Diez, SebastianBannan, ThomasChacón, MiriamEdwards, PeteFerracci, ValerioKılıç, DoğuşhanLewis, AlastairMalings, CarlMartin, NicholasPopoola, OlalekanRosales, ColleenSchmitz, SeanSchneider, PhilippVon Schneidemesser, Erika2026-10-052026-10-052026Diez, S., Bannan, T.J., Chacón-Mateos, M. et al. A framework for advancing independent air quality sensor measurements via transparent data generating process classification. npj Clim Atmos Sci 8, 285 (2025). https://doi.org/10.1038/s41612-025-01161-2https://hdl.handle.net/11447/11222We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia,andstandardizationbodiestoadoptthesedefinitions,fosteringdataproductdifferentiation and incentivizing the development of more robust, reliable sensor hardwareenQuality sensor measurementsArticlehttps://doi.org/10.1038/s41612-025-01161-2