Data review

# Overall

I think the overall webpage, data, and metadata is well done and will likely lead to successful download, use, and reuse by folks reproducing the data. My notes for each data product are below:

- On a 19 inch the summary paragraph on the webpage makes it so you don't see the much easier to understand data links (1-6) unless you click on the show summary button. Would be nice if these numbered data links were higher up so you see them right away (like lake information etc...)

## Lake information

1) A minor note, but why use shapefiles? these are often harder to track, proprietary, and come with multiple files. Geopackage (.gpkg) is faster, open, and comes with a single file.

2) The lake metadata column names either need more explanation (what is ws_mean?) or a link to the original dataset where these metadata comes from. (is this lake cat data?)

## Lake temp observations

- Why publish the source column? Seems like an internal tracking device that is not relevant.

- Is NHDhr_ ##### a NHD comid? Why not just use the label COMID for that column (or rather comid). Do you have non NHDHR lakes?

## Model config

- No comment

## Model inputs

No major issue, though the plethora of zips would bother me if I wanted all the data. Is this some kind of limit on how much data can be in a single object? Why not just put all these data into a single (much bigger zip)? Or give user both options like here's ALL the data and here's the broken apart data if you know where you want to work.

## Model predictions

This is where I struggled the most to evaluate what each dataset does and why there are so many different data objects. Can these not be unified slightly more to decrease object proliferation? Like just all pball predictions in one and all pbmtl in another? Looks like those objects would be ~ 1 gb and I just think it will be easier for an end user.

Other than that, the data is clear and well documented.

## Model evaluation

Again, I prefer one zipped folder with sub datasets inside of it instead of so many separate (relatively small) zipped folders. But fundamentally what is in the data is clear and understandable.

# Final comments

Overall this is an exciting dataset, a cool method, and a nice paper/approach. I'm excited to try doing something similar with remote sensing of water quality!

However, I think that the number of data objects could be cut back substantially which will make the data easier to work with and clearer. Additionally, I think a tutorial showing folks how to use your PGDL and MTL code with the datasets you published would amplify the value of this data by many times. Maybe you already have that in a separate code release, but showing us exactly how you go from all this raw data to your predictions would be really useful.

Nice work and if you need additional comments let me know,

Matt