Most labs rely on results that nobody can fully explain. The number is in a paper, a slide deck, or a decision that has already been made. Ask which inputs, which version of the code, and which run produced it, and often no one can say. This first episode is about that problem:
a result that is still in use after the record of how it was made has been lost.
A number is not a result
A result is a number plus its provenance: the inputs it came from, the code that computed it, the parameters used, and the run that produced it. Without those, you have a value and no way to check it. A lot of lab data is in this state, in spreadsheet cells and in folders named final_final_v2.
This works until someone asks how the number was produced. That might be an auditor, a regulator, a reviewer, or an AI system that reads your data and acts on it, which is more and more common.
If the record was never kept, there is nothing to show them. Usually nobody made a mistake. The tools they used simply didn’t record how each result was made.
The record has to come from the work itself
The usual fix is discipline: label everything, keep the notebook current, name files consistently. That holds until a busy week, a quick re-run, or a handoff to someone new. Provenance that depends on people remembering is missing at the moment it’s needed.
In a grounded workflow, each step records how its result was made at the moment it runs: what it used, what code ran, and what it produced. There is no separate documentation task. The design of the workflow is the operation, and every step writes its own record.
Ask how a result was made, and look it up
Once the record is automatic, the question that used to stall a meeting becomes a lookup. You can trace a result back through the analysis to the raw data, the instrument, and its origin. You can ask which results depend on an input that just changed.
A new scientist, an auditor, or an AI agent can follow any number back to the moment it was made, without needing the one person who happened to remember.
That is the difference between a result you report and one you can defend. The first asks for trust. The second shows the record.
Every result leaves a trail. The work is to build the workflow that keeps it.