The F.A.I.R (Findable, Accessible, Interoperable, and Reusable) principles for data management and Open Science have attracted the focus of the scientific community. Implementation of FAIR in environmental research data is a big challenge for scientists. For example, studies employ various devices and techniques to measure short-term environmental events in high temporal and spatial resolution in order to better understand their effects in the context of long-term environmental trends. However, to accurately interpret these trends, it is essential for scientific partners to reliably compare their sensor systems and their measurement parameters. Additionally, the unconditional comparability of joint measurement campaigns is a key element of collaborative research and joint data analysis.
Studies show that the comparability of measured data even with the same device is a crucial point to evaluate data uncertainty and needs to be taken into consideration, in particular when sharing the data for a joint analysis. Sensor comparison, calibration and baseline measurements are already in use, but the transfer of this information into the data management workflows, in terms of documentation and storage, is often overlooked and not well standardized. The question to be discussed is how and what additional information about the senor in the database can be used to improve the data comparability and joint data analysis routines. The focus of this session lies in (novel) approaches for data reliability validation and metadata tools to overcome this problem. It also considers possible Standard Operation Procedures (SOPs) for the inter-calibration/validation of sensors before/ during/ after joint measurement campaigns.