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5,371 results for “Flow”
Microtubule retrograde flow retains neuronal polarization in a fluctuating state
<p>In developing vertebrate neurons, a neurite is formed by more than a hundred microtubules. While individual microtubules are dynamic, the microtubule array has been regarded as stationary. Using live-cell imaging of neurons in culture or in brain slices, combined with photoconversion techniques and pharmacological manipulations, we uncovered that the microtubule array flows retrogradely within neurites to the soma. This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth. The motor protein dynein fuels this process. Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension. Thus, keeping neurites short is an active process. Microtubule retrograde flow is a novel type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.</p>
Dataset for the journal article: " In-situ Capillary Pressure and its Interrelationships with Flow Characteristics during Steady-state Three-phase Flow in Water-wet Berea Sandstone"
<p>Dataset 1 contains the summary results of the image analysis performed on a set of three-phase micro-CT images. <br> The analysis are:(1) Fluid saturations, (2) Characteristics of Gas clusters, (3) Characteristics of Oil clusters, (4) Pore-fluid occupancy, and (5) In-situ capillary pressure measurements. </p> <p>Dataset 2 contains the three-phase relative permeability data with the correction analysis. </p>
WaterGEMS models for flow starved water transmission network
<p><strong>This data contains WaterGEMS models of a Water Transmission Network (WTN) in India. It deploys a newly developed modeling approach, Pressure Driven Analysis for Flow Straved Condition(PDA-FSC). It is used for evaluating the performance of flow-starved WTN. </strong></p>
Routine to reproduce the figures from "Experimental study of the flows in a non-axisymmetric ellipsoid under precession"" JFM, 2022
<p>The Zip file contains the python notebook and all necessary datasets to reproduce the figures from the publication:</p> <ol> <li>Burmann, F. and <strong>Noir, J</strong>., 2022. Experimental study of the flows in a non-axisymmetric ellipsoid under precession. <em>Journal of Fluid</em> <em>Mechanics</em>, <em>932</em>,<a href="https://doi.org/10.1017/jfm.2021.932">https://doi.org/10.1017/jfm.2021.932</a></li> </ol> <p>The data are saved in .npz format, the structure of the data is explained in the header of the python jupyter notebook. </p>
Data on elevation of Heixiluo gully after a debris flow event
<p>The data is the elevation of gully after the debris flow in the Heixiluo, which is used in the paper submitted to Geophysical Research Letters (Modeling Progressive Erosion of Consolidated Landslide Dams by Debris Flows, [Paper # 2022GL101764]). </p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Lid-Driven Cavity Re=400 flow solution computed using LUMA and Code_Saturne coupled to each other
<p>This dataset is the result of running the Code_Saturne and LUMA codes coupled to each other to simulate a standard Re=400 Lid-Driven Cavity problemon ARCHER2. This is a test case for the coupling of the two codes.</p> <p>The domain is a unit cube. LUMA evolved the portion $x \le 0.6$, and Code\_Saturne evolved the portion $x \ge 0.4$. The boundary $x=0$ was driven with a velocity $u_y = 1$. Boundary data at the coupling boundaries is obtained from the other code using the PLE library.</p> <p>See https://github.com/cfdemons/cs-luma-archer/blob/main/tutorial.md for details to reproduce this dataset.</p> <p> </p>
Container flows on road, rail and waterways along Rhine-Alpine corridor (Rhine section) at NUTS-2 level with cost-time-emissions estimates and accessibility-frequency-availability of modes
<p>The present dataset is used to estimate the heterogeneous mode choice preferences of shippers, that are presented in the following article :<br> "A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data"<br> (Nicolet, A., Negenborn, R. R. & Atasoy, B., A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data. IEEE Open Journal of Intelligent Transportation Systems, Vol. 3, 2022, pp. 650-661.)</p>
Simulating flow forming/spinning of as-cast aluminum at elevated temperature: example implicit & explicit FEA
<p>This collection of files largely automates the creation and running of FEA models of rotary forming at temperature. The modelling approaches are detailed further here:</p> <p>M.J. Roy, D.M. Maijer, Analysis and modelling of a rotary forming process for cast aluminium alloy A356, Journal of Materials Processing Technology, Volume 226, 2015, Pages 188-204, ISSN 0924-0136, http://dx.doi.org/10.1016/j.jmatprotec.2015.06.036.<br> </p> <p>Briefly, the multi-stage model was developed with MATLAB 7.2 and Abaqus 11.2 and configured to run on a Linux-based system with PBS functionality, last tested on a muti-node CentOS 5 system. The main script is <strong>FFauto.m</strong>, which will write input files and user subroutines and call <strong>FFauto_Abaqus11Submit.m</strong> to run the input files or intermediate post-processing to set up the next job. More specifically:</p> <ol> <li>FFauto will write an input deck based on 2D implicit heat transfer and then run it.</li> <li>Once the analysis is complete, it will perform some cursory post processing by writing an Abaqus Python script and then submitting that.</li> <li>Based on the post-processing of the implicit analysis, it will then generate a 3D explicit forming analysis input file. Depending on the operator's input, this can include an explicit cooling step as well.</li> <li>It will then submit the 3D analysis.</li> </ol> <p>Note that steps can be skipped - that is, if the results for an implicit heat transfer calculation are available, then it will use those results to generate the explicit step(s).</p> <p>Note that the implicit user subroutine *uhard.f contains a DFLUX subroutine in addition to the UHARD subroutine. This was not employed in the published work. Leave the <em>ArtHeat</em> variable as <em>True</em> to both speed up simulation times and get similar results as published.</p> <p>Running <strong>FFauto.m</strong> is accomplished by submitting the <strong>FFauto4.pbs</strong> file.</p>
