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73 results for “performance results”
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 1: Operational scenario with 6.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 1. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p> <p> </p> <p> </p> <p> </p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 2: Operational scenario with 4.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 2. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 8: Damaged scenario with 1.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 8. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 7: Damaged scenario with 3.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 7. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 5: Damaged scenario with 6.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 5. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 6: Damaged scenario with 4.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 6. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 4: Operational scenario with 1.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 4. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 3: Operational scenario with 3.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 3. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
On the Performance of Method-Level Bug Prediction: A Negative Result. Appendix
<p>Abstract Bug prediction is aimed at identifying software artifacts that are more likely to be defective. Most approaches defined so far target the prediction of bugs at class/file level. Nevertheless, past research has provided evidence that this granularity might be too coarse-grained, thus reducing the usability of bug prediction in practice. As a consequence, researchers have started proposing defect prediction models targeting a finer granularity, particularly targeting methods, providing promising evidence that it is possible to operate at this granularity. Particularly, models based on a mixture of product and process metrics provided the best results. </p> <p>In this paper, we first replicate previous research on method-level bug- prediction using different systems and timespans. Afterward, based on the limitations of existing research, we (1) re-evaluate method-level bug prediction models more realistically and (2) analyze the whether textual features— previously shown as valuable sources of information for the evaluation of software quality (yet surprisingly unexplored in this research field)—can be exploited to improve method-level bug prediction abilities. Key results of our study include that (1) the performance of the previously proposed models, tested using the same strategy but with different systems/timespans, is con- firmed. However, (2) when evaluated with a more realistic strategy all the models show a dramatic drop in performance exhibiting results close to that of a random classifier. In addition, we find that (3) the contribution of textual features within such models is limited and unable to improve the prediction capabilities significantly. As a consequence, our replication and negative results indicate that method-level bug prediction is still an open challenge.</p>
GWAS results of selected binarized neurocognitive task performances from Philadelphia Neurodevelopmental Cohort
<p>This archive contains analysis results associated with the publication:</p> <p>Shraddha Pai, Shirley Hui, Philipp Weber, Soumil Narayan, Owen Whitley, Peipei Li, Viviane Labrie, Jan Baumbach, Anne L Wheeler, Gary D Bader. (2023). Multi-scale systems genomics analysis predicts pathways, cell types and drug targets involved in normative variation in peri-adolescent human cognition. Cerebral Cortex.</p> <p>-----------------------------------------------------------<br> *loco.mlma files: Univariate GWAS summary statistics for binarized performance of computerized neurocognitive tests in the Philadelphia Neurodevelopmental Cohorts. GWAS was performed on individuals ascertained to be of European genetic ancestry. For methods, please refer to the publication above.</p> <p>Phenotype codes:<br> lnb_tp2 Working memory LNB: Number of Correct Responses to 2-Back Trials (TP)<br> pcpt_t_tp Attention PCPT: Total of Correct Responses to Number Trials (TP) and Letter Trials (TP)<br> peit_cr Emotion identification PEIT: Total Correct Responses for All Test Trials, by genus<br> pfmt_ifac_tot Face memory PFMT: Total Correct Responses for All Test Trials<br> plot_tc Spatial reasoning PLOT: Total Correct Responses for All Test Trials, by genus<br> pmat_pc Nonverbal reasoning PMAT: Percent of Correct Responses for All Test Trials, by genus<br> pvrt_cr Verbal reasoning PVRT: Total Correct Responses for All Test Trials, by genus<br> pwmt_kiwrd_tot Word memory PWMT: Total Correct Responses for All Test Trials<br> volt_svt Object memory VOLT: Total Correct Responses for All Test Trial</p>
Raw results of the simulation experiments performed in the educacional study evaluating a guaranteed service
<p>This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation results presented in [1].</p> <p>[1] Suárez-González A., Rodríguez Rubio R., López-Ardao J.C., López-García C, Herrería-Alonso S. (2021). Educational case study: evaluation of guaranteed service on low earth orbit satellite networks. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 215-219. DOI: https://doi.org/10.46354/i3m.2021.emss.029</p>
Raw results of the simulation experiments performed to evaluate the Time-to-idle Control Variate ferformance in the single queue case
<p>This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation results presented in [1].</p> <p>[1] Suárez-González A., López-García C., López-Ardao J.C., Rodríguez Rubio R., Rodríguez Pérez M. (2021). "Time-to-idle Control Variate Performance in the Single Queue Case". Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 147-151. DOI: https://doi.org/10.46354/i3m.2021.emss.020</p>
Results Discharge Observation Uncertainty and Model Performance Pub
