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189 results for “WISE”
Precisely controlled colloids: A playground for path-wise non-equilibrium physics
<p>Particle Trajectories from Non Equilibrium Steady State (Driven) and Equilibrium state.</p> <p>Number of trajectories can be found in the Book1.xlx </p> <p>File format description on the individual *.dat files is explained in the instructions.pdf</p> <p> </p>
Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/Uq2qtb4_K2U">https://youtu.be/Uq2qtb4_K2U</a></p> <p><strong>This is a pre-print, please access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3503229.3547057">https://doi.org/10.1145/3503229.3547057</a></p> <p>Automatic analysis of variability is an important stage of <em>Software Product Line</em> (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.</p> <p>In this paper, we make use of the <em>Quality Variability Model</em> (QVM), based on <em>Category Theory</em> (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE -- the state-of-the-art CT tool.</p>
HVDC-WISE Deliverable 2.1 Appendix 3 Survey of Existing and Planned DC grids
<p>This excel sheet provides an overview as of 31st March 2023 of VSC and LCC links (to the best of the authors knowledge). This acts as as an Appendix to Deliverable 2.1 "Resilience Needs and Objectives" of the HVDC-WISE Horizon Europe project. The information contained in this excel workbook was used as part of the analysis on existing and planned HVDC grids.</p>
Pixel-wise Gaussian AD visualization (ResNet18, MVTecAD)
<p>A multivariate Gaussian model is fitted with the pixel-wise feature vectors [1] (all pixel positions confounded) extracted from a pre-trained convolutional neural network using the maximum likelihood estimator (empirical mean and empirical covariance matrix). From the eigendecomposition of the empirical covariance matrix, a whitening transformation is applied to the feature vectors, which yields a new vector of the same number of "components" (cf. [2]). </p> <p>This dataset shows visualizations of the component maps (cf. [2]) of all the categories in the MVTec-AD dataset. All the layer blocks [3] from a ResNet18 [4] are used as feature map extractors for the model described above. Each combination of category and layer is trained separately.</p> <p>The visualizations are composed of heatmaps of the square of the component maps; their sum (the Mahalanobis distance of each pixel, yielding an anomaly score map), and (for images containing defects, a.k.a. "anomalies") the ground truth annotation provided in the MVTec-AD dataset. </p> <p>For more details, refer to our paper.</p> <p>---</p> <p>[1] Pixel-wise feature vectors: given a stacked feature map of a 2D image (i.e. multiple 2D images, or "channels," containing information about the 2D input image), extract the values of all channels for a single pixel position.</p> <p>[2] The axes of the whitened [feature] vectors are referred to as "components" instead of "features" as a reminder that they come from a projection of the feature vectors along the eigenvectors, or eigen-<em>components</em>, of the covariance matrix. As the whitened vectors are in the same 2D grid structure as their original feature maps, we refer to them as "component maps." </p> <p>[3] "layer1", "layer2", "layer3", "layer4".</p> <p>[4] From torchvision version "0.15.2", model weights "ResNet18_Weights.IMAGENET1K_V1".</p> <p> </p> <p>TODO ref to paper</p>
Deaf Weight Wise: Community-engaged Implementation Research to Promote Healthy Lifestyle Change With Deaf ASL Users
ClinicalTrials.gov study NCT05211596. IPD Sharing: YES. Countries: 1. Publications: 2.
