hallMonitor 2.0
hallMonitor 2.0 is a data validation and quality assurance system developed for the Neural Dynamics of Control Lab. It ensures that datasets across multiple studies remain consistent with tracker files, metadata standards, and protocol-specific rules.
Philosophy
In research environments, silent errors accumulate quietly but cost dearly. This project is about making invisible problems visible early — and doing so in a way that respects modularity, transparency, and auditability.
Challenges
Rewriting the system from Bash to Python meant rethinking abstractions, error handling, logging, and validation structure. It had to scale to multiple data types and still provide clear, actionable feedback for research assistants and analysts.
Reflections
hallMonitor taught me how to build systems that last — and how to balance flexibility with reliability. It also deepened my commitment to writing code that explains itself, even as it scales.
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