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27 results for “error analysis”
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Software, Dataset, and Techreport: Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration
<p>This upload contains a techreport titled "Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration" together with the software (with documentation) and dataset generating the results. The software is also available on GitHub at https://github.com/croci/mpfem-paper-experiments-2024/ . The GitHub version may be updated in the future. This upload corresponds to commit number 8506dd368b84655201c8c72b1307239b9b4e43fd . See README.md file for installation instructions. The manuscript is also available on the arXiv: https://arxiv.org/abs/2410.12614.</p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"
<p>This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study. </p> <blockquote> <p>Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics.</p> </blockquote> <p>For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>).</p> <p><strong>The data is structured as follows:</strong></p> <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained <em>with the background section as input. </em>These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td><em>Results from Table 2</em></td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td><em>Results from Table 3</em></td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td><em>Results from Table 4</em></td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td><em>Results from Table 5</em></td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
<p>The performance of species distribution models is known to be affected by the analysis grain and the positional error of species occurrences. Coarsening of the spatial analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in predictive models developed for conservation and climate change studies it is increasingly important to test this assumption. Species distribution models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate species occurrences might easier end up in unsuitable environment, which can result in inappropriate conservation actions.</p> <p>Here, we examine the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated virtual species using tree canopy height, topography wetness index, and altitude derived from LiDAR point clouds at 5 x 5 m fine-resolution. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three common modelling techniques (MaxEnt, BRT and GLM) and four discrimination metrics to evaluate model performance (Sørensen index, overprediction and underprediction rate, AUC and TSS).</p> <p>We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance.</p> <p>We recommend fitting models with the finest possible analysis grain (i.e., depending on data availablity) even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrence data, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Global trade data of highly hazardous chemicals after data treatment and Python codes used for error analysis and mirror analysis
<p>This database includes all global trade data of the three types of highly hazaroudous chemicals listed under the Rotterdam Convention after error analysis and mirror analysis. Python codes used for error analysis and mirror analysis are also available.</p> <p> </p> <p> Corresponding authors: <a href="mailto:hongyan.zou@tjnu.edu.cn">hongyan.zou@tjnu.edu.cn</a>; <a href="mailto:Zhanyun.wang@empa.ch">Zhanyun.wang@empa.ch</a></p>
Analyzing sources of error in TIRM experiments and data analysis.
<p>The datasets contain the experimental and simulated intensity traces underlying the figures published in the paper "Analyzing sources of error in TIRM experiments and data analysis." by J. A. Rivera-Moran and P. R. Lang in the MDPI Polymers Special issue "Interaction and Dynamics of Polymers and Colloidal Particles Near Interfaces".</p>
Topographic data to support the analysis of error and uncertainty that degrade topographic corrections of remotely sensed data
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Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
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standard with together) corner left bottom (analysis genetic the in included species among bold gene in shown oxidase-I are SE cytochrome and species the within at) % (divergence divergence sequence average The . pairwise) corner showing right upper (Matrix) %;. SE 4 ABLE (T error in Description of a new species of the Rhinolophus trifoliatus-group (Chiroptera: Rhinolophidae) from Southeast Asia
standard with together) corner left bottom (analysis genetic the in included species among bold gene in shown oxidase-I are SE cytochrome and species the within at) % (divergence divergence sequence average The . pairwise) corner showing right upper (Matrix) %;. SE 4 ABLE (T error
A Statistical Analysis of Error in MPI Reduction Operations Dataset
<p>This is the dataset used to generate the figures and information contained in the paper <em>A Statistical Analysis of Error in MPI Reduction Operations </em>by Samuel D. Pollard and Boyana Norris, to appear in IEEE's<em> Fourth International Workshop on Software Correctness for HPC Applications</em>, 2020.<br> <br> A description is provided in the README.md as well as the software dependencies required to re-generate these data. The log files and tab-separated-values files (tsv) allows a user to analyze the same data we used for the paper. The file datasets-pollard-correctness2020.tar.bz2 decompresses to about 5.7GB.</p> <p>The source code used to generate these data is available <a href="https://github.com/sampollard/reduce-error">on Github</a>.</p>
Final geometries and energies, statistical analysis and estimated errors of single metals and bimetallics for CO2 to methanol conversion
<p>The dataset accommodate all the extra data discussed in:<br>Pisal, P., Krejčí, O. & Rinke, P. Machine learning accelerated descriptor design for catalyst discovery in CO<sub>2</sub> to methanol conversion. <em>npj Comput Mater</em> <strong>11</strong>, 213 (2025). https://doi.org/10.1038/s41524-025-01664-9 </p> <p>The datased contains four types of data:</p> <ol> <li>All the final geometries and energies of adsorbated (*H, *O, *OCHO & *OCH3) and all the 158 single metals and bimetallic alloys on all the surfaces with Miller indices in {-2, -1, ... 2} optimized with Open Catalyst Project (OCP) 20 <em>equiformer_V2</em> machine-learned force-field model. These are in the <a href="https://zenodo.org/api/records/15587232/draft/files/geometries_and_energies.zip/content" target="_blank" rel="noopener noreferrer">geometries_and_energies.zip</a> file organized by the metal/alloys name, with the final geometries and enerigies in a json file, using a json ASE format.</li> <li>All the estimated mean absolute errors (MAE) of predicted adsorption energies for all the considered metals and bimetallic alloys in <a href="https://zenodo.org/api/records/15587232/draft/files/Estimated_MAEs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">Estimated_MAEs_metals_bimetallics.csv</a> and xlsx file. The data content is identical, files differs only by a format.</li> <li>All the adsorption energy disctibutions (AEDs) for all the 158 metals/alloys and adsorbates in <span><a href="https://zenodo.org/api/records/15587232/draft/files/AEDs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">AEDs_metals_bimetallics.csv</a></span> and xlsx files. The data content is identical, files differs only by a format.</li> <li>All the statistical information of the adsorption energies for all the 158 metals/alloys and adsorbates in <span><a href="https://zenodo.org/api/records/15587232/draft/files/Statistics_AEDs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">Statistics_AEDs_metals_bimetallics.csv</a></span> and xlsx files. The data content is identical, files differs only by a format.</li> </ol>
Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework
<p>This repository contains code and data to re-create the numerical results shown in</p> <p>"Error analysis of kernel EDMD for prediction and control in the Koopman framework"</p> <p> <a href="http://arxiv.org/abs/2312.10460">http://arxiv.org/abs/2312.10460</a></p> <p>Please see the README file for detailed description of the codes in this repository.</p>
Data files for SWOT correlated error analysis
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Video Analysis of Errors and Technical Performance Within Minimally Invasive Surgery Short Title: Video Analysis in Minimally Invasive Surgery (VAMIS)
ClinicalTrials.gov study NCT05279287. IPD Sharing: NO. Countries: 1. Publications: 10.
Outpatient Prescription Errors: Detection, Analysis, and Impact on Safety
ClinicalTrials.gov study NCT00256568. IPD Sharing: Not stated. Countries: 1. Publications: 3.
A narrow window for geographic cline analysis using genomic data: effects of age, drift, and migration on error rates
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Estimating the redshift error in supernova data analysis_Code supplement
<p>Supplement for 'Estimating the redshift error in supernova data analysis'</p>
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