Gaia TGAS with 2MASS, WISE and Tycho2 photometry
<p>Crossmatch done taking the proper motions and epoch of the catalogues into account. This file should/will be replaced by one using the cross match tables provided by the Gaia Archive.</p> <p>Shared here by Ronald Drimmel for the Gaia Sprint NYC workshop, 2016.</p>
Step-wise reduction of graphene oxide using reactive MD
<p>We investigate step-wise reduction of graphene /graphite oxides using reactive molecular dynamics simulations. The visualized molecular configurations exemplify how the reduction occurs at elevated temperatures and ambient pressure.</p>
Bit-wise reversible integrations of the Solar System with JANUS
<p>This dataset contains 24 integrations of the Solar System. The integrator is JANUS, a bit-wise reversible algorithm that uses a mix of integer and floating-point arithmetic. The files are Simulation Archives, which can be opened with the REBOUND code. </p>
Voxel-wise maps for the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease
<p>This upload contains voxel-wise group level QSM data, and statistical maps for group level results associated with the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease.</p> <p>The assocaited code for reproducing these statistical maps can be found here: <a href="https://github.com/gecthomas/QSM_PD_longitudinal">gecthomas/QSM_PD_longitudinal (github.com)</a></p>
VaMoS 2022 Submission 12 - Continuous T-Wise Sampling: Increasing Coverage over Time
<p>Evaluation results for the VaMoS 2022 submission 12. For more details regarding the dataset please refer to the included readme.md file.</p>
A WISE view of extreme AGB stars
<p>The files in this distribution are related to the paper:</p> <p>"A WISE view of extreme AGB stars", by M.A.T. Groenewegen published in Astronomy & Astrophysics 659, A145 (2022).<br> </p>
Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States
<p>This study employs statistical techniques to evaluate climate model performance in wind speed and direction and their projected future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore across the Continental United States (CONUS). It extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution via a conditional approach. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. The proposed conditional approach provides a better way to characterize the directional wind speed distributions that offers additional insights for the joint assessment of speed and direction. </p> <p>WRF data: We focus on seasonal (December-January-February (winter hereafter) and June-July-August (summer hereafter) statistics computed from the 3-hourly RCM outputs on both wind speed and direction over ten locations with different local topological features. We use three WRF simulations driven by Community Climate System Model 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2G), and the Hadley Centre Global Environment Model version 2 (HadGEM2-ES). These three GCMs represent a range of climate sensitivities that encompasses most of the coupled model intercomparison project phase 5 (CMIP5) GCMs when projecting future temperature changes. In this work, we focus on RCP 8.5 scenario for future projections. A 16-member ensemble of one-year of RCM simulation using bias corrected CCSM-driven WRF is also generated for analyzing the uncertainty due to the RCM's internal variability (IV). </p> <p>Benchmark data: Reanalysis data are used as a verification dataset in order to evaluate the RCMs' wind conditions under study for the historical time period. For the seven inland locations, we use the second phase of the multi-institution North American Land Data Assimilation System project, phase 2, at a spatial resolution of 12 km and hourly resolution. NLDAS-2 is an offline data assimilation system featuring uncoupled land surface models driven by observation-based atmospheric forcing. The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are at a 32-km spatial resolution and 3-hourly temporal frequency.