Author(s): Tom Loree; Herve Squividant; Zahra Thomas
Linked Author(s):
Keywords: FAIR; EDR; OGC; FAIR; Timeseries
Abstract: This work presents an efficient framework for accessing, using, and sharing large environmental datasets by applying the FAIR principles through the Open Geospatial Consortium (OGC) API Environmental Data Retrieval (EDR) standard. The approach addresses common limitations of hydrometeorological big data, including heterogeneous formats and insufficient metadata, by converting and sharing source datasets into interoperable structures such as Zarr and by enriching them with standardized vocabularies. A Python-based lightweight API EDR enables metadata retrieval and flexible data extraction in multiple formats. The method is demonstrated using France’s SAFRAN gridded meteorological dataset, a 70-year daily archive originally provided as large CSV files. After FAIRification, the dataset is integrated into a spatiotemporal data infrastructure and made accessible through a viewer and standardized service metadata. Usage statistics reveal strong demand for fine-scale, short time-span extractions, highlighting the relevance of efficient subset access. The framework facilitates data reuse, reduces storage and bandwidth requirements for users, and minimizes the effort needed to understand heterogeneous acquisition methods.
Year: 2026