Access to high-quality data on existing bicycle infrastructure is a requirement for evidence-based bicycle network planning, which can support a green transition of human mobility. However, this requirement is rarely met: Data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality. Currently available tools for road network data quality assessment often fail to account for network topology, spatial heterogeneity, and bicycle-specific data characteristics. To fill these gaps, BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data will be introduced. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell, thus exposing spatial variation in data quality with a focus on network structure and connectivity. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. BikeDNA is based on open-source python libraries and Jupyter notebooks, requires minimal programming knowledge, and supports data quality assessments for a wide range of applications - from urban planning to OpenStreetMap data improvement or transportation network research. In this talk will introduce how to use BikeDNA to evaluate and improve local data sets on bicycle infrastructure, examine what BikeDNA can teach us on the current state of data for active mobility, and discuss the importance of local quality assessments to support increased uptake of open and crowd-sourced data.