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11,506 results for “Dynamics”
Investigating the interplay between RNA structural dynamics and RNA chemical probing experiments
<p>Normalized SHAPE reactivity for pre-miR20b and CDE2GG as reported in 'Investigating the interplay between RNA structural dynamics and RNA chemical probing experiments'.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-2121 (The Dynamics of Exposure, Phthalates and Asthma in a Randomized Trial (DEPART))
Title: The Dynamics of Exposure, Phthalates and Asthma in a Randomized Trial (DEPART) <br>Species: Homo sapiens <br>Number of samples: 281 <br>Number of named analytes: 30 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=41 <br>
Molecular Dynamics of Engineered Hen-Egg Lysozymes
<p>This dataset contains Molecular Dynamics (MD) simulation trajectories of engineered hen-egg lysozymes, stored in DCD format. The simulations were conducted in explicit solvent environments, but water molecules have been excluded from the trajectories for storage. Each trajectory captures the dynamic properties of various engineered lysozyme variants, providing valuable insights into structural changes, stability, and function.</p>
Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations in the Bulk
<p>Data set containing molecular dynamics (MD) simulations performed with <a href="https://www.gromacs.org/">Gromacs</a> to investigate the effect of polymer chain length and salt concentration on the atomistic structure and dynamics of PEO-LiTFSI polymer electrolytes in the bulk.</p> <p>PEO = Methoxy-terminated poly(ethylene oxide), sometimes also abbreviated as PEGDME for polyethylene glycol dimethyl ether<br>LiTFSI = Lithium bis(trifluoromethanesulfonyl)imide, sometimes also abbreviated as Li[NTf2].</p> <p>The data set contains:</p> <ul> <li>Gromacs input and output files (except trajectories due to their huge filesize)</li> <li>Processed data</li> <li>Various plots of the data</li> </ul> <p>The zip archives contain:</p> <ul> <li><code>coiling.zip</code>: MD simulations of single PEO chains in vacuum that were performed to produce coiled PEO chains, which were used to generate the starting structures for the bulk simulations.</li> <li><code>bulk.zip</code>: MD simulations of PEO-LiTFSI polymer electrolytes in the bulk.</li> <li><code>plots.zip</code>: Plots of various structural and dynamic quantities as function of the PEO chain length and the salt concentration.</li> </ul>
Impacts of Agriculture and Snow Dynamics on Catchment Water Balance in the U.S. and Great Britain
<p>Data required to reproduce the figures reported in the paper.</p> <p> </p>
Supporting data and code for the published paper: Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach
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Data for "Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks"
<p>This dataset contains snapshots of cell monolayers, represented as graphs, along with their corresponding average displacement measurements.</p>
Implementing amplified MRI in a computational fluid dynamics model of the cerebrospinal fluid
<div> <p>This folder respository contains files to implement measured amplified MRI motion into a CFD model. The files should be imported in FLUENT (Ansys Inc.) in the following order:</p> <ol> <li><strong>setup_case.jou</strong>: journal file containing the commands to setup the CFD model in FLUENT (Ansys Inc.). This file also compiles and loads the UDF <strong>mesh_motion_all_v2023.c</strong>. The UDF is directly based on the code provided in the Github respiratory (<a href="https://pyfsi.github.io/coconut/">CoCoNuT</a>). A define-on-demand function should then be executed to export the unique node ids from FLUENT. </li> <li>Run the Python file <strong>link_node_id_spline_interpolation.ipynb</strong> which returns .dat files for the different timesteps. </li> <li><strong>run_motion.jou</strong>: journal file that reads the .dat files generated in step 2 and applies the mesh motion in the CFD model.</li> </ol> </div>
Data in support of Plume Dynamics Reduced-Order Models
<p>This split tarball contains the data used in experiments in the paper "Coarse Graining and Reduced Order Models for Plume Dynamics". Unzipping the tarball in the experiment repository, or symlinking from the repository to the unzipped directory "plume_videos", will allow experiments to run.</p> <p>Data is laid out as it is in our machine. This includes movie files, numpy arrays of movies, and pickled coordinates of plume origins. </p> <p>Tarball is fragmented using the unix split command. Run `cat x* | tar -xzvf -`. See https://unix.stackexchange.com/questions/61774/create-a-tar-archive-split-into-blocks-of-a-maximum-size</p>
Data for "Mechanism of Solid-State 1H Photochemically Induced Dynamic Nuclear Polarization in a Synthetic Donor−Chromophore−Acceptor at 0.3 T"
<p>This data is associated with the publication "Mechanism of Solid-State 1H Photochemically Induced Dynamic Nuclear Polarization in a Synthetic Donor−Chromophore−Acceptor at 0.3 T" and contains the NMR and photo-CIDNP data discussed in the publication.</p>
Cross-Dock Trailer Scheduling with Workforce Constraints: A Dynamic Discretization Discovery Approach
