The research system starts before model training.

The first useful artifact in a research project is not always a model. It can be a clear data contract: where an observation comes from, what it means, when it becomes available, and what use is permitted. Without those answers, an impressive experiment can be difficult to interpret.

This is especially important in biomedical research, where an observation needs context. Study population, collection conditions, and intended use affect what can be concluded. These are general research considerations, not findings from a clinical study.

This also changes what completion means. A workflow can pass its engineering checks while the scientific question remains unanswered. Keeping those states separate makes the roadmap more honest and the next experiment easier to define.

Build the conditions for a trustworthy experiment before optimizing its result.