Network panel
Algae Dataset
Every data hub, dataset, sensor, variable, paper and limitation in one tree — and for each: where it comes from, how far back it goes, how often it captures, why it belongs in a cholera study, and why it does not work everywhere.
Highlight site
All three
Chakaria HDSS
Matlab HDSS
Dhaka City
Choosing a site greys out everything that does not apply there.
- +DATA HUBSwhere the data physically comes from
- +DATASET TYPESgrouped by what kind of water they see
- +SITE-WISEwhat actually applies at each site
- +DURATION & CAPTURE CADENCEdaily · weekly · 10-day · 16-day · monthly
- +VARIABLESwhat is computed from the bands
- −LIMITATIONSwhy not — typed and severity-ranked
- +PAPERSthe evidence, DOI-linked
ATMOSPHERIC limitations
HIGH Monsoon cloud destroys June-September optical coverage atmospheric
Every optical sensor here - Sentinel-2, Sentinel-3, Landsat, MODIS, ocean colour - is blinded by cloud. June to September is the wettest period in Bangladesh and also the period of highest cholera transmission, so the data is thinnest exactly when it matters most. Worse, cloudiness is not random with respect to blooms: cloudy weeks have different bloom dynamics, which makes the missingness informative rather than ignorable.
What to do: Carry Sentinel-1 SAR water extent as a cloud-proof companion, and model cloud_frac explicitly. The hospital codebook already contains ground sunshine hours and cloud cover (rows 306-307) - use them to characterise what you lost.