CRCNS Data Sharing: NeuroML Database for Multiscale Neuroscience Models

CRCNS 数据共享：用于多尺度神经科学模型的 NeuroML 数据库

• 批准号: 8257628
• 负责人: Sharon Marie Crook
• 金额: $ 8.04万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8257628
• 关键词: Archives; Area; Commit; Communities; Complex; Computer software; Data; Databases; Development; Discipline; Documentation; Education; Educational Curriculum; Educational process of instructing; Ensure; Excitatory Synapse; Extensible Markup Language; Feedback; Funding; Goals; Individual; International; Ion Channel; Language; Mentors; Minority; Modeling; Morphology; Neurons; Neurosciences; Neurosciences Research; Pathology; Potassium Channel; Publications; Publishing; Research; Research Infrastructure; Research Personnel; Stream; Structure; Students; Synapses; System; Testing; Training Programs; Woman; Work; base; computational neuroscience; computer science; data exchange; data modeling; data sharing; design; information model; multi-scale modeling; network models; portability; programs; relating to nervous system; role model; tool; user-friendly; web based interface

DESCRIPTION (provided by applicant): Intellectual Merit: The complexity of problems associated with structure and function in neuroscience requires that research from multiple groups across many disciplines be combined. In order to combine research from multiple groups, there must be an infrastructure for sharing data and exchanging models; however, the current use of multiple formats for encoding model information in the computational neuroscience community has hampered model exchange. The PI has collaborated extensively on the Neural Open Markup Language project, NeuroML, which is an international, collaborative initiative to develop a structured, declarative language for describing complex neuronal and neuronal network models. The goals of the project are to create a simulator-independent description language that facilitates data archiving, data and model exchange, database creation, and model publication. This collaborative initiative focuses on the key objects that need to be exchanged among existing software applications, such as descriptions of neuronal morphology, ion channels, synaptic mechanisms, and network structure. This modular approach brings additional benefits: not only can entire models be published and exchanged, but each individual object--such as a specific potassium channel or excitatory synapse--can be shared and re-implemented in a different model. The openness of the standards and the encouragement of feedback from many sections of the community are some of the guiding principles of the NeuroML initiative. The use of XML as a definition language provides the transparency, portability and extensibility required in these efforts, and also brings an infrastructure of established tools for efficient software and database development. The activities described in this proposal will take advantage of this infrastructure to provide a stream-lined set of tools for the computational neuroscience community to share, find, view, and test NeuroML models and their components. The specific aims of the proposed data sharing activities are to (1) develop and populate a XMLbased database system for multiscale models in neuroscience that are described using NeuroML, (2) integrate the web-based interface for the database with other NeuroML tools, including a LEMS-based model viewer, and (3) create user-friendly documentation and tutorials to ensure that the database will be useful for research and education. Broader Impacts: The database system proposed here is complementary to other existing model database efforts. In contrast to these other efforts, the modular design of NeuroML and use of XML provides for efficient searching and makes it much easier for a researcher to choose components from different models to combine for re-use. The development of a stream-lined tool chain for finding, viewing and testing these complex models and model components and the ease of re-implementing them on a different simulator could have a large impact on the field of computational neuroscience and also makes these complex models accessible for educational purposes. The PI and co-PI are both heavily committed to interdisciplinary teaching, bringing computer science and computational concepts into other areas of the curriculum. Drs. Crook and Dietrich also both work in areas where women are underrepresented and have demonstrated a commitment to serve as role models and mentors for other underrepresented groups in these fields. Dr. Crook has been involved with many training programs that target minority access to research. Dr. Crook works with underrepresented undergraduate students through programs funded by other mechanisms at ASU, and several of these students will have the opportunity to work on this project as part of those programs.

描述（由申请人提供）：智力优点：与神经科学中结构和功能相关的问题的复杂性要求将多个小组的研究组合在一起。为了结合多个小组的研究，必须有一个基础架构来共享数据和交换模型。但是，当前在计算神经科学界中多种格式在编码模型信息中的使用阻碍了模型交换。 PI在神经开放标记语言项目Neuroml上进行了广泛的合作，该项目是一项国际协作计划，旨在开发一种结构化的声明性语言，用于描述复杂的神经元和神经元网络模型。该项目的目标是创建独立于模拟器的描述语言，以促进数据归档，数据和模型交换，数据库创建和模型出版物。该协作计划的重点是需要在现有软件应用程序之间交换的关键对象，例如神经元形态，离子渠道，突触机制和网络结构的描述。这种模块化方法带来了其他好处：不仅可以发表和交换整个模型，而且可以在每个对象中（例如特定的钾通道或兴奋性突触） - 可以在不同的模型中共享和重新实现。标准的开放性以及社区许多部分的反馈是神经倡议的一些指导原则。将XML用作定义语言提供了这些努力所需的透明度，可移植性和可扩展性，还为有效的软件和数据库开发提供了建立工具的基础架构。本提案中描述的活动将利用本基础架构为计算神经科学社区提供一组衬里的工具，以共享，查找，查看，观察，测试神经元模型及其组件。 The specific aims of the proposed data sharing activities are to (1) develop and populate a XMLbased database system for multiscale models in neuroscience that are described using NeuroML, (2) integrate the web-based interface for the database with other NeuroML tools, including a LEMS-based model viewer, and (3) create user-friendly documentation and tutorials to ensure that the database will be useful for research and education.更广泛的影响：此处提出的数据库系统是其他现有模型数据库工作的补充。与这些其他努力相反，神经元的模块化设计和XML的使用提供了有效的搜索，并且使研究人员更容易从不同模型中选择组件以合并重复使用。开发用于查找，查看和测试这些复杂模型和模型组件的溪流式工具链，以及在不同模拟器上重新实现它们的易度性可能会对计算神经科学领域产生很大的影响，并使这些复杂模型可用于教育目的。 PI和Co-Pi都致力于跨学科教学，将计算机科学和计算概念带入课程的其他领域。博士。克鲁克（Crook）和迪特里希（Dietrich）都在女性代表性不足的领域工作，并表现出致力于作为这些领域其他代表性不足群体的榜样和导师的承诺。克鲁克博士参与了许多针对少数群体获得研究的培训计划。 Crook博士通过ASU的其他机制资助的计划与代表性不足的本科生合作，其中一些学生将有机会作为这些计划的一部分从事该项目。