Collaborative Conference: A Workshop to Explore Data Science in Oceanography

合作会议：探索海洋学数据科学的研讨会

• 批准号: 2038846
• 负责人: Nicholas Record
• 金额: $ 0.74万
• 依托单位: Bigelow Laboratory for Ocean Sciences
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038846&HistoricalAwards=false
• 关键词: Collaborative; Conference; Workshop; Explore; Data

Computational and data science skills have become essential in making scientific discoveries, especially in fields where the integration of diverse sources of observational and modeled data is required to understand the mechanisms and functions of complex systems, such as the Earth’s ocean. However, conventional ocean sciences education focuses predominantly on domain-specific knowledge and often falls short in explicitly incorporating these operational skills into the curriculum. The PIs will plan and host several workshops, a virtual workshop in 2020 and two in-person workshops in 2021, that aim to address this urgent need by combining immersive tutorials on state-of-the-art data science methodologies, peer-learning, and on-site collaborative hack project work in a 5-day intensive workshop. This is a domain-specific adaptation of the “hackweek” model, which has emerged in the computational data science community as a powerful model for educating new users, sharing technical expertise, and building an inclusive and cohesive community. The focus of the workshops is the use of ocean data, and specifically the use of data from the NSF-funded Ocean Observatories Initiative. The workshops, called "OceanHackWeek2020-2021", provide an avenue for expediting the adoption of data science methodologies and computational tools for data-intensive research in the ocean sciences community. These skills are indispensable for modern-day oceanographers to tackle complex scientific questions by harnessing the data revolution. OceanHackWeek differs from traditional academic conferences and one-off hackathons by its immersive curriculum, collaborative learning environment, and a set of carefully crafted participant selection criteria that helps promote diversity and inclusivity. These designs help spur collaborative interdisciplinary ideas and help promote best data science practices in ocean sciences. OceanHackWeek focuses not only on promoting data science literacy but also on building an inclusive and cohesive community of oceanographers for learning and sharing. The organizers encourage and empower participants to bring the skills gained and the sense of community formed during the hackweek back to their own labs and institutions. Beyond the intensive in-person workshop, OceanHackWeek tutorials will be maintained as persistent open online resources to allow anyone to partake and self-learn. By facilitating and catalyzing the growth and change of individuals through grassroot efforts that promote and embrace a diverse community, OceanHackWeek drives cultural change in the field of oceanography.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.

计算和数据科学技能对于建立科学发现至关重要，尤其是在需要观察和建模数据的潜水源以了解复杂系统（例如地球海洋）的机制和功能的领域。但是，传统的海洋科学教育主要集中在特定领域的知识上，并且通常在将这些运营技能纳入课程中的明确效果不足。 PIS将计划和举办几个研讨会，即2020年的虚拟讲习班，并在2021年举办两个面对面的研讨会，旨在通过结合有关最先进的数据科学方法的沉浸式教程，同行学习，以及现场合作的Hack Project Project在为期5天的高密集型研讨会上，以解决这一迫切需求。这是对“ hackweek”模型的特定领域的改编，该模型已在计算数据科学界出现，是一种有力的模型，用于教育新用户，共享技术专长以及建立一个包容性和凝聚力的社区。研讨会的重点是使用海洋数据，特别是使用NSF资助的海洋天文台计划的数据。该研讨会称为“ OceanHackWeek2020-2021”，为在海洋科学社区中采用数据科学方法和计算工具提供了加快数据科学方法和计算工具的途径。这些技能对于现代海洋学家来说是必不可少的，即通过利用数据革命来解决复杂的科学问题。 OceanHackWeek与传统的学术会议和一次性黑客马拉松不同，其沉浸式课程，协作学习环境以及一系列精心制作的参与者选择标准，有助于促进多样性和包容性。这些设计有助于刺激协作跨学科的思想，并有助于促进海洋科学中的最佳数据科学实践。 OceanHackWeek不仅着重于促进数据科学素养，还致力于建立一个具有包容性和凝聚力的海洋学家学习和共享社区。组织者鼓励并授权参与者将获得的技能以及在Hackweek期间形成的社区感带回自己的实验室和机构。除了密集的面对面研讨会外，OceanHackweek教程还将被视为持续的开放在线资源，以允许任何人参加和自学。通过通过促进和拥抱潜水员社区的基层努力来支持和催化个人的成长和变化，Oceanhackweek驱动了海洋学领域的文化变化。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得的支持。