NNPDF/nnpdf
Oct 2024 – Oct 2025
10 Months active
Languages Used
PythonTextYAMLCSVRSTTOMLreStructuredTextyaml
Technical Skills
Backend DevelopmentBuild ConfigurationCode CleanupCode RefactoringConfiguration ManagementData Configuration
Roy Stegeman
PROFILE
Roy Stegeman
Roy Stegeman contributed to the NNPDF/nnpdf repository by engineering robust backend features and data workflows for high-energy physics analysis. Over ten months, he delivered 48 features and resolved 23 bugs, focusing on reproducible data pipelines, configuration management, and uncertainty quantification. Using Python and YAML, Roy refactored legacy modules, enhanced CI/CD reliability, and expanded support for complex covariance and scale variation analyses. He improved data integrity through rigorous validation and metadata normalization, streamlined replica handling, and automated test coverage. His work emphasized maintainable code, clear documentation, and scalable tooling, enabling more accurate, efficient, and reliable scientific computation and collaboration.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
178Total
Bugs
23
Commits
178
Features
48
Lines of code
21,608
Activity Months10
Your Network
76 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary (NNPDF/nnpdf): Focused on correctness and expanded analysis capabilities. Delivered a critical bug fix in photon replica handling and extended the configuration space with new alpha scale variations to support broader, more robust analyses. These changes improve reliability of replica-based calculations and enable more comprehensive parameter studies, driving higher quality scientific results and better decision-making for downstream analyses.
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary (NNPDF/nnpdf): Focused on correctness and expanded analysis capabilities. Delivered a critical bug fix in photon replica handling and extended the configuration space with new alpha scale variations to support broader, more robust analyses. These changes improve reliability of replica-based calculations and enable more comprehensive parameter studies, driving higher quality scientific results and better decision-making for downstream analyses.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focused on developer experience improvements in NNPDF/nnpdf. Delivered tooling and formatting stability to enhance code quality, readability, and CI reliability. The work reduces formatting churn and aligns with long-term maintainability goals for YAML-driven configurations.
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focused on developer experience improvements in NNPDF/nnpdf. Delivered tooling and formatting stability to enhance code quality, readability, and CI reliability. The work reduces formatting churn and aligns with long-term maintainability goals for YAML-driven configurations.
June 2025
16 Commits • 6 Features
Jun 1, 2025In June 2025, the NNPDF/nnpdf workstream delivered targeted feature and reliability improvements that directly enhance analysis accuracy, configuration safety, and data integrity, while simplifying collaboration and onboarding.
16 Commits • 6 Features
Jun 1, 2025In June 2025, the NNPDF/nnpdf workstream delivered targeted feature and reliability improvements that directly enhance analysis accuracy, configuration safety, and data integrity, while simplifying collaboration and onboarding.
June 2025
May 2025
1 Commits
May 1, 2025May 2025: Focused on data integrity improvements in NNPDF/nnpdf. Delivered a targeted bug fix to align the ID field in theory card 41007000.yaml with its filename (41_007_000), ensuring data identifiers are consistent and correctly reference files. This work improves data traceability, reduces downstream errors in automated pipelines, and strengthens the overall robustness of theory card data management.
May 2025
1 Commits
May 1, 2025May 2025: Focused on data integrity improvements in NNPDF/nnpdf. Delivered a targeted bug fix to align the ID field in theory card 41007000.yaml with its filename (41_007_000), ensuring data identifiers are consistent and correctly reference files. This work improves data traceability, reduces downstream errors in automated pipelines, and strengthens the overall robustness of theory card data management.
March 2025
15 Commits • 3 Features
Mar 1, 2025This monthly summary covers the NNPDF/nnpdf work for 2025-03, focusing on feature-driven quality improvements, reproducibility, and documentation enhancements that collectively increase reliability and reduce time-to-validation for closure tests and Alpha_s studies.
15 Commits • 3 Features
Mar 1, 2025This monthly summary covers the NNPDF/nnpdf work for 2025-03, focusing on feature-driven quality improvements, reproducibility, and documentation enhancements that collectively increase reliability and reduce time-to-validation for closure tests and Alpha_s studies.
March 2025
February 2025
8 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for NNPDF/nnpdf: Focused on delivering features that improve uncertainty analyses, data quality, and reproducibility, while cleaning up dataset naming and metadata to support v2 deployments. Key features delivered include covariance handling enhancements in N3Fit to support multiple covariance sources and new configuration options (point_prescriptions, user_covmat_path) with control for resampling of negative pseudodata in baseline runs. Extended NNLO theory variations by adding EXA (Extended Model Variations) with alpha_s=0.120 to the scale variation configuration for more detailed uncertainty analyses. Also completed a dataset naming and metadata cleanup to fix YTTBAR to YT, standardize YAML filenames, correct kinematic labels, fix process_type, and add a v2 explanatory note. These changes were implemented through a series of targeted commits, ensuring backward compatibility and improved clarity. Business value and impact: This month’s work strengthens data integrity, expands uncertainty quantification capabilities, and accelerates reproducibility and review cycles for high-precision phenomenology, enabling more reliable comparisons with experimental results and faster iteration on analysis configurations.
