Raw data of indoor air microbiome and questionnaire analysis

Raw Data of Indoor Air Microbiome and Questionnaire AnalysisThe <b>raw data</b> consist of two primary components: (1) microbiome sequencing datasets obtained from indoor air and HVAC system samples, and (2) questionnaire responses collected from healthcare workers and occupants regarding indoor air quality (IAQ) perception and health symptoms.1. Indoor Air Microbiome DataThis dataset includes high-throughput sequencing data derived from dust, filter, and vent swab samples collected at multiple hospital and clinic sites. DNA extraction was conducted under sterile conditions, followed by library preparation using the Illumina platform (e.g., MiSeq or NovaSeq) targeting the bacterial 16S rRNA V3–V4 region.<b>Raw reads</b>: FASTQ files containing paired-end sequences.<b>Metadata</b>: Sampling site, surface type (e.g., duct, grille, vent, or filter), temperature, humidity, air-exchange rate, and occupancy.<b>Downstream analyses</b>: Alpha and beta diversity, taxonomic profiling, and correlation with environmental parameters were performed using QIIME2 and MicrobiomeAnalyst.The dataset allows for exploration of bacterial community composition, diversity gradients across sampling zones, and potential pathogenic taxa linked to hospital air quality.2. Questionnaire DataComplementing the microbiome profiles, questionnaire data capture human responses regarding <b>indoor air quality (IAQ), perceived comfort, and health symptoms</b>.<b>Participants</b>: Healthcare workers and staff (n ≈ 200).<b>Variables</b>: Demographics (age, gender, work department), duration of exposure, self-reported symptoms (respiratory irritation, allergies, fatigue), and perceived air freshness and ventilation quality.<b>Analysis</b>: Statistical association between microbiome diversity and reported health outcomes (e.g., correlation tests, multivariate regression).The integration of microbial and questionnaire datasets enables <b>multi-dimensional assessment</b> of the hospital indoor environment—linking microbial ecology with occupant health responses.3. Data UtilityThis comprehensive dataset provides a foundation for:Identifying <b>key microbial taxa</b> associated with respiratory or allergic symptoms.Establishing <b>microbial indicators</b> for air hygiene and risk assessment.Supporting future <b>machine learning models</b> for predicting IAQ-related health risks.