ArticleMethodsX2026
A reproducible R workflow for harmonizing indoor air quality sensor data and cleaning activity logs in school-based field campaigns.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
This article presents a reproducible workflow developed in R to process indoor air quality data by integrating wearable multi sensor measurements with tablet-based activity logs. The data come from two field campaigns in four primary schools and involve 20 female maintenance staff members. The method supports occupational exposure assessment under real world conditions, where sensor signals can be incomplete or noisy and self-reported activities may contain timing errors. The workflow organizes raw files by campaign, school and instrument, standardized timestamps (including daylight saving changes), and harmonizes quantitative signals (temperature, relative humidity, CO
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