Novel Use of Emergent Technologies to Improve Efficiency of Animal Model Research

 描述(由申请者提供)：该第二阶段项目旨在继续开发一种商业质量的创新云托管信息管理系统，称为GRAPH 2.0(Tm)，它将提高实验室效率，并为研究实验室提供更好的能力。GRAPE旨在提供集成的实验室流程管理模块，其中包括移动通信工具、数据监控和警报系统，以及对Microsoft Azure Cloud(Tm)机器学习和流分析服务的集成访问。最初，GRAPH 2.0将面向动物模型研究实验室；然而，该平台的核心设计为几乎适用于任何研究类型或相关行业。目前的研究信息管理系统主要设计为保存记录的工具，很少或根本不直接关注实验室效率或提高研究数据的价值。他们也没有利用新兴的移动设备技术、社交媒体框架以及云计算的数据分析和存储功能。许多实验室仍然使用纸张作为他们的主要记录系统。纸质数据记录之后是二次数据录入实验室数据库。这些系统容易出错、耗时，并导致实验室数据库在数据获取和数据录入之间存在显著的时间滞后。此外，他们不承认累积的数据关系，这可能会识别重要的趋势，研究人员经常错过对时间敏感事件采取行动的机会之窗。在第一阶段，RockStep Solutions展示了创新的云信息管理捆绑系统SCRAPE的可行性，该系统将提高动物模型数据管理的效率和能力。在第一阶段，作为概念验证，成功地开发了测试版本的SCRAPE，并针对严格的性能指标进行了测试。我们成功地构建了一个具有工作界面的原型，将实时通信技术与移动设备的媒体功能相结合。第二阶段提出了四个具体目标：1)开发技术基础设施，以支持针对企业商业版本的安全且可扩展的软件即服务(SaaS)部署；2)开发并扩展第一阶段原型数据监控和消息系统(DMM)，使其成为可用于生产的平台；3)扩展GRAPE的DMM，添加Stream Analytics引擎以支持物联网(IoT)设备和流媒体；4)在合作伙伴的研究实验室部署GRAPE的测试版，测试和优化产品以实现最终商业化。为了确保使用与研究机构相关的功能和工具来开发GRACH，RockStep Solutions已经与关键的测试版站点建立了合作，以测试本提案中开发的所有主要功能。影响：通过利用新兴技术和云计算，GRAPE提供了几个优势：1)支持研究小组成员之间使用熟悉的工具进行实时通信；2)降低实验受挫的风险；3)高效地进行复杂的实验。

 DESCRIPTION (provided by applicant): This Phase II project aims to continue development of a commercial quality, innovative cloud hosted information management system, called Climb 2.0(tm) that will increase laboratory efficiency and provide improved capabilities for research laboratories. Climb is designed to offer integrated laboratory process management modules that include mobile communications tools data monitoring and alert systems, and integrated access to Microsoft Azure Cloud(tm) Machine Learning and Stream Analytics services. Initially, Climb 2.0 will target animal model research laboratories; however, the core of the platform is designed to be adaptable to nearly any research type or related industry. Current research information management systems are primarily designed as record-keeping tools with little or no direct focus on laboratory efficiency or in enhancing value of the research data. They also do not leverage emergent mobile device technologies, social media frameworks, and data analysis and storage capabilities of cloud computing. Many laboratories still use paper as their primary recording system. Paper data logging is then followed by secondary data entry into a laboratory database. These systems are error prone, time consuming and lead to laboratory databases with significant time lags between data acquisition and data entry. Moreover, they do not recognized cumulative data relationships, which may identify important trends, and researchers often miss windows of opportunity to take action on time-sensitive events. In Phase I, RockStep Solutions demonstrated feasibility of an innovative Cloud Information Management Bundle system, Climb, which will increase efficiency and improve capabilities in animal model data management. During Phase I, a beta version of Climb was successfully developed and tested against strict performance metrics as a proof of concept. We successfully built a prototype with working interfaces that integrates real-time communication technologies with media capabilities of mobile devices. Phase II proposes four specific Aims: 1) Develop the technology infrastructure to support the secure and scalable Software as a Service (SaaS) deployment of Climb for enterprise commercial release; 2) Develop and extend the Phase-I prototype Data Monitoring and Messaging System (DMMS) into a platform ready for production use; 3) Extend Climb's DMMS adding a Stream Analytics engine to support Internet of Things (IoT) devices and streaming media; 4) Deploy a beta release of Climb at partner research labs, test and refine the product for final commercialization. To ensure Climb is developed with functionality and tools relevant to research organizations, RockStep Solutions has established collaborations with key beta sites to test all of the major functionality developed in this proposal. IMPACT: By leveraging emergent technologies and cloud computing, Climb offers several advantages: 1) enables real-time communications using familiar tools among members of research groups; 2) reduces the risk of experimental setbacks, and 3) enables complex experiments to be conducted efficiently.