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5,371 results for “Flow”
Three-dimensional crustal channel flows beneath the southeastern Tibetan Plateau revealed by full-waveform ambient noise tomography
<p>This is a new version of Vp and Vs models for paper titled "Three‐Dimensional Crustal Channel Flows Beneath the Southeastern Tibetan Plateau Revealed by Full‐Waveform Ambient Noise Tomography" published in Geophysical Research Letters.</p> <p>Modification history: new Vs model includes from surface downward to 120 km depth.</p> <p>Please ignore the models in version 1 and 2.</p>
Far-from-equilibrium attractors with Full Relativistic Boltzmann approach in 3+1D: moments of distribution function and anisotropic flows v_n
<p>Datasets of the figures of the paper "Far-from-equilibrium attractors with Full Relativistic Boltzmann approach in 3+1D: moments of distribution function and anisotropic flows v_n" available on https://arxiv.org/abs/2409.12123</p> <p> </p>
Mud Flows in the Southwestern Utopia Planitia, Mars
<p>Results of a mapping campaign of putative mud-volcanic landforms in southwestern parts of Utopia basin, Mars based on CTX imagery:</p> <p>Muddy_Landforms_Curin_et_al_2021: Hills, ridges, plateaus, and complexly layered units around Adamas Labyrinthus</p> <p>Polygonal_Troughs_Curin_et_al_2021: Major polygonal troughs around Adamas Labyrinthus</p>
Numerical simulation of groundwater flow in backfilled open-pits for BDZ deviation performance
<p>This dataset contains simulated groundwater flow entering multiple backfilled open-pits with mine wastes (flowrate results) and some Python script to visualize the data.<br> The objective was to predict the blast damage zone (BDZ) created by mine excavation on the flowrate through the backfilled wastes.<br> Flowrates were obtained by 3D numerical simulation using PFLOTRAN finite volume code (www.pflotran.org).<br> Conceptual model of the BDZ and equivalent permeability was derived from Rousseau and Pabst (in press) and Mourzencko et al. (2012) (see https://hal.archives-ouvertes.fr/hal-01196684/file/mourzenko2012.pdf).<br> Number of cases simulated: 40500, with 40496 converged numerical simulations.<br> Calculated were performed using the High Performance Computing Ressource of Calcul Quebec (www.calculquebec.ca/) and Compute Canada (www.computecanada.ca/)</p>
Continuous Flow Synthesis of Iron Oxide Nanoparticles Using Water-in-Oil Microemulsion_experimental_dataset
<p>This dataset contains the raw experimental data to the article Sopoušek et al., Continuous Flow Synthesis of Iron Oxide Nanoparticles<br> Using Water-in-Oil Microemulsion, Colloid Journal, 2020, Vol. 82, No. 6, pp. 727–734. </p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Data in support of manuscript "Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary" submitted to JGR-Oceans
<p>Data set in support of manuscript "Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary" submitted to JGR-Oceans in November 2021. Matlab script (makeFigs_barDragMix_upload.m) is used to generate the figures from the manuscript. Data files (*.mat) correspond with each figure (*.png). For questions or additional information please contact D. Ralston.</p>
NDVI, nocturnal road traffic noise and traffic flow over the canton of Geneva
<p>This dataset relates the spatial distribution of several environmental variables (including NDVI and nocturnal road traffic noise) over the territory of the canton of Geneva (Switzerland) with the aim of studying the potential attenuation effect of vegetation on road traffic noise. Traffic flow measurement points are in this regard also included.</p> <p>The dataset contains two distinct group of files. The first one (hec_grid_ge) contains the hectometric vector grid discretization of the canton of Geneva excluding Lake Geneva. Each cell is characterized by the mean, median and standard deviation of several environmental variables (NDVI, nocturnal road traffic noise, daily road traffic noise and land surface temperature). In addition, BiLISA local Moran's I, p-value and spatial association type for the spatial correlation of median NDVI and spatial lag of median nocturnal road traffic noise are defined for each cell. The second group of files (traffic_ge) contains the traffic flow measurements points which are characterized with "hec_grid_ge" layer variables of the hectometric cell to which they belong to.</p>
Material flow analysis of asphalt used in roads for an Austrian municipality
