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12,753 results for “Corrections”
Data from: Correcting a bias in the computation of behavioral time budgets that are based on supervised learning
<p>Supervised learning of behavioral modes from body-acceleration data has become a widely used research tool in Behavioral Ecology over the past decade. One of the primary usages of this tool is to estimate behavioral time budgets from the distribution of behaviors as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behavior. In this paper we show that the widespread computation of behavioral time budgets is biased, due to ignoring the classification model confusion probabilities. Next, we introduce <em>the confusion matrix correction for time budgets</em> -- a simple correction method for adjusting the computed time budgets based on the model's confusion matrix. Finally, we show that the proposed correction is able to eliminate the bias, both theoretically and empirically in a series of data simulations on body acceleration data of a fossorial rodent species (Damaraland mole-rat, <em>Fukomys damarensis</em>). Our paper provides a simple implementation of <em>the confusion matrix correction for time budgets</em>, and we encourage researchers to use it to improve accuracy of behavioral time budget calculations.</p>
Replication Data for: Exact two-component Hamiltonians for relativistic quantum chemistry: Two-electron picture-change corrections made simple
<p>Based on self-consistent field (SCF) atomic mean-field (amf) quantities, we present two simple, yet computationally efficient as well as numerically accurate algebraic approaches to remedy both scalar-relativistic <em>and</em> spin-orbit two-electron picture-change effects (PCE) arising within an exact two-component (X2C) Hamiltonian framework. Both approaches, dubbed as amfX2C and e(xtended)amfX2C, allow us to uniquely tailor PCE corrections to either mean-field models, <em>viz</em>. Hartree–Fock or Kohn–Sham DFT, in the latter case also avoiding the need of a point-wise calculation of exchange–correlation PCE corrections. We assess the numerical performance of these PCE correction models on spinor energies of group-18 (closed-shell) and group-16 (open-shell) diatomic molecules, achieving a consistent ≈ 10−5-Hartree accuracy with regard to reference four-component data. Additional tests include SCF calculations of molecular properties such as absolute contact density and contact density shifts in copernicium fluoride compounds (CnFn, n=2,4,6), as well as equation-of-motion coupled cluster calculations of X-ray core ionization energies of 5<em>d</em> and 6<em>d</em>-containing molecules where we observe an excellent agreement with reference data. To conclude, we are confident that our (e)amfX2C PCE correction models constitute a fundamental milestone towards a universal and reliable relativistic two-component quantum chemical approach, maintaining the accuracy of the parent four-component one at a fraction of its computational cost.</p>
Reproducibility package for: AllSynth: Transiently Correct Network Update Synthesis Accounting for Operator Preferences
<p>This is a reproducibility package for the paper "AllSynth: Transiently Correct Network Update Synthesis Accounting for Operator Preferences" to appear in TASE'22.</p>
Effects of variations of SUSY breaking scale on neutrino parameters at low energy scale under radiative corrections
<p>The paper addresses the effects of the variations of the SUSY breaking scale $m_s$ in the range (2-14) TeV on the three neutrino masses and mixings, in running the renormalization group equations (RGEs) for different input values of high energy seesaw scale $M_R$, in both normal and inverted hierarchical neutrino mass models. The present investigation is a continuation of the earlier works based on the variation of $m_s$ scale. Two approaches are adopted one after another - bottom-up approach for running gauge and Yukawa couplings from low to high energy scale, followed by the top-down approach from high to low energy scale for running neutrino parameters defined at high energy scale, along with gauge and Yukawa couplings. A self-complementarity relation among three mixing angles is also employed in the analysis. Significant effect due to radiative corrections on neutrino parameters with the variation of SUSY breaking scale $m_s$, is observed.</p>
Indoor Positioning Simulation For Examination And Correction Of Occupancy Limits In Architectural Design
<p>Dataset contains results of the simulation, statistical analysis and images. "Read me" file contains explanations on the content.</p>
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
<p>This RGB-D dataset is part is part of our publication</p> <p>Heindl, Christoph, et al. "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time." <em>Tenth International Conference on Machine Vision (ICMV 2017)</em>. Vol. 10696. SPIE, 2018.</p> <p>Our capture setup consists of a RGB-D sensor looking towards a known planar object. The sensor is coupled with an electronic linear axis to adjust distance. We captured data at distances [40cm, 90cm, 10cm steps] in the temperate range of [25°C, 35°C, 1°C steps]. At each temperature/distance tuple we grabbed 50 images from both RGB and IR (aligned with RGB) sensors. We then created an artificial depth map for all RGB images utilizing the known calibration target in sight.</p> <p>For more information visit https://github.com/cheind/rgbd-correction</p>
Data for "Workhorse minimally-empirical dispersion-corrected density functional, with tests for weakly-bound systems: r2 SCAN + rVV10"
