Data Science Core

数据科学核心

• 批准号: 10302201
• 负责人: Valerio Pascucci
• 金额: $ 8.46万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10302201
• 关键词: Algorithms; Architecture; Back; Big Data; Brain; Catalogs; Collaborations; Collection; Communities; Data; Data Science; Data Science Core; Data Sources; Data Store; Databases; Development; Docking; Dwarfism; Environment; FAIR principles; Face; Foundations; Goals; High Performance Computing; Hybrids; Image; Imaging Device; Individual; Infrastructure; Institution; Intuition; Investigation; Journals; Laboratories; Leg; Libraries; Link; Location; Massachusetts; Modeling; Paper; Performance; Process; Publishing; Reproducibility; Research Activity; Research Personnel; Research Project Grants; Resources; Science; Scientist; Services; Software Tools; Stream; System; Time; Time Management; Universities; Utah; base; brain research; computational platform; computerized data processing; cyber infrastructure; data dissemination; data management; data portal; data reuse; data sharing; data streams; design; digital; digital object identifier; experimental study; federated computing; flexibility; improved; interest; interoperability; parallel computer; portability; tool; virtual; web portal

SUMMARY / ABSTRACT – Data Science Core With the introduction of the Big Data paradigm, scientific investigation has become increasingly dependent on the ability to collect, manage, and process large amounts of data. Unfortunately, the scientific benefits of abundant data sources have often been dwarfed by the inadequacy of the existing data science tools, as scientists spent excessive time managing and processing data instead of focusing on the science. Worse, people would face insurmountable challenges to associate the results published in journal papers with the data and workflows needed to replicate the science. The Data Science Core of the Berghia Brain Project plans to provide the large distributed team of scientists the cyberinfrastructure needed to accomplish their aims without diverting time and energy from the science activities. In particular, each of the five Research Project's activities will require, to varying degrees, easy, intuitive access to a combination of massive storage, imaging devices, cloud resources, and high-performance computing (HPC) platforms to execute scientific workflows in a reliable and repeatable manner. Internal collaborations and external dissemination of data and results will challenge existing solutions, necessitating a tailored cyberinfrastructure for the Berghia Brain Project. The strategy used to develop this cyberinfrastructure relies on the following three complementary capabilities: (i) Create a data management infrastructure that connects all the institutions in a federated data store; (ii) Develop a scalable computing infrastructure that allows processing in a timely manner the massive amounts of data acquired by each project; and (iii) Develop services for interoperability and provenance tracking that enable sharing software tools, science workflows, and versioned data that will allow publishing the BBP findings with the best standards of reproducible science.

摘要/摘要 – 数据科学核心 随着大数据范式的引入，科学研究越来越依赖于 不幸的是，收集、管理和处理大量数据的能力并没有带来科学上的好处。 由于现有数据科学工具的不足，丰富的数据源往往显得相形见绌， 科学家花费过多的时间来管理和处理数据，而不是专注于科学。 将期刊论文中发表的结果与数据和数据联系起来将面临难以克服的挑战 Berghia 大脑项目的数据科学核心计划提供复制科学所需的工作流程。 网络基础设施需要庞大的分布式科学家团队来实现其目标而不转移 特别是，五个研究项目的每一项活动都需要花费时间和精力， 在不同程度上，轻松、直观地访问海量存储、成像设备、云的组合 资源和高性能计算 (HPC) 平台，以可靠且可靠的方式执行科学工作流程 可重复的方式。内部合作和外部传播数据和结果将挑战现有的。 解决方案，需要为 Berghia Brain 项目量身定制网络基础设施用于开发的策略。 该网络基础设施依赖于以下三个互补功能： (i) 创建数据管理 连接联合数据存储中所有机构的基础设施； (ii) 开发可扩展的计算； 能够及时处理每个项目获取的大量数据的基础设施； (iii) 开发互操作性和来源跟踪服务，以实现共享软件工具、科学 工作流程和版本化数据将允许以最佳的可重现标准发布 BBP 研究结果 科学。