</p> <p>In-situ measurement: Since reanalysis data can present errors and uncertainties, ground measurements and offshore buoy measurements are used to consolidate the evaluation of RCMs' wind conditions for inland and offshore locations in historical climates. Observational data are extracted from the Automated Surface Observing System (ASOS) network that consists stations covers the U.S. territory, available at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin. The offshore downscaled wind speeds from the historical decade are compared with National Data Buoy Center (NDBC) buoy observations of near-surface wind velocities available at https://www.ndbc.noaa.gov. The observed winds at the NBDC anemometers are adjusted to 10-m above ground height and at 3-hourly rate. </p> <p> </p> <p> </p>
Photometric redshifts for quasars from WISE-PS1-STRM
<p>Photometric redshift estimations for quasars</p> <p>In this repository we publish our photometric redshift catalogue of quasar candidates found in the cross-matched catalogue of the WISE All-Sky and PS1 3π DR2 sky surveys. Altogether there are 4,849,634 quasars in the catalog from which 2,879,298 are covered by our training set. When using this data please cite our work<br> https://doi.org/10.48550/arXiv.2206.01440. Below you can find the description of each column:</p> <ul> <li><strong>objID_PS1</strong>: object id in the PannSTARRS DR2 catalogue</li> <li><strong>raMean</strong>: rectascension of the object</li> <li><strong>decMean</strong>: declination of the object</li> <li><strong>w1mpro</strong>: PSF profile-fitting magnitude measured in WISE W1 filter</li> <li><strong>w1sigmpro</strong>: error estimation of w1mpro</li> <li><strong>w1mag</strong>: aperture magnitude related to 8.25" radius circular apertures measured in WISE W1 filter</li> <li><strong>w1sigm</strong>: error estimation of w1mag</li> <li><strong>w2mpro</strong>: PSF profile-fitting magnitude measured in WISE W2 filter</li> <li><strong>w2sigmpro</strong>: error estimation of w2mpro</li> <li><strong>w2mag</strong>: aperture magnitude related to 8.25" radius circular apertures measured in WISE W2 filter</li> <li><strong>w2sigm</strong>: error estimation of w2mag</li> <li><strong>gFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 g filter</li> <li><strong>gFPSFMagErr</strong>: error estimation of gFPSFMag</li> <li><strong>rFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 r filter</li> <li><strong>rFPSFMagErr</strong>: error estimation of rFPSFMag</li> <li><strong>iFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 i filter</li> <li><strong>iFPSFMagErr</strong>: error estimation of iFPSFMag</li> <li><strong>zFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 z filter</li> <li><strong>zFPSFMagErr</strong>: error estimation of zFPSFMag</li> <li><strong>yFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 y filter</li> <li><strong>yFPSFMagErr</strong>: error estimation of yFPSFMag</li> <li><strong>gFKronMag</strong>: Kron magnitude measured in PS1 g filter</li> <li><strong>gFKronMagErr</strong>: error estimation of gFKronMag</li> <li><strong>rFKronMag</strong>: Kron magnitude measured in PS1 r filter</li> <li><strong>rFKronMagErr</strong>: error estimation of rFKronMag</li> <li><strong>iFKronMag</strong>: Kron magnitude measured in PS1 i filter</li> <li><strong>iFKronMagErr</strong>: error estimation of iFKronMag</li> <li><strong>zFKronMag</strong>: Kron magnitude measured in PS1 z filter</li> <li><strong>zFKronMagErr</strong>: error estimation of zFKronMag</li> <li><strong>yFKronMag</strong>: Kron magnitude measured in PS1 y filter</li> <li><strong>yFKronMagErr</strong>: error estimation of yFKronMag</li> <li><strong>EBV_PS1</strong>: E(B-V) dust extinction values</li> <li><strong>EXT</strong>: flag denoting an object being extrapolated or not. It is 1 if the object is extraplolated and 0 if not.</li> <li><strong>z_pred_mean</strong>: photometric redshift estimation</li> <li><strong>z_pred_std</strong>: uncertainty of z_pred_mean</li> <li><strong>spectroz</strong>: spectroscopic redshift of the object if available and -999 otherwise</li> </ul>
Photometric redshifts for quasars from WISE-PS1-STRM
