Urban drainage research increasingly relies on monitoring data from sensors, experiments and models, but sharing such data in a reusable and reproducible form remains challenging. The information needed to interpret data is often spread across files, metadata, publications and analysis code. Reproducible research therefore requires not only well-described data, but also links to the code and workflows used to generate published results.INSCRIBE explores how Open Research Data practices can be applied in urban drainage research using existing platforms and standards. The project tests ORD workflows across different collaboration levels, from individual FAIR datasets to shared community data models, in use cases on sewer mixing experiments, urban drainage models and opportunistic rainfall monitoring. The main use case FAIRified sewer tracer experiments from Rieckermann et al. (2005). The data were converted into a common format and published on Zenodo, the analysis code on GitHub, and both were combined in Renku to enable reproducible analysis. A semantic data model was developed to describe the sewer system, experimental setup, monitoring and resulting data, and was later tested and extended with tracer data from another research group. INSCRIBE also investigates knowledge graphs, ontologies and semantic data models to improve data harmonization, discovery and future responsible use of large language models. The project engaged external communities, including IWA groups and OPENSENSE, to develop common urban drainage data models and reproducible workflows for opportunistic rainfall monitoring. Work on urban drainage model benchmarking highlighted limitations related to large model outputs. Results were presented at a data-FAIRifying workshop at the 11th IWA Sewer Processes and Networks conference in Trondheim. One joint peer-reviewed publication on the data model is being prepared for submission to Water Science and Technology, and another one on the FARIfication of the sewer mixing dataset, both by the end of 2027. A broader knowledge-transfer workshop is planned at Eawag on 16 November 2026.