February 2025
8 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for NNPDF/nnpdf: Focused on delivering features that improve uncertainty analyses, data quality, and reproducibility, while cleaning up dataset naming and metadata to support v2 deployments. Key features delivered include covariance handling enhancements in N3Fit to support multiple covariance sources and new configuration options (point_prescriptions, user_covmat_path) with control for resampling of negative pseudodata in baseline runs. Extended NNLO theory variations by adding EXA (Extended Model Variations) with alpha_s=0.120 to the scale variation configuration for more detailed uncertainty analyses. Also completed a dataset naming and metadata cleanup to fix YTTBAR to YT, standardize YAML filenames, correct kinematic labels, fix process_type, and add a v2 explanatory note. These changes were implemented through a series of targeted commits, ensuring backward compatibility and improved clarity. Business value and impact: This month’s work strengthens data integrity, expands uncertainty quantification capabilities, and accelerates reproducibility and review cycles for high-precision phenomenology, enabling more reliable comparisons with experimental results and faster iteration on analysis configurations.
January 2025
7 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for NNPDF/nnpdf highlighting feature delivery, bug fixes, and impact. Focused on enabling robust MT variation analyses, improving uncertainty handling, and maintaining high code quality to support reliable downstream physics studies.
7 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for NNPDF/nnpdf highlighting feature delivery, bug fixes, and impact. Focused on enabling robust MT variation analyses, improving uncertainty handling, and maintaining high code quality to support reliable downstream physics studies.
January 2025
December 2024
29 Commits • 9 Features
Dec 1, 2024Dec 2024 (Month: 2024-12) – NNPDF/nnpdf delivered stability, automation, and advanced physics capability gains that directly improve data integrity, reproducibility, and experiment throughput. The work focused on robust data handling, reliable IO, and expanding configurability for physics studies, while maintaining comprehensive documentation and user guidance.
December 2024
29 Commits • 9 Features
Dec 1, 2024Dec 2024 (Month: 2024-12) – NNPDF/nnpdf delivered stability, automation, and advanced physics capability gains that directly improve data integrity, reproducibility, and experiment throughput. The work focused on robust data handling, reliable IO, and expanding configurability for physics studies, while maintaining comprehensive documentation and user guidance.
November 2024
81 Commits • 19 Features
Nov 1, 2024November 2024 (NNPDF/nnpdf): Delivered substantial improvements to covariance and scale-variation workflows, completed major refactors of point-prescriptions, and upgraded documentation and CI stability. Key outcomes include multi-theory covmat support with alphas covariance, NNLO scale-variation IDs, and robust data updates; refactoring to remove 'n3lo' from point prescriptions and tightening covmat interaction; refreshed NNLO theory YAML data; and stabilized CI/build and testing pipelines, enhancing reliability and maintainability with clear business value for precise theory predictions.
81 Commits • 19 Features
Nov 1, 2024November 2024 (NNPDF/nnpdf): Delivered substantial improvements to covariance and scale-variation workflows, completed major refactors of point-prescriptions, and upgraded documentation and CI stability. Key outcomes include multi-theory covmat support with alphas covariance, NNLO scale-variation IDs, and robust data updates; refactoring to remove 'n3lo' from point prescriptions and tightening covmat interaction; refreshed NNLO theory YAML data; and stabilized CI/build and testing pipelines, enhancing reliability and maintainability with clear business value for precise theory predictions.
November 2024
October 2024
18 Commits • 5 Features
Oct 1, 2024Monthly summary for 2024-10 focused on delivering tangible business value through CI/build reliability, expanded data support, and codebase hygiene. Key outcomes include faster, more stable builds, streamlined test and release processes, and extended data/configuration support for experimental campaigns. The work emphasizes maintainability, reproducibility, and scalable tooling for ongoing development and analytics.
October 2024
18 Commits • 5 Features
Oct 1, 2024Monthly summary for 2024-10 focused on delivering tangible business value through CI/build reliability, expanded data support, and codebase hygiene. Key outcomes include faster, more stable builds, streamlined test and release processes, and extended data/configuration support for experimental campaigns. The work emphasizes maintainability, reproducibility, and scalable tooling for ongoing development and analytics.
Activity
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Quality Metrics
Correctness91.8%
Maintainability93.2%
Architecture90.0%
Performance85.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
BibTeXCSVDataPythonRSTShellTOMLTextYAMLreStructuredText
Technical Skills
Backend DevelopmentBuild ConfigurationCI/CDCode CleanupCode DocumentationCode MaintenanceCode RefactoringCode ReviewCommand-line InterfaceConfiguration ManagementCovariance MatrixData AnalysisData Analysis ConfigurationData CleaningData Configuration
Repositories Contributed To
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