<p><br>Dataset for Publication:<br>On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance</p> <p>Authors and Affiliations:<br>- Jerom P. M. Aerts<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Jannis M. Hoch<br> Department of Physical Geography, Utrecht University, Utrecht, the Netherlands<br> Fathom, Bristol, United Kingdom<br>- Gemma Coxon<br> Geographical Sciences, University of Bristol, Bristol, United Kingdom<br>- Nick C. van de Giesen<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Rolf W. Hut<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands</p> <p>Correspondence:<br>For inquiries about the dataset or publication, contact:<br>Jerom P. M. Aerts<br>Email: j.p.m.aerts@tudelft.nl</p> <p>---</p> <p>Overview:<br>This dataset supports the research article, "On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance."<br>It includes outputs and analyses from hydrological models and observational data across multiple flow categories and performance metrics. The dataset enables replication of the analyses and insights into the impacts of discharge observation uncertainty on model evaluation.</p> <p>---</p> <p>Dataset Structure:</p> <p>Folder: Model_Results<br>Contains results from hydrological model runs and associated analyses.</p> <p>Subfolders:</p> <p>1. MARRMoT_Models<br> - Contains results for six hydrological models implemented using the MARRMoT framework.</p> <p>2. PCR-GLOBWB<br> - Includes results for the PCR-GLOBWB hydrological model.</p> <p>3. wflow_sbm<br> - Contains results for the wflow_sbm hydrological model.</p> <p>Each Subfolder Contains:</p> <p>- Flow Categories<br> - Results categorized by flow regime:<br> - Low_Flow<br> - Average_Flow<br> - High_Flow</p> <p>- Gumboot Temporal Sampling Analysis<br> - Results of temporal sampling analyses conducted using the Gumboot method.</p> <p>- Objective Functions<br> - Files containing model performance metrics based on different objective functions.</p> <p>- Observations<br> - Observed discharge time series data used for model calibration and validation.</p> <p>- Simulation Time Series<br> - Simulated discharge time series generated by the models.</p> <p>---</p> <p>Data Description:</p> <p>- File Formats<br> - All data files are in CSV format with UTF-8 encoding.</p> <p>- Variable Descriptions<br> - Date: YYYY-MM-DD<br> - Discharge Values: Cubic meters per second (m³/s).<br> - Flow Categories: Indicates if data corresponds to low, average, or high flow conditions.</p> <p>- Performance Metrics:<br> - Includes common hydrological metrics (e.g., Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency) computed for each model and flow category.</p> <p>---</p> <p>Usage Guidelines:</p> <p>Citation:<br>If you use this dataset in your research, please cite the publication as follows:<br>> Aerts, J. P. M., Hoch, J. M., Coxon, G., van de Giesen, N. C., & Hut, R. W. (2024). On the importance of discharge observation uncertainty when interpreting hydrological model performance. [Journal Name, Volume, Pages]. DOI: [Insert DOI].</p> <p>Licensing:<br>This dataset is made available under the [Insert License, e.g., Creative Commons Attribution 4.0 International License (CC BY 4.0)].</p> <p>---</p> <p>Contact:<br>For further questions, feel free to contact:<br>Jerom P. M. Aerts<br>j.p.m.aerts@tudelft.nl</p>
Precalculated Results of Throughput Analysis in "Comprehensive Benchmarking of High-Performance Vector Field Representations"
<p>This repository contains results calculated by our throughput analysis. These results should be exactly and deterministically reproducible using the aforementioned software, but precomputed results are provided for the benefit of the reader, in case certain software is not available. Included in this artifact are the assembly files generated by the compilers, the throughput analysis of these aforementioned assembly files, and a collection of tables which summarize the results.</p> <p>These results were generated using <em>clang</em> version 14.0.6, <em>gcc</em> version 11.2.0, and <em>llvm-mca</em> version 14.0.6.</p>
Segmentation performance evaluation results and R scripts for generating figures and tables.
<p>This archive contains the evalutation results of 24 segmentation models and the R scripts to produce figures and tables published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>Please refer to the manuscript for further details.</p>
Results from network experiments conducted on ULiège testbed with the Network Performance Framework (NPF) tool
<p>This dataset results from network experiments on a router configuration with replayed traffic. The network trace comes from a border router of our campus network. The router is used in both directions, i.e. both for reception and transmission of packets (to and from our campus network). Each experiment was done 3 times (for a total of 11466 experiments).</p> <p> </p>
Academic Excellence, Website Quality, SEO Performance: Is there a Correlation? - Dataset of measurements, test results and calculated ratings.
<p>This Dataset, in two files of xlsx format, contains the data of all measurements, test results and calculated ratings as they are described in the methodology of the research article "Academic Excellence, Website Quality, SEO Performance: Is there a Correlation".</p>
MongoDB Performance Test Result Dataset
<p>This artifact contains performance test results from MongoDB’s internal performance testing system.</p>
Data analysis results for: "MoDLE: High-performance stochastic modeling of DNA loop extrusion interactions"
<p>Due to technical issues we are unable to upload the updated version of this dataset on Zenodo.<br> <br> The latest version of this dataset can be found on the NRID research data archive at DOI <a href="https://doi.org/10.11582/2022.00056">10.11582/2022.00056</a>.</p>
Inconsistent performance of multi-type genomic data in phylogenomics of neuropteridan insects, with solutions toward conflicting results
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