Computational Infrastructure for Brain Research: EAGER: BrainLab CI: Collaborative, Community Experiments with Data-Quality Controls through Continuous Integration

脑研究的计算基础设施：EAGER：BrainLab CI：通过持续集成进行数据质量控制的协作社区实验

• 批准号: 1649880
• 负责人: Randal Burns
• 金额: $ 29.46万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649880&HistoricalAwards=false
• 关键词: Computational; Infrastructure; Brain; Research; EAGER

The brain research community needs to increase the practice of sharing and combining data sets to increase the power of statistical analyses and to gain the most knowledge from collected data. This project aims to build a prototype system called BrainLab CI that will facilitate meaningful integration of thousands of publicly available Magnetic Resonance Imaging (MRI) and neurophysiology data sets, and allow researchers to define and conduct new large-scale community-level experiments on these data. BrainLab CI has the potential to transform research practice in neuroscience by overcoming major obstacles to data sharing: Scientists will be able to share data without losing control over data quality, and will maintain full visibility into how all subsequent experiments use their data and algorithms. This project may consequently drive a change in scientific culture by encouraging data sharing and the development of common analysis tools, and resulting accelerated discovery from connecting ideas, tools, data, and people. This project therefore aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare. The BrainLab CI prototype system will provide new paradigms for combining different analytic methods, meta-analysis with raw data, comparing the results of different laboratories and even synthesizing new experiments by combining different studies. An experimental-management software system will be deployed that allows users to construct community-wide experiments that implement data and metadata controls on the inclusion and exclusion of data. Example of controls include: requiring specific metadata, that data are registered to a given atlas, or that data are collected using specific experimentation protocols. BrainLab CI will initially focus on two different experimental patterns: (1) An incremental experiment defines an experiment against an existing data set which then opens to additional community contributions of data; and (2) a derived experiment forks/branches an existing experiment, allowing a researcher to change properties, such as an acceptance criteria or analysis algorithm, but otherwise run the same pipeline against the same inputs. The system will allow each experiment to maintain online dashboards showing how additional data changes results with complete provenance. To develop and validate the BrainLab CI prototype, several community experiments will be developed for MRI and for neurophysiology (including both optical and electrical physiology) data. These research domains were chosen because of the great potential gains for increased sharing of laboratory data in these domains. This Early-concept Grants for Exploratory Research (EAGER) award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the SBE Division of Behavioral and Cognitive Sciences, with funds associated with the NSF Understanding the Brain activity including for developing national research infrastructure for neuroscience, and alignment with NSF objectives under the National Strategic Computing Initiative.

大脑研究界需要增加共享和合并数据集的做法，以提高统计分析的能力，并从收集的数据中获得最多的知识。该项目旨在构建一个名为BrainLab CI的原型系统，该系统将促进数千个公开可用的磁共振成像（MRI）和神经生理学数据集的有意义的整合，并允许研究人员在这些数据上定义和进行新的大规模社区级实验。BrainLab CI有可能通过克服数据共享的主要障碍来改变神经科学的研究实践：科学家将能够在不失去对数据质量控制的情况下共享数据，并将保持对所有后续实验如何使用其数据和算法的完全可见性。因此，该项目可能会通过鼓励数据共享和开发通用分析工具来推动科学文化的变革，并通过连接思想，工具，数据和人员来加速发现。因此，该项目符合NSF的使命，以促进科学的进步和推进国家的健康，繁荣和福利。BrainLab CI原型系统将为结合不同的分析方法、原始数据的荟萃分析、比较不同实验室的结果，甚至通过结合不同的研究来合成新的实验提供新的范例。将部署一个实验管理软件系统，使用户能够构建全社区范围的实验，对数据的纳入和排除实施数据和元数据控制。控件示例包括：需要特定的元数据，将数据登记到给定的图谱中，或者使用特定的实验协议收集数据。 BrainLab CI最初将专注于两种不同的实验模式：（1）增量实验定义了针对现有数据集的实验，然后向其他社区贡献的数据开放;（2）衍生实验分叉/分支现有实验，允许研究人员更改属性，例如验收标准或分析算法，但在其他方面针对相同的输入运行相同的管道。该系统将允许每个实验维护在线仪表板，显示额外的数据如何改变具有完整出处的结果。为了开发和验证BrainLab CI原型，将为MRI和神经生理学（包括光学和电生理学）数据开发几个社区实验。之所以选择这些研究领域，是因为在这些领域增加实验室数据共享具有巨大的潜在收益。CISE高级网络基础设施分部的探索性研究（EAGER）早期概念赠款奖由SBE行为和认知科学分部共同支持，与NSF理解大脑活动相关的资金，包括开发神经科学的国家研究基础设施，并与国家战略计算计划下的NSF目标保持一致。

项目成果

Forest Packing: Fast Parallel, Decision Forests

森林包装：快速并行、决策森林

• DOI: 10.1137/1.9781611975673.6
• 发表时间: 2019
• 期刊: Proceedings of the 2019 SIAM International Conference on Data Mining
• 作者: James Browne, Disa Mhembere

FlashR: parallelize and scale R for machine learning using SSDs

FlashR：使用 SSD 并行化和扩展 R 以进行机器学习

• DOI: 10.1145/3200691.3178501
• 发表时间: 2018
• 期刊: ACM SIGPLAN Notices
• 作者: Zheng, Da;Mhembere, Disa;Vogelstein, Joshua T.;Priebe, Carey E.;Burns, Randal
• 通讯作者: Burns, Randal