Collaborative Research: Disciplinary Improvements: Creating a FAIROS Materials Research Coordination Network (MaRCN) in the Materials Research Data Alliance

协作研究：学科改进：在材料研究数据联盟中创建 FAIROS 材料研究协调网络 (MaRCN)

• 批准号: 2226414
• 负责人: David Elbert
• 金额: $ 44万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226414&HistoricalAwards=false
• 关键词: Collaborative; Research; Disciplinary; Improvements; Creating

The MaRCN (Materials research coordination network) will advance and coordinate FAIR data and support Open Science materials research nationally and internationally, bridging the fundamental gap between materials data and data-intensive methods including artificial intelligence and machine learning. The project will build on a range of planning and preparatory activities including the MGI (U.S. Materials Genome Initiative) and MaRDA (Materials Research Data Alliance), a community-based network spanning stakeholders in academia, industry, and publishing. By building on these initiatives to propose ways of advancing materials science to allow data to be leveraged in new ways, the MaRCN will enable the acceleration of materials design and deployment. This project will address multiple national research goals by advancing critical applications in renewable energy and sustainability, health, agriculture, energy storage, quantum computing, and advanced manufacturing.MaRCN will organize its activities around four over-arching themes: Data, Models, Training, and Impact. Activities undertaken as part of the Data theme include connecting and mediating the needs of data producers, data users, repository users, and publishers to make data deposition and access in trusted repositories the norm. The Models Theme will advance efforts and build community with a particular focus on the domains of electron microscopy and X-ray science. Training Theme activities will encompass a large number of convenings, including focus groups and workshops to seed and grow the network of educators and practitioners fluent in FAIR principles. The Impacts Theme will integrate and maximize the beneficial outcomes of the other themes and actively seek to harmonize the project’s activities with a large number of intersecting organizations with which the project is connected.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Mathematical and Physical Sciences.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.

MaRCN（材料研究协调网络）将推进和协调 FAIR 数据，支持国内外的开放科学材料研究，弥合材料数据与人工智能和机器学习等数据密集型方法之间的根本差距。 该项目将建立在一系列规划和筹备活动的基础上，包括 MGI（美国材料基因组计划）和 MaRDA（材料研究数据联盟），这是一个涵盖学术界、工业界和出版界利益相关者的社区网络。 通过在这些举措的基础上提出推进材料科学的方法，从而以新的方式利用数据，MaRCN 将加速材料设计和部署。该项目将通过推进可再生能源和可持续发展、健康、农业、能源存储、量子计算和先进制造等领域的关键应用来实现多个国家研究目标。MaRCN 将围绕四个总体主题组织活动：数据、模型、培训和影响。 作为数据主题一部分而开展的活动包括连接和协调数据生产者、数据用户、存储库用户和发布者的需求，以使可信存储库中的数据存储和访问成为常态。 模型主题将推进工作并建立社区，特别关注电子显微镜和 X 射线科学领域。 培训主题活动将包括大量的会议，包括焦点小组和研讨会，以培育和发展精通 FAIR 原则的教育工作者和从业者网络。 影响主题将整合并最大化其他主题的有益成果，并积极寻求协调该项目的活动与与该项目相关的大量交叉组织。该奖项由先进网络基础设施办公室颁发，并得到数学和物理科学理事会的共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。 优点和更广泛的影响审查标准。