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60 results for “surface code”
Datasets and Code for "Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes"
<p>This is the set of data and computer code used for reproducing the results in Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” <em>IISE Transactions</em>, in press.</p>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Datasets and code of the manuscript 'Insights into the Aerodynamic versus Radiometric Surface Temperature Debate in Thermal-based Evaporation Modeling'
<p>This contains the datasets and codes that were used to generate the results and discussions in the manuscript</p>
Data for "Suppressing quantum errors by scaling a surface code logical qubit"
<p>Includes the circuits that were executed, the samples that were collected, the corrections predicted by several decoders, as well as other intermediate files. See the README.txt file at the root of the ZIP archive for a more detailed overview.</p>
Data for "Relaxing Hardware Requirements for Surface Code Circuits using Time-dynamics"
<p>Includes the circuit files for each benchmarked stim circuit (circuits.zip), the raw statistics for each memory experiment (stats.csv), the 'fused' stats combining the X and Z error rates to represent the liklihood that neither logical observable fails (fused_stats.csv), the svg files for the main figures in the manuscript (svg_figures.zip), and a snapshot of the full code repo (midout.zip)</p>
Survival data and code: Camouflage using 3D surface disruption
<p class="MsoNormal">Disruptive markings are common in animal patterns and can provide camouflage benefits by concealing the body's true edges and/or by breaking the surface of the body into multiple depth planes. Disruptive patterns that are accentuated by high contrast borders are most likely to provide false depth cues to enhance camouflage, but studies to date have used visual detection models or humans as predators. We presented 3D-printed moth-like targets to wild bird predators to determine whether: (1) 3D prey with disrupted body surfaces have higher survival than 3D prey with continuous surfaces, (2) 2D prey with disruptive patterns or enhanced edge markings have higher survival than non-patterned 2D prey. We found a survival benefit for 3D prey with disrupted surfaces, even after accounting for luminance differences among the treatments. There was no evidence that false depth cues provided the same protective benefits as physical surface disruption in 3D prey, perhaps because our treatments did not mimic the complexity of patterns found in natural animal markings. Our findings indicate that disruption of surface continuity is an important strategy for concealing a 3D body shape.</p>
Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing for "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea" paper.
<p>Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing as well as selected model output used for analysis and producing figures in the paper "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea". Contact Bronwyn Cahill if you have questions at: bronwyn.cahill@io-warnemuende.de</p>
Data and code from: Sub-surface deformation of individual fingerprint ridges during tactile interactions
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Code from: When and where do waterbirds need water? Inferring candidate restoration areas from spatio-temporal variation in surface water availability
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Survival data and code: Camouflage using 3D surface disruption
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Fresh snow, peak accumulation, and ablation-Sun Cup snow surface datasets for evaluation of geometry aerodynamic roughness code
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data and code for Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing
<p>Data and radiative transfer code used for the manuscript "Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing". See Readme for details</p>
Model codes, data, and plot scripts for the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning"
<p>The model codes, data, and plot scripts used in the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning".</p><ul><li>Mods: the modified CAM model code used in these three experiments.</li><li>Results: post-processing NCL scripts and the results used for making plots.</li><li>figs: the NCL scripts and figures used in the paper.</li><li>Parcel: parcel model that represents the ice nucleation process.</li></ul><p> </p>
Data for "Yoked Surface Codes"
<p>Statistics collected, circuits generated, and some of the code written as part of the paper "Yoked Surface Codes".</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Data and code for "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations"
<p>This record contains data and scripts pertaining to the publication "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations".</p>
Code and data for "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction" By Guan et al. Submitted to JGR Oceans.
<p>This repository contains the code and data for the machine learning analysis of "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction”By Guan et al. Submitted to JGR Oceans.</p> <p>Specifically, this repository contains the following items:<br> (1) Codes for assessing the representation skill of the machine learning and linear regression (LR) methods. Three machine learning methods are considered: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN).<br> (2) Codes for assessing the prediction skill of the machine learning and LR methods. <br> (3) Seasonal-mean and annual-mean input data to run these codes. <br> (4) The package needed to run the random forest code, i.e. the RF_MexStandalone-v0.02 program package from https://code.google.com/archive/p/randomforest-matlab/downloads .</p>
Code for Atmospheric Research publication - Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields
<p>In this repository, we include the source codes for WRF namelist, figures, and height correction used in the Atmospheric Research publication "Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields"</p> <p>The namelist.wps and namelist.input in WRF namelist are using for making input and running simulation, and Fig scripts in Figure scripts are using for plotting the figures in the paper.</p> <p>hgt_corr in Height correction method is a code to correct the disparity of the 10-m height definition between the model and observation by applying the developed the height correction algorithm based on the Monin-Obukhov similarity theory.</p>
Data and QPI codes for "Evidence for chiral superconductivity on a silicon surface"
<p>Data files and supplementary codes for the paper "Evidence for chiral superconductivity on a silicon surface." To appear in Nature Physics. A preprint is available at: https://arxiv.org/abs/2210.06273.</p>
National Weather Service Coded Surface Bulletins, 2003-
<p>The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003. The latitude/longitude precision in the initial version of the bulletin is 1°. Beginning in 2009, a high-resolution version began to be distributed alongside the original version. The high-resolution version of the bulletin has a latitude/longitude precision of 0.1°. The format and content of the original bulletin can be found at <a href="https://www.wpc.ncep.noaa.gov/html/read_coded_bull.shtml">this link</a>. The format and content of the high-resolution bulletin can be found at <a href="https://www.wpc.ncep.noaa.gov/html/read_coded_bull_hr.shtml">this link</a>.</p> <p>The bulletins within a rolling 2-week window can be obtained from the WPC at <a href="https://ftp.wpc.ncep.noaa.gov/coded_sfc/">this link</a>. The files in this dataset were provided by WPC personnel from an internal archive.</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.