Data Science Core

数据科学核心

• 批准号: 10460154
• 负责人: Sandeep R Datta
• 金额: $ 40.16万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10460154
• 关键词: Address; Algorithms; Animals; Architecture; Area; Behavior; Behavioral; Benchmarking; Boston; Brain; Catalogs; Charge; Code; Communities; Computer Models; Corpus striatum structure; Data; Data Analyses; Data Science Core; Data Storage and Retrieval; Development; Dimensions; Dopamine; Evolution; Generations; Grant; Institution; Machine Learning; Metadata; Methods; Midbrain structure; Modeling; Online Systems; Procedures; Process; Reproducibility; Research; Research Personnel; Resolution; Rodent Control; Role; Series; Signal Transduction; Source Code; Techniques; Testing; Time; Validation; computerized data processing; data analysis pipeline; data archive; data format; data modeling; data repository; data resource; data sharing; data standards; diverse data; flexibility; graphical user interface; improved; insight; lens; machine vision; open data; open source; peer; programs; relating to nervous system; theories

Project abstract Understanding how the brain generates behavior requires two types of very sharp lenses: one focused on neural activity, and the other on the animal itself as it interacts with the world. The lenses available to us for peering at the brain and for characterizing behavior have steadily improved, which is both a boon and a curse: a boon because it is likely that key insights will arise from neural and behavioral data that approximate each other in terms of resolution, but a curse because organizing and modeling such dense and diverse data is a significant challenge. The Projects in this grant combine advanced techniques in machine vision, machine learning and neural recordings to address how neural activity in midbrain and striatal circuits orchestrate the behavior of rodents and control its evolution. The experimental aspects of this project are to be carried out by researchers located both in the Boston and the Bay area. Here we propose a Data Science Core (DSC) that will enable these diverse research groups to store, retrieve, and analyze their data using modular programs that can be hot- swapped for easy experimentation and iteration. In this structural role the DSC will adhere to a series of core principles for open science, including the use of open source code and data standards whenever possible, creating frameworks that allow for easy data sharing, and the careful curation of metadata to allow pooling of information from multiple labs to enable large-scale statistical testing and validation. The DSC will also be responsible for the distribution of generated data (including raw data, metadata, and analyzed datastreams) to the broader community, including to other U19 consortia. The DSC will therefore sit at the center of this proposal, acting as a crucial hub through which data and insight pass freely between investigators and labs.

项目摘要 了解大脑如何产生行为需要两种类型的非常锐利的镜头：一种专注于 神经活动，以及动物本身与世界互动时的另一种活动。我们可用的镜头 观察大脑和描述行为的能力已经稳步提高，这既是福也是祸： 这是一个福音，因为关键的见解很可能会从近似每个的神经和行为数据中产生 就分辨率而言，这是另一个问题，但这是一个诅咒，因为组织和建模如此密集和多样化的数据是一个 重大挑战。 该资助项目结合了机器视觉、机器学习和神经网络的先进技术 记录中脑和纹状体回路的神经活动如何协调啮齿类动物的行为 并控制其演化。该项目的实验部分将由位于 波士顿和湾区都有。在这里，我们提出了一个数据科学核心（DSC），它将支持这些 不同的研究小组使用模块化程序来存储、检索和分析他们的数据，这些程序可以是热门的 交换以方便实验和迭代。在这个结构角色中，DSC 将遵循一系列核心 开放科学的原则，包括尽可能使用开源代码和数据标准， 创建允许轻松数据共享的框架，并仔细管理元数据以允许汇集 来自多个实验室的信息，以实现大规模统计测试和验证。 DSC 也将 负责将生成的数据（包括原始数据、元数据和分析数据流）分发给 更广泛的社区，包括其他 U19 联盟。因此 DSC 将处于该问题的中心 提案，充当数据和见解在研究人员和实验室之间自由传递的重要枢纽。