<p>Circular economy gain increasing popularity to ensure a sustainable development. The road construction sector has a major contribution to the circular approach as asphalt has high recycling rates and waste materials have potential application in the production of asphalt mixtures.</p> <p>A study on the asphalt flow in an Austrian municipality was conducted and the underlying material flow analysis model and primary data construction and demolition data of the study are presented. In addition, transfer coefficients and the particle emission model are included. </p>
Post-50 Ma evolution of India-Asia collision zone from paleomagnetic and GPS data: Greater India indentation to eastward Tibet flow
<p>Review of paleomagnetic data from Tibet and N Indochina, supporting the paper "Post-50 Ma evolution of India-Asia collision zone from paleomagnetic and GPS data: Greater India indentation to eastward Tibet flow".</p>
Gene flow influences the genomic architecture of local adaptation in six riverine fish species
<p>Understanding how gene flow influences adaptive divergence is important for predicting adaptive responses. Theoretical studies suggest that when gene flow is high, clustering of adaptive genes in fewer genomic regions would protect adaptive alleles from recombination and thus be selected for, but few studies have tested it with empirical data. Here, we used RADseq to generate genomic data for six fish species with contrasting life histories from six reaches of the Upper Mississippi River System, USA. We used four differentiation-based outlier tests and three genotype-environment association analyses to define neutral SNPs and outlier SNPs that were putatively under selection. We then examined the distribution of outlier SNPs along the genome and investigated whether these SNPs were found in genomic islands of differentiation and inversions. We found that gene flow varied among species, and outlier SNPs were clustered more tightly in species with higher gene flow. The two species with the highest overall <em>F</em><sub>ST</sub> (0.0303 - 0.0720) and therefore lowest gene flow showed little evidence of clusters of outlier SNPs, with outlier SNPs in these species spreading uniformly across the genome. In contrast, nearly all outlier SNPs in the species with the lowest <em>F</em><sub>ST</sub> (0.0003) were found in a single large putative inversion. Two other species with intermediate gene flow (<em>F</em><sub>ST</sub> ~ 0.0025 - 0.0050) also showed clustered genomic architectures, with most islands of differentiation clustered on a few chromosomes. Our results provide important empirical evidence to support the hypothesis that increasingly clustered architectures of local adaptation are associated with high gene flow. </p>
51011 - Instream Flow Protection: Southwest Alaska - Phase 1
<p>Streamflow and discharge measurement data collected under AKSSF Grant 51011 - Instream Flow Protection: Southwest Alaska - Phase 1.</p>
Pyzoflex technology testing for powder flow monitoring
<p>These data represent a signal collection from the Pyzoflex(R) sensor when exposed to diverse amounts of powder driven by Argon gas flow, to be used in the laser cladding process.</p>
Current flow files mapped by Omniscape representing the dispersal of saltwater crocodiles
<p>This folder contains raster data in asc format to be used with Omniscape to visualise predicted flow across a study area raster, using the core breeding habitat cells ('Saltwater crocodile core breeding habitat cells.asc') for saltwater crocodiles (<em>Crocodylus porosus</em>) in the Northern Territory, Australia, the optimized resistance surface ('Saltwater crocodile optimized resistance surface.asc'), and core habitat cells ('Saltwater crocodile core habitat cells.asc') as ground nodes, effectively representing locations where current could 'settle'.</p>
Metamorphic evolution of the Greater Himalayan Sequence, Bhagirathi Valley (India) through a combination of channel flow and in-sequence shearing
<p>Dataset of a manuscript entitled "Metamorphic evolution of the Greater Himalayan Sequence, Bhagirathi Valley (India) through a combination of channel flow and in-sequence shearing " submitted in Tectonics (AGU)</p>
Observations and Modeling of a Buoyant Plume Exiting into a Tidal Cross-flow and Exhibiting Along-front Instabilities
