Here is my result! I produced these plots using Python with numpy and matplotlib. The analysis supports the claim that an inexpensive and open hardware can match the performance of a commercial pulse oximeter. There is a lot of available data too.
The hardest bit so far has been trying to get 2 serial devices to synchronize within 2 milliseconds of one another. I had some success. Using 2 of the researchers pulse oximeters, and at least 7 subjects, I managed to get clean photoplethysmograms (PPG waveforms)!
Over the course of the week, I also managed to synchronize 2 Pulse Sensor Amped and get at least 2 clean waveforms with the promise of more and I also synchronized one CMS D50+ pulse ox and one Pulse Sensor Amped pulse sensor and get a few clean waveforms.
These data are meant to help my fellow teacher researchers in answering their questions more so than mine, but it is nice to be helpful.
Best timing results for all systems (once synchronized by hand) was time resolution of ±3 m/s. This is enough to characterize a single waveform and even to find the pulse transit time.