Data Science Core

数据科学核心

• 批准号: 10490235
• 负责人: Krishna V Shenoy
• 金额: $ 73.07万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10490235
• 关键词: Address; Adopted; Algorithmic Analysis; Algorithms; Animal Experiments; Animals; Architecture; Behavior; Behavioral; Benchmarking; Brain; Catalogs; Code; Collaborations; Communication; Communities; Computer Analysis; Computer Models; Computer software; Consumption; Creativeness; Custom; Data; Data Analyses; Data Science Core; Data Set; Data Storage and Retrieval; Databases; Discipline; Doctor of Philosophy; Documentation; Environment; Feedback; Genetic; Goals; Guidelines; Industrialization; Lead; Link; Measurement; Metadata; Mus; Neurosciences; Online Systems; Optics; Postdoctoral Fellow; Process; Reproducibility; Research; Research Personnel; Rest; Retrieval; Route; Running; Software Engineering; Sorting - Cell Movement; Source Code; Speed; Standardization; Statistical Data Interpretation; Students; System; Testing; Time; Training; Universities; Work; Writing; base; computerized data processing; data access; data analysis pipeline; data archive; data format; data handling; data management; data modeling; data pipeline; data sharing; data standards; database query; experience; experimental study; flexibility; graphical user interface; member; open data; open source; recruit; relating to nervous system; repository; searchable database; success; tool; working group

Data Science Core (DSC) Leads: Krishna Shenoy PhD and Chris Roat PhD (with Surya Ganguli PhD) Project Summary Given the large volumes of optical, electrical, genetic and behavioral data that will be generated, stored and computationally analyzed, it is essential to establish a comprehensive and yet streamlined DSC. There are four major data challenges that the DSC will address. (1) Data size. Each experimental lab will generate very large, and rapidly increasing, datasets. We must contend with storing, pre-processing (e.g., spike sorting) and processing (e.g., single-trial analyses) these large and growing datasets. (2) Metadata. Collaborations between groups are often hampered by not fully capturing – in a searchable database and linked to the bulk data – all animal and experiment conditions, or so-called metadata. We will build in capabilities and requirements to electronically capture full metadata. (3) Data format. Collaborations are also often hampered by the effort required to understand each lab’s dataset format. Data format often depends on whether a given measurement system was custom built or relies on a commercial system. We will capture this information as part of the metadata for historical data relevant to this U19, and moving forward we will adopt the increasingly-popular NeuroData Without Borders (NWB) data format. Finally, (4) Across animals and labs. Performing large- scale analyses across many animals and labs is often truly onerous. This is because all three of the challenges listed above combine, causing one to shy away from anything other than essential analyses (e.g., pooling results across just a few mice in one specific condition). We will both build our own data pipelines to automatically query our metadata database and, subsequently, retrieve the indicated experimental data as well as adopt the increasingly-popular DataJoint pipeline. Our DSC will be led by Prof. Shenoy, Dr. Roat (with considerable industrial-scale data handling experience, and now at Stanford) and Prof. Ganguli (RP3 lead). Two full-time software engineers (TBD) will implement the DSC architecture, including bulk data server, relational meta-database, data standards and data pipeline. The software engineers will work closely with the rest of the team to help assure good communication, and to help migrate analysis code and documentation into professional software standards for dissemination. This will enable storage, retrieval and analysis of data in an efficient and modular way, which enables rapid replacement of any piece of the data analysis pipeline as is essential for a creative environment that also promotes rapid feedback of emerging ideas to subsequent experiments. We believe in Open Science, including open source code (e.g., github) and data formats. We will share data with the broader community, including with other U19 consortia. Thus our DSC is critical to the success of our proposed research, and serves as the central hub of our U19 research.

数据科学核心（DSC） 负责人：Krishna Shenoy博士和Chris Roat博士（与Surya Ganguli博士） 项目摘要 鉴于将产生、存储和分析大量的光学、电学、遗传和行为数据， 通过计算分析，必须建立一个全面而又精简的DSC。有四 DSC将解决的主要数据挑战。(1)数据大小。每个实验室都会产生非常大的， 并迅速增长的数据集。我们必须与存储、预处理（例如，尖峰排序）和 处理（例如，单次试验分析）这些大型且不断增长的数据集。(2)元数据.之间的合作 小组经常受到阻碍，因为没有在可搜索的数据库中并与批量数据相链接， 动物和实验条件，或所谓的元数据。我们将建立能力和要求， 电子捕获完整的元数据。(3)数据格式。合作也经常受到努力的阻碍 需要了解每个实验室的数据集格式。数据格式通常取决于给定的测量 系统是定制的或依赖于商业系统。我们将捕获此信息作为 与此U19相关的历史数据的元数据，并且向前迈进，我们将采用越来越受欢迎的 NeuroData Without Borders（NWB）数据格式。最后，（4）跨动物和实验室。表演大- 在许多动物和实验室中进行规模分析通常是非常繁重的。这是因为这三个挑战 以上所列的联合收割机，导致人们回避除了基本分析之外的任何东西（例如，汇总结果 在一种特定条件下的几只小鼠中）。我们都将建立自己的数据管道， 查询我们的元数据数据库，随后检索指示的实验数据并采用 越来越流行的DataJoint管道。 我们的DSC将由Shenoy教授和Roat博士领导（具有相当大的工业规模数据处理能力 经验，现在在斯坦福大学）和Ganguli教授（RP 3负责人）。两名全职软件工程师（TBD）将 实现DSC体系结构，包括批量数据服务器、关系元数据库、数据标准和 数据管道软件工程师将与团队的其他成员密切合作，以帮助确保良好的 通信，并帮助将分析代码和文档迁移到专业软件标准， 传播。这将使数据的存储、检索和分析能够以有效和模块化的方式进行， 能够快速替换数据分析管道的任何部分，这对于创造性环境至关重要 这也促进了新出现的想法对后续实验的快速反馈。我们相信开放科学， 包括开放源代码（例如，github）和数据格式。我们将与更广泛的社区共享数据， 包括其他U19联盟。因此，我们的DSC对我们提出的研究的成功至关重要， 作为我们U19研究的中心枢纽。