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25 results for “Brownian”
Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state (Data Underlying Figures)
<p>Data underlying figures in A. Bain 'Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state' RSC Environmental Science: Nano, 2022. </p> <p>CA = citric acid<br> NaCl = sodium chloride<br> AS = ammonium sulfate</p> <p>rho = difference in density (g/cm^3)<br> Rh = % relative humidity<br> radius is in micrometers<br> Pe0 are the calculated dimensionless Peclet numbers<br> </p>
Brownian Relaxation Shakes and Breaks Magnetic Iron Oxide-Polymer Nanocomposites to Release Cargo
<p>Original data supporting the findings of the manuscript and supplementary materials sorted after Figures and their respective panels.</p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - photon timetag simulation files
<p>Attached are the photon timestamp and channels simulated for different 3D diffusing molecules simulations at different conditions (simulation was performed by PyBroMo).</p> <p>Each of the files has, in its name, a code. The meaning of the codes are as following:</p> <pre>f32445 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model </pre> <pre>a01f8f - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>9ff667 - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>71154a - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>ad926d - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>1ab235 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a numerical PSF model</pre> <pre>d00978 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a numerical PSF model</pre> <p> </p> <pre>2469bb - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model </pre> <pre>4be121 - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>a7088f - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>023983 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>653f61 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>4f06ee - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <pre>dec32c - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <p> </p> <pre>85b0a1 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Numerical PSF model</pre> <pre>964ef3 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Numerical PSF model</pre> <p> </p> <pre>f28f6e - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Gaussian PSF model</pre> <pre>c311dd - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Gaussian PSF model</pre>
Data associated with "Decoding the hydrodynamic properties of microscale helical propellers from Brownian fluctuations"
<p>Deskewed data, thresholded data, and data analysis associated with https://arxiv.org/abs/2208.13854</p>
Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
Open the record for dataset details and reuse information.
Multiscale simulation data for demonstrating the analogy between grain boundaries and Brownian ratchets
Open the record for dataset details and reuse information.
Dataset for SciPost Phys. 9, 064 (2020) - Engineered swift equilibration of brownian particles: consequences of hydrodynamic coupling
<p> </p> <p><strong>1) "indep_tf25_Kf15_std18" and "sym3_tf35_Kf14_std25" Matlab files. </strong></p> <p>Each file is in Matlab format and contains 2 variables : "header" which contains the date, the number of samples and the sampling frequency of the acquisition, and "data" which is a 1D array. This array stores side by side the voltage corresponding to each of the four quadrants of the photodiode and the controled voltage that drives the intensity of the first laser beam (sent to the AOD).</p> <p>For example, in order to put together the voltage of the 1st quadrant of the photodiode we have to read one every 5 points from the array.</p> <p>Each file contains several protocols, depending on the duration of the protocol.</p> <p> "indep_tf25_Kf15_std18" : Those files corresponds to the response of the 1st particle to the Coupled ESE of the Figure 6</p> <p><strong>2) "Analyse_data_ESE_protocol" Matlab file </strong></p> <p>This script is written to analyse data format like the ones of this datasheet. If uses the data array to build the variance of the particle during the protocol. </p> <p>It was used to plot all the experimental results of the paper.</p> <p>"sym3_tf35_Kf14_std25" : Those files corresponds to the response of the 1st particle to the Symmetric ESE of the Figure 6</p> <p><strong>3) The "_result"</strong> is the post-treatment file obtained with the "Analyse_data_ESE_protocol" script.</p> <p><strong>4) "Figure_2" "Figure_6" "Figure_8" Matlab figures</strong></p> <p>Those Matlab files contains all the treated data (including error bars) used to plot the figures 6, 2, 8. </p> <p><strong>5) "equilibrium_correlation_fuctions' Mathematica file</strong></p> <p>This file is a computation notebook in Mathematica language that computes the expression of the correlation functions and plot them as in Figure 3</p> <p><strong>6) "Response_to_sym_protocol" Mathematica file </strong></p> <p>This file is a computation notebook in Mathematica language that, knowing the distance between the particles, predicts the variance evolution of the particles during the symmetric protocol by solving the coupled evolution equations. It was used to plot Figure 4 and for all theoretical predictions in other figures.</p> <p> </p> <p> </p> <p>All the programs are filled of comments that detail how to use them. </p>
