From Sensors to Semantics: Leveraging Ontologies for Recording Sensor Maintenance Metadata in the MOIN4Herbie Project

The collection and use of sensor data are vital for scientists monitoring the Earth's environment. It allows for the evaluation of natural phenomena over time and is essential for validating experiments and simulations. Assessing data quality requires understanding the sensor's state, including operation and maintenance, such as calibration parameters and maintenance schedules. In the HMC project MOIN4Herbie, digital recording of FAIR sensor maintenance metadata is developed using the electronic lab notebook Herbie. In this talk, we will describe the process of configuring Herbie with ontology-based forms for sensor maintenance metadata in our two pilot cases, the Boknis Eck underwater observatory and the Tesperhude research platform. This includes the development of a sensor maintenance ontology and task-specific ontologies tailored for each use case. Ontologies, in information science, are a formalization of concepts, their relations, and properties. They allow for the collection of input that is immediately fit for purpose as findable, machine-readable, and interoperable metadata. By using ontologies, we can ensure the use of controlled vocabularies and organize the knowledge stored within for accessibility and reusability. A further focus will be the translation of maintenance tasks into Shapes Constraint Language (SHACL) documents that can be rendered as forms to the users of Herbie. Herbie relies on these ontologies and vocabularies to generate user-friendly web forms for collecting, validating, and provisioning FAIR (Findable, Accessible, Interoperable, and Reusable) metadata in the form of Resource Desription Framework (RDF) graphs. We developed a module to facilitate metadata integration from sensor registries, such as the O2A Registry. This will allow seamless integration of the sensor maintenance metadata sourced from MOIN4Herbie with the sensor metadata stored in the registries and the data flows of the sensors. This approach ensures that the metadata collected is not only standardized but also easily accessible and usable for future research and applications. MOIN4Herbie is funded 2024-2026 by the Initiative and Networking Fund of the Helmholtz Association (Helmholtz Metadata Collaboration HMC, Project Cohort 2023).