NNPDF/nnpdf
Oct 2025 – Feb 2026
4 Months active
Languages Used
PythonMarkdownYAML
Technical Skills
Python programmingPython scriptingUser interface designconfiguration managementdata analysisdebugging
Jelle Koorn
PROFILE
Jelle Koorn
Worked on the NNPDF/nnpdf repository over four months, delivering eleven features and addressing key aspects of data analysis, reporting, and model training. Focused on enhancing user interface elements, refining plotting logic, and improving configuration management to streamline analytics workflows and reduce operational risk. Leveraged Python, YAML, and Markdown to implement robust data validation, visualization, and documentation practices. Integrated new datasets and tuned model parameters to strengthen training pipelines, while maintaining clean code and stable tests. Emphasized maintainability by removing deprecated code and improving reporting for mismatched datasets, resulting in more reliable, user-friendly, and reproducible scientific computing processes.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
28Total
Bugs
1
Commits
28
Features
11
Lines of code
2,291
Activity Months4
Your Network
31 peopleSame Organization
@nikhef.nl6
Shared Repositories
25
Andrew PietraszkiewiczMember
achiefaMember
Giovanni De CrescenzoMember
Eva GroenendijkMember
Ella ColeMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eleanor ColeMember
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — NNPDF/nnpdf. Key delivery: enhanced model training by adding datasets and tuning parameters; improved generalization and accuracy. No major bugs fixed this month. Overall impact: more robust training pipeline with reproducible configurations and faster iteration. Technologies demonstrated: dataset integration, hyperparameter tuning, configuration management, Git-based version control.
1 Commits • 1 Features
Feb 1, 2026February 2026 — NNPDF/nnpdf. Key delivery: enhanced model training by adding datasets and tuning parameters; improved generalization and accuracy. No major bugs fixed this month. Overall impact: more robust training pipeline with reproducible configurations and faster iteration. Technologies demonstrated: dataset integration, hyperparameter tuning, configuration management, Git-based version control.
February 2026
December 2025
13 Commits • 6 Features
Dec 1, 2025December 2025 monthly summary for NNPDF/nnpdf focused on delivering user-visible plotting improvements, enhanced data integrity reporting, and streamlined fit comparison workflows, complemented by configuration hardening, code cleanup, and more stable tests. These efforts reduce user friction, improve data quality signals, and raise release confidence through maintainable changes and robust test configurations.
December 2025
13 Commits • 6 Features
Dec 1, 2025December 2025 monthly summary for NNPDF/nnpdf focused on delivering user-visible plotting improvements, enhanced data integrity reporting, and streamlined fit comparison workflows, complemented by configuration hardening, code cleanup, and more stable tests. These efforts reduce user friction, improve data quality signals, and raise release confidence through maintainable changes and robust test configurations.
November 2025
11 Commits • 2 Features
Nov 1, 2025November 2025: Delivered two key features in NNPDF/nnpdf focused on data usability and robust reporting, with notable improvements in performance and maintainability. Improvements enhance user clarity for positivity plots and strengthen reporting for datasets excluded from fits, alongside documentation and code quality enhancements that support long-term scalability.
11 Commits • 2 Features
Nov 1, 2025November 2025: Delivered two key features in NNPDF/nnpdf focused on data usability and robust reporting, with notable improvements in performance and maintainability. Improvements enhance user clarity for positivity plots and strengthen reporting for datasets excluded from fits, alongside documentation and code quality enhancements that support long-term scalability.
November 2025
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for NNPDF/nnpdf focused on delivering targeted features, stabilizing the analysis workflow, and improving maintainability. The work emphasized business value in data handling fidelity, onboarding UX, and code hygiene to reduce operational risk in analytics pipelines.
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for NNPDF/nnpdf focused on delivering targeted features, stabilizing the analysis workflow, and improving maintainability. The work emphasized business value in data handling fidelity, onboarding UX, and code hygiene to reduce operational risk in analytics pipelines.
Activity
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Quality Metrics
Correctness91.4%
Maintainability88.6%
Architecture88.6%
Performance89.2%
AI Usage24.2%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
PythonPython programmingPython scriptingSoftware DevelopmentUser interface designbackend developmentclean code practicesconfiguration managementcontent organizationdata analysisdata modelingdata processingdata validationdata visualizationdebugging
Repositories Contributed To
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