coding-for-reproducible-research/CfRR_Courses
Feb 2026 – Feb 2026
1 Month active
Languages Used
PythonR
Technical Skills
PythonR programmingalgorithm designconditional logicdata analysisdata manipulation
dorotheavellame
PROFILE
Dorotheavellame
Worked on the coding-for-reproducible-research/CfRR_Courses repository to enhance educational notebooks focused on the Iris dataset. Applied Python and R programming skills to refactor code for improved readability, reliability, and maintainability, including simplifying conditionals, correcting assignment operators, and updating loop structures. Addressed tutorial accuracy by fixing data filtering thresholds, clarifying boolean literals, and correcting misleading references and typos in both code and documentation. Updated column naming conventions for consistency and replaced kable-based output with direct printing to streamline demonstrations. These changes improved the learner experience, strengthened reproducibility, and made onboarding easier for future contributors and students engaging with the material.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
15Total
Bugs
1
Commits
15
Features
2
Lines of code
1,650
Activity Months1
Your Network
16 peopleWork History
February 2026
15 Commits • 2 Features
Feb 1, 2026February 2026 — coding-for-reproducible-research/CfRR_Courses: Delivered Iris dataset notebook enhancements for educational clarity, corrected tutorial content, and improved notebook code quality. Switched to the iris dataset for consistency, updated column names with a Flower. prefix, removed kable-based output in favor of direct printing, and clarified iris loading. Addressed accuracy and readability issues in tutorials, fixed typos, boolean literals, and misleading references. Implemented internal code refactors to improve readability and reliability (loops, assignments, simplified conditionals, and removal of unnecessary functions). These changes strengthen learning outcomes, reproducibility, and maintainability while reducing cognitive load for learners.
15 Commits • 2 Features
Feb 1, 2026February 2026 — coding-for-reproducible-research/CfRR_Courses: Delivered Iris dataset notebook enhancements for educational clarity, corrected tutorial content, and improved notebook code quality. Switched to the iris dataset for consistency, updated column names with a Flower. prefix, removed kable-based output in favor of direct printing, and clarified iris loading. Addressed accuracy and readability issues in tutorials, fixed typos, boolean literals, and misleading references. Implemented internal code refactors to improve readability and reliability (loops, assignments, simplified conditionals, and removal of unnecessary functions). These changes strengthen learning outcomes, reproducibility, and maintainability while reducing cognitive load for learners.
February 2026
Activity
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Quality Metrics
Correctness98.6%
Maintainability93.4%
Architecture93.4%
Performance98.6%
AI Usage21.4%
Skills & Technologies
Programming Languages
PythonR
Technical Skills
PythonR programmingalgorithm designconditional logicdata analysisdata manipulationdata visualizationdocumentationeducational content developmentnotebook development
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