Applying Random Sampling methods to data analysis for uncertainty production, with an Open source and Open science outlook.

By Greg Henning

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The Large Language Model predictor GPT (versions 3.5 and 4) from OpenAI have been used to proofread part of this document. The texts, data and figures are the product of my own work. The ideas and conclusions are my own.

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Content:

• Preamble
  • Thanks and acknowledgments
  • Foreword
• Context and motivation
  • Nuclear data for application
  • ¹⁸³W
• Using random sampling to produce uncertainties and correlation matrices
  • Uncertainty and covariance ?
  • The Monte Carlo method
• Analysis method and practical implementation
  • Method of experimental data analysis
  • Implementation of the Monte Carlo analysis
• Results and discussions
  • Results
  • Discussion
• Open Science
  • Open Access
  • Open Data
  • Open Source
  • Distribution licenses
• Management methods for software programming and doing research
  • Workflow cycle inspired from Integrated Safety Management
  • Gamification: Seeing projects as an adventure
  • The Pomodoro method
  • Lean software programming
  • The Zen of Python
  • Note-taking
• Conclusions
• Appendix
  • Experimental (n, n’ \(\gamma\)) and (n, 2n \(\gamma\)) cross sections
  • ``--help``
  • File format for (n, xn gamma) cross section
  • Metadata standards
  • Updates
  • Glossary

Code and Data availability Statement

The analysis code described in this manuscript is available publicly, under the CeCILL-C FREE SOFTWARE LICENSE AGREEMENT, on the University of Strasbourg gitlab.

Other scripts and softwares of interest can be found in the extra section of the manuscript repository, and are distributed openly (check repository for specific license information).

Some data used in the analysis is made available in the analysis repository.

The results from the analysis presented here will be made available publicly on the Recherche Data Gouv platform, alongside previous datasets from the same setup.

Acknowledgments

The work presented in this document was supported in parts by the CNRS multipartner NEEDS program, PACEN/GEDEPEON, and by the European Commission within the Sixth Framework Program through I3-EFNUDAT (EURATOM contract n\(^{\circ}\) 036434) and NUDAME (Contract FP6-516487), within the Seventh Framework Program through EUFRAT (EURATOM contract n\(^{\circ}\) FP7-211499), through ANDES (EURATOM contract n\(^{\circ}\) FP7-249671), from the Euratom research and training program 2014-2018 under grant agreement n\(^{\circ}\) 847594 (ARIEL) and under grant agreement n\(^{\circ}\) 847552 (SANDA).

Footnotes

[1]

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