<p>Photometric redshift estimations for quasars</p> <p>In this repository we publish our photometric redshift catalogue of quasar candidates found in the cross-matched catalogue of the WISE All-Sky and PS1 3π DR2 sky surveys. Altogether there are 4,849,607 quasars in the catalog from which 2,879,273 are covered by our training set. When using this data please cite our work<br> https://doi.org/10.48550/arXiv.2206.01440. Below you can find the description of each column:</p> <ul> <li><strong>objID_PS1</strong>: object id in the PanSTARRS DR2 catalogue</li> <li><strong>raMean</strong>: rectascension of the object</li> <li><strong>decMean</strong>: declination of the object</li> <li><strong>w1mpro</strong>: PSF profile-fitting magnitude measured in WISE W1 filter</li> <li><strong>w1sigmpro</strong>: error estimation of w1mpro</li> <li><strong>w1mag</strong>: aperture magnitude related to 8.25" radius circular apertures measured in WISE W1 filter</li> <li><strong>w1sigm</strong>: error estimation of w1mag</li> <li><strong>w2mpro</strong>: PSF profile-fitting magnitude measured in WISE W2 filter</li> <li><strong>w2sigmpro</strong>: error estimation of w2mpro</li> <li><strong>w2mag</strong>: aperture magnitude related to 8.25" radius circular apertures measured in WISE W2 filter</li> <li><strong>w2sigm</strong>: error estimation of w2mag</li> <li><strong>gFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 g filter</li> <li><strong>gFPSFMagErr</strong>: error estimation of gFPSFMag</li> <li><strong>rFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 r filter</li> <li><strong>rFPSFMagErr</strong>: error estimation of rFPSFMag</li> <li><strong>iFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 i filter</li> <li><strong>iFPSFMagErr</strong>: error estimation of iFPSFMag</li> <li><strong>zFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 z filter</li> <li><strong>zFPSFMagErr</strong>: error estimation of zFPSFMag</li> <li><strong>yFPSFMag</strong>: PSF profile-fitting magnitude measured in PS1 y filter</li> <li><strong>yFPSFMagErr</strong>: error estimation of yFPSFMag</li> <li><strong>gFKronMag</strong>: Kron magnitude measured in PS1 g filter</li> <li><strong>gFKronMagErr</strong>: error estimation of gFKronMag</li> <li><strong>rFKronMag</strong>: Kron magnitude measured in PS1 r filter</li> <li><strong>rFKronMagErr</strong>: error estimation of rFKronMag</li> <li><strong>iFKronMag</strong>: Kron magnitude measured in PS1 i filter</li> <li><strong>iFKronMagErr</strong>: error estimation of iFKronMag</li> <li><strong>zFKronMag</strong>: Kron magnitude measured in PS1 z filter</li> <li><strong>zFKronMagErr</strong>: error estimation of zFKronMag</li> <li><strong>yFKronMag</strong>: Kron magnitude measured in PS1 y filter</li> <li><strong>yFKronMagErr</strong>: error estimation of yFKronMag</li> <li><strong>EBV_PS1</strong>: E(B-V) dust extinction values</li> <li><strong>EXT</strong>: flag denoting an object being extrapolated or not in the 12-dimensional color index space. It is 1 if the object is extrapolated and 0 if not.</li> <li><strong>DIST</strong>: average distance measured between the inference data points and their spectroscopic neighbors in the 12-dimensional color index space.</li> <li><strong>z_pred</strong>: photometric redshift estimation (predicted from color indices)</li> <li><strong>z_pred_std</strong>: uncertainty of z_pred</li> <li><strong>spectroz</strong>: spectroscopic redshift of the object if available and -999 otherwise</li> <li><strong>EXT2</strong>: flag denoting an object being extrapolated or not in the 13-dimensional color index+magnitude space. It is 1 if the object is extrapolated and 0 if not.</li> <li><strong>DIST2</strong>: average distance measured between the inference data points and their spectroscopic neighbors in the 13-dimensional color index+magnitude space.</li> <li><strong>z_pred2</strong>: photometric redshift estimation (predicted from color indices+magnitude)</li> <li><strong>z_pred_std2</strong>: uncertainty of z_pred2</li> </ul>
Fig. 3 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 3: Workflow of the quality control procedure and quality flag (QF) attribution.
