RCN: Building a Sediment Experimentalist Network (SEN)

当泥沙在山体滑坡、洪水和飓风等事件中穿过山坡、河流和三角洲时，我们的动态景观会演变成一条小路。在自然界，这些过程通常发生在巨大的长度和时间尺度上，使得直接研究它们变得困难。实验室沉积实验能够改进对这些地球表面过程的控制和观察。然而，这样的调查往往是孤立进行的，我们的科学界几乎没有任何协调。我们将成立沉积物实验者网络(SEN)，以帮助整合沉积物实验者的努力，并建立一个知识库，指导数据收集和管理的最佳做法。我们还将促进跨机构合作实验，并就基于沉积物的实验的数据和元数据标准与研究社区进行沟通和教育。这一努力将提高野外地质学家、模型师和实验学家的沉积研究的效率和透明度。SEN的主要成果将是1)创建一个知识库(SEN-KB)，2)协调实验合作实验室(SEN-EC)，以及3)将教育努力和数据标准开发(SEN-ED)与传播新技术和方法的工具相结合。SEN-KB将是一个在线资源的集合，用于管理和发现实验数据、元数据、分析工具、方法和其他用户驱动的需求。SEN-EC将试点基础设施，以促进多个实验室在实验方面的合作，以解决单一实验室难以解决的广泛和跨学科的重大挑战：1)将实验外推到自然系统和理论，2)来自不同设施的实验结果的可比性，以及3)实验中观察到的外部和内部过程的分离。教育局局长将通过收集和分享实验数据的讲习班和外联活动，为数据管理提供培训。该项目将促进实验者社区内的广泛用户以及建模人员和野外地质学家访问和使用新的和现有的数据。

Our dynamic landscape evolves as sediment travels a path through mountain slopes, rivers, and deltas in events like landslides, floods, and hurricanes. In nature, these processes typically occur over vast length and time scales, making them difficult to study directly. Laboratory sedimentary experiments enable improved control and observation of these Earth-surface processes. However, such investigations often occur in isolation, and there is little if any coordination for our scientific community. We will form a Sediment Experimentalist Network (SEN) to help integrate the efforts of sediment experimentalists and build a knowledge base for guidance on best practices for data collection and management. We will also facilitate cross-institutional collaborative experiments, and communicate with and educate the research community about data and metadata standards for sediment-based experiments. This effort will improve the efficiency and transparency of sedimentary research for field geologists and modelers as well as experimentalists.Major outcomes from SEN will be 1) creation of a Knowledge Base (SEN-KB), 2) coordination of Experimental Collaboratories (SEN-EC), and 3) integration of Educational efforts and Data standards development (SEN-ED) with tools for propagating new technology and methods. SEN-KB will be a collection of online resources for management and discovery of experimental data, metadata, analysis tools, methodologies, and other user-driven needs. SEN-EC will pilot infrastructure to foster multi-laboratory collaborations on experiments addressing broad and interdisciplinary grand challenges that are difficult to solve in a single laboratory: 1) extrapolation of experiments to natural systems and theory, 2) comparability of experimental results from disparate facilities, and 3) decoupling of external versus intrinsic processes observed in experiments. SEN-ED will provide training for data management through workshops and outreach for collecting and sharing experimental data. The project will facilitate access and use of new and existing data for a wide range of users in the experimentalist community and beyond toward modelers and field geologists.