Elements: Science-i Cyberinfrastructure for Forest Ecosystem Research

森林在整个历史上一直维持着食物、水、能源安全和人类福祉，现在正日益受到气候变化、生物多样性丧失、森林砍伐和森林退化的威胁。合作森林研究是应对这些全球挑战的核心，需要从世界各地收集的大量森林调查数据、高性能计算设施以及国际和跨学科专业知识，以提供以证据为基础的森林养护和恢复做法。然而，缺乏研究数据、计算能力和制定研究问题的专家支持是开展森林合作研究的主要障碍，特别是对来自代表性不足社区的研究科学家而言。为了克服这一障碍，该项目创建了一个具有定制数据治理框架的网络基础设施“Science-I”，围绕该框架，数据贡献者、研究人员和社区(包括土著利益相关者)在一个安全、可查找、可访问、可互操作和可重复使用的平台上连接起来，共同产生知识，以拯救世界森林生态系统。Science-I利用来自57个国家的343名注册研究科学家组成的日益壮大的社区，他们将贡献数据和专业知识，支持合作森林研究、气候适应规划和社区决策。Science-I还通过一个框架实现高价值的科学查询，该框架将来自当地数据贡献者的森林调查数据与不同级别的数据共享限制相结合，并允许未得到充分代表的研究人员共享使用先进的分析工具、研究代码和高性能计算资源。该项目将美洲原住民的经验和专业知识融入到全球一致和与当地相关的知识的联合生产中。该项目的目标是开发Science-I网络基础设施，为数据驱动的协作森林研究提供必要和及时的支持，以解决拯救世界森林生态系统的核心科学问题。Science-I基于一个创新的动态数据治理框架，该框架通过动态策略实施实现多源数据管理。该框架是为应对合作森林研究中的数据共享挑战而定制的，它支持并整合了多个数据共享策略，这些策略由本地原始数据集的数据贡献者在动态系统中预定义，以便在整个数据生命周期中执行这些策略。Science-I收集来自世界各地的地方原始森林调查数据集，并将其整合到不同保密级别的全球数据集中，以便为一些正在进行的研究项目使用。整个团队的合作得到了一个安全的项目沙箱的支持，该沙箱使项目成员能够利用机器学习工具包和内置的社区协作功能来共同产生全球一致和与当地相关的知识，这些知识将促进我们对全球森林系统生态过程的理解，并阐明识别和解释陆地生物多样性及其随空间和时间与环境相互作用的基本原则。该项目通过各种知识联合制作、教育和推广活动使社区参与进来，并支持由女性PI、研究生、博士后和/或其他职业生涯早期研究人员领导的12个科学-I研究项目。Science-I还通过协作外展活动，例如与联合国粮食及农业组织(粮农组织)共同主办的年度全球网络研讨会，让不同的受众和社区参与进来。科学一号的几个研究项目旨在促进农村和土著社区适应气候变化的森林管理和养护。在全国印第安人碳联盟的协调下，美洲原住民利益攸关方将在基于研究结果的共同制定政策和实用指导方针方面发挥主导作用。将组织两个虚拟研讨会和两个面对面研讨会，以吸引用户、利益相关者和开发人员，其中包括来自代表不足的社区的25名参与者，他们将支持参加面对面的研讨会。该奖项由NSF高级网络基础设施办公室获得，由NSF生物基础设施部门(BIO/DBI)联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Forests, which have been sustaining food, water, energy security, and human well-being throughout history, are increasingly threatened by climate change, biodiversity loss, deforestation, and forest degradation. Central to tackling these global challenges, collaborative forest research requires massive forest inventory data collected from around the world, high-performance computing facilities, as well as international and interdisciplinary expertise to provide evidence-based forest conservation and restoration practices. However, a lack of research data, computing capacity, and expert support for framing research questions poses a major obstacle to collaborative forest research, especially for research scientists from under-represented communities. To overcome this obstacle, this project creates a cyberinfrastructure “Science-i” with a customized data governance framework around which data contributors, researchers, and communities including indigenous stakeholders are connected in a secure, findable, accessible, interoperable, and reusable platform to co-produce knowledge for saving the world’s forest ecosystems. Science-i leverages a growing community of 343 registered research scientists from 57 countries who will contribute data and expertise to support collaborative forest research, climate adaptation planning, and community decision-making. Science-i also enables high-value scientific inquiries with a framework that integrates forest inventory data from local data contributors with various levels of data-sharing restrictions, and allows shared use of advanced analysis tools, research codes, and high-performance computing resources by the under-represented researchers. The project incorporates Native American experience and expertise into the co-production of globally consistent and locally relevant knowledge.The objective of this project is to develop Science-i cyberinfrastructure to provide essential and timely support for data-driven collaborative forest research that addresses scientific questions central to saving the world’s forest ecosystems. Science-i is based on an innovative dynamic data governance framework that enables multi-source data management with dynamic policy enforcement. Customized to address the data-sharing challenges in collaborative forest research, this framework supports and incorporates multiple data-sharing policies, pre-defined by data contributors of local raw datasets, in a dynamic system so that these policies are enforced throughout the data lifecycle. Science-i collects local raw forest inventory datasets from all over the world, and integrates them into global datasets of different confidentiality levels so that they can be utilized by a number of ongoing research projects. The team- wide collaboration is supported by a secure project sandbox that enables project members to utilize a machine learning toolkit and built-in community collaboration functions to co-produce globally consistent and locally relevant knowledge that will advance our understanding of the ecological processes of global forest systems, and elucidate fundamental principles that identify and explain terrestrial biodiversity and its interactions with the environment over space and time. This project engages communities with various knowledge co-production, education, and outreach activities, and supports twelve research projects in Science-i led by female PIs, graduate students, postdocs, and/or other early-career researchers. Science-i also engages diverse audiences and communities through collaborative outreaching events such as annual global webinars co-hosted with the Food and Agriculture Organization of the United Nations (FAO). Several Science-i research projects are aimed at promoting climate-resilient forest management and conservation among rural and indigenous communities. Native American stakeholders, coordinated by the National Indian Carbon Coalition will take a leading role in co-producing policies and practical guidelines based on the research results. Two virtual and two in-person workshops will be organized to engage users, stakeholders, and developers, including 25 participants from under-represented communities who will besupported to attend in-person workshops.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the NSF Division of Biological Infrastructure (BIO/DBI).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.