Glickfeld-And-Hull-Laboratories/ImagingCode-Glickfeld-Hull
Dec 2024 – Jan 2026
13 Months active
Languages Used
MATLABRPython
Technical Skills
Behavioral AnalysisData AnalysisData OrganizationData ProcessingExperimental Data AnalysisMATLAB Scripting
ArdentRodent
PROFILE
Ardentrodent
Jerry Hu developed and maintained the ImagingCode-Glickfeld-Hull repository, delivering a robust data analysis and processing pipeline for neuroscience experiments. He engineered scalable workflows for multi-day imaging, behavioral, and eye-tracking data, integrating GPU-accelerated image registration, parallel computing, and advanced MATLAB scripting. His work included refactoring experimental configurations, enhancing trial segmentation with photodiode and locomotion data, and implementing tools for flexible trial management and neural response analysis. By aligning data structures and automating quality control, Jerry improved reproducibility, data integrity, and analysis speed. The depth of his engineering ensured reliable, extensible pipelines supporting evolving experimental protocols and large-scale scientific datasets.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
75Total
Bugs
5
Commits
75
Features
30
Lines of code
74,374
Activity Months13
Your Network
10 people
Work History
January 2026
8 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01) monthly summary for ImagingCode-Glickfeld-Hull: Delivered structural overhaul for DREADD datasheets and data model, enhanced VIP PEG data tracking, fixed critical data-analysis parameter, and cleaned data processing workflow. These changes improved data integrity, cross-experiment consistency, and processing efficiency, enabling faster insights and better decision-making for experiments.
8 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01) monthly summary for ImagingCode-Glickfeld-Hull: Delivered structural overhaul for DREADD datasheets and data model, enhanced VIP PEG data tracking, fixed critical data-analysis parameter, and cleaned data processing workflow. These changes improved data integrity, cross-experiment consistency, and processing efficiency, enabling faster insights and better decision-making for experiments.
January 2026
December 2025
6 Commits • 2 Features
Dec 1, 2025Month: 2025-12 — concise monthly summary focusing on delivered features, major fixes, and overall impact for ImagingCode-Glickfeld-Hull. Highlights include expansion of YM90K-DART dataset with data from multiple mice (including i3335) and a new VIP_YM90K green/red FOV processing file to improve neural response analysis; DART pipeline enhancements including robust trial indexing handling, support for multiple indexing sources, and conditional data loading by source type; repository cleanup and maintainability improvements. These changes enhance data fidelity, enable broader analyses, reduce maintenance overhead, and improve reproducibility.
December 2025
6 Commits • 2 Features
Dec 1, 2025Month: 2025-12 — concise monthly summary focusing on delivered features, major fixes, and overall impact for ImagingCode-Glickfeld-Hull. Highlights include expansion of YM90K-DART dataset with data from multiple mice (including i3335) and a new VIP_YM90K green/red FOV processing file to improve neural response analysis; DART pipeline enhancements including robust trial indexing handling, support for multiple indexing sources, and conditional data loading by source type; repository cleanup and maintainability improvements. These changes enhance data fidelity, enable broader analyses, reduce maintenance overhead, and improve reproducibility.
November 2025
14 Commits • 3 Features
Nov 1, 2025November 2025 – ImagingCode-Glickfeld-Hull: Key features delivered and quality improvements to the DART pipeline, focusing on timing accuracy, flexible stimulus sources, and robust data handling. Highlights include: DART Trial Timing Enhancements with a new MATLAB analysis script; Multi-Source Stimulus Timing Support enabling PD/MW/cStimOn timing selection; Trial Data Management Enhancements with trialDropper integration and inputStructure naming; Data Extraction Integrity and Maintenance improving error handling, timing source fixes, and obsolete-file cleanup. This work improves data reliability, analysis speed, and reproducibility, delivering clear business value for experimental throughput and scientific insights.
