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

物质样本是关于自然界的最基本的数据形式。它们的收集和研究是实现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用于未来的科学几乎是不可能的，因为它们通常是不可查找的、可访问的、可互操作的和可重用的（FAIR）。大量的科学材料样本已经并将继续被收集，但大多数样本仍然是未知的，研究人员无法接近。围绕材料和数据共享的知识、技术、社会、法律和监管问题的复杂格局，往往导致难以逾越的障碍，使其难以公平。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，并结合培训和推广以支持采用。采样自然研究协调网络（RCN）将最大化自然历史价值链，在野外、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；并将结果材料和派生数据存储在不同的分布式数据库和存储库中。通过一系列专题研讨会和定期虚拟活动，采样自然将建立一个合作研究人员社区，以开发可访问的综合材料样本数据语料库的潜力。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，并对其短期和长期治理提出建议；关于材料样品收集、运输、储存、保存和共享的政策建议；涵盖样本识别和元数据最佳做法的培训材料；这是一项创新的社交媒体活动，旨在传达材料样本在科学、社会和可持续性方面的重要性。采样自然汇集了一个新的跨学科pi小组，将吸引来自多个NSF董事，联邦机构，土著群体，博物馆，出版商和基础设施的研究人员。该奖项反映了美国国家科学基金会的法定使命，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。

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.