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12,753 results for “Corrections”
Fig. 1 in Antilochus (Neaeretus) pterobrachys sp. nov. and the correct name of the subgenus Afroantilochus (Hemiptera: Heteroptera: Pyrrhocoridae)
Fig. 1. Antilochus (Neaeretus) pterobrachys sp. nov., J holotype. (Photo: L. Dembický).
Fig. 4 in Correction
Fig. 4. Map showing the sites where Acentrogobius ocyurus has been recorded (corrected).
Supporting information for: The correct name for an Aquilegia (Ranunculaceae) hybrid of the parentage Aquilegia flavescens × A. formosa
<p><span><em>Aquilegia</em> </span><span>x</span><span> <em>miniana</em></span><span> (J.F.Macbr. & Payson) Cronk, hybr. & stat. nov. is the correct name for the hybrid <em>Aquilegia</em> <em>flavescens</em> S.Watson x <em>A</em>. <em>formosa</em> Fisch. ex DC. var. <em>formosa</em>. In 1916, Payson and Macbride, while exploring the mountains of Idaho, found populations of <em>Aquilegia</em> that were pink in flower colour and appeared intermediate between the yellow-flowered <em>A</em>. <em>flavescens</em> and red-flowered <em>A. formosa</em>. They named these plants <em>A. flavescens</em> var. <em>miniana</em> J.F.Macbr. & Payson. There has been uncertainty over whether their type collections (in GH, RM, MO, US, E, CM, CAS, NY) do indeed represent hybrids or pink-flowered morphs of <em>A. flavescens</em>. Using a Wells diagram, the holotype (in the Gray Herbarium of Harvard University) is shown to be intermediate, allowing its identification as a clear hybrid. However, some of the isotype material is indistinguishable from <em>A. flavescens</em>. The holotype matches material from British Columbia that has been determined as being of hybrid origin using molecular and morphological data. <em>A. flavescens</em> var. <em>miniana</em> J.F.Macbr. & Payson is, therefore, an available name for the hybrid, which is here raised to the status of hybrid binomial.</span></p>
Stability of the next-to-tribimaximal mixings under radiative corrections with the variation of SUSY breaking scale
<pre>We analyse the stability of the next-to-tribimaximal mixings ($NTBM$) under radiative corrections with the variation of SUSY breaking scale ($m_s$) for both normal and inverted hierarchical cases at the fixed value of seesaw scale $M_R = 10^{15}$ GeV and $\tan \beta = 30$. All the neutrino oscillation parameters receive varying radiative corrections irrespective of the $m_s$ values at the electroweak scale, which are all within $3\sigma$ range of the latest global fit data. NH case is found to be more stable than IH case for all four cases.</pre>
Supporting information for "Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network"
<p>This dataset includes 2 files that represent supporting information for McJannet, D and Desilets, D (Submitted 2023)"Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network" Water Resources Research.</p> <p>File 1 - Supporting Information 1 - Example calculation: Excel sheet showing demonstration calaculations using the neutron intensity correction described in the paper</p> <p>File 2 - Supporting information 2 - List of neutron moniotr stations used in the paper and acknowledgment of their contribtuion</p>
Data From: B1 Field inhomogeneity correction for qDESS T2 mapping: application to rapid bilateral knee imaging
<p><strong>Purpose</strong>: T2 mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee T2 and high-resolution morphometry for cartilage and meniscus.<br> The qDESS uses an analytical signal model to compute T2 relaxometry maps, which require knowledge of the flip angle (FA). <br> In the presence of B1 inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of T2 measurements.<br> We propose a pixel-wise B1 correction method for qDESS T2 mapping exploiting an auxiliary B1 map to compute the actual FA used in the model.<br> <br> <strong>Methods</strong>: The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. <br> T2 measurements of femoral cartilage (FC) of both knees of six healthy participants were repeated longitudinally to investigate the association between T2 variation and B1.</p> <p><strong>Results</strong>: The results showed that applying the B1 correction mitigated T2 variations that were driven by B1 inhomogeneities. <br> Specifically, T2 left-right symmetry increased following the B1 correction (rc = 0.74 > rc = 0.69). Without the B1 correction, T2 values showed a linear dependence with B1. The linear coefficient decreased using the B1 correction (from 24.3 <span class="math-tex">\(\pm\)</span> 1.6 ms to 4.1 <span class="math-tex">\(\pm\)</span> 1.8) and the correlation was not statistically significant after the application of the Bonferroni correction (p-value > 0.01).