权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CC*Data: National Cyberinfrastructure for Scientific Data Analysis at Scale (SciDAS)

CC*Data：大规模科学数据分析国家网络基础设施 (SciDAS)

基本信息

批准号：
1659300
负责人：
Frank Feltus
金额：
$ 295.22万
依托单位：
Clemson University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-02-15 至 2022-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659300&HistoricalAwards=false
关键词：
CC Data National Cyberinfrastructure Scientific

项目摘要

Scientific discovery is increasingly dependent on huge datasets that require computing at unprecedented scale. Laboratory computers and spreadsheets simply cannot handle the data flowing from modern measurement devices, such as DNA sequencers. Scientific experiments now require understanding of both the underlying science and the cyberinfrastructure (CI) ecosystem to design and execute necessary computations. Fortunately, significant and strategic support from the public and private sectors is creating a distributed computational ecosystem at the national level to help meet the computational demands of large datasets. This project, the Scientific Data Analysis at Scale (SciDAS) is designed to improve flexibility and accessibility to national resources, helping researchers more effectively use a broader array of these resources. SciDAS is developed using large-scale systems biology and hydrology datasets, but is extensible to many other domains.On a technical level, SciDAS federates access to multiple national CI resources including NSF Cloud, Open Science Grid, the Extreme Science and Engineering Discovery Environment (XSEDE v2.0), petascale supercomputers such as COMET, and campus resources. Central to SciDAS is the use of ExoGENI dynamic networked infrastructure to enable Layer-2 connectivity and data movement between these resources and data repositories. SciDAS relies on the integrated-Rule-Oriented-Data-System (iRODS), enhanced with software-defined-networking (SDN) capabilities, to support network-aware data management decisions and efficient use of network resources. The distributed and scalable nature of both the data-sharing and the compute infrastructure are exploited to optimize for computer and data locality, boosting the performance of workflows and scientific productivity. Scientific discovery use cases in systems biology and hydrology will drive cyberinfrastructure development at the petascale level while simultaneously generating useful results for domain scientists.

科学发现越来越依赖于需要以前所未有的规模进行计算的庞大数据集。实验室计算机和电子表格根本无法处理来自现代测量设备（如DNA测序仪）的数据。科学实验现在需要了解基础科学和网络基础设施（CI）生态系统，以设计和执行必要的计算。幸运的是，来自公共和私营部门的重大战略支持正在国家层面创建分布式计算生态系统，以帮助满足大型数据集的计算需求。这个项目，科学数据分析规模（SciDAS）的目的是提高灵活性和可访问性的国家资源，帮助研究人员更有效地利用更广泛的这些资源。 SciDAS使用大规模系统生物学和水文学数据集开发，但可扩展到许多其他领域。在技术层面上，SciDAS联合访问多个国家CI资源，包括NSF Cloud，开放科学网格，极端科学和工程发现环境（XSEDE v2.0），千万亿次超级计算机（如COMET）和校园资源。 SciDAS的核心是使用ExoGENI动态网络基础设施，以实现这些资源和数据存储库之间的第2层连接和数据移动。 SciDAS依赖于集成的面向规则的数据系统（iRODS），增强了软件定义网络（SDN）功能，以支持网络感知数据管理决策和网络资源的有效使用。利用数据共享和计算基础设施的分布式和可扩展性来优化计算机和数据本地性，提高工作流程的性能和科学生产力。系统生物学和水文学的科学发现用例将推动千万亿次级别的网络基础设施发展，同时为领域科学家产生有用的结果。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

GPU Implementation of Pairwise Gaussian Mixture Models for Multi-Modal Gene Co-Expression Networks

DOI：
10.1109/access.2019.2951284
发表时间：
2019-01-01
期刊：
IEEE ACCESS
影响因子：
3.9
作者：
Shealy, Benjamin T.;Burns, Josh J. R.;Ficklin, Stephen P.
通讯作者：
Ficklin, Stephen P.

NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles

DOI：
10.1534/g3.120.401067
发表时间：
2020-09-01
期刊：
G3-GENES GENOMES GENETICS
影响因子：
2.6
作者：
Husain, Benafsh;Hickman, Allison R.;Feltus, F. Alex
通讯作者：
Feltus, F. Alex

Transcriptomic analysis of peripheral leukocytes in dairy cows with and without evidence of metritis and associated early postpartum disease

DOI：
10.15232/aas.2020-02092
发表时间：
2020-12-01
期刊：
APPLIED ANIMAL SCIENCE
影响因子：
1.5
作者：
McConnel, Craig;Crisp, Sierra;Ficklin, Stephen
通讯作者：
Ficklin, Stephen

Exploring Lossy Compression of Gene Expression Matrices

探索基因表达矩阵的有损压缩

DOI：
10.1109/drbsd-549595.2019.00010
发表时间：
2019
期刊：
2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5
影响因子：
0
作者：
McKnight, Coleman B.;Poulos, Alexandra L.;Bender, M. Reed;Calhoun, Jon C.;Feltus, F. Alex
通讯作者：
Feltus, F. Alex

Co-expression networks provide insights into molecular mechanisms of postharvest temperature modulation of apple fruit to reduce superficial scald