<p>This post-processed dataset contains the sUAS imagery and numerical modeling output used in the paper "Observations and Modeling of a Buoyant Plume Exiting into a Tidal Cross-flow and Exhibiting Along-front Instabilities". The provided code details the frontal processing routine used for both the observations and modeling results. </p>
Seagrass deformation affects fluid instability and tracer exchange in canopy flow
<p>Data and code used for the preparation of the manuscript "Seagrass deformation affects fluid instability and tracer exchange in canopy flow" (Vieira, Allshouse & Mahadevan 2022).</p> <p><em>Data and Code Repository Organization</em></p> <ul> <li><strong>data/ </strong>: contains the data presented in the manuscript (in .cdf and .mat format);</li> <li><strong>code/ </strong>: contains the code used for the numerical simulations (PSOM) and in processing the data and generating figures (MATLAB)</li> </ul> <p><em>Manuscript Abstract:</em></p> <p>Monami is the synchronous waving of a submerged seagrass bed in response to unidirectional fluid flow. Here we develop a multiphase model for the dynamical instabilities and flow-driven collective motions of buoyant, deformable seagrass. We show that the impedance to flow due to the seagrass results in an unstable velocity shear layer at the canopy interface, leading to a periodic array of vortices that propagate downstream. Each passing vortex locally weakens the along-stream velocity at the canopy top, reducing the drag and allowing the deformed grass to straighten up just beneath it. This causes the grass to oscillate periodically. Crucially, the maximal grass deflection is out of phase with the vortices. A phase diagram for the onset of instability shows its dependence on the fluid Reynolds number and an effective buoyancy parameter. Less buoyant grass is more easily deformed by the flow and forms a weaker shear layer, with smaller vortices and less material exchange across the canopy top. While higher Reynolds number leads to stronger vortices and larger waving amplitudes of the seagrass, waving is maximized at intermediate grass buoyancy. All together, our theory and computations correct some misconceptions in interpretation of the mechanism and provide a robust explanation consistent with a number of experimental observations.</p>
Wake vortices and dissipation in a tidally modulated flow past a three-dimensional topography
<p>LES simulation data of tidally modulated flow past an abyssal hill. Data used in the figures are available</p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 21 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 21\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td><strong>Item</strong></td> <td><strong><span class="math-tex">\(v_s\)</span></strong></td> <td><strong><span class="math-tex">\(B\)</span></strong></td> </tr> <tr> <td>R1</td> <td>2.20</td> <td>10</td> </tr> <tr> <td>R2</td> <td>0</td> <td>0</td> </tr> <tr> <td>R3</td> <td>2.20</td> <td>30</td> </tr> <tr> <td>R4</td> <td>2.20</td> <td>50</td> </tr> <tr> <td>R5</td> <td>1.10</td> <td>10</td> </tr> <tr> <td>R6</td> <td>0.22</td> <td>10</td> </tr> <tr> <td>R7</td> <td>4.39</td> <td>10</td> </tr> <tr> <td>R8</td> <td>1.76</td> <td>10</td> </tr> <tr> <td>R9</td> <td>2.85</td> <td>10</td> </tr> <tr> <td>R10</td> <td>6.58</td> <td>10</td> </tr> <tr> <td>R11</td> <td>2.20</td> <td>20</td> </tr> <tr> <td>R12</td> <td>5.49</td> <td>10</td> </tr> <tr> <td>R13</td> <td>3.29</td> <td>10</td> </tr> <tr> <td>R14</td> <td>3.60</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 59 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 59\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td>Item</td> <td><span class="math-tex">\(v_s\)</span></td> <td>B</td> </tr> <tr> <td>Tr</td> <td>0</td> <td>0</td> </tr> <tr> <td>T13</td> <td>1</td> <td>10</td> </tr> <tr> <td>T16</td> <td>3</td> <td>10</td> </tr> <tr> <td>T18</td> <td>5</td> <td>10</td> </tr> <tr> <td>T19</td> <td>10</td> <td>0.3</td> </tr> <tr> <td>T25</td> <td>10</td> <td>1</td> </tr> <tr> <td>T27</td> <td>10</td> <td>3</td> </tr> <tr> <td>T29</td> <td>10</td> <td>10</td> </tr> <tr> <td>T30</td> <td>30</td> <td>10</td> </tr> <tr> <td>T31</td> <td>10</td> <td>30</td> </tr> <tr> <td>T33</td> <td>10</td> <td>50</td> </tr> <tr> <td>T38</td> <td>20</td> <td>10</td> </tr> <tr> <td>T43</td> <td>15</td> <td>10</td> </tr> <tr> <td>T50</td> <td>25</td> <td>10</td> </tr> <tr> <td>T56</td> <td>8</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <p> </p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
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.