<br> <br> <strong>Conclusion</strong>: the study showed that B1 correction could mitigate variations driven by the sensitivity of the qDESS T2 mapping method to B1, therefore increasing the sensitivity to detect real biological changes. The proposed method may improve the robustness of bilateral qDESS T2 mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies.</p> <p><strong>This repository contains the raw data for reproducing the results reported in the paper. The data are provided in NIFTI format. The repository also contains the MALTAB scripts used to compute B<sub>1</sub> maps from the NIFTI data.</strong></p>
Flow-field correction method to constrain AMOC in coupled model (IPSL-CM6A-LR)
<p>We use the standard version of IPSL-CM6A-LR (Boucher et al., 2020). The ocean component of IPSL-CM6A-LR is the NEMO oceanic model Version 3.6. The dynhpg.F90 file is a modified version of the routine that implements the flow-field correction method, and the namelist_ORCA1_cfg is the modified namelist used to activate the flow field correction, set the parameters, and read the input temperature, salinity, and mask. The mask specifies the region where this method is applied.</p> <p>To implement this method using the IPSL coupled model, consider following these steps:</p> <p>1. Install the standard configuration in a path<br> 2. Copy the provided dynhpg.F90 in modipsl/modeles/NEMOGCM/CONFIG/ORCA1_LIM3_PISCES/MY_SRC<br> 3. Compile<br> 3. Copy and modify the namelist_ORCA1_cfg in modipsl/config/IPSLCM6/testffc/PARAM</p> <p>The input conservative temperature (in degC), absolute salinity (in g/kg), and mask are provided to the model for the flow field correction are available in the following files:</p> <p>data_sal_sa_1.5LFC.nc (salinity) <br> data_sal_sa_weak1.5LFC.nc (salinity)<br> data_tem_bigthetao_1.5LFC.nc (temperature)<br> data_tem_bigthetao_weak1.5LFC.nc (temperature)<br> rhdmsk_data_bothBC2.v2.nc (mask)</p> <p>Note that data_sal_sa_1.5LFC.nc and data_tem_bigthetao_1.5LFC.nc are used to constrain the AMOC to the strong state in Jiang et al., (2023). data_sal_sa_weak1.5LFC.nc and data_tem_bigthetao_weak1.5LFC.nc are used to constrain it to the weak state in Jiang et al., (2023).</p> <p>Finally, some of the main simulated outputs are given. All the outputs are the annual mean ensemble mean (3 members) data lasting for 100 years. The initial states of the 3 members are sampled in the years of 1850, 2000 and 2080 of the CMIP6 piControl simulation available on ESGF, corresponding to neutral, strong, and weak AMOC states respectively.</p> <p>heatc_strong_3runs_1Y.nc (ocean heat content in J/m2)<br> sos_strong_3runs_1Y.nc (sea surface salinity in psu)<br> tos_strong_3runs_1Y.nc (sea surface temperature in degC)<br> precip_strong_3runs_1Y.nc (precipitation in kg/(s*m2))<br> diaptrW_strong_3runs_1Y.nc (meridional streamfunction in Sv)<br> slp_strong_3runs_1Y.nc (sea level pressure in Pa)<br> geop500_strong_3runs_1Y.nc (geopotential height at 500 hPa in m)<br> nettop0_strong_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2)<br> nettop_strong_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2)<br> t2m_strong_3runs_1Y.nc (air temperature at 2-m in K)<br> vitu850_strong_3runs_1Y.nc (zonal wind at 850 hPa in m/s)<br> cld_strong_3runs_1Y.nc (low-level and high-level cloudiness, unitless)<br> tauuo_strong_3runs_1Y.nc (surface downward stress in the x-direction in N/m2)<br> tauvo_strong_3runs_1Y.nc (surface downward stress in the y-direction in N/m2)</p> <p>The files listed above are the results from simulations where the AMOC is constrained to the strong state (i.e., using the input data_tem_bigthetao_1.5LFC.nc and data_sal_sa_1.5LFC.nc). Similarly, the same fields from simulations where the AMOC is constrained to the weak state (i.e., using the input data_tem_bigthetao_weak1.5LFC.nc and data_sal_sa_weak1.5LFC.nc) are:</p> <p>heatc_weak_3runs_1Y.nc (ocean heat content in J/m2)<br> sos_weak_3runs_1Y.nc (sea surface salinity in psu)<br> tos_weak_3runs_1Y.nc (sea surface temperature in degC)<br> precip_weak_3runs_1Y.nc (precipitation in kg/(s*m2))<br> diaptrW_weak_3runs_1Y.nc (meridional streamfunction in Sv)<br> slp_weak_3runs_1Y.nc (sea level pressure in Pa)<br> geop500_weak_3runs_1Y.nc (geopotential height at 500 hPa in m)<br> nettop0_weak_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2)<br> nettop_weak_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2)<br> t2m_weak_3runs_1Y.nc (air temperature at 2-m in K)<br> vitu850_weak_3runs_1Y.nc (zonal wind at 850 hPa in m/s)<br> cld_weak_3runs_1Y.nc (low-level and high-level cloudiness, unitless)<br> tauuo_weak_3runs_1Y.nc (surface downward stress in the x-direction in N/m2)<br> tauvo_weak_3runs_1Y.nc (surface downward stress in the y-direction in N/m2)</p> <p> </p>