14 Commits • 3 Features
Nov 1, 2025November 2025 – ImagingCode-Glickfeld-Hull: Key features delivered and quality improvements to the DART pipeline, focusing on timing accuracy, flexible stimulus sources, and robust data handling. Highlights include: DART Trial Timing Enhancements with a new MATLAB analysis script; Multi-Source Stimulus Timing Support enabling PD/MW/cStimOn timing selection; Trial Data Management Enhancements with trialDropper integration and inputStructure naming; Data Extraction Integrity and Maintenance improving error handling, timing source fixes, and obsolete-file cleanup. This work improves data reliability, analysis speed, and reproducibility, delivering clear business value for experimental throughput and scientific insights.
November 2025
October 2025
9 Commits • 4 Features
Oct 1, 2025October 2025 — Key deliverables across the ImagingCode-Glickfeld-Hull repo: multi-day eye-tracking data processing tooling; bug fix improving timing alignment in DART; VIP mice analysis across multi-day experiments with SSIx, DART, pupil and locomotion data; enhanced DART index calculation and piecewise regression across cell types; and core DART pipeline enhancements including tooling, secure execution, and visualization.
October 2025
9 Commits • 4 Features
Oct 1, 2025October 2025 — Key deliverables across the ImagingCode-Glickfeld-Hull repo: multi-day eye-tracking data processing tooling; bug fix improving timing alignment in DART; VIP mice analysis across multi-day experiments with SSIx, DART, pupil and locomotion data; enhanced DART index calculation and piecewise regression across cell types; and core DART pipeline enhancements including tooling, secure execution, and visualization.
September 2025
4 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for ImagingCode-Glickfeld-Hull. This period focused on delivering two major features that accelerate experimentation, improve data quality, and increase pipeline scalability in the ImagingCode project. Key features delivered: - Experimental Configuration Update for New Mouse i3331: Added configurations for mouse i3331 in celine/DART_V1_YM90K_Celine.m and aligned related scripts in the jerry directory (mouse IDs, dates, frame counts, and session lists). Commit: 14477ac3099bfde47b17c9fdedbc6add40be031c. - Pupil Data Extraction and Analysis Enhancements with Photodiode Integration: Refactored and enhanced pupil data processing to incorporate photodiode data, introduced extractEyeData_jerry.m, updated alignment to use the new extraction method, and enabled CPU core pooling for parallel day-level processing. Commits: 3986bbed2831803713cfe3a629cfcfc79dd33f02; a9b55af220eeeb86186b2a7c5903b1c711e52bc5; a1814f97e1c795a5855f9d4d1f7c4e1ccd861c52. Major bugs fixed: - No explicit major bugs were documented in this period. The work focused on feature delivery, reliability improvements, and performance enhancements in the pupil analytics pipeline. Overall impact and accomplishments: - Enabled rapid onboarding of a new subject (i3331) with consistent data configuration across datasets. - Improved pupil tracking accuracy through photodiode integration and a new extraction workflow. - Achieved faster multi-day analyses via CPU core pooling, increasing scalability for larger experiments. Technologies/skills demonstrated: - MATLAB data processing and scripting, cross-directory workflow alignment (celine/jerry), and data pipeline refactor. - Integration of photodiode data into pupil analytics and the introduction of extractEyeData_jerry.m. - Parallel processing and performance optimization through CPU core pooling.