<p>VASP inputs and outputs for the:</p> <ul> <li>Ar2 binding energy curve (Ar2_bpara.tar.gz)</li> <li>L28 set of layered-solid geometries and interlayer binding energies (L28.tar.gz)</li> <li>S22 set of interaction energies of weakly-bound complexes (S22.tar.gz)</li> </ul> <p>All POTCAR files have been replaced by "potcar.txt" files containing the title(s) of the POTCAR(s) needed to reproduce the calculations. A preprint is available from the arXiv:2204.11717 (<a href="https://arxiv.org/abs/2204.11717">link</a>).</p>
Tibetan zenith wet delay model with refined vertical correction
<p>MATLAB scripts and coefficient matrix of the TZ model, which corresponds to the paper "Tibetan zenith wet delay model with refined vertical correction"</p>
Dataset to adjusted spectral correction method for calculating extreme winds in tropical cyclone affected water areas
<p>This is a dataset of the 50-year wind of an effective temporal resolution of 10 min over three areas with the presence of tropical cyclones at 10 m, 50 m, 100 m and 150 m.</p> <p>There are 18 files in total, with 12 files for the 50-year winds:</p> <p>'XXcfsru50atYYmcorr_revision.dat'</p> <p>and 6 files for the corresponding latitudes and longitudes:</p> <p>'TC_XX_lat.dat' and 'TC_XX_lon.dat'</p> <p>The three areas are indexed as E1, W1, W2 (as in the file names 'XX').</p> <p>The heights are 10 m, 50 m, 100 m and 150 m (as in the filenames 'YY').</p> <p> </p> <p> </p>
JAG Bias Corrected SPP
<p>This dataset belongs to the paper: Comparison of Bias-Corrected MultiSatellite Precipitation Products by Deep Learning Framework</p> <p>Daily dataset from 01/01/2016 to 12/2019</p>
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Correction occurrence analysis spreadsheet
<p>The accompanying "Correction occurrence" spreadsheet for the paper:</p> <p>这个文件-“纠正事件”Excel表格是下面论文的附属文件:</p> <blockquote> <p>Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training, Rong Gong, Xavier Serra, Chinese traditional music technology session - China conference on sound and music technology 2017, Suzhou, China</p> </blockquote> <p>Each occurrence is annotated with (1) teacher's feedback (2) signal analysis method (3) classified dimension or detailed elements.</p> <p>每一个纠正事件都标注有(1)老师的反馈,(2)所使用的信号分析方法,(3)分类的维度和细节元素。</p>
Data supplementing the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal
<p>These data supplement the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal</p> <p>The directory contains the following files:</p> <p>1<strong>5 fastq files raw reads (5 mock communities, 3 replicates)</strong><strong>.rar </strong>- contains the 15 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>15 fastq files information.xlsx</strong> :</p> <p>- contains the information relative to the 15 fastq files corresponding to the PGM raw data of the 5 mock communities (sequenced with 3 replicates), including: the ID of the fastq files, the mock community name, the replicate number, the final sample Id and the number of raw reads per fastq file.</p> <p>- contains the information of the proportion of the 8 diatoms species (%) used to create the 5 mock communities (estimated from microscopy).</p>
FIGURE 3. A in Rollo H. Beck's Visits to Isla Guadalupe, Mexico, with Additions and Corrections to the Island's Avifauna
FIGURE 3. A portion of Rollo Beck's collecting catalog from Isla Guadalupe (CAS).
Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law
<p>Included are central data and codes for the manuscript "Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law", published May 2, 2024.</p> <p>Reference for full article:</p> <p>Ashley, A.W., Mookherjee, M., Xu, M., Yu, T., Manthilake, G., & Wang, Y. (2024). Viscosity measurements at high pressures: A critical appraisal of corrections to Stokes' Law. Journal of Geophysical Research: Solid Earth, 129, e2023JB028489. https://doi.org/10.1029/2023JB028489</p>
Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material)
<p>These are the nested sampling inference products used to obtain the results for <span><a href="https://arxiv.org/abs/2404.16428">arXiv:2404.16428</a>. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given. </span></p> <p><span>Folders:</span></p> <p><span>outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms. </span></p> <p><span>outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100. </span></p> <p><span>outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run.</span></p>
Cost Management in Cloud Computing: a study on financial waste and correction strategies
<p>This project explores the identification and mitigation of financial wastage in Cloud Computing (CC) operations by professionals in the field, examining the alignment of their strategies with FinOps guidelines. Through a survey distributed via social media and messaging apps to CC professionals, the study engaged 28 participants from various companies, sizes, and sectors. It revealed that these professionals have encountered cost-related issues in cloud applications, particularly financial wastage. The investigation aimed to understand the prevalence of financial wastage in CC and evaluate the effectiveness of the adopted practices in addressing these inefficiencies within the framework of FinOps principles.</p>
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Data from: Correction of preferred-orientation induced distortion in cryo-electron microscopy maps
<p>Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy, modifications to grids or to air-water-interfaces. However, these approaches often require time-consuming experiments and the effect was always protein dependent. Here, we developed a procedure containing removing mis-aligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct such distortion by recovering missing data using a purely computational algorithm. This procedure called Signal-to-Noise Ratio Iterative Reconstruction Method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.</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.