SI2-SSE: GenApp - A Transformative Generalized Application Cyberinfrastructure

SI2-SSE：GenApp - 变革性通用应用程序网络基础设施

• 批准号: 1912444
• 负责人: Emre Brookes
• 金额: $ 19.72万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-31 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912444&HistoricalAwards=false
• 关键词: SI2; SSE; GenApp; Transformative; Generalized

Scientific computing and computational analysis are becoming integral aspects of virtually any field of science, be it exact sciences like Physics, Chemistry and Biology, or social sciences. Efforts of many research laboratories are focused on creation of scientific codes for data generation, analysis and interpretation. However, publicly funded and often hard won scientific codes developed in a typical research laboratory too frequently become unsustainable beyond the lifetime of funding or shortly after staff rotation. Projects that are funded to afford expensive computer science expertise simply to maintain and update existing software divert scarce resources from the lab's primary goals and often translates the problem without resolving it. Only a select number of researchers receive sufficient funding to maintain and update software, limiting the dissemination of new ideas and techniques. The diversity and continually changing nature of software environments compounds the issues. Enabling user utilization presents hurdles in deployment, access and training. These issues also create barriers to the implementation of new ideas embodied in new codes. The GenApp project's goals are to address these issues. To begin with, GenApp enables the rapid dissemination of scientific codes to researchers with minimal software expertise. As more researchers use these codes, more of them become vested in the codes, which helps their sustainability. The fundamental goal of this project is to advance the GenApp framework into a transformative tool to broadly benefit the scientific software developer community. GenApp is a generalized application generation framework intended for rapid deployment of scientific codes, which can generate both science gateways and stand-alone applications. Among the main unique features of GenApp are the minimal technical expertise requirement for the end user and an open-end design ensuring sustainability of generated applications. To produce fully functional applications, GenApp weaves libraries of fragments and user defined modules as directed by simple definition files, created from a uniform, logical, and simple-to-encode general interface definition file provided by GenApp. This general definition file and the underlying software can be reused indefinitely to produce applications in a variety of existing and yet-to-be defined software environments. Preserving such simplicity with GenApp's maturation is one of the main developmental strategies. To achieve the goal of GenApp four focus Aims have been proposed. The first is infrastructure development, which includes general enhancements to the capabilities of GenApp. The second is documentation, training, dissemination, outreach and sustainability - all important aspects to produce a software product that is useful to the community. The third is simply feedback, since user and developer feedback will help drive the first two Aims. The final Aim includes two structural biology domain science applications that will adopt and drive GenApp development. GenApp will see its primary practical utilization in making highly demanding novel computational and analysis tools accessible to experimentalists and theoreticians working in the nuclear magnetic resonance (NMR) and small-angle scattering (SAS) domains of structural biology. The GenApp framework will serve as a platform for applications utilizing advanced tools requiring efficient use of HPC resources, tools for modeling SAS data with molecular simulations, and a large software suite for a combined analysis of NMR and SAS measurements coupled to computational modeling. Easy access to these powerful tools will enable hitherto impossible studies of a number of fundamental biological problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