ALIMA range-corrected photon count profiles and spectral analysis output
<p>The dataset includes a .nc file of range-corrected and background-corrected photon counts measured by ALIMA (Airborne Lidar for Middle Atmosphere research) from six research flights (ST08, ST09, ST10, ST11, ST12, ST14) during the SouthTRAC-GW campaign in September 2019. These corrected photons counts are proportional to the atmospheric density. The data resolution (t, z) is (10s, 100m) but not yet vertically smoothed nor temporally integrated. A vertical smoothing of 900 m and a temporal integration of 1 min is recommended to obtain a sufficient signal-to-noise-ratio.</p> <p>Furthermore, .nc files containing spectral power density (PSD) for selected legs of each research flight averaged over 10 km altitude ranges based on vertically smoothed and temporally integrated range- and background-corrected photon counts by ALIMA are included.</p>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
Code for Producing a Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2021) Combining DMSP-OLS and NPP-VIIRS
<p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL, 1992-2021).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p>
Dataset for "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans et al.
<p>Code and data products associated with "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans V., Robel A., Thompson A. F., and Seroussi H.</p> <p>Please see readme file for all the information.</p> <p>Contact: vverjans3@gatech.edu</p> <p>Author: Vincent Verjans</p>
Attitude Corrected Broadband Radiation and LibRadtran Simulation data based on helicopter-borne observations during MOSAiC
<p>This data set includes broadband radiation measurements after attitude correction of 14 research flights during MOSAiC.<br> In total 12 helicopter and 2 HELiPOD flights. Supportive data includes relative azimuth to sun position to identify shading effects on the CMP22 pyranometer installed.<br> Furthermore, libRadtran simulations are provided to validate cloudfree flights or flightsections.<br> In connection with surface type classifications (e.g. Sperzel, Tim R; Jäkel, Evelyn; Lampert, Astrid; Birnbaum, Gerit; Pätzold, Falk; Neckel, Niklas; Rosenburg, Sophie; Fuchs, Niels; Wendisch, Manfred (2023): Surface type classifications from helicopter-borne imagery during the MOSAiC expedition. PANGAEA, https://doi.org/10.1594/PANGAEA.958997) multiple linear regression can be applied as described by Sperzel, T. R. Codes for: Surface Albedo Measurements and Surface Type Classification from Helicopter-based Observa371<br> tions during MOSAiC, https://doi.org/10.5281/zenodo.7974021 (2023), which results in albedo values per fraction (e.g. Sperzel, Tim R; Jäkel, Evelyn; Lampert, Astrid; Birnbaum, Gerit; Pätzold, Falk; Neckel, Niklas; Rosenburg, Sophie; Fuchs, Niels; Wendisch, Manfred (2023): Helicopter-borne solar broadband irradiance measurements from the MOSAiC expedition. PANGAEA, https://doi.org/10.1594/PANGAEA.958999).<br> Radiation data file names start with "BBR_<date>", simulation file names with "BBR_fdn_clear".</p>
Stability of the next-to-tribimaximal mixings under radiative corrections with the variation of SUSY breaking scale in MSSM
<pre>We analyse the radiative stability of the next-to-tribimaximal mixings ($NTBM$) with the variation of SUSY breaking scale ($m_S$) in MSSM, for both normal and inverted hierarchical cases at the fixed input value of seesaw scale $M_R = 10^{15}$ GeV and $\tan \beta = 30$. All the neutrino oscillation parameters receive varying radiative corrections irrespective of the $m_S$ values at the electroweak scale, which are all within $3\sigma$ range of the latest global fit data. NH case is found to be more stable than IH case for all four cases.</pre>
Stability of the next-to-tribimaximal mixings under radiative corrections with the variation of SUSY breaking scale in MSSM
<pre>We analyse the radiative stability of the next-to-tribimaximal mixings ($NTBM$) with the variation of SUSY breaking scale ($m_S$) in MSSM, for both normal and inverted hierarchical cases at the fixed input value of seesaw scale $M_R = 10^{15}$ GeV and $\tan \beta = 30$. All the neutrino oscillation parameters receive varying radiative corrections irrespective of the $m_S$ values at the electroweak scale, which are all within $3\sigma$ range of the latest global fit data. NH case is found to be more stable than IH case for all four cases.</pre>
