EarthCube Domain End-User Workshop: Science-Driven Cyberinfrastructure Needs in Solar-Terrestrial Research; Newark, New Jersey; Spring 2014

这个为期一年的项目的目的是组织一个为期2.5天的EarthCube研讨会，汇集了太阳-地球系统的观测，理论，数据分析，数据驱动的3D建模，数据管理和高性能计算方面的专家。 目标是确定当前的挑战和网络基础设施需求，这些挑战和网络基础设施需求涉及现有和未来的大型多波长数据集的访问、共享、可视化和综合，这些数据集涵盖从太阳表面到地球高层大气的日地观测和建模。 这项活动的智力价值将以两种方式实现。讲习班将向日地界介绍地球立方体框架背后的总体愿景，同时提供日地界对地球立方体举措的共识反馈。 本次研讨会期间确定的科学挑战和相关网络基础设施需求将推动旨在实施网络基础设施解决方案的后续活动。 这些活动将扩大参与大学的计算基础设施，并容纳来自物理学，计算机科学和数学等广泛学科的博士后和研究生参与;因此，这些活动将促进发现和理解，同时促进教学，培训和学习，这将直接影响学生的职业生涯。 这些活动将有助于将创新的数据分析、数据可视化和数据驱动建模技术转移到可能受益于类似框架的其他研究领域。

The purpose of this 1-year project is to organize a 2.5-day EarthCube Workshop that brings together experts in observations, theory, data analysis, data-driven 3D modeling, data management and high performance computing of the Sun-Earth system. The goal is to identify the current challenges and cyberinfrastructure needs related the access, sharing, visualization and synthesis of the existing and future large, multi-wavelength datasets covering solar-terrestrial observations and modeling all the way from the surface of the Sun to the Earth's upper atmosphere. The intellectual merit of this activity will materialize in two ways. The workshop will introduce the solar-terrestrial community to the general vision behind the EarthCube framework while providing consensus feedback from this community to the EarthCube initiative. The scientific challenges and related cyberinfrastructure needs identified during this workshop will drive subsequent activities aiming to implement cyberinfrastructure solutions. These activities will result in expanding the computing infrastructure of the participating universities and accommodate postdoctoral and graduate student involvement from a wide range of disciplines including physics, computer sciences, and mathematics; thus, these activities will advance discovery and understanding while promoting teaching, training and learning, which will directly impact the students' careers. Such activities will facilitate the transfer of innovative data analysis, data visualization, and data-driven modeling techniques to other fields of research that may benefit from a similar framework.