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11,506 results for “Dynamics”
Design and assembly of core/shell nanostructures as investigated by microfluidics and molecular dynamics simulation_dataset_DLS_TEM_MD
<p><span>He we like to publish data related to modified and non-modified MSN cores analysed using microfluidics platform against acetalated dextran (AcDEX)/spermine modified acetalated dextran (SpAcDEX) polymers. </span></p> <p><span>The data contains Dynamic light scattering (DLS) and TEM images which help us to to the demarcation of combinations which formed successful core/shell particles along with, <em>in-silico</em> modelling and molecular dynamics (MD) simulations data showing molecular interactions between the core particles and <a>the encapsulant </a></span><span><span></span></span><span>polymer. </span></p>
Data for: Nitrogen dynamics and carbon sequestration in soil following application of digestates from one- and two-step anaerobic digestion
<p>Data set for article <span>Nitrogen dynamics and carbon sequestration in soil following application of digestates from one- and two-step anaerobic digestion (https://doi.org/10.1016/j.scitotenv.2022.158177).</span></p>
Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning
<div> <p>Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.</p> </div> <div></div>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 02)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p> </p> <p>Please cite us if you find this data useful!</p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 03)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p> </p> <p>Please cite us if you find this data useful!</p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 01)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p>Please cite us if you find this data useful!</p>
Data for "Diel Variability and Decoupled pH-Oxygen Dynamics Drive Metabolic Plasticity in Kelp Crabs from an Upwelling Seascape".
<p>Data for “<span>Diel Variability and Decoupled pH-Oxygen Dynamics Drive Metabolic Plasticity in Kelp Crabs from an Upwelling Seascape</span>”.</p> <p>By Vargas, C.A. et al.</p> <p> </p> <p>Two files:</p> <p>Readme.txt</p> <p>Diel cycle Taliepus upwelling dataset.xlsx</p> <p> </p> <p>-Sheet “Upwelling index” Time-series of “u” and “v” component of the wind, and calculated upwelling index (UI) for two coastal sites in Arauco Gulf, an upwelling shadow (US) area, and an active upwelling (AU) area. Dataset used in Figure 1A.</p> <p>-Sheet “Time series”. Time-series data of seawater temperature (T), dissolved oxygen (DO), and pH for an upwelling shadow (US) area, and an active upwelling (AU) area. T in ºC, DO in mg L-1, and pH units. Dataset used in Figure 1D, E, Figure 2A, B, C, and Figure 3A, B, C, D.</p> <p>-Sheet “O2 consumption and C production”. Results data for oxygen consumption (umol O2 g-1 h-1), carbon production (umol C g-1 h-1), and respiratory quotient (RQ) during incubations. Datast used in Figure 5A, B, C, D, E, F.</p> <p>-Sheet “Glucose and lactate content”. Results data for hemolymph pH, glucose and lactate content (mg mg-tissue-1) in Taliepus dentatus muscle during incubations. Datast used in Figure 6A, B, C, D, E, F.</p> <p>All date and time (Date_Time) data format (mm/dd/yyyy hh:mm:ss)</p>
Fault zone material files for dynamic rupture modeling of the 2019 Ridgecrest earthquakes
<p>This repository contains the material files used to add a low-velocity fault zone to a dynamic rupture model of the 2019 Ridgecrest sequence (Taufiqurrahman et al., 2023, Nature).</p>
Dynamic Person Follower path generation using filtering and parametric curves
<p>These videos showcase results from a PhD thesis focusing on person path filtering and Bézier curve path generation and following, employing a parametric trajectory controller.</p> <div><a href="https://zenodo.org/api/records/14117216/draft/files/StraightLineParametricPersonFollwer.mp4/content" target="_blank" rel="noopener noreferrer">StraightLineParametricPersonFollwer.mp4</a>: Demonstrates the filtering process of a simulated person walking in a straight line with significant variance in position data.</div> <div> </div> <div><span><a href="https://zenodo.org/api/records/14117216/draft/files/C1ContinuityInPersonFollowing.mp4/content" target="_blank" rel="noopener noreferrer">C1ContinuityInPersonFollowing.mp4</a></span>: Highlights the generation of Bézier curves that maintain C1 continuity, ensuring smooth transitions between curve segments.</div> <div> </div> <div><span><a href="https://zenodo.org/api/records/14117216/draft/files/DynamicPersonFollowerRealWorldTest.mp4/content" target="_blank" rel="noopener noreferrer">DynamicPersonFollowerRealWorldTest.mp4</a></span>: Features a real-world test where the Robot Model2E dynamically follows a person using parametrically generated curves.</div>
Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells
<p>The raw data presented in this folder corresponds to the publication<br># Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells</p> <p>Johanna E. M. Dickmann 1,2, Marjolaine Martin 1,§, Claire Lionnet 1,§, Zoe Nemec-Venza 1, Olivier Hamant 1,2</p> <p>1 Laboratoire Reproduction et Développement des Plantes, ENS de Lyon, INRAE, CNRS, UCBL1 <br>2 Correspondence: olivier.hamant@ens-lyon.fr, johanna.dickmann@ens-lyon.fr <br>§ Equal contribution </p> <p>ORCIDs:<br>* Johanna Dickmann: 0000-0002-0861-4440<br>* Zoe Nemec-Venza: 0000-0002-2346-2596<br>* Olivier Hamant: 0000-0001-6906-6620</p> <p>Submitted to bioRxiv in November 2024 </p> <p>This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 521501033 to J.D. and the European Research Council (ERC-2021-AdG-101019515 “Musix” to O.H.).</p> <p>## Data organization</p> <p>The data are organized according to the figure panels in the publication. For large experiments, the folders may contain subfolders for each experimental repeat and/or condition. For explanations on the data and the methods, please refer to the publication.</p> <p>All data presented here are the original raw data output of the microscopes in the CZI format, a proprietary format developed by Zeiss, encapsulating both 4D image data and metadata, i.e. acquisition settings. This format is supported by open-source software such as Fiji and Open Microscopy Environment.</p> <p>Refer to the README files in the subfolders for information on which exact file was used to display in the figure.</p> <p>## Explanation of the file names</p> <p>### Arabidopsis experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* an experiment identifier (e.g.”PLA001”, “PRO077”)</p> <p>* the line of the imaged plant material (e.g. “pUBQ10-LTi6B-TdTomato”)</p> <p>* sometimes information on the ecotype of the line (e.g. “Col-0”)</p> <p>* the age of the plants (e.g. “7d” = 7 day old plants)</p> <p>* sometimes information on a stain added (e.g. “FM4-64_0_5ugPml” = FM4-64 dye at a final concentration of 5 ug/ml)</p> <p>* sometimes information on a treatment (e.g. “beforeFDA” = image taken before FDA was added; “FDA2.5ugPml” = after adding FDA at a final concentration of 2.5 ug/ml, sometimes with additional information on the time between adding the FDA and imaging, e.g. “25min”)</p> <p>* sometimes information on the centrifugation speed (e.g. “100g”)</p> <p>* sometimes information on the imaging support (e.g. “bucket” = NOA73 container, “wells” = NOA73 microwells, “coverslip”)</p> <p>* sometimes information on the imaging mode (“z-stack”, “t-series” = time series/timelapse, “6x” = zoom of 6 in Zen software)</p> <p>* the solution the sample was imaged in (“Solution A” or “A” = hyperosmotic buffer with D-mannitol, “AS” = hyperosmotic buffer with D-sorbitol)</p> <p>* sometimes information on experimental setup (“ON” = overnight timelapse imaging)</p> <p>* increasing numbers at the end of the file name indicate subsequent fields of view or positions imaged with the same settings</p> <p>* for Fig. 1A: information about the length of the plasmolysis (“50 min”)</p> <p>* for Fig. 4c,d: information on which solution the protoplasts are and have been imaged in: “A” = hyperosmotic buffer solution with 600 mM D-mannitol. “B” = hyperosmotic buffer solution with 280 mM D-mannitol. Times indicate time between addition of new buffer and onset of imaging of the position list.</p> <p>* for Fig. 4e-f: the concentration of the hyperosmotic buffer solution is indicated. For the control, the number of additions of hyperosmotic buffer solution with 600 mM D-mannitol is indicated.