RCN: Reimagining a Sustainable Data Network to Accelerate Agricultural Research and Discovery

RCN：重新构想可持续数据网络以加速农业研究和发现

• 批准号: 2126334
• 负责人: Leonore Reiser
• 金额: $ 60万
• 依托单位: Phoenix Bioinformatics Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126334&HistoricalAwards=false
• 关键词: RCN; Reimagining; Sustainable; Data; Network

The AgBioData Research Coordination Network (RCN) will accelerate research in agricultural science by increasing the accessibility and reuse of large- scale biological data. In the face of increasing human populations, global climate change and other challenges, there is a critical need to discover new ways to increase agricultural yields while reducing environmental impacts. Such advances in agricultural science are increasingly led by data-driven approaches, which rely on data that are properly stored and made available to the research community through online resources. With NSF support, this RCN will leverage grass roots efforts of the AgBioData Consortium (www.agbiodata.org) to expand the network of database experts and biocurators to include research scientists, funders, and data publishers to establish and disseminate standards, best practices, and recommendations for agricultural data management. The training component will increase participation of a broadly representative constituency, including those traditionally underrepresented in the sciences and junior researchers, and will prepare them to be leaders in agricultural data sciences.The AgBioData RCN will focus on increasing the value of Big Scientific Data through the FAIR (findable, accessible, interoperable, and reusable) model. Annual meetings will bring together multi-disciplinary scientists from all the major U.S. agricultural genomic, genetic, and breeding (GGB) databases and allied resources, accelerating synergistic efforts to make the huge amount of data curated by AgBioData databases FAIR. This will facilitate shortened data processing/curation times, simplified data management, and more standardized data handling between databases, making it easier for researchers to find and use data. The larger network of all data stakeholders will work together to identify and prioritize the most pressing data and metadata standardization needs and to develop and implement processes to solve them. Working groups will be created to focus on key community data issues, including unified nomenclature, metadata standards, data federation, and recommendations for emerging data types. The network will directly support data-generating scientists by developing clearly defined FAIR data management guides with a framework on how to maximize data visibility and data reuse for common types of biological data and a foundational FAIR data management educational curriculum appropriate for academic courses or short training modules. Lastly, a sustainability review and report will provide a roadmap for the future of GGB databases. The AgBioData Consortium is a model for how database managers, researchers, educators, and publishers can work together to be more resource-efficient and how, as central resources for the communities they serve, they can use a collective voice to benefit all scientists, both domestically and abroad.This project is jointly funded by the Plant Genome Research Program of the Division of Integrative Organismal Biology and the Capacity: Cyberinfrastructure program of the Division of Biological Infrastructure in NSF's Biological Sciences Directorate.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.

农业生物数据研究协调网络（RCN）将通过提高大规模生物数据的可访问性和重用性来加速农业科学研究。 面对人口增长、全球气候变化和其他挑战，迫切需要发现新的方法来提高农业产量，同时减少对环境的影响。农业科学的进步越来越多地由数据驱动的方法引领，这些方法依赖于正确存储并通过在线资源向研究界提供的数据。在 NSF 的支持下，该 RCN 将利用 AgBioData 联盟 (www.agbiodata.org) 的基层努力，扩大数据库专家和生物管理者网络，将研究科学家、资助者和数据发布者纳入其中，以建立和传播农业数据管理的标准、最佳实践和建议。培训内容将增加具有广泛代表性的群体的参与，包括传统上在科学领域代表性不足的群体和初级研究人员，并使他们成为农业数据科学领域的领导者。AgBioData RCN 将专注于通过 FAIR（可查找、可访问、可互操作和可重用）模型提高科学大数据的价值。年会将汇集来自美国所有主要农业基因组、遗传和育种 (GGB) 数据库及相关资源的多学科科学家，加速协同努力，使 AgBioData 数据库管理的大量数据变得公平。这将有助于缩短数据处理/管理时间、简化数据管理以及数据库之间更加标准化的数据处理，使研究人员更容易查找和使用数据。所有数据利益相关者组成的更大网络将共同努力，确定最紧迫的数据和元数据标准化需求并确定优先顺序，并开发和实施解决这些需求的流程。将成立工作组，重点关注关键的社区数据问题，包括统一术语、元数据标准、数据联合和新兴数据类型的建议。该网络将通过开发明确定义的 FAIR 数据管理指南，以及如何最大限度地提高常见类型生物数据的数据可见性和数据重用的框架，以及适合学术课程或短期培训模块的基础 FAIR 数据管理教育课程，来直接支持数据生成科学家。最后，可持续性审查和报告将为 GGB 数据库的未来提供路线图。 AgBioData 联盟是数据库管理员、研究人员、教育工作者和出版商如何共同努力以提高资源效率以及如何作为他们所服务的社区的核心资源，如何利用集体声音使国内外所有科学家受益的典范。该项目由综合有机体生物学司的植物基因组研究计划和生物司的能力：网络基础设施计划共同资助 NSF 生物科学理事会的基础设施。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。