SilverLining: A highly scalable cloud-based platform for data distribution and user collaboration

堪萨斯大学研究中心。获得了一笔赠款，以评估利用基于云的平台即服务（PaaS）进行数据发布的潜力，以解决现有分布式数据服务架构（如分布式通用信息检索（DiGIR）和TDWG信息检索访问协议（TAPIR））目前面临的无数挑战。具体目标是：1）简化和降低发布数据的持续成本; 2）提高数据质量; 3）提供对已发布数据的可扩展、有效的访问; 4）通过为新的数据交互应用程序创建一个简单、高度可扩展的平台来激励创新; 5）开发一套展示新架构能力的参考应用程序。将基于云的解决方案与生物多样性信息学社区目前使用的现有分布式基础设施进行比较，将量化两种根本不同方法的相对优点。低成本的数据发布是实现促进发现、分析并最终促进新知识和教育的应用程序的第一步。该项目中采用的方法通过提供概念证明来支持这一进展的各个层面，该概念证明将证明基于云的解决方案对当前数据发布障碍的可行性，为广泛部署这些解决方案铺平道路。这些解决方案将特别有利于资源和/或技术专长很少的研究人员或机构。本项目试验台的数据范围将以生物多样性发生信息为基础，这是一个具有高度优先权、充分证明和理解需求的社区。然而，这些解决方案将普遍适用于任何动态数据发布联盟，为研究、协作、培训、指导和教育提供可扩展的、基于标准的平台。

The University of Kansas Center for Research, Inc. is awarded a grant to evaluate the potential for leveraging a cloud-based Platform as a Service (PaaS) for data publication to address myriad challenges currently faced by existing distributed data service architectures such as Distributed Generic Information Retrieval (DiGIR) and TDWG Access Protocol for Information Retrieval (TAPIR). Specific goals are to 1) simplify and reduce the ongoing cost of publishing data, 2) improve data quality at the source, 3) provide scalable, effective access to published data, 4) stimulate innovation by creating a simple, highly scalable platform for new applications for data interaction, and 5) develop a suite of reference applications demonstrating capacities of the new architecture. Metrics comparing the cloud-based solution with the existing, distributed infrastructure currently utilized by the biodiversity informatics community will quantify the relative merits of the two fundamentally different approaches. Low-cost data publishing is the preliminary step to enable applications that promote discovery, analysis, and ultimately new knowledge and education. The approach taken in this project supports all levels of this progression by providing a proof of concept that will demonstrate the viability of the cloud-based solution to current impediments in data publishing, paving the way to deploying these solutions broadly. The solutions will be especially beneficial to researchers or institutions with few resources and/or little technical expertise. The scope of data in the test bed for this project will be based on biodiversity occurrence information, a community with a high priority, well demonstrated and understood need. However, the solutions will be applicable generally to any dynamic data publishing federation, providing a scalable, standards based platform for research, collaboration, training, mentoring, and education.