<p>The zip file contains the dataset used in the research published under the title: Cross-Dock Trailer Scheduling with Workforce Constraints: A Dynamic Discretization Discovery Approach. There are 640 instances in this dataset.</p> <p>There are a total of 1920 .csv files with names starting with: </p> <ol> <li>LoadingDoor (640 files for the 640 instances): contains the loading door index, loading door deadline, and the penalty for violating the deadline at the corresponding loading door</li> <li>Trailer_&_Processing (640 files for the 640 instances): contains the trailer index, unloading door index, and the processing time of the trailer at the unloading door</li> <li>Trailer_&_Shipment (640 files for the 640 instances): contains the trailer index, the arrival time of the trailer, loading door index, and the number of shipments to be cross-docked from the trailer to the loading door.</li> </ol>
NMR data: Dynamic nuclear polarization solid-state NMR spectroscopy as a tool to rapidly determine degree of modification in dialcohol cellulose
<p>Solid-state NMR data underlying figures and tables in the article: <span>Dynamic nuclear polarization solid-state NMR spectroscopy as a tool to rapidly determine degree of modification in dialcohol cellulose</span></p>
Unveiling the Natural Dynamics of Parthenocarpy and Self-Compatibility in Apple Trees
<p><strong><span>Supplementary Materials:</span></strong></p> <p><span>Table S1. Three years parthenocarpy and self-compatible accession survey result.</span></p> <p><span>Table S2. Accessions used in GWAS for parthenocarpic and self-compatible traits. </span></p> <p><span>Table S3. Fruit set rate of parthenocarpy and self-compatible accessions. Light blue colour in the column of Number of fruits per cluster flower indicated fruit setting rates were comparable between bagged flowers and open-pollinated (OP) flowers. </span></p> <p><span>Table S4. Functional annotations of the associated SNPs located within the candidate genes for parthenocarpy trait. The significant SNPs (-Log10 (P) ≥3) associated <em>Malus</em> genes were identified within 20kb either side of the SNP on GDT2T genome database.</span></p> <p><span>Table S5. Functional annotations of the associated SNPs located within the candidate genes for self-compatible trait. The significant SNPs (-Log10 (P) ≥3) associated <em>Malus</em> genes were identified within 20kb either side of the SNP on GDT2T genome database.</span></p>
Information diffusion assumptions can distort our understanding of social network dynamics (code and data)
<p>This repository contains the data (<code>cascade_reconstruction.tar.gz</code>) and code (<code>code_repository.tar.gz</code>) for a paper titled "Information diffusion assumptions can distort our understanding of social network dynamics" by <a href="https://www.matthewdeverna.com/">Matthew R. DeVerna</a>, <a href="https://pierri.faculty.polimi.it/">Francesco Pierri</a>, <a href="https://rachithaiyappa.github.io/">Rachith Aiyappa</a>, <a href="https://diogofpacheco.github.io/">Diogo Pachecho</a>, <a href="https://jbryden.co.uk/home/">John Bryden</a>, and <a href="https://cnets.indiana.edu/fil">Filippo Menczer</a> .</p> <p>Please see the README.md file for important details! If you would like to report issues with the code, you can do so through an associated GitHub repository that houses the project's code, which can be found <a href="https://github.com/osome-iu/cascade_reconstruction">here</a>.</p>
Molecular Dynamics Trajectories of Membrane-bound Influenza Hemagglutinin (A/duck/Alberta/35/76) in Complex with 0-3 Copies of FISW84 Fab Fragments
<p>MD simulation trajectories of influenza hemagglutinin (A/duck/Alberta/35/76) in a bilayer mimicking the viral membrane, with 0-3 copies of FISW84 antibody's Fab domains bound.</p> <p>Files are named as (copy number of Fab).(simulation replica ID).(file type extension). The systems were constructed in CHARMM format PSF files and the trajectories were recorded in DCD format.</p> <p>Simulations were performed with NAMD3. The trajectories were re-centered and re-wrapped about the periodic boundary from the raw trajectories, in order to keep the HA-Fab complex and the lipid bilayer appearing as a single continuous entity instead of isolated molecules at the opposite side of the periodic boundary. Water molecules in the simulation were removed in these trajectories due to file size limitations.</p>
DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY
<p>The rapid advancement of technology has transformed the financial services sector, leading to the rise of fintech companies that leverage cutting-edge tools such as artificial intelligence (AI) and machine learning (ML) to offer innovative solutions. One area where fintech is particularly impactful is dynamic pricing, which involves adjusting prices in real-time based on market conditions, user behavior, and external factors. The ability to optimize pricing in response to fluctuating conditions is critical for maximizing profitability, improving customer satisfaction, and maintaining competitiveness. In this context, machine learning algorithms provide a powerful framework for making data-driven pricing decisions by learning from historical data and predicting future trends.</p>
Data used in the manuscript "Pioneering evidence of the dynamics of water vapor and CO2 fluxes in Sahara Desert soils"
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Data from: Predicting the spatio-temporal dynamics of biological invasions: Have rapid responses in Europe limited the spread of the Asian yellow-legged hornet (Vespa velutina nigrithorax)?
