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

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，以应对国家重要性的挑战，并将在不久的将来为社会提供有价值的成果。今天，管理我们的生态系统、保护我们的社会和发现新疗法的全球需求与提供解决我们时代最紧迫挑战所需的数据和模型的全球能力之间存在巨大差距。该项目旨在通过将研究人员，政策制定者和行业连接到未来可扩展的生物群落监测网络来弥合这一差距-通过开发统一的生物群落数据集，交叉模型和政策范式，使这些学科能够加速，创新和融合。如果成功，这将导致学科如何研究和管理地球的根本范式转变。它将有助于新一代科学家开发生物群落的预测人工智能模型，并开发基于科学的方法和工具，以制定政策并提供政策意识工具来解决社会规模的挑战。我们期望，对生物群落的深度监测以及由此产生的新科技生态系统将对人类健康、农业、国家安全和生态产生广泛影响。该项目的技术目标已经被仔细地实例化，以便朝着融合的进展对一系列科学问题产生持久的影响。首先，生命科学、工程和政策领域不断面临管理和统一不同生物群落和生态数据集的挑战。这些问题通过汇集独特的深度和最先进的生物群落和生态数据集，确定硬统一问题，并提供统一的参考解决方案来解决。其次，重点是新的统一的基于代理的模型，用于预测蚊子种群，因为蚊子传播的疾病每年已经造成6亿多例人类疾病，对撒哈拉以南非洲的弱势社区产生了不成比例的巨大影响。通过加速开发新的蚊子预测模型--特别是将其推广到其他物种--该项目将为人类健康和大流行病防备做出长期贡献。第三，随着深层生物群落数据呈指数级增长，生命科学将被基因组信息淹没。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.