NSF Convergence Accelerator Track D: Deep Monitoring of the Biome Will Converge Life Sciences, Policy, and Engineering

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. Today there is a huge gap between the global need to manage our ecosystems, protect our societies, and discover new therapeutics – and the global capacity to deliver the data and models needed to solve the most pressing challenges of our time. This project is intended to bridge that gap by connecting researchers, policy makers, and industries to the scalable biome monitoring networks of the future – by developing the unified biome datasets, cross-cutting models, and policy paradigms that will empower these disciplines to accelerate, innovate, and converge. If successful, this would lead to a fundamental paradigm shift in how disciplines study and manage the planet. It will contribute to the advent of a new generation of scientists developing predictive AI models of the biome, and to developing science-based methods and tools for shaping policies and delivering policy-aware tools to solve societal-scale challenges. We expect that deep monitoring of biome and the new science and technology ecosystem emerging from it will have wide impact on human health, agriculture, national security, and ecology.

The technical goals of this project have been carefully instantiated so that progress towards convergence makes a lasting impact on a range of scientific problems. First, the life sciences, engineering, and policy domains continually face the challenge of managing and unifying disparate biome and ecological datasets. These issues are addressed head on by bringing together uniquely deep and state-of-the-art biome and ecological data sets, identifying the hard unification problems, and providing a reference solution to unification. Second, there is a focus is on new unified agent-based models for predicting mosquito populations, as mosquito-borne diseases already account for over 600 million cases of human disease per year, with a disproportionately large impact on disadvantaged communities in sub-Saharan-Africa. By accelerating the development of new predictive mosquito models – especially by generalizing them to additional species – this project will provide long lasting contributions to human health and pandemic preparedness. Third, as deep biome data exponentially scales, the life sciences will become overwhelmed with genomic information. Convergence must lead to new methods to efficiently harness these data and autonomously derive insights.