TL;DR: I am expected to work on a dataset with missing/wrong information about measurement units. Is this a common practice?
I am studying a Masters's program composed mostly of machine learning. This semester I need to complete a 12 credit Project Module. Project Module means that there are no lectures, just a problem formulation, consultations with a teacher, and then a final report, source code and presentation are submitted.
The problem is to identify rocks by melting them with a laser and measuring the spectrum of the resulting plasma.
The training data I have is a bunch of measured spectra (example visualization). Each file comprises of comma separated tuples (wevelength, intensity).
My teacher didn't have the time to figure out what is being measured when we say "intensity" and sent me the "example" above to figure for myself. Well, I figured it's radiance, based on information of the same source as the example I was sent(here)
Now, radiance is measured in watt divided by solid angle and surface area. This is strictly non-negative. The example graph also cuts the ordinate at 0.
Yet when I look at the provided dataset, I can identify regions such as this one:
188.5999999999995,15.175680381419365 188.6999999999995,0.872615069518224 188.7999999999995,-8.345751621890303 188.8999999999995,-10.279252960079765 188.9999999999995,-4.22895372957017 189.09999999999948,3.978827692507486 189.19999999999948,16.603400130260713 189.29999999999947,-12.791825667786197 189.39999999999947,-4.859145351410255 189.49999999999946,4.452390240582065 189.59999999999945,-14.106010857508583 189.69999999999945,7.039306431256487 189.79999999999944,-7.3039003395809505
Negative line intensities with values far from 0.
And here is my question. Both my teacher and a couple of more knowledgeable colleagues agree that there is nothing wrong with this:
The data you have is real world data collected with a hand held. Your model should be able to deal with the data as it is. You can apply preprocessing steps if you want to avoid negative data values.
To me it is absurd to even consider a dataset without first sanity checking it. Just as unacceptable I would call working with a preprocessed dataset without knowing how was it preprocessed (maybe I am seeing centered values, but no one told me the mean; maybe I am seeing a logarithmic scale).
Is it customary for a task to be presented this way?