lter/lterwg-caged
Jan 2025 – Jul 2026
17 Months active
Languages Used
MarkdownR
Technical Skills
DocumentationProject ManagementData AnalysisStatistical ModelingData HarmonizationData Cleaning
Jamie McDevitt-Irwin
PROFILE
Jamie Mcdevitt-irwin
Developed and maintained the lter/lterwg-caged repository, delivering a robust data pipeline for ecological analysis. Over 17 months, engineered workflows for data harmonization, validation, and statistical modeling using R and dplyr, with extensive documentation in Markdown. Implemented modular scripts for data cleaning, filtering, and visualization, supporting reproducible analyses and effect size calculations such as mean log response ratio. Enhanced data integrity through rigorous quality control, metadata management, and bug fixes, while expanding analytical capabilities for beta diversity and habitat comparisons. The work emphasized maintainability, traceability, and stakeholder-ready outputs, enabling reliable ecological insights and streamlined onboarding for collaborative research teams.
Overall Statistics
Feature vs Bugs
81%Features
Repository Contributions
200Total
Bugs
15
Commits
200
Features
62
Lines of code
17,728
Activity Months17
Your Network
16 peopleShared Repositories
16
raine-detmerMember
Maowei LiangMember
Dan DetmerMember
hillarykrumbholzMember
Kelly SpeareMember
luisagenesMember
Luisa GenesMember
Marc HenselMember
Marc HenselMember
Work History
July 2026
5 Commits • 4 Features
Jul 1, 2026July 2026: Completed a focused set of feature enhancements and maintainability improvements in lter/lterwg-caged. Key changes include a more accurate dominance metric based on direct summation of abundances, introduction of replicates averaging for robustness, correction of the alpha diversity plot to reflect mean LRR values, and clarifications/removals of outdated notes in data processing scripts. These changes improve metric accuracy, stability, and maintainability, delivering clearer insights for ecological analysis and reducing downstream ambiguity for analysts and stakeholders.
5 Commits • 4 Features
Jul 1, 2026July 2026: Completed a focused set of feature enhancements and maintainability improvements in lter/lterwg-caged. Key changes include a more accurate dominance metric based on direct summation of abundances, introduction of replicates averaging for robustness, correction of the alpha diversity plot to reflect mean LRR values, and clarifications/removals of outdated notes in data processing scripts. These changes improve metric accuracy, stability, and maintainability, delivering clearer insights for ecological analysis and reducing downstream ambiguity for analysts and stakeholders.
July 2026
June 2026
61 Commits • 19 Features
Jun 1, 2026June 2026 work on lterwg-caged delivered end-to-end LRR tooling, data enrichment, and pipeline improvements that accelerate robust effect-size analyses and decision-making. Key features included a new Mean Log Response Ratio (LRR) tooling script based on Nick's mean diff workflow, LRR data enrichment with metadata and duplicate handling, and enhanced data validation and plotting for LRR and effect sizes. Critical bugs were fixed, including correcting the LRR mean calculation to average across caged and uncaged, addressing a sample size discrepancy, and improving data ingestion, joins, and NA handling. The month also advanced the data pipeline: ingesting new data, tidy data organization, and rerunning statistics with the latest data, plus expanded figure generation for LRR and diversity metrics. Technologies demonstrated include R scripting, data wrangling, metadata integration, plotting, loess modeling experimentation, and end-to-end pipeline maintenance, contributing to greater data quality, reproducibility, and faster insights.
June 2026
61 Commits • 19 Features
Jun 1, 2026June 2026 work on lterwg-caged delivered end-to-end LRR tooling, data enrichment, and pipeline improvements that accelerate robust effect-size analyses and decision-making. Key features included a new Mean Log Response Ratio (LRR) tooling script based on Nick's mean diff workflow, LRR data enrichment with metadata and duplicate handling, and enhanced data validation and plotting for LRR and effect sizes. Critical bugs were fixed, including correcting the LRR mean calculation to average across caged and uncaged, addressing a sample size discrepancy, and improving data ingestion, joins, and NA handling. The month also advanced the data pipeline: ingesting new data, tidy data organization, and rerunning statistics with the latest data, plus expanded figure generation for LRR and diversity metrics. Technologies demonstrated include R scripting, data wrangling, metadata integration, plotting, loess modeling experimentation, and end-to-end pipeline maintenance, contributing to greater data quality, reproducibility, and faster insights.
