Translational Data Science Workshop

转化数据科学是一个新兴领域，它将数据科学原理、技术和方法应用于科学、经济和社会挑战，有望产生重要的国家影响。为了理解数据如何被使用和应用于日常生活的许多方面的重要转变，重要的是要在我们多样化的研究环境中有效地应用这些原则和工具，以成功地应对这些挑战，造福人类和社会福利。 本次研讨会将建立在前两次NSF资助的转化数据科学研讨会的基础上，重点讨论参与转化数据科学的挑战;与技术问题相关的挑战;以及与组织和文化变革相关的挑战。 来自工业界、政府、非营利组织和学术界的观点和见解，加上对可操作挑战和可衡量成果的讨论，将有助于推动数据科学领域以及相关领域和应用领域的发展。本次研讨会将为未来的研究、培训和合作提供确定高优先级主题和高影响力机会的机制，其目标是促进与转化数据科学相关的新兴社区的发展;加强现有的联系;并让学术界参与工业和公共部门合作伙伴提供的观点和见解。另一个目标将是讨论和协调关于转化数据科学中最重要的挑战的不同观点，并明确确定指导研究和资助的机会和高度优先领域。 通过召集能够为转化数据科学路线图和生态系统的战略发展做出贡献的多元化利益相关者社区，本次研讨会将建立和加强跨部门的联系。 研讨会的另一个主题是“负责任的数据科学”，探讨如何将负责任地使用数据的基本问题转化为数据科学实践，重点是公共部门的用例。研讨会还将讨论与转化数据科学的概念是否以及如何从根本上改变数据科学的实践和教学方式有关的开放性问题，以及在此背景下可能出现的应用和基础研究以及教学问题。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知识价值和更广泛的影响审查标准进行评估来支持。

Translational data science is an emerging field that applies data science principles, techniques and technologies to scientific, economic, and societal challenges that hold the promise of having an important national impact. In order to understand the important shifts in how data is used and applied to many aspects of daily life, it is important to effectively apply these principles and tools within our diverse research environments to successfully address these challenges for the benefit of human and societal welfare. This workshop will build upon discussions initiated at two previous NSF-funded workshops on translational data science and focus on the challenges of engaging in translational data science; challenges related to technical issues; and challenges related to organizational and cultural change. The perspectives and insights shared from industry, government, nonprofits, and academia, coupled with discussions focused on actionable challenges and measurable outcomes, will serve to advance the field of data science as well as related fields and application areas. This workshop will provide mechanisms for identifying high-priority topics and high-impact opportunities for future research, training, and collaboration.The objectives of this workshop will be to facilitate the growth of the emerging community related to translational data science; strengthen existing connections; and engage the academic community with the perspectives and insights available from industrial and public sector partners. Another objective will be to discuss and align different views about the challenges that are most important in translational data science, and clearly identify opportunities and high priority areas to guide research and funding. By convening a diverse community of stakeholders who can contribute to the strategic development of a translational data science roadmap and ecosystem, this workshop will build and strengthen connections across sectors. Another theme of the workshop will be "responsible data science", to explore how foundational issues in responsible use of data can be translated to data science practice, with a focus here on public sector use cases. The workshop will also discuss open issues related to if and how the notion of translational data science fundamentally changes how data science is practiced and taught, and the applied and basic research and pedagogical issues that may arise in this context.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.