Proteobench/ProteoBench
Nov 2024 – Feb 2026
7 Months active
Languages Used
CSVPythonJSONJavaScriptJupyter NotebookTOMLMakefile
Technical Skills
Configuration ManagementConfiguration ParsingData ParsingData ProcessingError HandlingFile Handling
Witold Wolski
PROFILE
Witold Wolski
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
40Total
Bugs
5
Commits
40
Features
16
Lines of code
2,745,863
Activity Months7
Your Network
19 people
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 ProteoBench monthly wrap-up focusing on expanding DIA-NN support for DDA workflows, UI and parsing refinements, and repository hygiene to improve reliability and user productivity.
2 Commits • 1 Features
Feb 1, 2026February 2026 ProteoBench monthly wrap-up focusing on expanding DIA-NN support for DDA workflows, UI and parsing refinements, and repository hygiene to improve reliability and user productivity.
February 2026
December 2025
8 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Proteobench/ProteoBench. Focused on stabilizing and expanding the benchmarking workflow, improving error handling, and delivering richer, reproducible reporting with end-to-end tooling.
December 2025
8 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Proteobench/ProteoBench. Focused on stabilizing and expanding the benchmarking workflow, improving error handling, and delivering richer, reproducible reporting with end-to-end tooling.
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025 — focus on reliability, usability, and analytics expansion for ProteoBench/ProteoBench. Delivered user-friendly error handling for benchmarking submissions to reduce data-upload friction and provide clear guidance for common issues; introduced ROC-AUC as a new performance metric for species classification, with computation, detection of unchanged species, and plotting/tests; stabilized tests by adding a species column to the QuantDatapoint DataFrame to cover edge cases and preserve accurate metrics. These improvements increase system reliability, reduce user support load, and broaden benchmarking capabilities for species-level analysis. Technologies include Python data processing, metric computation, plotting, and robust test design; improved CI/QA collaboration.
3 Commits • 2 Features
Nov 1, 2025November 2025 — focus on reliability, usability, and analytics expansion for ProteoBench/ProteoBench. Delivered user-friendly error handling for benchmarking submissions to reduce data-upload friction and provide clear guidance for common issues; introduced ROC-AUC as a new performance metric for species classification, with computation, detection of unchanged species, and plotting/tests; stabilized tests by adding a species column to the QuantDatapoint DataFrame to cover edge cases and preserve accurate metrics. These improvements increase system reliability, reduce user support load, and broaden benchmarking capabilities for species-level analysis. Technologies include Python data processing, metric computation, plotting, and robust test design; improved CI/QA collaboration.
November 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for Proteobench/ProteoBench. Focused on accelerating data ingestion and improving data parsing reliability. Implemented Calamine-powered XLSX data processing, added a new dependency, and reinforced parsing with unit tests to deliver faster, more robust data pipelines.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for Proteobench/ProteoBench. Focused on accelerating data ingestion and improving data parsing reliability. Implemented Calamine-powered XLSX data processing, added a new dependency, and reinforced parsing with unit tests to deliver faster, more robust data pipelines.
May 2025
8 Commits • 6 Features
May 1, 2025Month: 2025-05. This period delivered measurable business value through offline/local data testing capabilities, maintainability improvements, automated notebook execution readiness, and performance/code quality enhancements for ProteoBench/ProteoBench. This summary highlights the key features delivered, major bugs fixed, overall impact and accomplishments, and technologies demonstrated.
8 Commits • 6 Features
May 1, 2025Month: 2025-05. This period delivered measurable business value through offline/local data testing capabilities, maintainability improvements, automated notebook execution readiness, and performance/code quality enhancements for ProteoBench/ProteoBench. This summary highlights the key features delivered, major bugs fixed, overall impact and accomplishments, and technologies demonstrated.
May 2025
January 2025
10 Commits • 3 Features
Jan 1, 2025January 2025 highlights for ProteoBench/ProteoBench focused on delivering end-to-end PEAKS workflow support, improving data integrity, and enhancing cross-platform reliability. Key work included core and extended PEAKS parameter parsing, robust file handling, and deeper Spectronaut integration with PEAKS data. The outcomes reduce manual steps, improve quantification interpretation, and enable reproducible proteomics analyses across different OS environments.
