Transparency & Reproducibility#
Scientific integrity requires reproducible computational work
Why reproducibility matters:
Validates scientific claims
Enables building on previous work
Facilitates collaboration and review
Increases research impact
Many factors affect whether work can be reproduced
Best practices require effort:
Automated pipelines instead of manual steps
Explicit parameter documentation
Version control for all code and scripts
Clear documentation of the complete workflow
“It Works on My Machine”
Software and hardware environments critically affect results
Environment inconsistencies are a primary cause of reproducibility failures
Results depend on data, but sharing is complex
Mitigation strategies:
✓ Share subsets or synthetic data
✓ Document data characteristics
✓ Provide preprocessing code
✓ Describe model training data properties