Correlates of Political Correctness and Woke Ideology (data and codebook)
<p>This study explored the different correlates of political correctness and Woke philosophy. Based on the existing<br> literature, a new 42-item questionnaire called Controversial Beliefs was created and completed by 400 adults.<br> Participants were invited to write comments on each item and had the option to not respond (Prefer not to say). Various factors were devised based on a content analysis conducted around the themes of Race, Gender and Feminism, which had acceptable<br> internal reliability. </p>
Muon000 and Muon002 raw and pressure temperature corrected data set
<p>Data from Two detectors Muon000 and Muon002. Raw coincidence counts and pressure temperature corrected data set.</p>
CSFD: The Corrected SFD Milky Way Dust Map
<p>This package contains the full-sky CSFD Galactic dust reddening E(B−V) map and associated data products described in <a href="https://arxiv.org/abs/2306.03926">Chiang 2023 (arXiv:2306.03926)</a>.</p> <p>This dust map improves upon the widely used <a href="https://ui.adsabs.harvard.edu/abs/1998ApJ...500..525S/abstract">Schlegel, Finkbeiner, & Davis (1998, SFD)</a> map by removing a tomographically reconstructed cosmic infrared background (CIB) component that contaminates the original SFD. This new dust map enables more accurate Galactic extinction corrections for extragalactic photometry. More information can be found on the <a href="https://idv.sinica.edu.tw/ykchiang/CSFD.html">project website</a>.</p>
UKCP18 RCM precipitation and temperature bias corrected using non-parametric quantile mapping method
<p>The UKCP18 RCM PPE (Met Office Hadley Centre, 2018) projections of precipitation and daily average temperature were bias adjusted using a non-parametric quantile mapping method based on empirical quantiles (Boe et al, 2007, Gudmundsson et al, 2012). The datasets cover the period from December 1980 to November 2080 and are intended for use in climate change impact assessments, where the bias correction helps reduce biases in multiple statistics while <span>maintaining projected climatic changes</span>.</p> <p>-------------------------------------------------</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, <em>8 March 2022</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Boe, J.; Terray, L.; Habets, F. & Martin, E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 2007, 27, 1643-1655, doi: 10.1002/joc.1602.</p> <p>Gudmundsson, L.; Bremnes, J. B.; Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 2012, 16, 3383-3390, doi:10.5194/hess-16-3383-2012.</p> <p><strong>Paper Citation:</strong><br>We kindly ask users of this dataset to cite the paper that describes the dataset. The paper is published and can be accessed via the following link: <a href="https://doi.org/10.5194/essd-17-2113-2025" target="_new" rel="noopener">https://doi.org/10.5194/essd-17-2113-2025</a>.</p> <p><br>Please reference the paper as:<br>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025.</p>
Correction of 4sU induced quantification bias of Spt6 data set
<p>This data set contains the GRAND-SLAM output of the Spt6 data set (https://zenodo.org/record/4275956) after correcting the 4sU induced quantification bias using the correction approach described <a href="https://www.biorxiv.org/content/10.1101/2023.04.21.537786v1">here</a> and implemented in the <a href="https://www.nature.com/articles/s41467-023-39163-4">grandR package</a>.</p> <p>The zip file contains the full GRAND-SLAM output, the *.tsv.gz file is the GRAND-SLAM output table. The RData file contains the grandR object as analyzed in the original original Spt6 data set (see respective zenodo repository).</p> <p> </p> <p> </p> <p> </p>
Monte Carlo based dataset to train a deep learning based partial volume correction for 177Lu-SPECT imaging
<p>This dataset contains pairs of random activity distribution and Monte Carlo simulated SPECT reconstructions used for the training of a deep learning based partial volume correction method. More detailed information on the creation of the data set, the partial volume correction and the use of the data can be found in the publication listed below and in the readme file.</p> <p> </p> <p>Publication: Will be updated, when published.</p>
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OpenNeuro
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