Reproducibility

Mike Nelson discusses reproducibility in image analysis.
The presentation with clickable links is available for download:
docs.google.com/presentation/...
00:20 - Different levels of reproducibility
01:35 - Diverse evidence for lack of reproducibility
02:30 - Underreporting of imaging methods
04:40 - Resources to improve reproducibility
05:45 - Make your data Findable, Accessible, Interoperable, and Reusable
06:10 - Data hosting options
07:00 - Best practices with scripting
07:30 - StarDist and Cellpose - examples of well-documented, accessible, and reusable software
08:50 - How to make your machine learning workflows reproducible
09:20 - Batch effects and controls
10:20 - Summary and conclusion
12:00 - Image analysis can help with reproducibility