childhealthbiostatscore/CHCO-Code
Mar 2026 – Mar 2026
1 Month active
Languages Used
R
Technical Skills
R programmingbioinformaticsdata analysisdata cleaningdata visualizationfile management
sleidholt
PROFILE
Sleidholt
In March 2026, Sam Leidholt developed two core features for the childhealthbiostatscore/CHCO-Code repository, focusing on scalable analytics for PFAS exposure and multi-omics data integration. Using R programming and bioinformatics techniques, Sam enhanced longitudinal PFAS-kidney association workflows, refined blood and plasma volume calculations, and standardized data loading and directory management to improve reproducibility. For multi-omics studies, Sam built a comprehensive pipeline for cleaning, harmonizing, and integrating metabolomics, lipidomics, and proteomics data, streamlining output path handling. The work emphasized robust data analysis and statistical modeling, resulting in maintainable, end-to-end workflows that support future longitudinal and integrative omics research.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
2
Lines of code
979
Activity Months1
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Work History
March 2026
10 Commits • 2 Features
Mar 1, 2026March 2026 performance summary for CHCO-Code (childhealthbiostatscore): Delivered two major features enhancing PFAS exposure analysis and multi-omics data workflows, with robust improvements to data loading, directory/path management, and analysis reproducibility. The work positions the project to deliver scalable, end-to-end analytics for PFAS longitudinal studies and integrative omics pipelines.
10 Commits • 2 Features
Mar 1, 2026March 2026 performance summary for CHCO-Code (childhealthbiostatscore): Delivered two major features enhancing PFAS exposure analysis and multi-omics data workflows, with robust improvements to data loading, directory/path management, and analysis reproducibility. The work positions the project to deliver scalable, end-to-end analytics for PFAS longitudinal studies and integrative omics pipelines.
March 2026
Activity
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Quality Metrics
Correctness94.0%
Maintainability90.0%
Architecture92.0%
Performance90.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
R
Technical Skills
R programmingbioinformaticsdata analysisdata cleaningdata visualizationfile managementstatistical modeling
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