WaterDataHub 1.0: A Curated Discovery Resource for AI-Driven Water Research

The increasing availability of publicly accessible data has created new opportunities for data-driven and AI-supported water research. Such data are particularly attractive due to their transparency, cost-effectiveness, and ability to support reproducible scientific studies. Moreover, publicly available datasets offer strong potential for methodological transferability across geographical locations, enabling comparative analyses, model generalization, and knowledge sharing across regions with diverse environmental and socio-economic conditions. In this work, we explore both the benefits and limitations of conducting AI-driven research using publicly available datasets, emphasizing their role in accelerating innovation and enabling broader participation in scientific inquiry. We specifically focus on water-related data, including environmental monitoring datasets that capture various aspects of water quality, availability, and ecosystem dynamics. These datasets typically rely on measurements such as chemical composition, temperature, flow rates, and pollutant levels, often collected through governmental or international monitoring initiatives. We introduce WaterDataHub 1.0, a curated discovery resource for publicly available data sources relevant to water research and artificial intelligence applications. The principal contribution of this work is WaterDataHub 1.0 itself: a centralized, cross-domain, and AI-oriented discovery resource that organizes fragmented water-related data sources within a common framework and facilitates their identification and reuse for data-driven research.