lter/lterwg-caged
May 2025 – Oct 2025
3 Months active
Languages Used
R
Technical Skills
Data CleaningData ProcessingData WranglingEcological Data AnalysisData AnalysisData Transformation
hillarykrumbholz
PROFILE
Hillarykrumbholz
Over three months, Hannah Krumbholz developed and refined experimental treatment classification features for the lter/lterwg-caged repository, focusing on standardizing 'caged', 'uncaged', and 'partial' labels across ecological datasets. She applied data cleaning, wrangling, and R scripting to resolve classification inconsistencies, implement dataset-specific mappings, and introduce diagnostic exports. Her work included updating data processing pipelines, improving data upload reliability to Google Drive, and clarifying treatment definitions to enhance data quality and reproducibility. By codifying classification logic and documenting rationale for edge cases, Hannah enabled more accurate ecological analyses and fostered cross-organizational consistency in data governance and research workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
3
Lines of code
423
Activity Months3
Your Network
13 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025In Oct 2025, delivered a feature to standardize caging treatments across datasets in lter/lterwg-caged. Clarified and codified definitions for 'caged', 'partial', and 'uncaged' to improve data categorization accuracy and consistency for ecological analyses. Implemented targeted code fixes (commit aae7289a601a838f9646f6c38c4fc19b8185d648) to refine standardization and documented rationale, including excluding Wang Mongolia due to misclassification risk. Result: higher data quality, reproducibility, and more reliable downstream analyses.
1 Commits • 1 Features
Oct 1, 2025In Oct 2025, delivered a feature to standardize caging treatments across datasets in lter/lterwg-caged. Clarified and codified definitions for 'caged', 'partial', and 'uncaged' to improve data categorization accuracy and consistency for ecological analyses. Implemented targeted code fixes (commit aae7289a601a838f9646f6c38c4fc19b8185d648) to refine standardization and documented rationale, including excluding Wang Mongolia due to misclassification risk. Result: higher data quality, reproducibility, and more reliable downstream analyses.
October 2025
June 2025
4 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for lter/lterwg-caged: Implemented Unified Experimental Treatment Classification Across Datasets with Diagnostics and Processing Pipeline Updates, delivering standardized labels (caged/uncaged/partial), dataset-specific mappings, and new diagnostic exports. Updated data upload paths to Google Drive for processed data to improve pipeline reliability and data accessibility. Fixed critical classification inconsistencies and edge-case handling to reduce mislabeling across datasets.
June 2025
4 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for lter/lterwg-caged: Implemented Unified Experimental Treatment Classification Across Datasets with Diagnostics and Processing Pipeline Updates, delivering standardized labels (caged/uncaged/partial), dataset-specific mappings, and new diagnostic exports. Updated data upload paths to Google Drive for processed data to improve pipeline reliability and data accessibility. Fixed critical classification inconsistencies and edge-case handling to reduce mislabeling across datasets.
May 2025
2 Commits • 1 Features
May 1, 2025Delivered Experimental treatment caging classification improvements in lterwg-caged to standardize 'caged' vs 'uncaged' labeling across organizations and enhanced QC labeling to boost data robustness for ecological analyses. This work improves data quality, cross-site consistency, and reproducibility, enabling more reliable downstream research and decision-making.
2 Commits • 1 Features
May 1, 2025Delivered Experimental treatment caging classification improvements in lterwg-caged to standardize 'caged' vs 'uncaged' labeling across organizations and enhanced QC labeling to boost data robustness for ecological analyses. This work improves data quality, cross-site consistency, and reproducibility, enabling more reliable downstream research and decision-making.
May 2025
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Quality Metrics
Correctness85.8%
Maintainability82.8%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
R
Technical Skills
Data AnalysisData CleaningData ProcessingData TransformationData WranglingEcological Data AnalysisEcological ModelingRR Scripting
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