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1,651 results for “Planes”
All raw data for the transport part of 'In-plane selective area InSb-Al nanowire quantum networks'
<p>This folder contains all the raw data gathered on the devices used in the transport part of the paper "In-plane selective area InSb–Al nanowire quantum networks" https://doi.org/10.1038/s42005-020-0324-4.</p>
FIGURE. Floral asymmetry patterns found in the studied species. Flower with the adaxial petal like a standard in Ch. celiae (A), Ch. megacycla (B), Ch. pachyclada (C), Ch. crenulata (D), Ch. tocantinensis (E), Ch. orbiculata (F). Flower with four slightly elevated flat petals and one asymmetric lower lateral Ch. claussenii. (G). Flower with one of the inner petals small and the other coiled in the androecium in Ch. cercidifolia (H). Flower with adaxial petal and right upper lateral similar in shape and size in the same plane in Ch. cyclophylla (I), Ch. claussenii (J), Ch. rigidifolia (K) e Ch. veadeirana (L). in Taxonomic review of Chamaecrista sect. Absus subsect. Absus ser. Paniculatae (Benth.) H.S. Irwin & Barneby (Leguminosae, Caesalpinioideae)
FIGURE. Floral asymmetry patterns found in the studied species. Flower with the adaxial petal like a standard in Ch. celiae (A), Ch. megacycla (B), Ch. pachyclada (C), Ch. crenulata (D), Ch. tocantinensis (E), Ch. orbiculata (F). Flower with four slightly elevated flat petals and one asymmetric lower lateral Ch. claussenii. (G). Flower with one of the inner petals small and the other coiled in the androecium in Ch. cercidifolia (H). Flower with adaxial petal and right upper lateral similar in shape and size in the same plane in Ch. cyclophylla (I), Ch. claussenii (J), Ch. rigidifolia (K) e Ch. veadeirana (L).
Movie of density evolution in the plane perpendicular to the orbital plane
<p>Similar to Fig. 1, in this movie the evolution of the density is shown in the plane perpendicular to the orbital plane.</p>
Quantitative Virus-Plane electrostatics
<p>Virus plane electrostatic interactions</p>
Cutting Plane Selection with Analytic Centers and Multiregression - Density Filtering Plots
<p>This repository contains the detailed plots for the density filtering experiments of the paper "Cutting Plane Selection with Analytic Centers and Multiregression".<br> The data were generated by the experiments available on the associated [repository](https://github.com/Opt-Mucca/Analytic-Center-Cut-Selection).</p> <p>In the following naming conventions, `X` stands for density filtering at X%.<br> The plots `root_direct_filter_X.pdf` show the relative primal-dual difference at the end of the separation at the root node for each instance-seed pair.<br> The plots `root_avg_filter_X.pdf` show the relative primal-dual difference at the end of the separation at the root node for each instance with all seeds averaged with a geometric mean.<br> The plots `root_mindev_filter_X.pdf` show the relative primal-dual difference at the end of the separation at the root node for each instance with the minimum deviation between the baseline and filtered method.</p> <p>In the following naming conventions, `Y` stands for the quantity of interest including:<br> - `lp_iterations`: number of LP iterations to solve the problem (only when solved to optimality)<br> - `node`: number of nodes taken to solve the problem (only when solved to optimality)<br> - `time`: time taken to solve the problem<br> - `node_throughput`: number of nodes processed per second<br> - `lpiter_throughput`: number of LP iterations performed per second</p> <p>The plots `tree_direct_filter_Y_rel_X.pdf` show the relative primal-dual difference at the end of the separation at the root node for each instance-seed pair.<br> The plots `tree_agg_filter_Y_rel_X.pdf` show the relative primal-dual difference at the end of the separation at the root node for each instance with seeds aggregated with a geometric mean.</p> <p> </p>
IG. 6. Ordination diagram of PCA of the Patagonian bat assemblage for craniodental variables using A) data set not size-corrected; and B) data set size-corrected. Polygons include specimens from each species: H. macrotus (▲), H. magellanicus (), H. montanus (▲), L. varius (■), M. chiloensis (●), and T. brasiliensis (£). Vectors show the strengh of correlation of each variable with the plane of PC1 and PC2. See text for abbreviations in Ecomorphological diversity in the Patagonian assemblage of bats from Argentina
IG. 6. Ordination diagram of PCA of the Patagonian bat assemblage for craniodental variables using A) data set not size-corrected; and B) data set size-corrected. Polygons include specimens from each species: H. macrotus (▲), H. magellanicus (), H. montanus (▲), L. varius (■), M. chiloensis (●), and T. brasiliensis (£). Vectors show the strengh of correlation of each variable with the plane of PC1 and PC2. See text for abbreviations
Keeping Up to Date With P4Runtime: An Analysis of Data Plane Updates on P4 Switches
