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8 results for “Uncertainty Propagation”
Nuclear Data Uncertainty Propagation for the Molten Salt Fast Reactor Design (dataset)
<p>This repository contains the dataset, post-processing script and models needed to reproduce the results presented in the article "Nuclear Data Uncertainty Propagation for the Molten Salt Fast Reactor Design", published in the special issue of Nuclear Science and Engineering dedicated to the 1st Young Molten Salt Reactor Conference (held in Lecco in June 6th and 9th 2022).</p> <p>The dataset includes:</p> <ul> <li>the perturbed nuclear data files (in ACE and ENDF-6 formats) generated with the open-source, python package <a href="https://github.com/luca-fiorito-11/sandy">SANDY</a> and processed with the processing code <a href="https://github.com/njoy">NJOY</a> by using the <a href="https://github.com/nicoloabrate/ndl">NDL</a> code.</li> <li>the <a href="https://serpent.vtt.fi/serpent/">Serpent 2</a> Monte Carlo calculations for two models of the Molten Salt Fast Reactor design, conceived during the <a href="http://samofar.eu/">SAMOFAR</a> EU project</li> </ul> <p>The python scripts include pre- and post-processing tools used to generate the perturbed data and to analyse the Serpent 2 output.</p>
Citation count error data for "Data inaccuracy quantification and uncertainty propagation for bibliometric indicators"
<p>This is the original collected data on citation count errors resulting from citation matching errors in Web of Science data for the publication "Data inaccuracy quantification and uncertainty propagation for<br>bibliometric indicators". The first column, <code>CITCOUNT_ALL</code>, gives the total (corrected) citation count for a publication, which is the citation count according to WoS plus the additionally manually identified citations (missed by WoS's algorithm). The second column, <code>CITCOUNT_WOS</code>, is the WoS citation count. The numeric difference between the two column values in one row is the number of additionally manually identified citations.</p>
Data for: When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation
<p>This is the data repository for our study on “When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation” submitted to the Journal of Industrial Ecology (JIE).</p> <p>I contains:</p> <ul> <li>The data behind the all numeric plots</li> <li>The data needed to reproduce the case-study results in the Supplementary Information (check out V1)</li> </ul> <p> </p> <p>To reproduce our results you need to download the files from this repo, our code from Github (https://github.com/simschul/uncertainty_disaggregation) and put the data into the `./data` folder. </p> <p>The data is an intermediate output from an earlier study: https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html </p> <p>For more information how those intermediate results were created please refer to the paper and the code (https://github.com/simschul/uncertainty_GHG_accounts)</p> <p> </p> <p> </p>
Validation of an Uncertainty Propagation Method for Moving-Boat ADCP Discharge Measurements
<p>ADCP intercomparisons data from Génissiat (2010) and Chauvan (2016). Data used in the article <strong>Validation of an Uncertainty Propagation Method for Moving-Boat ADCP Discharge Measurements</strong>, <em>Water Resources Research</em>, Despax et al..</p>
A non-intrusive nuclear data uncertainty propagation study for the ARC fusion reactor design
<p>Dataset of perturbed nuclear data files employed in the Serpent Monte Carlo code for the nuclear data uncertainty propagation analysis of the Affordable, Robust and Compact (ARC) fusion reactor. </p>
Dataset for: Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems
<p>Dataset containing the response data for the calibration of the MB5 microbarometer (Figure 5), the WNRS (Figure 6) and the sensor (Figure 8).</p>
RRR/RAPID input and output files corresponding to "Analytical Propagation of Runoff Uncertainty into Discharge Uncertainty through a Large River Network"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>David, C. H., J. M. Hobbs, M. J. Turmon, C. M. Emery, J. T. Reager, and J. S. Famiglietti (2019), Analytical Propagation of Runoff Uncertainty into Discharge Uncertainty through a Large River Network, Geophysical Research Letters, 46, 8102–8113, DOI: 10.1029/2019GL083342.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p>
Data and Codes for: Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis
<p>Phase segmentation is a crucial step in X-ray computed tomography (CT) for image-based analysis (CT-IBA) to derive soil and root information. How segmentation uncertainty (SU) affects CT-IBA of vegetated soil has never been explored. The enclosed data and codes are used to assist the analysis of SU quantification and propagation in the journal paper published in Plant & Soil. The title of the paper is Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis. </p>
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