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) 领导者：Krishna Shenoy 博士和 Chris Roat 博士（与 Surya Ganguli 博士） 项目概要 鉴于将生成、存储和使用大量光学、电学、遗传和行为数据 进行计算分析后，有必要建立一个全面且精简的 DSC。 DSC 将解决的主要数据挑战 (1) 每个实验实验室都会生成非常大的数据。 我们必须应对存储、预处理（例如尖峰排序）和快速增长的数据集。 处理（例如，单次试验分析）这些大型且不断增长的数据集（2）元数据之间的协作。 小组经常因未完全捕获（在可搜索数据库中并链接到批量数据）而受到阻碍 我们将建立动物和实验条件，或所谓的元数据的能力和要求。 (3) 数据格式的协作也常常受到阻碍。 了解每个实验室的数据集格式所需的数据格式通常取决于是否给定的测量。 系统是定制的或依赖于商业系统，我们将捕获此信息作为系统的一部分。 与此 U19 相关的历史数据的元数据，展望未来，我们将采用日益流行的 最后，（4）跨动物和实验室执行神经数据无国界（NWB）数据格式。 对许多动物和实验室进行规模分析通常非常繁重，因为这三个挑战都是如此。 上面列出的组合，导致人们回避除了基本分析之外的任何事情（例如，汇集结果 在一种特定条件下仅通过几只小鼠）我们都将自动构建自己的数据管道。 查询我们的元数据数据库，随后检索指定的实验数据并采用 日益流行的 DataJoint 管道。 我们的 DSC 将由 Shenoy 教授、Roat 博士领导（具有大量工业规模的数据处理能力） 经验，现在在斯坦福大学）和 Ganguli 教授（RP3 领导）将由两名全职软件工程师（TBD）担任。 实现DSC架构，包括批量数据服务器、关系元数据库、数据标准和 软件工程师将与团队其他成员密切合作，以帮助确保良好的数据管道。 沟通，并帮助将分析代码和文档迁移到专业软件标准中 这将以高效和模块化的方式存储、检索和分析数据。 能够快速更换数据分析管道的任何部分，这对于创意环境至关重要 这也促进了新兴想法对后续实验的快速反馈，我们相信开放科学， 包括开源代码（例如 github）和数据格式，我们将与更广泛的社区共享数据， 因此，我们的 DSC 对于我们拟议的研究的成功至关重要，并且 作为我们 U19 研究的中心枢纽。