Aggregated purity results across four services, with caveats — a second dataset posts 31–60

Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

I read post #45 twice before replying, because I had assumed the opposite.

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.

This follows post #45 rather than contradicting it.

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

Community-collected datasets: some members have compiled datasets from their own experience and shared them. They are self-reported, unblinded, and therefore limited as evidence. But they show real patterns that people experience.

Privacy: if contributing data, only share data you are comfortable making permanent and public. Once posted, data is persistent.

Collection methods: ask how the data were collected. Longitudinal tracking over months is stronger than retrospective recall. Prospective measurement (done while experiencing something) is stronger than memory afterward.

Limitations of datasets: all community-collected data has limitations. The population is self-selected (people in this community are not representative of all people using these compounds). Reporting bias is real (remarkable outcomes get reported; mundane outcomes do not).

post #52 answers the question as asked. The question underneath it is different.

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.

On post #50 — agreed on the reasoning, with one qualification.

Combining data from different sources: datasets from this site are not directly comparable to published trials because the populations are different. They are worth reading separately, not merged together.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

I read post #54 twice before replying, because I had assumed the opposite.

Reproducibility: if sharing data, include enough context (compound, dose, timeframe, method) that someone reading it understands what it represents.

post #56 is right about the mechanism and I think understates the practical bit.

Using data in discussions: datasets are useful as reference points when someone claims something unusual. "I have not seen that reported in the data" is different from "that is impossible", but data gives you something to say.

Community-collected datasets: some members have compiled datasets from their own experience and shared them. They are self-reported, unblinded, and therefore limited as evidence. But they show real patterns that people experience.

Picking up post #56: that is the part I would want checked first.

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

Coming back to post #58, because the follow-up matters more than the original answer.

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.