4 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for ImagingCode-Glickfeld-Hull. This period focused on delivering two major features that accelerate experimentation, improve data quality, and increase pipeline scalability in the ImagingCode project. Key features delivered: - Experimental Configuration Update for New Mouse i3331: Added configurations for mouse i3331 in celine/DART_V1_YM90K_Celine.m and aligned related scripts in the jerry directory (mouse IDs, dates, frame counts, and session lists). Commit: 14477ac3099bfde47b17c9fdedbc6add40be031c. - Pupil Data Extraction and Analysis Enhancements with Photodiode Integration: Refactored and enhanced pupil data processing to incorporate photodiode data, introduced extractEyeData_jerry.m, updated alignment to use the new extraction method, and enabled CPU core pooling for parallel day-level processing. Commits: 3986bbed2831803713cfe3a629cfcfc79dd33f02; a9b55af220eeeb86186b2a7c5903b1c711e52bc5; a1814f97e1c795a5855f9d4d1f7c4e1ccd861c52. Major bugs fixed: - No explicit major bugs were documented in this period. The work focused on feature delivery, reliability improvements, and performance enhancements in the pupil analytics pipeline. Overall impact and accomplishments: - Enabled rapid onboarding of a new subject (i3331) with consistent data configuration across datasets. - Improved pupil tracking accuracy through photodiode integration and a new extraction workflow. - Achieved faster multi-day analyses via CPU core pooling, increasing scalability for larger experiments. Technologies/skills demonstrated: - MATLAB data processing and scripting, cross-directory workflow alignment (celine/jerry), and data pipeline refactor. - Integration of photodiode data into pupil analytics and the introduction of extractEyeData_jerry.m. - Parallel processing and performance optimization through CPU core pooling.
September 2025
August 2025
6 Commits • 4 Features
Aug 1, 2025Focused on delivering scalable data-processing enhancements and advanced analysis capabilities for ImagingCode-Glickfeld-Hull. Augmented data integrity and flexibility with trial-level modification history and skipAction controls; enabled robust multi-session data handling and seamless integration of new subject data; expanded neural analysis toolkit with surround suppression metrics and SSIx plotting, supporting pre/post-condition comparisons and locomotion/pupil effects. These developments accelerate longitudinal studies, improve reproducibility, and broaden experimental parameter support.
August 2025
6 Commits • 4 Features
Aug 1, 2025Focused on delivering scalable data-processing enhancements and advanced analysis capabilities for ImagingCode-Glickfeld-Hull. Augmented data integrity and flexibility with trial-level modification history and skipAction controls; enabled robust multi-session data handling and seamless integration of new subject data; expanded neural analysis toolkit with surround suppression metrics and SSIx plotting, supporting pre/post-condition comparisons and locomotion/pupil effects. These developments accelerate longitudinal studies, improve reproducibility, and broaden experimental parameter support.
July 2025
4 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for ImagingCode-Glickfeld-Hull: Delivered two core features to strengthen data logging, analysis workflow, and experimental data management, while pruning legacy components to reduce maintenance risk. The work enhances data provenance, analysis reliability, and experimental control, with measurable improvements in processing efficiency and reproducibility.
4 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for ImagingCode-Glickfeld-Hull: Delivered two core features to strengthen data logging, analysis workflow, and experimental data management, while pruning legacy components to reduce maintenance risk. The work enhances data provenance, analysis reliability, and experimental control, with measurable improvements in processing efficiency and reproducibility.
July 2025
June 2025
10 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for ImagingCode-Glickfeld-Hull repository focused on improving experimental data management, expanding experiment configurations, and stabilizing the data pipeline across multi-day processing. Delivered concrete features, fixed critical merge-related issues, and demonstrated solid technical execution with clear business value in reproducibility and throughput.
June 2025
10 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for ImagingCode-Glickfeld-Hull repository focused on improving experimental data management, expanding experiment configurations, and stabilizing the data pipeline across multi-day processing. Delivered concrete features, fixed critical merge-related issues, and demonstrated solid technical execution with clear business value in reproducibility and throughput.
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for ImagingCode-Glickfeld-Hull: Delivered major performance and analysis enhancements with GPU-accelerated registration and data processing improvements. This work reduces CPU usage, improves per-frame alignment speed, and enhances data quality and visualization to support faster, more reliable decision-making in experiments.
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for ImagingCode-Glickfeld-Hull: Delivered major performance and analysis enhancements with GPU-accelerated registration and data processing improvements. This work reduces CPU usage, improves per-frame alignment speed, and enhances data quality and visualization to support faster, more reliable decision-making in experiments.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04) monthly summary for the ImagingCode-Glickfeld-Hull project. Delivered DART imaging data processing and video analysis enhancements, including DART experimental data integration, updated PV/SST data analysis scripts, a new video generator for 2-photon imaging data, and epilepsy-related data analysis with updated movie formats and ITI/polynomial mask support. Commits underpinning these efforts include: 3fb5bc48023d899b40760ad18c698dbe75561ae3 (DART_expt_info and epilep analysis updates), d05dc40758e4de5e291a28064a5ad92eabef3844 (Script to make videos from raw 2p data), and 7037e6736158acb800105362d9ab7eb13d44eef1 (epilepsy plots).
