Researchers developed standardized procedures to grow fungal spores in laboratories and prepare them for testing with automated detection devices. They tested 17 different fungal species commonly found in the air and created reference datasets to train computer algorithms to identify these spores. Two different detection technologies were evaluated, showing promising accuracies (55-95%) for identifying various fungal spores. This work provides a blueprint for other scientists to create reliable training data for automated air quality monitoring systems that track allergens and disease-causing fungi.