Collective dynamics and pair-distribution function of active Brownian ellipsoids
<p>Supplemental data for the following manuscript: Stephan Bröker, Michael te Vrugt, Raphael Wittkowski,</p> <p>"Collective dynamics and pair-distribution function of active Brownian ellipsoids"</p>
Data for "FC2DES: Modeling 2D electronic spectroscopy for the Generalized Brownian Oscillator Molecular Hamiltonian"
<p>Within is the 2DES presented in the manuscript: "FC2DES: Modeling 2D electronic spectroscopy for the Generalized Brownian Oscillator Molecular Hamiltonian" as well as the parameter and input files used to create the spectra via the Python code package MolSpecPy.</p>
Dataset for "Decoupling elasticity and electrical conductivity of carbon black gels filled with insulating non-Brownian grains"
<p>A unique bistable transition has been identified in granular/colloidal gel-composites, resulting from shear-induced phase separation of the gel phase into dense blobs. In energy applications, it is critical to understand how this transition influences electrical performance. Mixing conductive colloids with conductive inclusions, we find the conductivity and elasticity move in concert, both decreasing in the collapsed phase-separated state. Surprisingly, with insulating inclusions these properties can become decoupled, with the conductivity instead increasing despite the collapse of the gel structure.</p>
Brownian Dynamics simulations of yeast Nuclear Pore Complex FG repeats
<p>The files in this folder can be used to reproduce the Brownian Dynamics simulations of FG repeats using IMP as described in Kim et al., 2018.</p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - raw figures of all quantities from all simulations
<p>We performed many simulations in many conditions, calculated many quantities and reported them in the main paper. However, the journal manuscript did not place for so many figures. Nevertheless, the reported values of all simulations rely on the results that were also summarized in the figures. These figures were produced by the Jupyter notebooks analyzing the simulation results.</p> <p>Each figure file has the following nomenclature:</p> <p>TypeOfFigure_concentration_diffusionCoefficient_1or2pop_simulationDuration(long)_Type of PSF.</p> <p>For example:</p> <p><em>molecule_positions_pure_and_impure_bursts_varying_F_C62_D22_5_psfNumeric</em> - report on the molecular position histograms of pure and impure bursts for varying F values, in the simulation with C=62pM, D=22.5 um^2/s, a NUmerical PSF model and 60s duration of simulation (if nothing appears, it was 60s; if the word <em>long</em> appears, it was 180s).</p>
Grain boundaries are Brownian ratchets
<p># Phase-field crystal simulations for paper "Grain Boundaries are Brownian Ratchets"</p> <p>Openly available Matlab simulation files used to produce the phase-field crystal results from https://www.science.org/doi/10.1126/science.adp1516 see also https://zenodo.org/records/13319941</p> <p>Runable Matlab files can be found within the respective folders BICRYSTAL, POLYCRYSTAL_MAINTEXT and POLYCRYSTAL_LARGER_ACCELERATION and are named by the corresponding figures.</p> <p>## Update<br>Since the acceptance and publication of our paper, many friends and colleagues have been interested in whether a larger acceleration in grain growth rate through cyclic annealing can be achieved. We updated the dataset by adding a new version with an additional set of cyclic annealing simulations, indicating even more robust evidence of cyclic annealing-accelerating grain growth. The additional code can be found in the folder POLYCRYSTAL_LARGER_ACCELERATION, and the details and discussion can be found in the README file in the same folder.</p> <p>## Other simulations<br>The remaining input files for the MD simulations, neb calculations and python codes for the Markov modellings are not included in this repository and can instead be found under https://doi.org/10.5061/dryad.0k6djhb8v</p> <p><br>## Disclaimer<br>The software is released here under the MIT license. We kindly ask to refer to/cite the related publication https://www.science.org/doi/10.1126/science.adp1516 for any usage and extension. The authors are thankful for any advice considering typos, mistakes, and/or discussions around the code/implementation or the topic of the related publication in general. Please do not hesitate to contact one of the authors, Maik Punke, via: maik.punke@tu-dresden.de</p>
Orientation-dependent propulsion of active Brownian spheres: from self-advection to programmable cluster shapes
<p>Supplemental data for the following manuscript: Stephan Bröker, Jens Bickmann, Michael te Vrugt, Michael E. Cates, Raphael Wittkowski, "Orientation-dependent propulsion of active Brownian spheres: from self-advection to programmable cluster shapes".</p>
Active Brownian particles in external force fields: field-theoretical models, generalized barometric law, and programmable density patterns
<p>Supplementary data for the following manuscript: Jens Bickmann, Stephan Bröker, Michael te Vrugt, Raphael Wittkowski, "Active Brownian particles in external force fields: field-theoretical models, generalized barometric law, and programmable density patterns".</p>
Crossing the Brownian Bridge: A Discussion of Infrastructure Capacity Expansion Policies
<p>This spreadsheet supports the model developed in the corresponding paper</p>
Data from: Sediment availability provokes a shift from Brownian to Lévy-like clonal expansion in a dune building grass.
