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114 results for “Flow structure”
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
3D Flow Field of a subaqueous cylindrical pendulum from large eddy simulation with fluid structure interaction
<p>Relevant data of fluid structure interaction large eddy simulation of a subaqueous cylindrical pendulum to reproduce the major findings of the article "Fluid structure interaction of a subaqueous pendulum: Analyzing the effect of wake correction via large eddy simulations" published in Physics of Fluids. The article has been published as open access: https://doi.org/10.1063/5.0086557</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Рис. 6. Структура сообществ зообентоса оз. УΑыΛь и впаΑающих в него рек и ручьев, июΛь 2021 г. Fig. 6. The structure of zoobenthic communities of Udyl Lake and rivers and streams flowing into it, July 2021 in Quantitative zoobenthos characteristics of the Udyl Lake basin (Udyl Nature Reserve, Khabarovsky Region)
Рис. 6. Структура сообществ зообентоса оз. УΑыΛь и впаΑающих в него рек и ручьев, июΛь 2021 г. Fig. 6. The structure of zoobenthic communities of Udyl Lake and rivers and streams flowing into it, July 2021
Fig. 4 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 4. Results of the Geneland analysis. A: Bar plot of posterior probability density according to the number of clusters; B: posterior probability maps for the delimited clusters.
Fig. 3 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 3. Pairwise FST between localities of Telmatobius pefauri obtained using mitochondrial (A) and microsatellite (B) data. The colour scale corresponding to the values of FST is shown to the right of each matrix. Significant (Bonferroni corrected) comparisons showing p <0.05, p <0.01 and p <0.001 are denoted by *, ** and ***, respectively.
Fig. 2 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 2. Median-joining network based on the fragment of the analysed control region. Table 1. Indices of mitochondrial diversity, nuclear diversity, and inbreeding coefficients (FIS) by locality
Fig. 1 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 1. Study area, distribution of Telmatobius pefauri. Localities, 1: Socoroma (Socoroma River); 2: Murmuntani; 3: Copaquilla; 4: Chapiquiña; 5: Belén; 6: Lupica; 7: Saxamar. Localities 2 and 3 belong to the Seco River drainage; localities 4–7 belong to the Tignamar River drainage. Basin limits are indicated with dashed lines. The inset map shows the study area (highlighted by a red box) in relation to South America. SAAD = South American Arid Diagonal.
Fig. 5 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.
Fig. 5. Scatter plot for the first two principal components obtained in the Principal Components Analysis using SSR data.
FIGURE 4 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 4. Visualization of the flow within the 3D printed model (Re = 0.376, see Table 1). Flow in the folds consists of horizontal bands of distinct red and blue color, indicating no adoral component to flow and no mixing within the folds, consistent with Hypothesis 2 (see text, Figure 2.2). The still used in the print version of this paper is a single frame from the flow pattern observed, showing the steady-state flow pattern after nine minutes of flow. The animation is sped up 16x (for video see palaeo-electronica.org/content/2015/1073-blastoid-hydrospire-fluid-flow).
FIGURE 2. Schematic showing hypothesized flow patterns within the hydrospire folds. 2.1 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 2. Schematic showing hypothesized flow patterns within the hydrospire folds. 2.1, In Hypothesis 1, the flow has an adoral component representing respiratory leakage. 2.2, In Hypothesis 2, the flow is entirely radial, without leakage. See text for further discussion.
FIGURE 3 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 3. Digital and physical models use to visualize fluid flow. 3.1, Digital solid model of approximately the lower quarter of a hydrospire of Pentremites rusticus, using Blender (see text). 3.2, 3D-printed rendering of the digital model, shown with inlet headers connected.
FIGURE 1 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 1. Anatomy of the hydrospires of the blastoid Pentremites rusticus. 1.1, Location of one of the five radially distributed hydrospires within the calyx, showing incurrent hydrospire pores, and excurrent spiracle (inferred direction of water flow indicated by the arrows). 1.2, Oblique view of a section of a hydrospire and associated structures. Modified from Schmidtling and Marshall (2010).
