@Drew I like your suggestion of trying an experiment to validate your ideas. If you haven’t read back carefully through Howard’s posts for the past 2 years, you might find useful ideas and observations there. The forum’s user profile activity tab is a good way to find such things.
Sorry to keep dragging this out, but it sounds like you have a genuine interest…
As an analogy, research applies to OpenAg’s sharing of data in the way that having something coherent to say and sharing a common language with readers applies to writing. Sharing data is essentially an instance of human written communication, so all the typical problems around syntax, semantics, grammar, vocabulary, semiotics, logic, and style apply. Things like pictures, timestamps, and temperature measurements are comparable to letters in an alphabet or syllables in spoken words. We need layers of commonly agreed upon structure on top of those things to make them useful.
You and I are able to have this conversation because we share a lot of common understanding about the use of the English language. OpenAg did not inherit a common language that is suitable for exchanging data sets that can be used to create optimized growth recipes with machine learning. Various companies and researchers have their own internal systems, but those are the linguistic equivalent of local dialects that outsiders would not be able to understand.
The Growth Chamber Handbook from NCERA-101 is an example of a common language for exchanging data about controlled environment agriculture, but it’s meant for writing papers in journals rather than feeding machine learning algorithms. Caleb has mentioned genome research as an example he would like to follow. We could look more closely at how the genetics folks share their data. As I write this, I’m thinking that needs to go on my todo list.