科学计算和计算分析正在成为几乎所有科学领域不可或缺的一部分，无论是物理、化学和生物学等精确科学，还是社会科学。许多研究实验室的工作重点是为数据生成、分析和解释制定科学代码。然而，在典型的研究实验室中开发的公共资助且往往来之不易的科学规范往往在资助期限结束后或员工轮换后不久就变得不可持续。那些仅仅为了维护和更新现有软件而资助昂贵的计算机科学专业知识的项目，将稀缺的资源从实验室的主要目标中转移出来，并且经常在没有解决问题的情况下转化问题。只有少数研究人员获得足够的资金来维护和更新软件，限制了新思想和技术的传播。软件环境的多样性和不断变化的性质使问题更加复杂。 用户利用率的实现在部署、访问和培训方面存在障碍。 这些问题也阻碍了新法典所体现的新思想的实施。 GenApp项目的目标就是解决这些问题。开始，GenApp使科学代码能够快速传播给具有最少软件专业知识的研究人员。随着越来越多的研究人员使用这些代码，他们中的更多人成为代码的归属，这有助于他们的可持续性。该项目的基本目标是将GenApp框架推进为一个变革性工具，以广泛造福科学软件开发人员社区。GenApp是一个通用的应用程序生成框架，旨在快速部署科学代码，可以生成科学网关和独立应用程序。GenApp的主要独特功能包括对最终用户的最低技术专门知识要求和确保生成应用程序可持续性的开放式设计。为了产生功能齐全的应用程序，GenApp根据简单定义文件的指导编织片段和用户定义模块的库，这些简单定义文件是从GenApp提供的统一、逻辑和易于编码的通用接口定义文件创建的。 这个通用定义文件和底层软件可以无限期地重复使用，以在各种现有的和尚未定义的软件环境中生成应用程序。随着GenApp的成熟，保持这种简单性是主要的发展战略之一。为了实现GenApp的目标，提出了四个重点目标。第一个是基础设施开发，包括对GenApp功能的一般增强。第二是文件编制、培训、传播、推广和可持续性-所有这些都是制作对社区有用的软件产品的重要方面。第三个是简单的反馈，因为用户和开发人员的反馈将有助于推动前两个目标。 最终目标包括两个结构生物学领域的科学应用，将采用和驱动GenApp的发展。 GenApp的主要实际用途是为结构生物学核磁共振（NMR）和小角散射（SAS）领域的实验学家和理论家提供高要求的新型计算和分析工具。GenApp框架将作为一个平台，用于使用需要高效使用HPC资源的高级工具的应用程序，用于使用分子模拟对SAS数据进行建模的工具，以及用于将NMR和SAS测量结果与计算建模相结合进行综合分析的大型软件套件。轻松获得这些强大的工具将使迄今为止不可能的一些基本生物学问题的研究成为可能。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

GenApp, Containers and Abaco: Technical Paper

GenApp、Containers 和 Abaco：技术论文

• DOI: 10.1145/3332186.3332191
• 发表时间: 2019
• 期刊: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning
• 作者: Brookes, Emre;Stubbs, Joe
• 通讯作者: Stubbs, Joe

ROTDIF-web and ALTENS: GenApp-based Science Gateways for Biomolecular Nuclear Magnetic Resonance (NMR) Data Analysis and Structure Modeling

ROTDIF-web 和 ALTENS：基于 GenApp 的生物分子核磁共振 (NMR) 数据分析和结构建模科学网关

• DOI: 10.17605/osf.io/t4gkh
• 发表时间: 2019
• 期刊: Gateways 2019
• 作者: Chen, Yuexi;Jeong, Cheol;Savelyev, Alexey;Krueger, Susan;Curtis, Joseph E;Brookes, Emre H;Fushman, David
• 通讯作者: Fushman, David

BEES: Bayesian Ensemble Estimation from SAS

• DOI: 10.1016/j.bpj.2019.06.024
• 发表时间: 2019-08-06
• 期刊: BIOPHYSICAL JOURNAL
• 影响因子: 3.4
• 作者: Bowerman, Samuel;Curtis, Joseph E.;Wereszczynski, Jeff
• 通讯作者: Wereszczynski, Jeff

Measuring Success: How Science Gateways Define Impact

衡量成功：科学网关如何定义影响力

• 发表时间: 2019
• 期刊: Proceedings of Gateways 2019
• 作者: Wilkins-Diehr, Nancy;Miller, Mark;Brookes, Emre H;Arora, Ritu;Chourasia, Amit;Calyam, Prasad;Jennewein, Douglas M;Nandigam, Viswanath;LaMar, M Drew;Cleveland, Sean B
• 通讯作者: Cleveland, Sean B

Enabling rich data sharing for Science Gateways via the SeedMeLab platform

通过 SeedMeLab 平台实现科学网关的丰富数据共享

• DOI: 10.17605/osf.io/wv3bf
• 发表时间: 2019
• 期刊: Proceedings of Gateways 2019
• 作者: Chourasia, Amit;Nadeau, David;Luo, Jiaping;Chen, Tony;Miller, Mark;Brookes, Emre H
• 通讯作者: Brookes, Emre H