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） 当我们寻求解锁大脑如何产生行为时，衡量大脑在观察到 它产生的行为，建立可能模仿过程的模型至关重要。作为成像和 记录大脑活动和行为的改善，我们的模型和计算的复杂性增加，因此 我们测得的数据集的密度和多样性是否存在。处理这些大量数据是一个智力挑战 本身，如果我们要了解大脑和 行为。 U19中的项目涵盖了各种不同的方法来剖析电路通路 在清醒中，行为鼠标。由我们的高级成像和 仪器核心几乎是原型系统，无法在架子上使用，因此将 提供前所未有的数据，并提出标准化，分析和信息的新挑战 萃取。为了支持这些活动，我们的数据科学资源核心将借鉴 具有计算机基础架构，分析和建模，数据的专业知识的特殊跨学科团队 科学，统计，机器学习以及数据科学方法的教学和培训。与这个团队一起 希望构建一条可扩展，强大且能够满足即时需求的模型数据管道和 缺陷，同时建立最佳实践前进。 U19项目的位置 哥伦比亚的新扎克曼思维大脑行为研究所将进一步增强这项工作的影响，首先 通过我们建立碎片的能力，为我们的框架和管道优化了最新基础设施的状态， 其次，通过使我们能够建立可以扩展为整个研究所服务的新标准和 超过。 我们的核心将有3个主要组成部分：1）建立硬件和网络基础架构 从任何地方启用集中式，索引，安全但很容易共享的数据存储和高功率分析。 该基础架构还将包括直接访问我们的高性能集群中的主机数据 资源和通过云计算。 2）开发，跟踪，模块化和共享新型算法 以及建模方法集中，启用版本控制以及索引，汇总和共享 处理的数据。 3）建立核心努力，专注于培训和研究人员的日常支持 需要在大数据分析，建模和统计方法中建立技能和专业知识。后者的努力 认识到当用户缺乏编程技能和分析时，数据分析成为瓶颈 经验或仅对他们开发自己的实验设计的能力充满信心并分析他们的 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，最佳实践以及 硬件配置，原始数据和处理的数据，算法和模型以及我们的新教学法 方法和成功。