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04) monthly summary for the ImagingCode-Glickfeld-Hull project. Delivered DART imaging data processing and video analysis enhancements, including DART experimental data integration, updated PV/SST data analysis scripts, a new video generator for 2-photon imaging data, and epilepsy-related data analysis with updated movie formats and ITI/polynomial mask support. Commits underpinning these efforts include: 3fb5bc48023d899b40760ad18c698dbe75561ae3 (DART_expt_info and epilep analysis updates), d05dc40758e4de5e291a28064a5ad92eabef3844 (Script to make videos from raw 2p data), and 7037e6736158acb800105362d9ab7eb13d44eef1 (epilepsy plots).
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Glickfeld-Hull ImagingCode work: Delivered substantial enhancements to neurophysiological data analysis by upgrading power spectrum analysis, introducing SST/PV epileptiform activity quantification, and refactoring MATLAB scripts to accommodate new experimental datasets and improve data organization. These changes streamline the data analysis pipeline and position the team to rapidly adapt to evolving experiments.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Glickfeld-Hull ImagingCode work: Delivered substantial enhancements to neurophysiological data analysis by upgrading power spectrum analysis, introducing SST/PV epileptiform activity quantification, and refactoring MATLAB scripts to accommodate new experimental datasets and improve data organization. These changes streamline the data analysis pipeline and position the team to rapidly adapt to evolving experiments.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered imaging data processing enhancements in ImagingCode-Glickfeld-Hull. Implemented trigger synchronization, power spectrum analysis, and new diagnostic/analysis scripts; refactored code for modularity and maintainability; updated experiment parameter handling to support flexible workflows. This work improves data processing speed, analysis capabilities, and reproducibility across experiments.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered imaging data processing enhancements in ImagingCode-Glickfeld-Hull. Implemented trigger synchronization, power spectrum analysis, and new diagnostic/analysis scripts; refactored code for modularity and maintainability; updated experiment parameter handling to support flexible workflows. This work improves data processing speed, analysis capabilities, and reproducibility across experiments.
December 2024
5 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for ImagingCode-Glickfeld-Hull (2024-12). Focused on delivering a more robust, reproducible data pipeline and richer behavioral analytics. Key accomplishments include a comprehensive refactor of the experimental data organization and processing pipeline, the introduction of photodiode-based trial segmentation with diagnostic visualizations, and the implementation of locomotion-aware segmentation and analysis tools. No major defects were reported; the month emphasized stability, data governance, and end-to-end analysis improvements that accelerate downstream research and collaboration.
5 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for ImagingCode-Glickfeld-Hull (2024-12). Focused on delivering a more robust, reproducible data pipeline and richer behavioral analytics. Key accomplishments include a comprehensive refactor of the experimental data organization and processing pipeline, the introduction of photodiode-based trial segmentation with diagnostic visualizations, and the implementation of locomotion-aware segmentation and analysis tools. No major defects were reported; the month emphasized stability, data governance, and end-to-end analysis improvements that accelerate downstream research and collaboration.
December 2024
Activity
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Quality Metrics
Correctness84.4%
Maintainability81.8%
Architecture80.6%
Performance77.0%
AI Usage21.0%
Skills & Technologies
Programming Languages
MATLABPythonR
Technical Skills
Behavioral AnalysisCode RefactoringData Acquisition ConfigurationData AnalysisData ManagementData OrganizationData ProcessingData VisualizationDebuggingExperimental ConfigurationExperimental Data AnalysisExperimental Data ManagementExperimental DesignExperimental Parameter ConfigurationExperimental Setup
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