May 2026
7 Commits • 4 Features
May 1, 2026May 2026 monthly summary for lter/lterwg-caged: Key data quality improvements and new analytical capabilities delivered; enabled more reliable ecological analyses and clearer data governance. Resolved a metadata attachment error in the R script related to the year variable, improved ETL data integrity, introduced a beta diversity model for late successional stages, added sample size calculations across succession and habitat with concise summaries, and documented the rationale for Alderson dataset exclusion to enhance transparency.
7 Commits • 4 Features
May 1, 2026May 2026 monthly summary for lter/lterwg-caged: Key data quality improvements and new analytical capabilities delivered; enabled more reliable ecological analyses and clearer data governance. Resolved a metadata attachment error in the R script related to the year variable, improved ETL data integrity, introduced a beta diversity model for late successional stages, added sample size calculations across succession and habitat with concise summaries, and documented the rationale for Alderson dataset exclusion to enhance transparency.
May 2026
April 2026
22 Commits • 4 Features
Apr 1, 2026April 2026 focused on stabilizing and expanding the lter/lterwg-caged data pipeline, with emphasis on throughput accuracy, data integrity, model coverage, and clearer documentation. The changes reduce data loss, improve model fidelity, and streamline pipeline operations, delivering measurable business value in reliability, speed of analysis, and stakeholder confidence.
April 2026
22 Commits • 4 Features
Apr 1, 2026April 2026 focused on stabilizing and expanding the lter/lterwg-caged data pipeline, with emphasis on throughput accuracy, data integrity, model coverage, and clearer documentation. The changes reduce data loss, improve model fidelity, and streamline pipeline operations, delivering measurable business value in reliability, speed of analysis, and stakeholder confidence.
March 2026
4 Commits • 2 Features
Mar 1, 2026March 2026 — CAGED data pipeline enhancements and validation improvements in lter/lterwg-caged focused on delivering business value through reliable, reproducible data processing and stronger data quality controls. Key outcomes include streamlined data harmonization, QC, and analysis workflows, expanded documentation for reproducibility, and robust validation to prevent loss of sources/experimental names during processing. The repository also stayed aligned with upstream changes by merging latest main branch updates.
4 Commits • 2 Features
Mar 1, 2026March 2026 — CAGED data pipeline enhancements and validation improvements in lter/lterwg-caged focused on delivering business value through reliable, reproducible data processing and stronger data quality controls. Key outcomes include streamlined data harmonization, QC, and analysis workflows, expanded documentation for reproducibility, and robust validation to prevent loss of sources/experimental names during processing. The repository also stayed aligned with upstream changes by merging latest main branch updates.
March 2026
February 2026
11 Commits • 3 Features
Feb 1, 2026February 2026 – Monthly performance summary for repository lter/lterwg-caged. Delivered enhancements to the Data Processing Pipeline and supporting artifacts, with a focus on data quality, reliability, and clarity for downstream analyses and stakeholder reporting. Key features delivered: - Data Processing Pipeline Refinements: standardization of caging classifications for zamin and sellers; improved sampling point logic; refined data sources handling; augmented quality control; updated sampling points and outputs for unique sources/names; and overall pipeline improvements. In-flight and historical data handling were stabilized through targeted commits (02, 03, 05a) to ensure consistent data lineage and outputs. - Visualization Enhancement for Successional Stage Representation: updated plot colors to improve interpretability of successional stages across visualizations. - Documentation and Naming Consistency for Experimental Design: updated dataset experiment names; added guidelines for experimental data handling; clarified filtering of confounding treatments in experimental design documents; refreshed meta-document to reflect current conventions. Major bugs fixed / stability improvements: - Resolved data ingestion gaps and corrected end-of-year date handling for new datasets (burkepile, duran, sellers); stabilized last-time-point logic per year; ensured new data can flow through the pipeline (notably through 05a scripts). - Strengthened data source tracking and naming consistency to reduce mismatches between input sources and outputs. Overall impact and accomplishments: - Significantly improved data quality, traceability, and reproducibility across the caged dataset pipeline, enabling more reliable analyses and faster onboarding of new data sources. - Enhanced stakeholder communication via clearer visualizations and up-to-date documentation. Technologies/skills demonstrated: - Data pipeline engineering and automation; scripting and workflow stabilization across multiple pipeline stages (scripts 02/03/05a); data quality control; data visualization; and documentation governance (naming conventions, meta-doc updates).
