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10,801 results for “data correction”
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>
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All POTCAR files have been replaced by "potcar.txt" files containing the title(s) of the POTCAR(s) needed to reproduce the calculations.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>
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>
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>
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>
Figure 1 in New Data On Distribution Of Two Palaearctic Leptaleus Laferté-Sénectère, 1849 (Coleoptera: Anthicidae) Species With New Records And A Correction
Figure 1. Leptaleus chaudoiri (Kolenati, 1846), specimen from Islamabad, Pakistan.
Chromophore-driven optical path reduction as an indirect inner filter effect correction strategy in fluorescence microplate assays - experimental data
<p>Experimental data for the paper entitled <em>Chromophore-driven optical path reduction as an indirect inner filter effect mitigation strategy in fluorescence microplate assays</em> (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.saa.2025.127049" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.saa.2025.127049</span></span></a>). </p> <p>An Excel file with multiple worksheets is provided for the following:</p> <p>1. Fluorescence data:</p> <p>The worksheets contain fluorescence values measured in tetraplicates using two microplate readers (Tecan Spark M10, Tecan, Austria and SpectraMax iD3, Molecular Devices, USA), with baseline values recorded in octuplicates. The results were averaged and baseline-corrected. Detailed notes on the titration experiments are included in the manuscript. Data include both IFE-uncorrected fluorescence and fluorescence corrected (fully or partially) by the addition of an absorbing chromophore.</p> <p>2. Absorbance data:</p> <p>All measured absorbance values obtained using the Tecan Spark M10 microplate reader with UV-Vis transparent microplates are included. The absorbance values at specific wavelengths suitable for the Lakowicz IFE correction method or for determining the concentration of the compound are given separately.</p> <p>3. Fluorescein spectra:</p> <p>UV-Vis and fluorescence excitation/emission spectra for highly dilute fluorescein concentrations where the inner filter effect (IFE) is negligible are reported. These measurements were performed to obtain “true” excitation and emission spectra without the influence of IFE quenching effects.</p> <p>4. ZINFE/NINFE IFE corrections:</p> <p>The dataset contains the results of ZINFE/NINFE IFE corrections (<a href="https://doi.org/10.1021/acs.analchem.2c01031">https://doi.org/10.1021/acs.analchem.2c01031</a>) applied to the fluorescence data and processed using the web service available at <a href="https://ninfe.science">https://ninfe.science</a>.</p> <p>The notation of the fluorophores and microplates is the same as in the manuscript. For reasons of visual clarity, the numerical titration indices are not in subscript.</p> <p> </p>
Tomographic data for demonstrating distortion correction methods
<p>Datasets were used to demonstrate the improvement of a tomographic data after distortion correction was applied.</p> <p>Details in the conference paper: </p> <p>Nghia T. Vo, Robert C. Atwood, and Michael Drakopoulos "Preprocessing techniques for removing artifacts in synchrotron-based tomographic images", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131I (10 September 2019); https://doi.org/10.1117/12.2530324</p> <p>Distortion coefficients were calculated using vounwarp package: </p> <p><a href="https://github.com/DiamondLightSource/vounwarp/blob/master/vounwarp/examples/example_01.py">https://github.com/DiamondLightSource/vounwarp/blob/master/vounwarp/examples/example_01.py</a></p> <p>Slices can be reconstructed using the <a href="https://github.com/DiamondLightSource/Savu">Savu</a> pipeline: <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/corrections/dark_flat_field_correction.py">flat-field correction</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/corrections/distortion_correction_dev.py">distortion correction</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/centering/vo_centering.py">center of rotation calculation</a>, <a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/ring_removal/remove_all_rings.py">ring artifact removal</a>, and<a href="https://github.com/DiamondLightSource/Savu/blob/master/savu/plugins/reconstructions/astra_recons/astra_recon_gpu.py"> FBP reconstruction</a>.</p>
Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study
<p>Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C.</p> <p>O conjunto de dados atualizado está organizado nas seguintes pastas:</p> <ol> <li> <p><strong>c_programs</strong>: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas.</p> <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> <p><strong>codes_suggested_by_the_tools</strong>: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode.</p> <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> <p><strong>complexity_of_codes</strong>: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado.</p> <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> <p>Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.</p>
Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction
<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></p>
Neural data for North Sami compound correction
<p>Data for the paper:</p> <p>Wiechetek, L, Pirinen, F., Hämäläinen, M. & Argese, C. (2021) <a href="https://www.researchgate.net/publication/354254222_Rules_Ruling_Neural_Networks_-_Neural_vs_Rule-Based_Grammar_Checking_for_a_Low_Resource_Language">Rules ruling neural networks – Neural vs. rule-based grammar checking for a low resource language</a>. In <em>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021).</em></p> <p> </p> <p><em>Data.zip</em> includes the data (test, train, validation) in a JSON format and <em>code.zip</em> has the source code.</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </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>
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>
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>
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>
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>
Data-driven characterization and correction of the orientation dependence of magnetization transfer measures using diffusion MRI
<p>These are the produced plots for every subjects and every sessions of the study. Here are the characterization plots, the corrected plots, and the tractometry plots.</p>
Topographic data to support the analysis of error and uncertainty that degrade topographic corrections of remotely sensed data
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Allen Brain Atlas
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DANDI Archive for NWB 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.