Workshop: Extending Open Access By Bridging the Data Steward Gap: GO FAIR Train-the-Trainer (T3)

研讨会：通过弥合数据管理员差距来扩展开放获取：GO FAIR 培训师培训 (T3)

• 批准号: 1937953
• 负责人: Christine Kirkpatrick
• 金额: $ 5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937953&HistoricalAwards=false
• 关键词: Workshop; Extending; Open; Access; Bridging

There are national calls for improved production of research data that are FAIR (Findable, Accessible, Interoperable, and Reusable) in order to increase scientific discovery and enhance the societal and economic benefit of federal research and development (R&D) funding. To stimulate growth of such a FAIR data ecosystem in the U.S., the concrete action taken by this project is to increase expert knowledge of FAIR-oriented tools and techniques, thereby creating new capacity for increasing the value of new and existing data sets. Developing a community of practice for making data FAIR and increasing the network of available expert trainers available will serve to accelerate awareness and build new knowledge in application of FAIR techniques in the U.S. By means of a two-and-a-half-day intensive training session, the project will bring together practitioners and consultants from academia, non-profits, and industry. The intended audience for the course is seasoned data stewards and researchers, research computing staff with depth in data-driven research, and library and informaticists. The trained teachers will be prepared to teach researchers, data experts, and research library staff at universities and research institutions who are dealing with the ever-growing complexity of data integration.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.

为了增加科学发现和提高联邦研究与开发（R D）资金的社会和经济效益，国家呼吁改进FAIR（可查找、可解释、可互操作和可重用）研究数据的生产。为了刺激美国公平数据生态系统的增长，该项目采取的具体行动是增加面向FAIR的工具和技术的专业知识，从而为增加新的和现有的数据集的价值创造新的能力。开发一个使数据公平的实践社区，并增加可用的专家培训师网络，将有助于加速认识和建立新的知识，在美国通过两天半的强化培训课程，该项目将汇集来自学术界，非营利组织和行业的从业者和顾问。 该课程的目标受众是经验丰富的数据管理员和研究人员，在数据驱动研究方面有深度的研究计算人员，以及图书馆和信息学家。经过培训的教师将为大学和研究机构的研究人员、数据专家和研究图书馆工作人员提供培训，帮助他们处理日益复杂的数据集成问题。该奖项反映了NSF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。