Coming back to: A community side-effect dataset, with its response rate and biases

Temporal bias: older data in a dataset might reflect conditions (supplier, formulation, context) that have changed. Newer data is more current.

How to contribute: if you have longitudinal data you want to add, the format is simple: date, measurement, context. Contact the maintainer of the specific dataset.

Small point, but it is the one that usually catches people.

Bias toward positive outcomes: datasets collected by members are biased toward people who found the compounds useful. People who did not respond do not return. People who had bad outcomes might have left the community.

Not disagreeing with anyone above, just adding the bit I keep having to look up.

Everything in post #11 holds. The case it does not cover is the one I have.

Where a value is derived rather than measured, mark it. Derived columns get treated as observations the moment the file leaves your hands.

Not the answer, but possibly the question that gets there.

A dataset is only useful if the collection method is described alongside it. Numbers without a protocol are a list rather than data.

Reporting the observation and leaving the explanation open deliberately.