Recommendations for Advancing FAIR and Open Data Standards in the Water Treatment Community

Water treatment researchers will maximize insights from data by implementing FAIR and Open (FAIR/O) principles for data storage and management. Progress toward domain-specific FAIR/O data infrastructure and practices advance data synthesizability, data extensibility, and stakeholder diversity. Embracing FAIR/O data principles in domain research would enable datadriven research breakthroughs that benefit a diverse array of water treatment research stakeholders. These breakthroughs arise from increased data density to improve research scope, better crossmode data translation between experimental and modeling research outputs, and improved multiscale data integration to answer key questions across research scales. This paper addresses the gap between the current state of data sharing and the envisioned potential of FAIR/O data by offering practical steps toward building effective and sustainable water treatment data infrastructure, engaging key stakeholders, and motivating their participation. We clarify essential activities of a water treatment research data repository and suggest opportunities for communicating and growing its value.

Automated summary

This paper discusses the implementation of FAIR/O principles to maximize insights from data in water treatment research, and proposes practical steps and opportunities for building effective and sustainable water treatment data infrastructure.