RCN: Sampling Nature: A Network to Enhance the Natural History Value Chain for Sustainability Science

RCN：自然采样：增强可持续科学自然历史价值链的网络

• 批准号: 2129268
• 负责人: Kerstin Lehnert
• 金额: $ 50万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129268&HistoricalAwards=false
• 关键词: RCN; Sampling; Nature; Network; Enhance

Material samples are the most basic form of data about the natural world. Their collection and study are fundamental for solving sustainability goals. Unfortunately, harnessing existing samples and sample-based data for future science is nearly impossible, because they are often not Findable, Accessible, Interoperable, and Reusable (FAIR). A vast number of scientific material samples has already and continues to be collected, yet most samples remain unknown and inaccessible to researchers at large. The complex landscape of intellectual, technical, social, legal, and regulatory issues surrounding material and data sharing often results in insurmountable barriers to making them FAIR. Overcoming these barriers demands not only robust transdisciplinary infrastructure, but also community-driven metadata standards and effective data policies, combined with training and outreach to support adoption. The Sampling Nature Research Coordination Network (RCN) will maximize the natural history value chain that samples nature at field sites, labs, repositories, and museums; processes those samples for scientific analyses; and stores the resulting materials and derived data in diverse and distributed databases and repositories. Through a series of thematic workshops and regular virtual events, Sampling Nature will build a community of collaborative researchers to exploit the potential of an accessible, integrated corpus of material sample data. The RCN will deliver an extensible, domain neutral, community-driven metadata standard, with recommendations for its short- and long-term governance; policy recommendations around the collection, transport, storage, preservation, and sharing of material samples; training materials covering best practices for sample identification and metadata; an innovative social media campaign to communicate the importance of material samples in science, society, and sustainability. Sampling Nature brings together a new and interdisciplinary group of PIs that will engage researchers from multiple NSF directorates, federal agencies, Indigenous groups, museums, publishers, and infrastructure.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.

物质样本是自然界最基本的数据形式。它们的收集和研究是解决可持续发展目标的基础。不幸的是，利用现有的样本和基于样本的数据来进行未来的科学研究几乎是不可能的，因为它们通常是不可查找的、不可解释的、不可互操作的和可重用的（FAIR）。已经并继续收集了大量的科学材料样本，但大多数样本仍然不为广大研究人员所知，无法获得。围绕材料和数据共享的知识、技术、社会、法律的和监管问题的复杂局面往往导致难以克服的障碍，使它们变得公平。克服这些障碍不仅需要强大的跨学科基础设施，还需要社区驱动的元数据标准和有效的数据政策，以及支持采用的培训和推广。采样自然研究协调网络（RCN）将最大限度地发挥自然历史价值链的作用，在实地、实验室、储存库和博物馆对自然进行采样;处理这些样本进行科学分析;并将所得材料和衍生数据存储在各种分布式数据库和储存库中。通过一系列专题研讨会和定期虚拟活动，Sampling Nature将建立一个协作研究人员社区，以利用可访问的综合材料样本数据库的潜力。RCN将提供一个可扩展的、领域中立的、社区驱动的元数据标准，并就其短期和长期治理提出建议;围绕物质样本的收集、运输、储存、保存和共享提出政策建议;提供涵盖样本识别和元数据最佳做法的培训材料;一个创新的社交媒体活动，以传达材料样品在科学，社会和可持续发展中的重要性。Sampling Nature汇集了一个新的跨学科的PI组，将吸引来自多个NSF董事会，联邦机构，土著团体，博物馆，出版商和基础设施的研究人员。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。