Data-quality checks before analysing mapping data
Statistics computed on bad data are precise and wrong. Check the data first.
Before any minimum, maximum or mean is trusted, the dataset should pass a set of checks that catch export, merge and sensor problems.
Checks
- Unit: confirmed explicitly, never inferred from the magnitude of the values.
- Timestamps: monotonic per sensor, no duplicates, consistent interval, no time-zone or daylight-saving jumps.
- Completeness: gaps identified per sensor, with duration; a sensor missing a large fraction of the study cannot support conclusions.
- Flatlines: long runs of identical values suggest a stalled logger or a fault.
- Abnormal jumps: step changes larger than physically plausible in one interval.
- Oscillation: rapid alternation that suggests electrical noise or a failing sensor.
- Identifiers: every column mapped to a unique sensor with a location.
Each finding should be visible in the report with a decision: excluded, corrected (with a record), or accepted with a stated limitation. ValiTrac's quality stage produces exactly these findings with PASS / WARNING / REVIEW REQUIRED / CRITICAL states.
Frequently asked questions
- What is a flatline in logger data?
- A long run of identical readings that suggests a stalled logger, a disconnected probe or a sensor fault; the affected period cannot support conclusions about the space.
- How much missing data is acceptable in a mapping study?
- The protocol should say. A sensor missing a large fraction of the study, or missing data around events of interest, generally cannot be used for hot/cold-spot conclusions.
References
- [1]WHO Technical Supplement 8 to TRS 961 Annex 9: Temperature mapping of storage areas (2015)
General technical guidance written against the cited sources. It is not regulatory or legal advice and does not replace the applicable standard, guideline or a qualified reviewer's judgement.
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