An open-source quality control pipeline for SeaExplorer glider data: a case study in La Palma, Canary Islands

Autonomous underwater gliders are pivotal for sustained ocean observations; however, maximizing effective data utilization requires highly standardized, end-to-end data processing workflows. Despite the availability of generic glider toolboxes, to our knowledge, no published open-source workflow currently provides an end-to-end, SeaExplorer-specific processing chain from vendor raw files to CF-compliant, quality-controlled outputs. Here, we present a Python-based pipeline that standardizes post-mission raw SeaExplorer data, applies automated Quality Control (QC) tests, and generates both diagnostic and user-oriented products designed for reproducible scientific use. This framework is designed to automate, under expert supervision, the entire processing chain, from raw data ingestion to the generation of CF-compliant NetCDF files. The proposed workflow implements a QC approach, adhering to the principle of data preservation over elimination, ensuring that original data are preserved for future scientific re-evaluation. The framework applies a suite of fourteen QC tests and produces two distinct outputs: a granular diagnostic file preserving detailed per-test flags for technical validation, and an aggregated file providing a single quality indicator per variable for immediate scientific use. The pipeline was demonstrated using data from two SeaExplorer missions conducted in La Palma (Canary Islands). Designed according to FAIR (Findable, Accessible, Interoperable, Reusable) principles, this tool supports reproducible data management and facilitates the integration of operational engineering data into scientific workflows.