Supplementary data: Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments
<p>The files in this record represent supplementary data for the article titled “Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments” in Water Resources Research. The files contain simulations of the HBV rainfall-runoff model for 14 alpine catchments in Switzerland. The model simulated different water balance components (such as runoff, snow water equivalent and evapotranspiration) for the reference period 1980-2009 and the three scenario periods (2020-2049, 2045-2074 and 2070-2099) using the A1B emission scenario.</p>
Laboratory data - physical modelling of gravel bed rivers under unsteady flow conditions
<p>Sediment transport and topography data from laboratory experiments under unsteady flow conditions.</p> <p>Data supporting the manuscript:<br> Redolfi, M., Bertoldi, W., Tubino, M., & Welber, M. (2018). Bed load variability and morphology of gravel bed rivers<br> subject to unsteady flow: A laboratory investigation. Water Resources Research, 54, 842–862. https://doi.org/<br> 10.1002/2017WR021143<br> Details about the physical model and the experimental procedure can be found in the paper.</p>
Research data supporting "Platinum Nanocatalyst Amplification: Redefining the Gold Standard for Lateral Flow Immunoassays with Ultra-Broad Dynamic Range"
<p>Research data supporting the publication: Loynachan C. N., et al., 2017, ACS Nano, DOI: http://dx.doi.org/10.1021/acsnano.7b06229.</p>
Data for: Quantifying Bankfull Flow Width Using Preserved Bar Clinoforms from Fluvial Strata
<p>Reconstruction of active channel geometry from fluvial strata is critical to constrain the water and sediment fluxes in ancient terrestrial landscapes. Robust methods—grounded in extensive field observations, numerical simulations, and physical experiments—exist for estimating the bankfull flow depth and channel-bed slope from preserved deposits; however, we lack similar tools to quantify bankfull channel widths. We combined high-resolution lidar data from 134 meander bends across 11 rivers that span over two orders of magnitude in size to develop a robust, empirical relation between the bankfull channel width and channel-bar clinoform width (relict stratigraphic surfaces of bank-attached channel bars). We parameterized the bar cross-sectional shape using a two-parameter sigmoid, defining bar width as the cross-stream distance between 95% of the asymptotes of the fit sigmoid. We combined this objective definition of the bar width with Bayesian linear regression analysis to show that the measured bankfull flow width is 2.34 ± 0.13 times the channel-bar width. We validated our model using field measurements of channel-bar and bankfull flow widths of meandering rivers that span all climate zones (R2 = 0.79) and concurrent measurements of channel-bar clinoform width and mud-plug width in fluvial strata (R2 = 0.80). We also show that the transverse bed slopes of bars are inversely correlated with bend curvature, consistent with theory. Results provide a simple, usable metric to derive paleochannel width from preserved bar clinoforms.</p>
Key dataset used in the paper of "Emergent constraints reveal lower estimates of global river flow"
<p>This dataset includes key data used in the emergent constraint approach for refined partitioning of global water cycle components. </p>
531MVFSP - Multivision flow SP
<p>Multivision flow SP</p>
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 5 (2023) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Tourists and Visitors Flows in Lombardy
<p><span>The dataset provides information about the aggregate yearly flow of individuals travelling across 120 municipalities located in Lombardy (Italy) with details on the origin and destination of the movement. The dataset refers to the calendar year 2022.</span></p> <p><span>The dataset also distinguishes among two different travelling profiles, namely visitors and tourists. In particular, visitors are defined as those individuals who make a visit outside of the municipality of usual residence for at least four hours without an overnight stay. On the other hand, Tourists are defined as those users with a night cell referring to a municipality that differs from the phone residence. Such definitions of these two travelling behaviour are consistent with those provided by official statistical offices.</span></p> <p><span>In particular, the dataset “db_tourists.csv” provides information on the number of tourists moving from the municipality specified in the column “Origin_id” to the municipality specified in the column “Destination_id”.</span></p> <p><span>The dataset “db_visitors.csv” provides information on the number of visitors moving from the municipality specified in the column “Origin_id” to the municipality specified in the column “Destination_id”.</span></p> <p><span>Municipalities are anonymized and indicated through an index ranging between 1 and 120. </span></p> <p><span>The mobile network data used in this paper have been made available to the authors by Polis, a public entity collaborating with the Lombardy region. These data are provided by a main telecommunication company in an anonymised and irreversibly aggregated form, in compliance with the privacy legislation, and the provisions of the EU GDPR, according to the Privacy by Design methodology.</span></p>
Chemical and electromagnetic parameters of NOx-rich gas flow under the influence of multi-frequency electromagnetic field (numerical data)
<p>Metastable and non-stable states lifetime dependence on the exciting electromagnetic field parameters for the quantum series of the main ground state – dissociation consequence.</p> <p>Overtones and main frequencies spectrum and probabilities for NO molecule resonance excitation.</p>
A novel flow chamber for biodegradable alloy assessment in physiologically realistic environments: Supporting Data
<p>Experimental data including Excel analysis and microscopy supporting the publication 'A novel flow chamber for biodegradable alloy assessment in physiologically realistic environments' <a href="https://doi.org/10.1063/1.4821498" target="_blank" rel="noopener">https://doi.org/10.1063/1.4821498</a></p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</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.