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
<p>Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Direct measurements of 4-dimensional variability in oceanic flow structures with a new towed phased array Doppler sonar
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Going with the flow? Relative importance of riverine hydrologic connectivity versus tidal influence for spatial structure of genetic diversity and relatedness in a foundational submersed aquatic plant
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Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
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Understanding Near-Ground Tornado Flows - Pressure, Shear and Turbulence, and their Importance in Structural Loading
<h1>Dataset Description</h1> <p>The research focuses on the velocity and pressure field of the near-ground region within one of the Wind Engineering, Energy, and Environment (WindEEE) Dome's tornadic-like flow simulations. This research also aims to provide valuable insight in the behaviour between tornado-building interactions. Measurement regions of interest range from the apparent centre of the tornado-like flow structure extending beyond twice the radius of maximum tangential velocity.</p> <p>Two vortex cases were simulated and measured:</p> <p>(1) TV0 - a stationary vortex where the bell mouth (updraft centre) aligns with the centre of the test chamber, and</p> <p>(2) TV1 - a vortex translating for a distance of 4.5 m at a nominal translation speed of about 1-1.2 m/s.</p> <div> <h2>S0. Documentation</h2> <p>Contains information documents regarding the instrumentation specifications, test plan, and other important diagrams.</p> <div> <h2>S1. Near-Ground Flow Model</h2> </div> <div> <p>TV0 and TV1 simulations were tested at the WindEEE Dome testing facility. Velocity measurement devices were used to characterize simulated vortex's horizontal flow field at heights of 3.2, and 7.2. Pressure measurement devices were used to characterize the vortex's ground surface pressure. These measurements aim to provide data for more accurate tornado wind field models.</p> <h3>E1. Near-Ground Velocity Field</h3> <p>During this test, 3-D point measurements were taken to aid in characterizing TV0 and TV1 simulations. Measurements were taken at locations R = 0, 60 and 105 cm measured perpendicularly from the bell mouth's path of translation. Two heights and four point positions were captured at each location. These measurements aim to provide detailed characteristics of the wind field.</p> <h3>E2. Ground Surface Instantaneous Pressure Field</h3> <p>A ground surface pressure model covering an area of 120 cm by 300 cm was used to characterize the ground pressure distribution of simulations: TV0 and TV1. Instantaneous pressure measurement devices captured the flow distribution throughout a 2-D grid of pressure taps mounted flush with the ground surface panels.</p> <div> <h2>S2. Low-Rise Building Model</h2> </div> <div> <p>A 3:12 roof slope, 1:100 scale, low-rise building model was instrumented with external surface pressure taps and tested under TV0 and TV1 flow simulations at WindEEE. The low-rise building model was measured in locations R = 0, 60, and 105 cm measured perpendicularly from the bell mouth translation pat, and at azimuth angles 0, 45, and 90 degrees relative to the path of the bell mouth.</p> <h3>E1. Low-Rise Building Aerodynamics</h3> <p>The effects of stationary and translating vortices on a low-rise building model were investigated.</p> <h2>S3. Small-Portable Temporary Building Model</h2> <div> <p>A temporary low-rise building was instrumented with external surface pressure taps and tested under TV0 and TV1 flow simulations at WindEEE. The small-portable building model of size 1.2 cm by 1.2 cm by 3.2 cm (1:70 scale) and ground surface model covering 120 cm by 300 cm. The small-portable building was measured in locations R = 0, 60, and 105 cm measured perpendicularly from the bell mouth translation path.</p> <h3>E1. Small-Portable Temporary Building Aerodynamics</h3> <p>The effects of stationary and translating vortices on a small-portable building model were investigated.</p> <div> <h2>S4. Near-Ground Particle Image Velocimetry (PIV)</h2> </div> <div> <p>A Continuum laser was focused and split through a cylindrical lens into a thin laser sheet, fog machines were used to seed the entire test chamber with long lasting fog fluid, and finally synchronized high-resolution cameras (flares) were used to capture laser light refraction. Flares send image data to the DVR Express Cores during test measurements. This PIV system was used to measure the horizontal velocity field of simulations: TV0 with an approximate total coverage of 80 cm by 240 cm.</p> <h3>E1. Near-Ground Horizontal Velocity Field</h3> <p>The PIV system was used to measure the 2-D velocity field at a height of 7.2 cm from the chamber floor. The plane of measure covers regions from the tornado-like flow's apparent centre to beyond the region of maximum tangential velocity.</p> <h3>E2. Low-Rise Building Wake Aerodynamics</h3> <p>A low-rise building model was measured at the centre of the test chamber, subject to the tornado vortex TV0. The model was made black to absorb energy and light from the PIV laser. The PIV system was used to measure the 2-D velocity field at a height of 7.2 cm from the chamber floor.</p> <p> </p> <p><strong>Note: </strong>Due to storage limitations we cannot include the entire PIV raw dataset, however data can be made available apon request. Please email the project contact person or the hosting instution (<a href="https://windeee.ca/contact/">Contact – WindEEE Research Facility</a>) for information or access to the complete dataset. </p> </div> </div> </div> </div> </div>
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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.