How PostgreSQL and QField Cloud can streamline data collection and improve data security: experience from a collaborative, interdisciplinary water reuse project in Flanders, Belgium.

Data is at the heart of every project, and its quality determines the reliability of research outcomes. In ever more collaborative geoscientific research, where data comes from many sources and in various formats, the right tools must be used to ensure data integrity and seamless access for all involved.

Since 2019, in the project Grow, we routinely monitored groundwater quality and levels within an agricultural field where water is reused for irrigation and groundwater recharge. With multiple participants and analysis factors and fine temporal resolution of the monitoring, problems were encountered with a large volume of unstructured data with poor version control. Standard tools for collaborative research, such as cloud-based Excel spreadsheets, proved ineffective and threatened data integrity. A more robust data management system was urgently needed.

Here we provide an overview of a framework based on a popular, open-source PostgreSQL relational database management system deployed in Amazon Web Services that helps to overcome data management issues in groundwater monitoring projects. Among main features are user-based, minimum privilege access which protects the data from, e.g., accidental deletions, and a hardcoded set of data correctness checks decreasing the likeliness of data input errors. Field data is collected using QField Cloud mobile application running a preconfigured QGIS project, sending the data directly to the database. Users also have access to all historical records, helping them detect anomalies on the spot. Laboratory analysis results and data from automatic data loggers without Internet of Things (IoT) modules are processed and uploaded to the database using custom-developed, open-source Python software, providing full transparency. Several IoT devices upload data directly to the database.

So far, the new management system has proved a far superior platform for collaborative data analysis compared to existing tools. It significantly improved fieldwork efficiency and provided assurance of the data quality by improving the collection and handling process transparency. Thanks to the hard work of the QGIS and QField communities, as well as the developers and maintainers of PostgreSQL, we are better equipped for the future of geodata analysis.