February 2026
11 Commits • 3 Features
Feb 1, 2026February 2026 – Monthly performance summary for repository lter/lterwg-caged. Delivered enhancements to the Data Processing Pipeline and supporting artifacts, with a focus on data quality, reliability, and clarity for downstream analyses and stakeholder reporting. Key features delivered: - Data Processing Pipeline Refinements: standardization of caging classifications for zamin and sellers; improved sampling point logic; refined data sources handling; augmented quality control; updated sampling points and outputs for unique sources/names; and overall pipeline improvements. In-flight and historical data handling were stabilized through targeted commits (02, 03, 05a) to ensure consistent data lineage and outputs. - Visualization Enhancement for Successional Stage Representation: updated plot colors to improve interpretability of successional stages across visualizations. - Documentation and Naming Consistency for Experimental Design: updated dataset experiment names; added guidelines for experimental data handling; clarified filtering of confounding treatments in experimental design documents; refreshed meta-document to reflect current conventions. Major bugs fixed / stability improvements: - Resolved data ingestion gaps and corrected end-of-year date handling for new datasets (burkepile, duran, sellers); stabilized last-time-point logic per year; ensured new data can flow through the pipeline (notably through 05a scripts). - Strengthened data source tracking and naming consistency to reduce mismatches between input sources and outputs. Overall impact and accomplishments: - Significantly improved data quality, traceability, and reproducibility across the caged dataset pipeline, enabling more reliable analyses and faster onboarding of new data sources. - Enhanced stakeholder communication via clearer visualizations and up-to-date documentation. Technologies/skills demonstrated: - Data pipeline engineering and automation; scripting and workflow stabilization across multiple pipeline stages (scripts 02/03/05a); data quality control; data visualization; and documentation governance (naming conventions, meta-doc updates).
January 2026
2 Commits • 1 Features
Jan 1, 2026Month 2026-01: Delivered targeted improvements to data analysis guidance and data download workflow for the lter/lterwg-caged project, aligning user expectations with actual behavior and reinforcing data integrity during updates.
2 Commits • 1 Features
Jan 1, 2026Month 2026-01: Delivered targeted improvements to data analysis guidance and data download workflow for the lter/lterwg-caged project, aligning user expectations with actual behavior and reinforcing data integrity during updates.
January 2026
December 2025
16 Commits • 3 Features
Dec 1, 2025December 2025 focused on delivering end-to-end feature work for lter/lterwg-caged, strengthening model robustness and data governance, and preparing stakeholder-ready visuals. The work enhanced habitat-level analyses with caging effects, improved model reliability, and clarified data provenance to support reproducibility and decision-making.
December 2025
16 Commits • 3 Features
Dec 1, 2025December 2025 focused on delivering end-to-end feature work for lter/lterwg-caged, strengthening model robustness and data governance, and preparing stakeholder-ready visuals. The work enhanced habitat-level analyses with caging effects, improved model reliability, and clarified data provenance to support reproducibility and decision-making.
November 2025
15 Commits • 4 Features
Nov 1, 2025November 2025: Delivered major enhancements to lterwg-caged, expanding the analysis dataset, standardizing beta diversity modeling, and introducing uncaged/caged data models with enhanced visualizations. Strengthened data validation and function reliability, clarified documentation, and improved reproducibility. These changes increase statistical power, reduce onboarding time, and enable robust cross-condition comparisons for downstream decision-making.
15 Commits • 4 Features
Nov 1, 2025November 2025: Delivered major enhancements to lterwg-caged, expanding the analysis dataset, standardizing beta diversity modeling, and introducing uncaged/caged data models with enhanced visualizations. Strengthened data validation and function reliability, clarified documentation, and improved reproducibility. These changes increase statistical power, reduce onboarding time, and enable robust cross-condition comparisons for downstream decision-making.