<p>This dataset contains the measurement scripts and the results of their execution for the paper “Keeping up to Date with P4Runtime: An Analysis of Data Plane Updates on P4 Switches”.</p> <p>For post-processing, the script <code>post-processing.py</code> was used. Its execution results in CSV suitable for inclusion in tikz figures as included in the paper.</p> <p>In the paper, the following experiment runs are considered:</p> <table> <thead> <tr> <th>experiment</th> <th>runs</th> <th>figures</th> </tr> </thead> <tbody> <tr> <td><code>2022-08-19_11-59-49_801994</code></td> <td>01, 05, 09</td> <td>5</td> </tr> <tr> <td><code>2022-08-19_21-19-07_490218</code></td> <td>01, 05, 09</td> <td>6</td> </tr> <tr> <td><code>2022-08-20_10-28-26_900864</code></td> <td>1</td> <td>4, 5</td> </tr> <tr> <td><code>2023-01-28_16-54-28_462097</code></td> <td>0</td> <td>4</td> </tr> <tr> <td><code>2023-01-28_17-51-17_645739</code></td> <td>1</td> <td>4</td> </tr> <tr> <td><code>2023-01-28_19-15-05_255922</code></td> <td>0</td> <td>4</td> </tr> <tr> <td><code>2023-01-28_20-10-43_259639</code></td> <td>1</td> <td>4</td> </tr> <tr> <td><code>2023-01-29_17-50-08_687660</code></td> <td>1</td> <td>4</td> </tr> </tbody> </table> <p><em>Note</em>: Only a subset of capture CSV <em>not</em> discussed in the paper are included, to reduce the dataset size limit.</p>
Ultrasound Plane Wave Raw Data 75 Angles - Breast Phantom and Calibration Phantom Dataset
<p><strong>Summary</strong></p> <p>This dataset is a collection of raw ultrasound plane wave data from a breast mimicking phantom and a calibration phantom. The breast phantom data contains samples of hyperechoic lesions, hypoechoic lesions and no lesions. Whereas the calibration phantom contains samples to experimentally validate resolution and contrast. This dataset was acquired to do experimental validation of physics based deep learning for image registration, about which a paper is a current work in progress.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>One the one hand this dataset contain 220 samples of in-vitro breast phantom ultrasound plane wave data (identified by CIRS073_RUMC). This data consists of three lesion types: hyperechoic lesions, hypoechoic lesions, and no lesions. On the other hand this dataset contains data from a calibration phantom (identified by CIRS040GSE). This data consists of: 5 samples of hypoechoic cysts, 5 samples from wire targets of 100 micrometer, 5 samples with -6dB and -3dB lesions in the field of view, and 5 samples with +3dB and +6dB in the field of view. All recordings were obtained twice: once in a low attenuating area 0.7 dB/cm/mHz, and once in a high attenuating area 0.95 dB/cm/mHz . The calibration phantom used is: Multi-Purpose Multi-Tissue Ultrasound Phantom Model 040GSE (CIRS, Norfolk, USA).</p> <p><em>Acquisition Information</em></p> <p>Data was acquired using a Verasonics Vantage T256 R256 system with an L12-5 50 mm linear array ultrasound transducer operating at a center frequency of 7.8 MHz. Plane wave data has been acquired consisting of 75 steering angles with an angle range of -16 degrees to +16 degrees. Verasonics scripts used for the acquisition are provided along with the data.</p> <p><em>Image Reconstruction</em></p> <p>The <a href="https://github.com/RALSchoop/DeepUS">GitHub repository</a> contains code to subsample any amount of angles from the 75 acquired angles and to do image reconstruction with the f-k migration algorithm on this dataset. This processing code is available in Python.</p> <p><strong>Involved Parties</strong></p> <p>This dataset was produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands in collaboration with the Medical Ultrasound Imaging Center in Radboud UMC Nijmegen, The Netherlands.</p> <p><strong>Contact Details</strong></p> <p>r [dot] schoop [at] nki [dot] nl</p>
Data for "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>This file gives the datasets of our manuscript "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary".</p>
Data set for "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>Data set for the paper titled "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary".</p>
Out-of-plane performance of structurally and energy retrofitted masonry walls: Geopolymer versus cement-based textile-reinforced mortar combined with thermal insulation
<p>Data corresponding to all figures and tables presented in the publication</p>
Assessment of Spread of Transversus Abdominis Plane Block
ClinicalTrials.gov study NCT01024868. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Use of Anatomical Landmark in Locate Occlusal Plane.
ClinicalTrials.gov study NCT04694196. IPD Sharing: NO. Countries: 1. Publications: 9.
Ultrasound Guided Serratus Anterior Plane Block in ED Patients With Rib Fractures
ClinicalTrials.gov study NCT06299137. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Lumbar Erector Spinae Plane Block for Back Surgery
ClinicalTrials.gov study NCT03825198. IPD Sharing: NO. Countries: 1. Publications: 1.
Intermittent Erector Spinae Plane Block Via Subcutaneous Port for Cancer Pain
ClinicalTrials.gov study NCT07396558. IPD Sharing: NO. Countries: 1. Publications: 3.
Erector Spinae Plane Block With Bupivacaine for Medical Thoracoscopy
ClinicalTrials.gov study NCT06313632. IPD Sharing: NO. Countries: 1. Publications: 1.
Lateral Quadratus Lumborum Block Versus Transversus Abdominis Plane Block in Laparoscopic Surgery
ClinicalTrials.gov study NCT04553991. IPD Sharing: NO. Countries: 1. Publications: 3.
Comparison of Erector Spina Plane Block and Thoracic Epidural Block
ClinicalTrials.gov study NCT04702061. IPD Sharing: Not stated. Countries: 1. Publications: 0.
NOL-Guided Superficial Parasternal Intercostal Plane Block Versus Erector Spinae Plane Block
ClinicalTrials.gov study NCT06070701. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
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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.