Making the record useful elsewhere.

Carry specimen identity, units and uncertainty alongside the observation so that an API response remains scientifically interpretable outside the website.

A numerical response would carry source and specimen identifiers, endpoint, original units, conditions, uncertainty basis and transformation history. FAIR principles support explicit identifiers, provenance and reuse metadata (Wilkinson et al., 2016). The proposed API would preserve those alongside values rather than returning a number whose scientific context depends on a separate marketing page. Missing fields would have defined states, and a client could request source-exact or explicitly transformed records. This makes the response useful for an internal comparison while preventing a receiving system from assuming that a normalised field is an original measurement or that a complete response has been experimentally validated.

(Wilkinson et al, 2016)

Retain eligibility rules and snapshot identity with each query so that another scientist can recover why particular records entered the response.

The proposed query contract would return records satisfying explicit scientific and reuse criteria, with the chosen snapshot version retained. Filtering by temperature or endpoint should not silently remove source limitations or turn an unreported condition into a match. A formal record-set definition can make those rules testable. It would describe which records belong in the response, not predict which material is best. The client should also receive an exclusion summary where useful, so a zero-result response can be distinguished from no evidence, incompatible conditions or unavailable rights. This makes the API’s behaviour inspectable and supports a meaningful acceptance task with the intended user.

(Wilkinson et al, 2016)

Give unsupported quantities and unresolved source fields explicit error states, preventing a technically valid response from concealing missing scientific information.

An invalid unit conversion, unsupported endpoint or unavailable source should return a clear state rather than a plausible empty value. The proposed contract would distinguish a missing observation from zero and a restricted source from an incompatible experiment. It would define stable identifiers, pagination and version selection while retaining the scientific eligibility rule. A client could then handle an unresolved comparison explicitly in its own workflow. This matters commercially because technical uptime alone is insufficient: a reliable connection delivering ambiguous scientific quantities can still create bad decisions, and the value of a curated service depends on those quantities remaining interpretable.

Choose one receiving workflow and acceptance task that can demonstrate useful integration without claiming demand from the existence of a prototype.

The next useful commitment is a client task with a finite record set, an explicit output and a scientist who can judge whether it improves the workflow. It should include at least one difficult case involving units, missingness or source restrictions, rather than only easy numerical rows. If the exported record is sufficient, a narrow prototype may be worth developing. If the buyer mainly needs a one-off interpretation, a scoped brief may be the better service. The proposed API should therefore emerge from repeated decision value, with a clear acceptance test and a stop criterion before substantial infrastructure or paid access is considered.