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: 1740097
• 负责人: Emre Brookes
• 金额: $ 26.96万
• 依托单位: University of Texas Health Science Center San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740097&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任何科学领域不可或缺的方面，无论是物理、化学和生物等精确科学，还是社会科学。许多研究实验室的工作重点是为数据生成、分析和解释创建科学代码。然而，在一个典型的研究实验室中制定的由公共资金资助的、往往来之不易的科学代码，往往在资金使用期限过后或工作人员轮换后不久就变得不可持续。那些仅仅是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，会将稀缺的资源从实验室的主要目标上转移出来，而且往往会在没有解决问题的情况下翻译出来。只有少数研究人员获得了足够的资金来维护和更新软件，限制了新思想和新技术的传播。软件环境的多样性和不断变化的性质使问题变得更加复杂。实现用户利用率在部署、访问和培训方面存在障碍。这些问题也给新准则中体现的新思想的实施制造了障碍。GenApp项目的目标就是解决这些问题。首先，GenApp使科学代码能够快速传播给只需最少软件专业知识的研究人员。随着更多的研究人员使用这些代码，更多的代码被赋予代码，这有助于它们的可持续性。该项目的基本目标是将GenApp框架推进为一种变革性的工具，使科学软件开发人员社区广泛受益。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，既可以生成科学网关，也可以生成独立的应用程序。GenApp的主要独特功能包括对最终用户最低限度的技术专长要求，以及确保生成的应用程序的可持续性的开放式设计。为了生成功能齐全的应用程序，GenApp按照简单定义文件的指示编织片段和用户定义模块的库，这些定义文件是从GenApp提供的统一、逻辑且易于编码的通用接口定义文件创建的。该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。首先是基础设施开发，包括对GenApp功能的全面增强。第二是文件编制、培训、传播、推广和可持续性--所有这些都是产生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，它们将采用并推动GenApp的开发。GenApp将看到其主要的实际用途，使高要求的新型计算和分析工具可供从事结构生物学的核磁共振(核磁共振)和小角散射(SAS)领域的实验者和理论家使用。GenApp框架将作为应用程序的平台，使用需要高效使用HPC资源的高级工具、使用分子模拟对SAS数据进行建模的工具，以及用于将核磁共振和SAS测量与计算建模相结合的组合分析的大型软件套件。容易获得这些强大的工具将使迄今为止不可能对一些基本的生物学问题进行研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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