research technical write-up
UCSC SIP Research Automation
Transcript cleanup and qualitative research workflow support
This automation project supported qualitative research by reducing repetitive transcript cleanup and preparing text for downstream analysis.
Problem / motivation
Research teams often spend valuable time on formatting and cleanup before analysis can even begin. Automation can remove repetitive friction while keeping researchers in control.
Key technical challenges
- Handling transcript inconsistency without overfitting to one file.
- Preserving meaning while removing mechanical noise.
- Explaining automation limits clearly to research collaborators.
Architecture / workflow
How the system fits together
Input transcripts are normalized and cleaned with Python scripts.
Regex passes handle speaker labels, timestamps, spacing, and repeated artifacts.
Outputs are structured for review, coding, and qualitative analysis.
Documentation explains assumptions and manual review points.
What I built
- Python cleanup utilities for recurring transcript artifacts.
- Regex rules for formatting and normalization.
- Research-support documentation for workflow handoff.
Outcomes / metrics
- Reduced manual cleanup time for qualitative research preparation.
- Demonstrated automation work that respects human review and domain context.
Lessons learned
- Good research automation is careful, auditable, and modest about what it changes.
- Technical communication is part of the tool when collaborators need to trust the output.
Screenshots / media
Visual evidence placeholders
Replace these panels with screenshots, demos, diagrams, or notebook exports as each artifact becomes ready for publishing.
Media
Cleanup workflow
Raw transcript, normalization passes, review output, and analysis handoff.