January 2025
10 Commits • 3 Features
Jan 1, 2025January 2025 highlights for ProteoBench/ProteoBench focused on delivering end-to-end PEAKS workflow support, improving data integrity, and enhancing cross-platform reliability. Key work included core and extended PEAKS parameter parsing, robust file handling, and deeper Spectronaut integration with PEAKS data. The outcomes reduce manual steps, improve quantification interpretation, and enable reproducible proteomics analyses across different OS environments.
November 2024
7 Commits • 2 Features
Nov 1, 2024ProteoBench monthly summary for 2024-11 focusing on the ProteoBench/ProteoBench repository. Delivered robust parameter parsing improvements for Spectronaut and DIANN, with an emphasis on reliability, default behaviors, and test coverage. Enhanced parsing logic, file handling, and normalization to support downstream analytics and standardized mass tolerance defaulting. Implemented and validated through tests and code reviews, reducing downstream parsing errors and enabling smoother integration with DIANN/Spectronaut workflows. Key deliverables include robust Spectronaut parameter parsing (reader, read_spectronaut_settings, robust IO, cleaning helpers, predictors_library normalization, and 40 ppm default for System Default) and DIANN parameter parsing enhancements (correct handling of mass tolerance values and expanded tests for a new DIANN report file). Also addressed formatting, upload-file handling, and parameter stripping to prevent parsing failures. Overall impact: improved reliability and consistency of parameter-driven data processing, enabling faster data validation and more accurate analytics. Technologies and skills demonstrated: Python, IO robustness, parameter parsing, data normalization, test coverage, and quality improvements in configuration handling. Top achievements (business value oriented): - Spectronaut parameter parsing enhancements enabling accurate parameter extraction and defaulting; reduces manual rework and supports downstream analytics. - DIANN parameter parsing enhancements improving mass tolerance handling and extending compatibility with new report formats. - Cross-cutting quality improvements including formatting fixes, upload-file handling, param stripping, and expanded tests, increasing maintainability and reliability.
7 Commits • 2 Features
Nov 1, 2024ProteoBench monthly summary for 2024-11 focusing on the ProteoBench/ProteoBench repository. Delivered robust parameter parsing improvements for Spectronaut and DIANN, with an emphasis on reliability, default behaviors, and test coverage. Enhanced parsing logic, file handling, and normalization to support downstream analytics and standardized mass tolerance defaulting. Implemented and validated through tests and code reviews, reducing downstream parsing errors and enabling smoother integration with DIANN/Spectronaut workflows. Key deliverables include robust Spectronaut parameter parsing (reader, read_spectronaut_settings, robust IO, cleaning helpers, predictors_library normalization, and 40 ppm default for System Default) and DIANN parameter parsing enhancements (correct handling of mass tolerance values and expanded tests for a new DIANN report file). Also addressed formatting, upload-file handling, and parameter stripping to prevent parsing failures. Overall impact: improved reliability and consistency of parameter-driven data processing, enabling faster data validation and more accurate analytics. Technologies and skills demonstrated: Python, IO robustness, parameter parsing, data normalization, test coverage, and quality improvements in configuration handling. Top achievements (business value oriented): - Spectronaut parameter parsing enhancements enabling accurate parameter extraction and defaulting; reduces manual rework and supports downstream analytics. - DIANN parameter parsing enhancements improving mass tolerance handling and extending compatibility with new report formats. - Cross-cutting quality improvements including formatting fixes, upload-file handling, param stripping, and expanded tests, increasing maintainability and reliability.
November 2024
Activity
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Quality Metrics
Correctness85.8%
Maintainability84.2%
Architecture82.2%
Performance75.0%
AI Usage24.6%
Skills & Technologies
Programming Languages
CSVJSONJavaScriptJupyter NotebookMakefilePythonTOML
Technical Skills
API integrationBackend DevelopmentBenchmarkingCode FormattingCode RefactoringConfiguration ManagementConfiguration ParsingCross-Platform CompatibilityData AnalysisData CleaningData IntegrationData ParsingData ProcessingData ValidationData Visualization
Repositories Contributed To
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