Wide-field Infrared Survey Explorer (WISE) Catalog of Periodic Variable Stars
<p> Wide-field Infrared Survey Explorer (WISE) Catalog of Periodic Variable Stars<br> Xiaodian Chen, Shu Wang, Licai Deng, Richard de Grijs and Ming Yang</p> <p> We have compiled the first all-sky mid-infrared variable-star catalog based on Wide-field<br> Infrared Survey Explorer (WISE) five-year survey data. Requiring more than 100 detections<br> for a given object, 50,282 carefully and robustly selected periodic variables are discovered,<br> of which 34,769 (69%) are new. Most are located in the Galactic plane and near the equatorial<br> poles. A method to classify variables based on their mid-infrared light curves is established<br> using known variable types in the General Catalog of Variable Stars. Careful classification of<br> the new variables results in a tally of 21,427 new EW-type eclipsing binaries, 5654 EA-type <br> eclipsing binaries, 1312 Cepheids, and 1231 RR Lyraes. By comparison with known variables <br> available in the literature, we estimate that the misclassi- fication rate is 5% and 10% for<br> short- and long-period variables, respectively. A detailed comparison of the types, periods, <br> and amplitudes with variables in the Catalina catalog shows that the independently obtained <br> classifications parameters are in excellent agreement. This enlarged sample of variable <br> stars will not only be helpful to study Galactic structure and extinction properties, <br> they can also be used to constrain stellar evolution theory and as potential candidates for<br> the James Webb Space Telescope.<br> <br> These supplementary materials contain ALLWISE and NEOWISE-R single-exposure photometry tables of variables list<br> in Table 2 and 6 of the paper, and light curve figures for the 50,282 periodic variables in Table 2. <br> SourceID is identifier join these attachments to Table 2 and 6.</p> <p>Example: For variable star WISEJ094812.4+093448 in Table 2, the SourceID=170 is adopted to search <br> corresponding single-exposure information in both 'allwise12.txt' and 'neowise12.txt'. </p> <p>File Description:</p> <p>allwise12.txt Single exposure photometry data of variables from ALLWISE.</p> <p> Bytes Format Units Label Explanations<br> ----------------------------------------------------------------------------------------- <br> 1- 8 I5 --- SourceID Internal source identifier <br> 10- 20 F11.7 deg RAdeg Right Ascension in decimal degrees (J2000) <br> 22- 32 F11.7 deg DEdeg Declination in decimal degrees (J2000) <br> 34- 47 F14.8 day MJD Modified Julian date of the mid-point of the observation <br> 49- 54 F6.3 mag W1mag Single exposure WISE W1 (3.35 micron) band magnitude<br> 56- 63 F6.3 mag eW1mag W1 band uncertainty<br> 65- 77 F6.3 mag W2mag Single exposure WISE W2 (4.6 micron) band magnitude <br> 79- 86 F6.3 mag eW1mag W2 band uncertainty<br> -----------------------------------------------------------------------------------------<br> <br> <br> neowise12.txt Single exposure photometry data of variables from NEOWISE-R.</p> <p> Bytes Format Units Label Explanations<br> -----------------------------------------------------------------------------------------<br> 1- 8 I5 --- SourceID Internal source identifier <br> 10- 21 F11.7 deg RAdeg Right Ascension in decimal degrees (J2000) <br> 23- 34 F11.7 deg DEdeg Declination in decimal degrees (J2000) <br> 36- 44 F6.3 mag W1mag Single exposure WISE W1 (3.35 micron) band magnitude<br> 46- 54 F6.3 mag eW1mag W1 band uncertainty<br> 56- 64 F6.3 mag W2mag Single exposure WISE W2 (4.6 micron) band magnitude <br> 66- 74 F6.3 mag eW1mag W2 band uncertainty<br> 76- 90 F14.8 day MJD Modified Julian date of the mid-point of the observation<br> -----------------------------------------------------------------------------------------</p> <p> <br> figure0.zip -- figure23.zip Full figures of 50282 WISE variables. They are divided into 24 <br> packages by the order of Right Ascension.</p>
Dataset_Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology
<p>Original micro-CT imaging data and output of processing via the OpenFiberSeg software of additively manufactured carbon fiber reinforced composites. </p> <p>This dataset accompanies the publication in Composite Science and Technology available at https://doi.org/10.1016/j.compscitech.2023.110261</p> <p> </p>
Data Analyses for Ancillary WISE Femhrt Hormone Replacement Study
ClinicalTrials.gov study NCT00600106. IPD Sharing: NO. Countries: 1. Publications: 42.
The WISE Trial - Walking Improvement for SCI With Exoskeleton
ClinicalTrials.gov study NCT02943915. IPD Sharing: NO. Countries: 1. Publications: 6.
The Wise App Trial for Improving Health Outcomes in PLWH
ClinicalTrials.gov study NCT03205982. IPD Sharing: NO. Countries: 1. Publications: 4.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.