Data Science Core

数据科学核心

• 批准号: 9983203
• 负责人: Rajendra Bose
• 金额: $ 43.69万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983203
• 关键词: Address; Algorithmic Software; Algorithms; Archives; Bayesian Modeling; Behavior; Big Data; Biological; Brain; Cloud Computing; Code; Collaborations; Communities; Complex; Computer software; Computers; Core Facility; Custom; Data; Data Analyses; Data Engineering; Data Science; Data Science Core; Data Set; Data Storage and Retrieval; Development; Dimensions; Documentation; Ecosystem; Educational process of instructing; Emerging Technologies; Engineering; Ensure; Experimental Designs; High Performance Computing; Image; Imaging Device; Individual; Information Retrieval; Infrastructure; Institutes; Laboratories; Libraries; Link; Location; Machine Learning; Measures; Metadata; Methodology; Methods; Mind; Modeling; Modernization; Motor Pathways; Mus; Nature; Network Infrastructure; Neural Network Simulation; Neurosciences; Neurosciences Research; Outcome; Output; Performance; Postdoctoral Fellow; Process; Protocols documentation; Publications; Research; Research Personnel; Resources; Risk; Secure; Services; Speed; Standardization; Statistical Data Interpretation; Statistical Methods; Supervision; System; Techniques; Technology; Testing; Time Series Analysis; Training; Training Support; algorithm development; awake; brain behavior; cloud based; cluster computing; computer infrastructure; cost; data analysis pipeline; data archive; data curation; data format; data management; data modeling; data pipeline; data sharing; density; design; dissemination research; experience; experimental study; faculty support; graduate student; high risk; improved; indexing; instrumentation; member; method development; model building; model development; motor control; novel; pedagogical content; power analysis; programs; prototype; relating to nervous system; skills; statistics; success; virtual

Abstract (30 lines) As we seek to unlock how the brain generates behavior, measuring what the brain is doing as we observe the behaviors it generates, and building models that might mimic the process are critical. As tools for imaging and recording brain activity and behavior improve, and the complexity of our models and computations increase, so does the density and diversity of our measured datasets. Handling these much data is an intellectual challenge in itself, while arguably even more data will be needed if we are to understand the relation between brain and behavior. The projects within the U19 span a wide range of different approaches to dissect the motor pathway in the awake, behaving mouse. The methodologies to be used, supported by our Advanced Imaging and Instrumentation core, are almost uniformly prototype systems, not available off the shelf, and therefore will provide both unprecedented data, and present new challenges for standardization, analysis and information extraction. To support these activities, our data science resource core will draw upon the expertise of an exceptional interdisciplinary team with expertise in computer infrastructure, analysis and modeling, data science, statistics, machine learning and the teaching and training of data science methods. With this team, we hope to build a model data pipeline that is scalable, robust and capable of addressing immediate needs and deficiencies, while establishing best practices moving forward. The location of the U19 project within Columbia's new Zuckerman Mind Brain Behavior institute will further enhance the impact of this effort, firstly through our ability to establish shard, state of the art infrastructure optimized for our framework and pipeline, and second by enabling us to establish new standards that can be scaled to serve the whole institute and beyond. Our core will have 3 main components: 1) Establishing the hardware and network infrastructure that will enable centralized, indexed, secure yet easily shared data storage and high-power analysis from anywhere. This infrastructure will also include direct access to analyze host data on our high performance cluster resources and through cloud computing. 2) Developing, tracking, modularizing and sharing novel algorithms and modeling approaches centrally, enabling version control as well as indexing, pooling and sharing of processed data. 3) Establishing a core effort focusing on training and day to day support of researchers needing to establish skills and expertise in big data analysis, modeling and statistical methods. This latter effort recognizes that data analysis becomes a bottleneck when users lack programming skills, and analytical experience or simply confidence in their ability to develop their own experimental designs and analyze their own data. We plan to share and disseminate all aspects of our core activities, from best practices and hardware configurations, raw and processed data, algorithms and models and our new pedagogical approaches and successes.

摘要（30行） 当我们试图解开大脑如何产生行为时，在观察大脑行为时测量大脑正在做什么 它生成的行为以及构建可能模仿该过程的模型至关重要。作为成像和 记录大脑活动和行为的能力得到改善，我们的模型和计算的复杂性也随之增加，所以 我们测量的数据集的密度和多样性。处理如此多的数据是一项智力挑战 就其本身而言，如果我们要了解大脑和大脑之间的关系，可以说需要更多的数据 行为。 U19 内的项目涵盖了多种不同的方法来剖析运动通路 在清醒、行为正常的老鼠中。我们的高级成像和支持所使用的方法 仪器核心几乎都是原型系统，不是现成的，因此将 提供了前所未有的数据，并对标准化、分析和信息提出了新的挑战 萃取。为了支持这些活动，我们的数据科学资源核心将利用专家的专业知识 杰出的跨学科团队，拥有计算机基础设施、分析和建模、数据方面的专业知识 科学、统计学、机器学习以及数据科学方法的教学和培训。有了这个团队，我们 希望建立一个可扩展、稳健且能够满足即时需求的模型数据管道 缺陷，同时建立前进的最佳实践。 U19项目所在地 哥伦比亚新​​成立的祖克曼心脑行为研究所将进一步增强这项努力的影响，首先 通过我们建立分片的能力，最先进的基础设施针对我们的框架和管道进行了优化， 其次，使我们能够建立新的标准，这些标准可以扩展以服务于整个研究所和 超过。 我们的核心将包含 3 个主要组成部分：1）建立硬件和网络基础设施 实现随时随地的集中式、索引式、安全且轻松共享的数据存储和高功率分析。 该基础设施还将包括直接访问以分析我们的高性能集群上的主机数据 资源并通过云计算。 2）开发、跟踪、模块化和共享新颖算法 集中建模方法，实现版本控制以及索引、池化和共享 处理过的数据。 3）建立专注于研究人员培训和日常支持的核心工作 需要建立大数据分析、建模和统计方法方面的技能和专业知识。后者的努力 认识到当用户缺乏编程技能时数据分析就会成为瓶颈，并且分析 经验或只是对他们开发自己的实验设计和分析他们的能力的信心 自己的数据。我们计划分享和传播我们核心活动的各个方面，从最佳实践和 硬件配置、原始和处理后的数据、算法和模型以及我们的新教学法 方法和成功。