Developing Cloud-based tools for Big Neural Data

开发基于云的大神经数据工具

• 批准号: 8830141
• 负责人: Joost B Wagenaar
• 金额: $ 19.22万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 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 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8830141
• 关键词: Animal Model; Applications Grants; Area; Big Data; Biomedical Engineering; Cardiac; Clinical; Collaborations; Communities; Complement; Complex; Computational Technique; Data; Data Analyses; Data Provenance; Data Set; Electrophysiology (science); Emergency Care; Epilepsy; Evaluation; Feedback; Fostering; Foundations; Funding; Genetic; Genomics; Goals; Health; Human; Image; Incentives; Individual; Institution; Knowledge; Laboratories; Learning; Machine Learning; Metadata; Methods; Mining; Modality; National Institute of Neurological Disorders and Stroke; Nature; Neurosciences; Organism; Performance; Physiological; Process; Protocols documentation; Research; Research Infrastructure; Research Personnel; Role; Science; Scientist; Series; Solutions; Standardization; Statistical Models; Techniques; Time; Training; base; career; cloud based; cohort; comparative; computer science; data acquisition; data integration; data management; data mining; data sharing; improved; nervous system disorder; novel; novel strategies; relating to nervous system; statistics; tool; tool development; translational neuroscience

DESCRIPTION (provided by applicant): Big data has the potential to dramatically advance the electrophysiology biodata sciences in similar ways that it has transformed Genetics. Differences between these two areas dictate separate approaches to apply Big Data tools, and methods in order to provide successful assets to the research community. For one, neural datasets are very heterogeneous by nature. The data is difficult to interpret without knowing specifics about the data acquisition protocol, the experimental paradigm and the physiological state of the recorded subject. Many neural datasets are complemented with complex meta-data sets, which should be an integral component in any effort to integrate and share these data with other researchers. The goal of this project is to develop novel, generalizable Big Data tools to facilitate cloud-base analysis of complex multi-scale neural data. Epilepsy research will be used as a specific use case to guide the development of the tools. A cohort of established senior investigators performing epilepsy research will use and validate these tools in their laboratories. Epilepsy research is currently limited by its narrow focus on single models (animal or human) in individual centers and laboratories. Just as Genetics was revolutionized through Big Data techniques, so too can Epilepsy research be transformed through novel approaches to standardize, share, and mine data across groups of investigators. Over the past several years I have co-developed a NINDS funded cloud-based data platform, ://ieeg.org, giving me a central role in developing Big Data solutions for neural data, such as customized data sharing, large-scale cloud-based data analysis, and search and interrogation techniques for complex data and metadata. My scientific objectives for this project are: (1) to develop generalizable tools to curate, analyze, and interrogate multi-scale neural data, and (2) to create a platform that will galvanize a research community focused on sharing data, and methods to advance Big Data research in the basic and translational neurosciences. Equally important to this proposal, I present a training plan to prepare me for an academic career focused on Big Data in the neurosciences. This plan supplements my background in bioengineering and statistical modeling of neural data with broader data-science expertise in data integration and machine learning, and deeper domain knowledge of the clinical neurosciences. I have assembled a group of collaborators, basic investigators and clinician scientists, who will use the tools developed in this project to analyze and validate their data and methods. I will use the results of this project as the foundation for a R01 Grant application, in which I will expand the developed platform and tools to target other research domains (TBI, Emergency Care, Cardiac), as well as integrate other data-modalities such as Imaging, and Genomics. OMB No. 0925-0001/0002 (Rev. 08/12 Approved Through 8/31/2015) Page Continuation Format Page

描述（由申请人提供）：大数据有可能以与转化遗传学相似的方式显着推进电生理生物生物科学。这两个领域之间的差异决定了应用大数据工具的单独方法，以及为研究社区提供成功资产的方法。首先，神经数据集本质上是非常异构的。在不了解有关数据采集方案，实验范式和记录主题的生理状态的细节的情况下，很难解释数据。许多神经数据集都配有复杂的元数据集，这应该是与其他研究人员集成和共享这些数据的任何努力的组成部分。该项目的目的是开发新颖的，可概括的大数据工具，以促进复杂多尺度神经数据的云基础分析。癫痫研究将用作指导工具开发的特定用例。进行癫痫研究的一批成熟的高级研究人员将在其实验室中使用和验证这些工具。癫痫研究目前受到单个中心和实验室中单个模型（动物或人的）的关注狭窄的限制。正如遗传学通过大数据技术彻底改变的一样，癫痫研究也可以通过新颖的方法来转化，以标准化，共享和挖掘研究人员群体的数据。在过去的几年中，我共同开发了基于NINDS的基于云的数据平台：//ieeg.org，使我在开发神经数据的大数据解决方案方面具有核心作用，例如自定义数据共享，基于云的大规模数据分析，以及用于复杂数据和元数据的搜索和询问技术。我对该项目的科学目标是：（1）开发可推广的工具来策划，分析和询问多尺度神经数据，（2）创建一个平台，该平台将使研究社区旨在共享数据，以共享数据，以促进基本和易位神经科学领域的大数据研究。对于这项建议同样重要的是，我提出了一项培训计划，以准备我为神经科学中的大数据提供的学术职业做好准备。该计划为我的生物工程和统计建模提供了我的背景神经数据的统计模型，并具有更广泛的数据集成和机器学习方面的数据科学专业知识，以及对临床神经科学的更深入的领域知识。我组建了一组合作者，基本研究人员和临床医生，他们将使用该项目中开发的工具来分析 并验证他们的数据和方法。我将使用该项目的结果作为R01赠款应用程序的基础，在该应用程序中，我将扩展开发的平台和工具，以针对其他研究领域（TBI，紧急护理，心脏护理），并整合其他数据模式，例如成像和基因组学。 OMB No. 0925-0001/0002（Rev. 08/12批准通过8/3​​1/2015）页面延续格式页面