November 2025
October 2025
8 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for lter/lterwg-caged: Delivered a cohesive feature set to stabilize analytics for beta dispersion and effect size calculations. The work focused on data wrangling improvements, cross-treatment data completeness, and diagnostic support, with refactoring for clearer modeling inputs and metadata-driven reruns. It also addressed data integrity gaps related to replicates and exp.name filtering, ensuring reliable downstream analyses across experiments.
October 2025
8 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for lter/lterwg-caged: Delivered a cohesive feature set to stabilize analytics for beta dispersion and effect size calculations. The work focused on data wrangling improvements, cross-treatment data completeness, and diagnostic support, with refactoring for clearer modeling inputs and metadata-driven reruns. It also addressed data integrity gaps related to replicates and exp.name filtering, ensuring reliable downstream analyses across experiments.
September 2025
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focused on enhancing observability for the data processing pipeline in lter/lterwg-caged and improving data readability for modeling data. Delivered observable outputs (unique sources and experiment names) and documented data loss points to enable faster traceability and issue diagnosis. Renamed modeling dataframes to improve readability and maintainability. These changes reduce data loss risk, streamline debugging, and enable more reliable modeling pipelines.
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focused on enhancing observability for the data processing pipeline in lter/lterwg-caged and improving data readability for modeling data. Delivered observable outputs (unique sources and experiment names) and documented data loss points to enable faster traceability and issue diagnosis. Renamed modeling dataframes to improve readability and maintainability. These changes reduce data loss risk, streamline debugging, and enable more reliable modeling pipelines.
September 2025
August 2025
6 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for the lterwg-caged repository. Focused on delivering data integrity verification, improving data quality controls, and refactoring beta-diversity analysis to support reliable statistical modeling. Achievements include robust data checks across caged_v1 and avg.caged_v1, improved handling and tracing of sources and experiment names, and a refactor to emphasize mean differences in beta diversity with NA debugging aids. These efforts reduce data loss risks, accelerate debugging, and enhance reproducibility for downstream analyses and reports. Key improvements have been integrated into the data wrangling pipeline and accompanying documentation.
August 2025
6 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for the lterwg-caged repository. Focused on delivering data integrity verification, improving data quality controls, and refactoring beta-diversity analysis to support reliable statistical modeling. Achievements include robust data checks across caged_v1 and avg.caged_v1, improved handling and tracing of sources and experiment names, and a refactor to emphasize mean differences in beta diversity with NA debugging aids. These efforts reduce data loss risks, accelerate debugging, and enhance reproducibility for downstream analyses and reports. Key improvements have been integrated into the data wrangling pipeline and accompanying documentation.
June 2025
17 Commits • 5 Features
Jun 1, 2025June 2025 monthly summary – lterwg-caged. Key accomplishments include modular data processing and enhanced visualization, beta-diversity modeling and dispersion analysis, and data handling improvements that collectively improve data integrity, reproducibility, and decision-ready outputs. Specific work included refactoring the 08 data-wrangling script into 08a/08b/08c with new raw-data figures, development and refinement of beta-diversity models and dispersion metrics (Figures 2 & 3 groundwork, beta regression experiments), and data quality fixes such as prairie dog disturbance handling, end-timepoint data filtering refinements, and coordinate standardization (lat/long). Visualization refinements for gamma richness and uncaged/caged plots enhanced clarity for upcoming meetings. These efforts were supported by code refactoring, model simplification (removing redundant random effects), and data normalization, demonstrating proficiency in R-based data science, statistical modeling, and reproducible workflows.
