list manipulation - Handy / flexible directory tree operations

Is there any shorter / more handy / more flexible way to get directory tree as a nested Rules/Associations list?

I tried to incorporate FileNames + GatherBy and GroupBy, but nothing was as short and handy as old fashioned:

ClearAll @ filesInDir;

filesInDir[dirname_, deep_] :=  If[
    DirectoryQ[dirname],
    FileBaseName[ dirname] -> (
          If[   deep == 1, 
                FileNameTake[#, -1], 
                filesInDir[#, deep - 1]
            ] & /@ FileNames["*", dirname]),
    FileNameTake[dirname, -1]
];



filesInDir[$UserBaseDirectory, 2]

"Mathematica" -> {"ApplicationData" -> {"CCompilerDriver", "CloudObject", "CUDALink", "Parallel"}, "Applications" -> {...}, "Autoload" -> {...}, "FrontEnd" -> {"init.m"}, "Kernel" -> {"init.m"}, "Licensing" -> {}, "Paclets" -> {"Configuration", "Repository", "Temporary"}, "SystemFiles" -> {"CharacterEncodings", "FrontEnd", "Kernel", "SpellingDictionaries"}}

And now, from this nested rules I want to go back to the output of:

FileNameSplit /@ FileNames["*", $UserBaseDirectory, 2]

p.s.

I've chosen nested lists/associations to reduce ByteCount and make querying more user friendly. But if you can convince me I should keep a full paths list it would be a valid answer too.

Comments

plotting - Plot 4D data with color as 4th dimension

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plotting - Filling between two spheres in SphericalPlot3D

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plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....

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