NNPDF/nnpdf
Oct 2024 – Sep 2025
6 Months active
Languages Used
PythonYAMLpythonyaml
Technical Skills
Data AnalysisPhysics SimulationConfiguration ManagementHigh Energy PhysicsHigh-Energy PhysicsPhysics Data Processing
enocera
PROFILE
Enocera
Emanuele Roberto Nocera developed and enhanced data analysis and visualization workflows in the NNPDF/nnpdf repository, focusing on high-energy physics datasets. He implemented standardized uncertainty definitions and improved data integration for ATLAS, CMS, and LHCb experiments, using Python and YAML for configuration management and data processing. His work included building PDG-style visualization bases for polarized PDFs, refining heatmap labeling for clearer interpretation, and streamlining systematic uncertainty handling in data-theory comparisons. By updating metadata governance and refactoring configuration code, Emanuele ensured reproducibility and maintainability. His contributions demonstrated depth in scientific computing, technical writing, and robust version control practices throughout the project.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
19Total
Bugs
1
Commits
19
Features
6
Lines of code
2,876
Activity Months6
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for NNPDF/nnpdf: Delivered a feature that enhances the Data Theory Comparison workflow by updating the RunCard and associated data inputs. Updated with_shift handling, refreshed example YAML datasets, and removed obsolete parsing from the configuration class, resulting in a cleaner, more reproducible experiment setup. No major bug fixes were recorded this month. Impact: streamlined experiment configuration, improved reproducibility, and alignment with review feedback (JCM). Technologies and skills demonstrated: Python configuration management, YAML data handling, code cleanup and refactoring, git-based collaboration and review, and documentation for tutorials.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for NNPDF/nnpdf: Delivered a feature that enhances the Data Theory Comparison workflow by updating the RunCard and associated data inputs. Updated with_shift handling, refreshed example YAML datasets, and removed obsolete parsing from the configuration class, resulting in a cleaner, more reproducible experiment setup. No major bug fixes were recorded this month. Impact: streamlined experiment configuration, improved reproducibility, and alignment with review feedback (JCM). Technologies and skills demonstrated: Python configuration management, YAML data handling, code cleanup and refactoring, git-based collaboration and review, and documentation for tutorials.
September 2025
August 2025
9 Commits • 1 Features
Aug 1, 2025During August 2025, delivered a major enhancement to the data-theory comparison workflow in NNPDF/nnpdf. Added shifts_from_systematics to account for correlated systematics, integrated shift handling into plot_fancy and the data-theory visualization pipeline, and expanded tests and documentation for the with_shift option and the data-theory workflow. Implemented an ATLAS ZpT 8 TeV variant replacing a large set of 100 correlated systematics with a compact mix (1 stat, 1 uncorrelated systematic, and 1 correlated systematic, plus luminosity and optional MC), improving stability, reproducibility, and maintainability. Updated metadata and expanded comments for clarity. Result: clearer, more accurate uncertainty representation, faster test cycles, and a smoother workflow for future systematic studies.
August 2025
9 Commits • 1 Features
Aug 1, 2025During August 2025, delivered a major enhancement to the data-theory comparison workflow in NNPDF/nnpdf. Added shifts_from_systematics to account for correlated systematics, integrated shift handling into plot_fancy and the data-theory visualization pipeline, and expanded tests and documentation for the with_shift option and the data-theory workflow. Implemented an ATLAS ZpT 8 TeV variant replacing a large set of 100 correlated systematics with a compact mix (1 stat, 1 uncorrelated systematic, and 1 correlated systematic, plus luminosity and optional MC), improving stability, reproducibility, and maintainability. Updated metadata and expanded comments for clarity. Result: clearer, more accurate uncertainty representation, faster test cycles, and a smoother workflow for future systematic studies.
June 2025
4 Commits • 1 Features
Jun 1, 2025June 2025 (2025-06) — NNPDF/nnpdf: Delivered data integration capabilities for inclusive cross-section datasets and completed metadata hygiene, enabling robust, reproducible analyses across ATLAS and CMS datasets (ttbar, WPWM, Z0).
4 Commits • 1 Features
Jun 1, 2025June 2025 (2025-06) — NNPDF/nnpdf: Delivered data integration capabilities for inclusive cross-section datasets and completed metadata hygiene, enabling robust, reproducible analyses across ATLAS and CMS datasets (ttbar, WPWM, Z0).
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for NNPDF/nnpdf: Focused on delivering a user-facing visualization improvement and maintaining stability. Key feature delivered: clarified heatmap color bar label in the theory covariance matrix, updating from '% of data' to '% of data value' to improve precision and interpretation. Implemented via commit a15a6c89dbd2d8e515f33fdd54ce1792b6e8256c. No major bugs reported in this period; the work maintained stability and touched a small portion of visualization code. Overall impact: improved interpretability of covariance analyses, enabling researchers to make more informed decisions and reducing potential misinterpretation risk. Technologies/skills demonstrated: visualization UX improvement, precise labeling, focused, release-ready change with a single commit, strong version control discipline.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for NNPDF/nnpdf: Focused on delivering a user-facing visualization improvement and maintaining stability. Key feature delivered: clarified heatmap color bar label in the theory covariance matrix, updating from '% of data' to '% of data value' to improve precision and interpretation. Implemented via commit a15a6c89dbd2d8e515f33fdd54ce1792b6e8256c. No major bugs reported in this period; the work maintained stability and touched a small portion of visualization code. Overall impact: improved interpretability of covariance analyses, enabling researchers to make more informed decisions and reducing potential misinterpretation risk. Technologies/skills demonstrated: visualization UX improvement, precise labeling, focused, release-ready change with a single commit, strong version control discipline.
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 – NNPDF/nnpdf: Focused on cross-experiment standardization of luminosity uncertainty definitions and correlation handling to improve data analysis accuracy across ATLAS, CMS, and LHCb for 7, 8, and 13 TeV datasets. Implemented updated identifiers, configuration changes, and cross-checks to ensure consistent uncertainty treatment and reproducibility.
3 Commits • 1 Features
Feb 1, 2025February 2025 – NNPDF/nnpdf: Focused on cross-experiment standardization of luminosity uncertainty definitions and correlation handling to improve data analysis accuracy across ATLAS, CMS, and LHCb for 7, 8, and 13 TeV datasets. Implemented updated identifiers, configuration changes, and cross-checks to ensure consistent uncertainty treatment and reproducibility.
February 2025
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — Delivered a foundational visualization capability for polarized PDFs in the NNPDF/nnpdf repository. Implemented a Polarized PDFs Visualization Basis in PDG-style format with a mapping for polarized parton components, enabling clear display and visualization of polarized PDF data. This work provides a stable basis for downstream visualization, analytics, and validation workflows and aligns with PDG conventions for data presentation.
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — Delivered a foundational visualization capability for polarized PDFs in the NNPDF/nnpdf repository. Implemented a Polarized PDFs Visualization Basis in PDG-style format with a mapping for polarized parton components, enabling clear display and visualization of polarized PDF data. This work provides a stable basis for downstream visualization, analytics, and validation workflows and aligns with PDG conventions for data presentation.
Activity
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Quality Metrics
Correctness90.0%
Maintainability89.4%
Architecture85.2%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAMLpythonyaml
Technical Skills
Configuration ManagementData AnalysisData ConfigurationData ManagementData ProcessingData VisualizationDocumentationHigh Energy PhysicsHigh-Energy PhysicsNumerical MethodsPhysics Data IntegrationPhysics Data ProcessingPhysics SimulationPlotting LibrariesPython
Repositories Contributed To
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