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
Dorothea Vellame enhanced the coding-for-reproducible-research/CfRR_Courses repository by refining educational notebooks focused on the iris dataset. She improved data clarity and consistency by updating column names and standardizing dataset usage, while removing unnecessary output formatting for direct, readable results. Using Python and R, Dorothea addressed tutorial inaccuracies, corrected boolean logic, and fixed documentation typos to ensure accuracy and ease of understanding. Her internal code refactors simplified loops and conditionals, improving both readability and maintainability. These updates reduced cognitive load for learners, strengthened reproducibility, and streamlined onboarding, reflecting a thoughtful approach to educational content and technical reliability.
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 peopleShared Repositories
16
Work 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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