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The ability to repeat research is vital in confirming the validity of scientific discovery and is relevant to ubiquitous sensor research. Investigation of novel sensors and sensing mechanisms intersect several Federal and non-Federal agencies. Despite numerous studies on sensors at different stages of development, the absence of new field-ready or commercial sensors seems limited by reproducibility. Current research practices in sensors needs sustainable transformations. The scientific community seeks ways to incorporate reproducibility and repeatability to validate published results. A case study on the reproducibility of low-cost air quality sensors is presented. In this context, the article discusses (a) open source data management frameworks in alignment with findability, accessibility, interoperability, and reuse (FAIR) principles to facilitate sensor reproducibility; (b) suggestions for journals focused on sensors to incorporate a reproducibility editorial board and incentivization for data sharing; (c) practice of reproducibility by targeted focus issues; and (d) education of current and the next generation of diverse student and faculty community on FAIR principles. The existence of different types of sensors such as physical, chemical, biological, and magnetic (to name a few) and the fact that the sensing field spans multiple disciplines (electrical engineering, mechanical engineering, physics, chemistry, and electrochemistry) call for a generic model for reproducibility. Considering the available metrics, the authors propose eight FAIR metric standards to that transcend disciplines: citation standards, design and analysis transparency, data transparency, analytical methods transparency, research materials transparency, hardware transparency, preregistration of studies, and replication.