egenomics/agb2025
Jun 2025 – Jun 2025
1 Month active
Languages Used
BashMarkdownPythonRShell
Technical Skills
BioinformaticsData AnalysisData VisualizationDocumentationPython ScriptingR
noelia-gl
PROFILE
Noelia-gl
Noelia Gil enhanced the egenomics/agb2025 repository by delivering targeted improvements to a Shiny dashboard for bioinformatics data analysis. She refactored the taxonomy tab, streamlining statistical test options and improving data parsing and relative abundance calculations using R and Shiny. Noelia also implemented a quality control filter that allows users to exclude samples flagged as FAIL, adding UI controls and status indicators for transparency. Addressing a statistical analysis issue, she resolved a metadata configuration problem to prevent analysis errors. Comprehensive documentation updates, including guidance on data visualization and QIIME file conversion, further supported user onboarding and workflow efficiency.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
610
Activity Months1
Your Network
25 peopleSame Organization
@estudiant.upf.edu3
Work History
June 2025
5 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for egenomics/agb2025: Delivered targeted Shiny dashboard enhancements, improved data quality controls, and expanded documentation to accelerate user onboarding and reduce support overhead. Major items include taxonomy tab refactor with improved data parsing and relative abundance calculations; a quality-control filter to exclude samples flagged as FAIL; a metadata/config fix addressing a statistical analysis issue; and comprehensive docs guiding data visualization and Shiny usage. These changes improve result reliability, streamline workflows, and demonstrate strong end-to-end capability from data handling to UI and user guidance.
5 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for egenomics/agb2025: Delivered targeted Shiny dashboard enhancements, improved data quality controls, and expanded documentation to accelerate user onboarding and reduce support overhead. Major items include taxonomy tab refactor with improved data parsing and relative abundance calculations; a quality-control filter to exclude samples flagged as FAIL; a metadata/config fix addressing a statistical analysis issue; and comprehensive docs guiding data visualization and Shiny usage. These changes improve result reliability, streamline workflows, and demonstrate strong end-to-end capability from data handling to UI and user guidance.
June 2025
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance80.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
BashMarkdownPythonRShell
Technical Skills
BioinformaticsData AnalysisData VisualizationDocumentationPython ScriptingRR ShinyShell ScriptingShiny
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