<p>This dataset accompanies the manuscript: 'Sediment availability provokes a shift from Brownian to Lévy-like clonal expansion in a dune building grass.' Reijers et al., accepted at Ecology Letters</p> <p>Abstract</p> <p>In biogeomorphic landscapes, plant traits can steer landscape development through plant-mediated feedback interactions. Interspecific differences in clonal expansion strategy can therefore lead to the emergence of different landscape organizations. Yet, whether landscape-forming plants adopt different clonal expansion strategies depending on their physical environment remains to be tested. Here, we use a field survey and a complementary mesocosm approach to investigate whether sediment deposition affects the clonal expansion strategy employed by dune-building marram grass individuals. Our results reveal a consistent shift in expansion pattern from more clumped, Brownian-like, movement in sediment-poor conditions, to patchier, Lévy-like, movement under high sediment supply rates. Additional model simulations illustrate that the sediment-dependent shift in movement strategies induces a shift in optimization of the cost-benefit relation between landscape engineering (i.e. dune formation) and expansion. Plasticity in expansion strategy may therefore allow landscape-forming plants to optimize their engineering ability depending on their physical landscape.</p> <p>Methods</p> <p>Data stored in this repository are collected during both a field survey and a one-year mesocosm experiment in which we measured traits from marram grass (<em>Ammophila arenaria</em>) growing in conditions with either low or high sediment availability or deposition. Focus was primarily on spatial shoot organization and the step size distribution of the distances between shoots as a proxy for clonal expansion strategy. These shoot coordinates were extracted from still images (photos from the field survey are included in the repository, images from the mesocosm experiment are available upon request) and distances were calculated using a nearest neighbour connecting algorithm. Detailed description of these methods can be found in the accompanying publication (Reijers <em>et al.</em> <em>Ecology Letters</em>) or in a previously published method protocol (Reijers & Hoeks, 2019 <em>Protocol Exchange)</em>. In addition we measured several plant growth related parameters such as: shoot length, shoot diameter, rhizome depth and leaf C:N ratio for the plant sampled during the field survey and shoot height, shoot numbers, shoot growth and leaf C:N ratio for the plants growing in the mesocosm experiment. Next to plant related data we measured several environmental characteristcs in the field including grain size, elevation, plant available nitrogen, organic matter, distance to sea and sediment deposition. For the mesocosm experiment we measured soil height, volume, organic matter, C:N and moisture content. A readme file was included to further explain file content. </p>
Data from: Revisiting Brownian motion as a description of animal movement: a comparison to experimental movement data
Characterization of patterns of animal movement is a major challenge in ecology with applications to conservation, biological invasions and pest monitoring. Brownian random walks, and diffusive flux as their mean field counterpart, provide one framework in which to consider this problem. However, it remains subject to debate and controversy. This study presents a test of the diffusion framework using movement data obtained from controlled experiments. Walking beetles (Tenebrio molitor) were released in an open circular arena with a central hole and the number of individuals falling from the arena edges was monitored over time. These boundary counts were compared, using curve fitting, to the predictions of a diffusion model. The diffusion model is solved precisely, without using numerical simulations. We find that the shape of the curves derived from the diffusion model is a close match to those found experimentally. Furthermore, in general, estimates of the total population obtained from the relevant solution of the diffusion equation are in excellent agreement with the experimental population. Estimates of the dispersal rate of individuals depend on how accurately the initial release distribution is incorporated into the model. We therefore show that diffusive flux is a very good approximation to the movement of a population of Tenebrio molitor beetles. As such, we suggest that it is an adequate theoretical/modelling framework for ecological studies that account for insect movement, although it can be context specific. An immediate practical application of this can be found in the interpretation of trap counts, in particular for the purpose of pest monitoring.
