Big Data for Discovery Science

发现科学的大数据

• 批准号: 8935807
• 负责人: ARTHUR W TOGA
• 金额: $ 284.14万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2018-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8935807
• 关键词: Address; Big Data; Biological Process; Biology; Biomedical Research; Cells; Collection; Communication; Communities; Computational algorithm; Computer software; Computers; Coupled; Data; Data Collection; Data Set; Documentation; Educational workshop; Elements; Fellowship Program; Generations; Genes; Genetic; Goals; Heterogeneity; Image; Imagery; Individual; Informatics; Information Resources Management; Instruction; Joints; Knowledge; Knowledge Discovery; Laboratories; Lead; Leadership; Methods; Mining; Modeling; Monitor; Neurosciences; Ontology; Organ; Phenotype; Process; Protocols documentation; Recording of previous events; Research; Research Personnel; Research Project Grants; Resolution; Resource Sharing; Role; Science; Scientist; Series; Site; Solutions; Source; Speed; System; Technology; Thirst; Training; Training Activity; Training Programs; Training Support; Transcend; Translating; Universities; Validation; Visit; biological systems; cloud based; computer science; computerized data processing; computing resources; data acquisition; data management; design; experience; innovation; next generation; novel; programs; public health relevance; systems research; technological innovation; tool; web site

DESCRIPTION (provided by applicant): Modern biomedical data acquisition, from genes to cells to systems, is producing exponentially more data due to increases in the speed and resolution of data acquisition methods. Yet, "big data" is a moving target. What is considered big data today, will be relatively "small data" tomorrow. Moreover, singularly large data sets arise from the efforts of single laboratories or are accumulated from a collection of more modest studies across common or heterogeneous study protocols. Simply having large-scale biomedical data and making it available online, however, is not a means to an end but only the next step in turning data into actionable knowledge. Our Big Data for Discovery Science (BDDS) Center, has the following aims: 1) create a user-focused graphical system to dynamically create, modify, manage and manipulate multiple collections of big datasets, 2) enrich next generation "Big Data" workflow technologies coupled to modern computation and communication strategies specifically designed for large-scale biomedical datasets, 3) develop a knowledge discovery interface to enable modeling, visualizing, and the interactive exploration of Big Data. In addition to these overarching aims, the goals of this BDDS Center include training and consortium activities. Here we will create university-level degree programs in big data informatics, develop annual workshops on strategies for big data best practices, and contribute to national BD2K consortium efforts. The innovations of our BDDS Center include: 1) providing a novel data science framework for characterizing and big data as a shared resource either singularly or collectively, 2) deriving novel computer algorithms for the joint processing o multi-modal data with an emphasis on the challenges that big data present for computation, 3) designing and deploying a unique data management system focused on the user experience which is ontology agnostic, easy to use, and puts the data first, 4) providing enhanced technologies for remote data access, scientific workflow construction, and cloud-based computation on big data sets, 5) providing compelling means for big data set visualization, interaction, and hypothesis generation. Building on these technologies, we will construct and validate tools so that they may be translated to any biological system or biomedical research domain. Our team is comprised of leading neuroscience, biology, and computer science researchers, with expertise in large-scale biomedical data, experience with the present challenges and promise of big data, and a demonstrable history of delivering unique computational resources, thereby insuring big data solutions which promote a "science of discovery".

描述（由申请人提供）： 现代生物医学数据采集，从基因到细胞到系统，由于数据采集方法的速度和分辨率的提高，正在产生呈指数级增长的数据。然而，“大数据”是一个移动的目标。今天被认为是大数据的东西，明天将是相对“小数据”。此外，非常大的数据集来自单个实验室的努力，或者是从跨共同或异质研究方案的更适度研究的集合中积累的。然而，仅仅拥有大规模的生物医学数据并将其在线提供并不是达到目的的手段，而只是将数据转化为可操作知识的下一步。 我们的大数据发现科学（BDDS）中心，有以下目标：1）创建以用户为中心的图形系统，以动态地创建、修改、管理和操纵大数据集的多个集合，2）丰富下一代“大数据”工作流技术，该技术与专门为大规模生物医学数据集设计的现代计算和通信策略相结合，3）开发知识发现接口，以实现大数据的建模、可视化和交互式探索。除了这些总体目标外，该BDDS中心的目标还包括培训和联合会活动。在这里，我们将创建大数据信息学的大学学位课程，开发关于大数据最佳实践战略的年度研讨会，并为国家BD2K联盟的努力做出贡献。我们BDDS中心的创新包括：1）提供一种新颖的数据科学框架，用于单独或共同地将大数据表征为共享资源，2）导出用于联合处理多模态数据的新颖的计算机算法，重点是大数据对计算提出的挑战，3）设计和部署专注于用户体验的独特数据管理系统，该系统是本体不可知的、易于使用的，并且将数据放在首位，4）提供用于远程数据访问的增强技术，科学的工作流程构建，以及基于云的大数据集计算，5）为大数据集可视化，交互和假设生成提供引人注目的手段。在这些技术的基础上，我们将构建和验证工具，以便它们可以被翻译到任何生物系统或生物医学研究领域。我们的团队由领先的神经科学、生物学和计算机科学研究人员组成，他们拥有大规模生物医学数据方面的专业知识，对大数据目前的挑战和前景有着丰富的经验，并且在提供独特的计算资源方面有着可证明的历史，从而确保大数据解决方案能够促进“发现科学”。