Michela Taufer

Addressing the intertwined goals of sustainability and computational progress requires coordinated multidisciplinary action that spans research, technology development, and governance.

Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here we integrate multi-source Earth observation and environmental datasets and use machine learning to develop a medium-resolution (30 m) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. We subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management.

Data-intensive science and artificial intelligence-enabled workflows now shape research and teaching, yet faculty at primarily undergraduate institutions (PUIs) face tight resources and steep barriers to adopting advanced computing tools. This article reports stewardship lessons drawn from interviews with three PUI faculty who use two National Science Foundation-supported cyberinfrastructure ecosystems: Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support and the National Science Data Fabric, which are relied on to support teaching and research. Across fields spanning physics, computer science, and environmental science, the faculty converged on three practical patterns: 1) lower the barrier to first success with low-friction onboarding and ready-to-run examples, 2) make support human and immediate through visible help channels and rapid triage, and 3) make science engaging by bringing real datasets and problems into student projects. We conclude with a compact stewardship checklist for teams building and operating national-scale computing and data services to make computing practical for everyone.