Raw data files for the analysis of Brownian motion of single DNA molecules
<p>Raw data files for the analysis of Brownian motion of single DNA molecules.</p>
Brownian Motion or Mean Reversion? A Parameter Based Approach to Stochastic Process Selection
<p>Data set and supporting files for article "Brownian Motion or Mean Reversion? A Parameter Based Approach to Stochastic Process Selection"</p> <ul> <li>ADF Tests log commodities SA (Excel and PDF files) – EViews outputs (18) of Augmented Dickey-Fuller tests run on all 9 Seasonally Adjusted time series (S A) with trend and intercept, and with only intercept.</li> <li>Commodities data historical x Inflation (Excel file) – Time series used in the paper in 3 versions: <ul> <li>In historical monthly averages prices US$,</li> <li>In December 2018 US$ prices (with US inflation adjustment in appropriate tab),</li> <li>In December 2018 US$ prices Seasonally Adjusted (S A) from EViews output.</li> </ul> </li> <li>commodities log SA data Ev1 (Excel file) – for the log of 9 commodities time series in Dec 2018 values, after STL decomposition in EViews: original series, seasonally adjusted, seasonal factor and trend</li> <li>commodities log SA Regressions (Excel and PDF files) - EViews outputs of OLS regression on log of seasonally adjusted times series: Dx = α + β x.</li> <li>GBMVaratAVG, GBMVaratPaths, MRMVaratAVG, MRMVaratPaths (Matlab codes) – Matlab codes for Variance ratio tests for GBM and MRM, sample paths (200) and averages of 2,500 paths.</li> <li>Series Seasonal data decomposition (PDF file) – graphical display of seasonal decomposition of log of 9 price series into: original series, trend, seasonal factor, remainder, and seasonally adjusted.</li> <li>Variance Ratio Comm SA (Excel file) - EViews outputs of variance ratio tests run of seasonal adjusted log of 9 price series in Dec 2018 values. </li> <li>ADF Tests log commodities SA (Excel and PDF files) – EViews outputs (18) of Augmented Dickey-Fuller tests run on all 9 Seasonally Adjusted time series (S A) with trend and intercept, and with only intercept.</li> <li>Commodities data historical x Inflation (Excel file) – Time series used in the paper in 3 versions: <ul> <li>In historical monthly averages prices US$,</li> <li>In December 2018 US$ prices (with US inflation adjustment in appropriate tab),</li> <li>In December 2018 US$ prices Seasonally Adjusted (S A) from EViews output.</li> </ul> </li> <li>commodities log SA data Ev1 (Excel file) – for the log of 9 commodities time series in Dec 2018 values, after STL decomposition in EViews: original series, seasonally adjusted, seasonal factor and trend</li> <li>commodities log SA Regressions (Excel and PDF files) - EViews outputs of OLS regression on log of seasonally adjusted times series: Dx = α + β x.</li> <li>GBMVaratAVG, GBMVaratPaths, MRMVaratAVG, MRMVaratPaths (Matlab codes) – Matlab codes for Variance ratio tests for GBM and MRM, sample paths (200) and averages of 2,500 paths.</li> <li>Series Seasonal data decomposition (PDF file) – graphical display of seasonal decomposition of log of 9 price series into: original series, trend, seasonal factor, remainder, and seasonally adjusted.</li> <li>Variance Ratio Comm SA (Excel file) - EViews outputs of variance ratio tests run of seasonal adjusted log of 9 price series in Dec 2018 values. </li> </ul>
ScienceDex guides
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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.