Glickfeld-And-Hull-Laboratories/ImagingCode-Glickfeld-Hull
Feb 2025 – Jul 2025
4 Months active
Languages Used
MATLAB
Technical Skills
Data AnalysisImage AnalysisMATLAB ScriptingNeuroscience Data ProcessingScientific ComputingNeuroscience
Sara Gannon
PROFILE
Sara Gannon
Contributed to the ImagingCode-Glickfeld-Hull repository by developing and enhancing MATLAB-based data analysis pipelines for neuroscience imaging experiments. Over four months, delivered new features including neural data analysis functions, expanded support for marmoset datasets, and improved plotting workflows with integrated statistical analysis. Leveraged MATLAB scripting, image processing, and scientific computing to streamline experiment inclusion, automate timecourse and cross-orientation adaptation analyses, and introduce eye-tracking and sinusoidal fitting tools. Consolidated local scripts into the repository to improve reproducibility and collaboration. The work focused on increasing analysis efficiency, data quality, and visualization capabilities, supporting ongoing research in two-photon imaging and neural response studies.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
11,317
Activity Months4
Your Network
31 peopleSame Organization
@duke.edu21
Humam Al-ShamiMember
Bryan ChanMember
Bo YuMember
CelineMember
Chris WalterMember
HyeYun ParkMember
HakenmuellerMember
Jude AlnasMember
Kelly XuMember
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for ImagingCode-Glickfeld-Hull focusing on analytics enhancements to the plotting workflow. Delivered Statistical Analysis Enhancements for Plotting Script, enabling built-in statistical functions and data processing adjustments to plots for improved visualization and data-driven insights. Achieved code alignment with the main branch to ensure up-to-date baseline. No separate major bugs fixed were documented in this period.
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for ImagingCode-Glickfeld-Hull focusing on analytics enhancements to the plotting workflow. Delivered Statistical Analysis Enhancements for Plotting Script, enabling built-in statistical functions and data processing adjustments to plots for improved visualization and data-driven insights. Achieved code alignment with the main branch to ensure up-to-date baseline. No separate major bugs fixed were documented in this period.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered substantial enhancements to cross-orientation adaptation experiments in ImagingCode-Glickfeld-Hull, focusing on data analysis pipeline improvements and MATLAB-based image analysis tooling. Key work includes updated data loading/processing, and new MATLAB scripts for image analysis, experiment inclusion criteria, timecourse plotting, sinusoidal fitting, and eye-tracking analysis for two-photon imaging. No major bugs fixed this month; maintenance included reorganizing local files into the repository to improve reproducibility and collaboration. This work reduces analysis time, improves data quality and reproducibility, and strengthens the foundation for ongoing research in two-photon imaging.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered substantial enhancements to cross-orientation adaptation experiments in ImagingCode-Glickfeld-Hull, focusing on data analysis pipeline improvements and MATLAB-based image analysis tooling. Key work includes updated data loading/processing, and new MATLAB scripts for image analysis, experiment inclusion criteria, timecourse plotting, sinusoidal fitting, and eye-tracking analysis for two-photon imaging. No major bugs fixed this month; maintenance included reorganizing local files into the repository to improve reproducibility and collaboration. This work reduces analysis time, improves data quality and reproducibility, and strengthens the foundation for ongoing research in two-photon imaging.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for the ImagingCode-Glickfeld-Hull project focused on neural data analysis enhancements and data handling improvements. Delivered a new neural data analysis function bigFits.m, integrated expt(113) into the experiment list, and refactored marmoset data handling to leverage the new function. Updated the cross-ori analysis and fitting routines to align with the new workflow, improving pipeline reliability, reproducibility, and data processing throughput across experiments.
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for the ImagingCode-Glickfeld-Hull project focused on neural data analysis enhancements and data handling improvements. Delivered a new neural data analysis function bigFits.m, integrated expt(113) into the experiment list, and refactored marmoset data handling to leverage the new function. Updated the cross-ori analysis and fitting routines to align with the new workflow, improving pipeline reliability, reproducibility, and data processing throughput across experiments.
May 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focused on extending data analysis capabilities in ImagingCode-Glickfeld-Hull. Delivered enhancements to PDS timecourse analysis and added Marmoset data support, improving both analysis accuracy and data coverage. Implemented updates to experiment lists, enhanced plotting (cell highlighting and improved subplot layouts), and introduced new marmoset summary and raw data processing scripts. All work consolidated under a single change to streamline review and maintenance, enabling faster insights and broader applicability across species.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focused on extending data analysis capabilities in ImagingCode-Glickfeld-Hull. Delivered enhancements to PDS timecourse analysis and added Marmoset data support, improving both analysis accuracy and data coverage. Implemented updates to experiment lists, enhanced plotting (cell highlighting and improved subplot layouts), and introduced new marmoset summary and raw data processing scripts. All work consolidated under a single change to streamline review and maintenance, enabling faster insights and broader applicability across species.
Activity
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Quality Metrics
Correctness76.0%
Maintainability74.0%
Architecture74.0%
Performance68.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MATLAB
Technical Skills
Data AnalysisImage AnalysisImage ProcessingMATLABMATLAB ScriptingNeuroscienceNeuroscience Data ProcessingScientific ComputingScriptingSignal Processing
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