17 Commits • 5 Features
Jun 1, 2025June 2025 monthly summary – lterwg-caged. Key accomplishments include modular data processing and enhanced visualization, beta-diversity modeling and dispersion analysis, and data handling improvements that collectively improve data integrity, reproducibility, and decision-ready outputs. Specific work included refactoring the 08 data-wrangling script into 08a/08b/08c with new raw-data figures, development and refinement of beta-diversity models and dispersion metrics (Figures 2 & 3 groundwork, beta regression experiments), and data quality fixes such as prairie dog disturbance handling, end-timepoint data filtering refinements, and coordinate standardization (lat/long). Visualization refinements for gamma richness and uncaged/caged plots enhanced clarity for upcoming meetings. These efforts were supported by code refactoring, model simplification (removing redundant random effects), and data normalization, demonstrating proficiency in R-based data science, statistical modeling, and reproducible workflows.
June 2025
May 2025
12 Commits • 5 Features
May 1, 2025May 2025 performance summary for lter/lterwg-caged. Focused on strengthening data integrity, reproducibility, and analytical capabilities. Delivered end-to-end data quality upgrades, improved duplicate handling in zero-fill, expanded EDA/modeling workflows with clearer visualizations, standardized dependencies, and mitigated automated data uploads by disabling Google Drive uploads. These efforts reduce analysis risk, accelerate reporting, and improve trust in downstream insights for decision-making.
May 2025
12 Commits • 5 Features
May 1, 2025May 2025 performance summary for lter/lterwg-caged. Focused on strengthening data integrity, reproducibility, and analytical capabilities. Delivered end-to-end data quality upgrades, improved duplicate handling in zero-fill, expanded EDA/modeling workflows with clearer visualizations, standardized dependencies, and mitigated automated data uploads by disabling Google Drive uploads. These efforts reduce analysis risk, accelerate reporting, and improve trust in downstream insights for decision-making.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 focused on metadata/documentation hygiene for the lterwg-caged project. Implemented clarifications to meta-documentation, refined data interpretation guidance, and standardized file naming conventions to improve clarity and maintainability. The changes reduce ambiguity in metadata and support reproducibility and onboarding for analysts and downstream users.
3 Commits • 1 Features
Mar 1, 2025March 2025 focused on metadata/documentation hygiene for the lterwg-caged project. Implemented clarifications to meta-documentation, refined data interpretation guidance, and standardized file naming conventions to improve clarity and maintainability. The changes reduce ambiguity in metadata and support reproducibility and onboarding for analysts and downstream users.
March 2025
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025: Delivered documentation and methodological improvements for the lterwg-caged project, focusing on data usability, reproducibility, and robust analysis. Key contributions include enhancements to data dictionary and experimental data key documentation, alignment of data-harmonization fields, and updates to meta documentation; plus a methodological upgrade to beta dispersion calculations for more robust results.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025: Delivered documentation and methodological improvements for the lterwg-caged project, focusing on data usability, reproducibility, and robust analysis. Key contributions include enhancements to data dictionary and experimental data key documentation, alignment of data-harmonization fields, and updates to meta documentation; plus a methodological upgrade to beta dispersion calculations for more robust results.
January 2025
6 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for lter/lterwg-caged: Focused on solidifying the data harmonization workflow through targeted documentation and guidance. Delivered a centralized meta-document, refreshed README, updated meeting-derived details, and added site-level metadata instructions to standardize data discovery and reuse. These changes lay the groundwork for consistent onboarding, reduce operational risk from misconfigurations, and improve discoverability of the workflow and scripts. Implemented through six commits across the repository, establishing clearer governance and repeatable processes.
6 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for lter/lterwg-caged: Focused on solidifying the data harmonization workflow through targeted documentation and guidance. Delivered a centralized meta-document, refreshed README, updated meeting-derived details, and added site-level metadata instructions to standardize data discovery and reuse. These changes lay the groundwork for consistent onboarding, reduce operational risk from misconfigurations, and improve discoverability of the workflow and scripts. Implemented through six commits across the repository, establishing clearer governance and repeatable processes.
January 2025
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Quality Metrics
Correctness88.6%
Maintainability87.2%
Architecture83.2%
Performance83.4%
AI Usage20.2%
Skills & Technologies
Programming Languages
MarkdownR
Technical Skills
Data AnalysisData CleaningData FilteringData HarmonizationData ManagementData PreparationData StandardizationData ValidationData VisualizationData WranglingDebuggingDocumentationEcological Data AnalysisExploratory Data AnalysisLinear Mixed-Effects Models
Repositories Contributed To
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