</p> <p>### Physcomitrium patens experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* an experiment identifier (e.g. "PyP001") </p> <p>* “Physco_wt” referring to Physcomitrium patens wild type</p> <p>* the age of the moss tissue used for protoplasting (e.g. “6d” = 6 days)</p> <p>* information on the stain added (e.g. “ Fm4-64_2ugPml” = FM4-64 dye at a final concentration of 2 ug/ml)</p> <p>* information on the imaging support (e.g. “bucket” = NOA73 container, “coverslip”)</p> <p>### Maize experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* the date on which the experiment was performed (yyyymmdd)</p> <p>* the plant species (“Maize”)</p> <p>* sometimes information in the solution used for digestion ("A+E" = hyperosmotic buffer solution with D-mannitol)</p> <p>### Bead experiments</p> <p>The filenames contain the following information, separated by underscores:<br>* an experiment identifier (e.g. “beads008”)</p> <p>* a description of the beads (“fluoresbrite1micron” = Fluoresbrite beads of a diameter of 1 um)</p> <p>* sometimes a short description of the protocol (e.g. “SolAwashed-2-3mLsolA” = NOA73 microwells were washed in hyperosmotic buffer with 600 mM D-mannitol 2x prior to imaging, beads were imaged in 3 ml hyperosmotic buffer solution with 600 mM D-mannitol.)</p> <p>* an information on the size of the field of view (e.g. “small FOV” = small field of view, i.e. one microwell with beads)</p> <p>* information at which approx. height of the microwell the image was taken (“TopOfWells” = close to the opening of the wells on the top)</p> <p>* sometimes information on the zoom of the Zen software (e.g. “7x”)</p>
Dynamical ising dataset for the paper Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
<p>This dataset provide the evolution in time for the magnetizaion in the 2D Ising model evolved with Gluber dynamics for a lattice of size 64 x 64.</p>
Spatiotemporal dynamics of grassland aboveground biomass in northern China and the alpine region: Impacts of climate change and human activities
<p>We employed CASA model to estimate grassland Net Primary Productivity and aboveground biomass A(AGB) from meteorological and GIMMS Normalized Difference Vegetation Index (NDVI) remote sensing data in northern China. We analyzed the dynamics of grassland AGB and impacts climate change and human activites.</p>
Spatiotemporal dynamics of grassland aboveground biomass in northern China and the alpine region: Impacts of climate change and human activities
<p>We employed CASA model to estimate grassland Net Primary Productivity and aboveground biomass A(AGB) from meteorological and GIMMS Normalized Difference Vegetation Index (NDVI) remote sensing data in northern China. We analyzed the dynamics of grassland AGB and impacts climate change and human activites.</p>
Plasticizer Content by TGA - Isothermal TGA runs at 250 °C and dynamic TGA runs on the residue of isothermal runs.
<p>Datasets obtained for the Plasticizer Content by TGA analysis, from the paper "An Integrated Characterization Strategy on Board for Recycling of poly(vinyl butyral) (PVB) from Laminated Glass Wastes"</p>
Distribution of Polyphosphate Kinase 2 Genes in Bacteria Underscores a Dynamic Evolutionary History
<p>This repository contains the supplementary files associated with the study 'Distribution of Polyphosphate Kinase 2 Genes in Bacteria Underscores a Dynamic Evolutionary History'.</p>
Supporting molecular simulations data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3"
<p>Supplementary data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3: <a href="https://doi.org/10.1126/sciadv.abe3326">10.1126/sciadv.abe3326</a>"</p>
Great Power Dynamics and International Economic Cooperation: Experimental Evidence from Parallel Surveys in China and the United States
Open the record for dataset details and reuse information.
Data from:Modelling the spatiotemporal dynamics of soil nitrogen in croplands of Northeast China from 1980 to 2023 using multisource data and machine learning
<p>This dataset include the spatiotemporal distribution and uncertainty of cropland soil total nitrogen content at 0-30, 30-60, 60-100 cm depths in Northeast China from 1980 to 2023. The long-time series of TN were estimated by using an space-time automatic machine learning. The detail information on the products were given below:</p> <p>Period: 1980-2023</p> <p>Spatial resolution: 0.004166667 degree (~500 m)</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Data format: GeoTIFF</p>
Data - Quantifying dynamic linkages between precipitation, groundwater recharge, and streamflow using ensemble rainfall‐runoff analysis
<p>The data support the analysis conducted in "Quantifying Dynamic Linkages Between Precipitation, Groundwater Recharge, and Streamflow Using Ensemble Rainfall‐Runoff Analysis", accepted for publication in Water Resources Research (https://doi.org/10.1029/2024WR037821) by Huibin Gao, Qin Ju, Dawei Zhang, Zhenlong Wang, Zhenchun Hao, and James Kirchner.</p> <p> </p>
Molecular Dynamics Assessment of Fusion Relevant Elements Figures
<p>Figures for "Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements"</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.