<p>This data was collated from multiple sources including aktion-wespenschutz.de (Germany), The Biological Records Centre, UKCEH (UK), GBIF, L'Inventaire national du patrimoine naturel (France), iNaturalist (Belgium), StopVelutina (Italy), Waarneming.nl (Netherlands), MAGRAMA. Inventario Español del Patrimonio Natural y la Biodiversidad (Spain). </p> <p>Coordinates of occurence records were obatained from the sources above. These records were used in ecological niche models and mechanistic models to simulate the spread of <em>Vespa velutina nigrithorax</em> in Belgium, Germany, The Netherlands and the United Kingdom. </p> <p>Please be sure to cite and credit the orginal data providers if you intend to use this data. </p>
Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS using Rapid Update 4DVar Assimilation of C-band Phased Array Weather Radar Data
<div> <p>These are data supporting the conclusions of the paper. The observation dataset including sounding data, automatic weather station data and radar data. And Vdras outputs are also provided here. The corresponding scripts are based on python(version 3.9) and NCL (version 6.6.2).</p> <p> </p> </div> <h2>Files</h2>
Molecular dynamics analysis of iPP-polymorphs; a dataset of alpha and beta atomic structures
<p>This is the dataset corresponding to the publication in the <em>Polymer </em>journal:</p> <p>"Molecular dynamics analysis of iPP-polymorphs; Investigating thermal expansion and elastic properties"</p> <p>Authors:<strong> H.N. Chávez Thielemann, J.A.W. van Dommelen, L.E. Govaert, M. Hütter</strong></p> <p>Year: 2024</p> <p> </p> <p>The dataset presented here provides the chemical structures of iPP crystals, including COMPASS forcefield parameters, as follows:</p> <p>α structures were obtained by repeating the crystalline unit cell 4 times in a, 2 times in b, and 4 times in c (comprising 32 chains and 3456 atoms)</p> <ul> <li><strong><a href="https://zenodo.org/records/14048060/files/alpha2.zip?download=1">alpha2.zip</a>: </strong>α2 is the unit cell with perfect up-down alternation.</li> <li><a href="https://zenodo.org/records/14048060/files/alpha1.zip?download=1"><strong>alpha1.zip</strong></a>: α1 is an α2 but with 50% random up-down alternation.</li> </ul> <p>A X% of regio defects means that X% of the monomers are incorporated with the inverse head-tail order than in a perfect α2 case.<br>Thus, number of atoms and chains remain unvaried.</p> <ul> <li><a href="https://zenodo.org/records/14048060/files/d2p.zip?download=1"><strong>d2p.zip:</strong></a> α2 containing 2% of regio defects.</li> <li><strong><a href="https://zenodo.org/records/14048060/files/d4p.zip?download=1">d4p.zip:</a> </strong>α2 containing 4% of regio defects.</li> </ul> <p>Vacancy, 31 chains, 3348 atoms:</p> <ul> <li><strong><a href="https://zenodo.org/records/14048060/files/v1.zip?download=1">v1.zip:</a> </strong>contains the same α2 but with a vacancy, i.e. a complete chain is missing.</li> </ul> <p>β structures were obtained by repeating the crystalline unit cell 3 times in a, 2 times in b, and 4 times in c (comprising 36 chains and 3888 atoms)</p> <ul> <li><strong><a href="https://zenodo.org/records/14048060/files/beta2.zip?download=1">beta2.zip:</a> </strong>β2 structure is a monochiral domain, with purely right-handed chains.</li> <li><a href="https://zenodo.org/records/14048060/files/beta1.zip?download=1"><strong>beta1.zip:</strong></a> β1 structure comprises twelve left- and twenty-four right-handed chains.</li> </ul> <p> </p> <p>File names ended with <strong>_img</strong> indicates that supplementary images are provided for that structure.</p> <p>In most of the cases, the LAMMPS data files are accompanied by a PDB file for completeness.</p> <p>To download them all including extra files at once, then download the archive file <a href="https://zenodo.org/api/records/14048060/files-archive"><strong>14048060.zip</strong>.</a></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.