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1,651 results for “Planes”
Data set for "Tracking the nearfield evolution of an initially shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
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Light-sheet microscopy enabled by a miniaturized plane illuminator
<p>Light-sheet microscopy enabled by a miniaturized plane illuminator </p>
The Dataset of Quantifying Alignment Deviations for the In-plane Biaxial Test System
<p><em><strong>For dataset in "alignmentDeviaitons_SP56_54976.csv":</strong></em></p> <p>The dataset consists of 12 alignment deviations of an in-plane biaxial tensile testing machine as well as 56 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 12 alignment deviations using 56 strain values on a shape-optimized cruciform specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 55000 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 12 distinct deviations, 56 strain values at the required location on the cruciform specimen are obtained using Python scripts.</p> <p><em><strong>The illustration of “Code Scripts of Quantifying Alignment Deviations.rar”:</strong></em></p> <p>To quantify the alignment deviations of the in-plane biaxial testing machine, the AutoML model built-in AutoGluon was used to map relationship between the 12 alignment deviations of the in-plane biaxial testing machine and 56 strain measurement locations on a shape-optimized cruciform specimen. The training data for the AutoML model was obtained by Optimal Latin Hypercube, an algorithm-designed DOE experimental strategy. DOE scheme designed 12 alignment deviations of the in-plane biaxial testing machine. Through the finite element simulation of each group of alignment deviation in the DOE design scheme, 54976 groups of 56 strain values for the cruciform specimen were obtained.</p> <ol> <li><em><strong>AutogluonProgram_ SP56_ 54976 with_ Jupyter. ipynb: </strong></em>the operation procedure of developing the mapping relationship between alignment deviation and strain measurement locations using an automl model is documented in jupyerbook.</li> <li><em><strong>datasetOutput_ to_ localDir. py: </strong></em>output 56 strain measurement points and related 12 centering deviations to the local data file.</li> <li><em><strong>measurePointsSets_ in_ AbaqusModel. py: </strong></em>create a set of 56 measuring points for cruciform specimens in ABAQUS.</li> </ol>
America Air Plane
Source: Objaverse 1.0 / Sketchfab
The effective transmissivity of a plane-walled fracture with circular cylindrical obstacles - dataset for JGR SE manuscript
<p>The data needed to reproduce the figures from the article "The effective transmissivity of a plane-walled fracture with circular cylindrical obstacles" - to be published in JGR SE.</p> <p>The data are provided in vtk (Paraview) or mat (Matlab) format. To the all mat-files are provided scripts (m-files, i.e. figXX_plot.m) to plot the figures.</p>
Healthy Aging Increases the Coupling Between the Margin of Stability and Hip Moment in the Frontal Plane during Single-Stance
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Figure 7e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7e Examples of use of the functions Duopoly and Star. - Example of use of the function Star. Parameter angle is set to 20º.
Figure 7b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7b Examples of use of the functions Duopoly and Star. - Example of use of the function Duopoly. Creation of a nefroid.
Figure 6e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6e Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - A modificaction of the first five iterations of the Koch's (c) by changing parameter f from 1 to 2.
Figure 6b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6b Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - First three first iterations of the Koch's curve.
Figure 6a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6a Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - First seven iterations of the Sierpinski triangle.
Figure 5c from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5c Different stages of the creation of a beehive structure with the aid of tessellations. - Once the contiguous hexagons are obtained, function Tessellation allows the creation of the structure.
Figure 5b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5b Different stages of the creation of a beehive structure with the aid of tessellations. - Creating two contiguous hexagons to the starting one. These hexagons are derived from the middle points of some of the sides of the initial hexagon.
Figure 5a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 5a Different stages of the creation of a beehive structure with the aid of tessellations. - Creating a regular hexagon that works as the start of the tessellation.
Figure 4a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 4a Partial results during the process of finding the circumcenter of the triangle of points (-1,0), (0,1) and (1,0). - Triangle creation and obtention of the middle points of the sides and three auxiliary points in the orthogonal direction of each of the sides.
Figure 6d from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 6d Examples of different fractals produced with the functions of the LearnGeom package. The examples obtained with FractalSegment show how minimal modifications of the parameters can lead to very different curves. - A modificaction of the first five iterations of the Koch's (c) by changing parameter angle from 60º to 90º.
Figure 3e from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3e Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A shear transformation.
Figure 3f from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3f Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A homothety.
Figure 3b from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 3b Examples of use of the functions included in the package that represent affine transformations in the plane. In all the pictures, the blue triangle, placed at the points A(0,0), B(2,0) and C(1,1), is the one passed to each of the functions, being the orange triangle the output resulting for each of the transformations. - A rotation.
Figure 7a from: Briz-Redón Á, Serrano-Aroca Á (2018) Novel pedagogical tool for simultaneous learning of plane geometry and R programming. Research Ideas and Outcomes 4: e25485. https://doi.org/10.3897/rio.4.e25485
Figure 7a Examples of use of the functions Duopoly and Star. - Example of use of the function Duopoly. Creation of an astroid.
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.