NIDCR Data Bank

NIDCR数据库项目是一个通用数据管理平台，旨在提供将数据从现场数据生产基础设施大规模转移到基于长期进步的档案云存储的能力。该流程旨在使数据所有者能够独立于技术支持人员进行简单的操作，并将确保捕获强大的元数据、创建数字对象标识符，并在摄取过程中提供高级一级、二级和三级数据分析机会。该平台还围绕强大的成本回收模式进行设计，这将使数据生产者和管理人员处于有利地位，以了解他们保留的数据，并对其保留做出明智的价值判断，以及确保数据保留合规性。该平台将是可扩展的，并能够通过API利用各种标准词汇和数据共享集成。这个完全开源的平台与推进ODSS的目标很好地结合在一起。 NIDCR数据库从一开始就被设想为数据可持续性解决方案。该平台是在Microsoft Azure云平台上开发的，利用了Microsoft资源管理最佳实践和功能，这将使采用该平台的组织能够从组织的数据生产者/管理人员那里收回平台运营的全部成本。收集的元数据将实现强大的数据治理，从而允许根据政策和数据估值做出数据保留知情决策。其他设计方面的考虑将使存储在数据库实例中的数据具有长期可持续性。例如，Azure数据存储提供了自动分层到成本较低的“冷”存储选项。此类基础架构的设计考虑因素将适应各种存储层可能需要的数据下载差异等技术挑战。NIDCR将使用和维护核心开源代码库。

The NIDCR Data Bank project is a generic data management platform that is intended to provide the ability to move data at scale from on-site data production infrastructure into long-term STRIDES based archival cloud storage. The process is designed to be simple for data owners to operate independently of technology support staff, and will ensure capture of strong meta-data, creation of digital object identifiers, and provide advanced primary, secondary data, and tertiary data analytical opportunities during ingestion. The platform is also designed around a robust cost-recovery model, which will put data producers and stewards in a strong position to understand the data they retain, and make informed value judgements about it's retention as well as ensure data retention compliance. The platform will be extensible and able to leverage a variety of standard vocabularies and data sharing integrations via API. This fully opened sourced platform, is well aligned to advance ODSS goals. The NIDCR Data Bank was conceived as a data sustainability solution from it's inception. The platform is, developed on the Microsoft Azure cloud platform, leverages Microsoft resource management best practices and capabilities which will enable full cost recovery for the operation of the platform for an adopting organization from the organization's data producer/steward. Meta data collected will enable strong data governance, thus allowing data retention informed decisions to be made based on policy and data valuation. Other design considerations will enable long-term sustainability of data stored in an instance of the Data Bank. For example, Azure data storage provides for automated tiering to less expensive "cold" storage options. Design considerations for this type of infrastructure will accommodate technical challenges such differences in data downloading that various tiers of storage may require. NIDCR will employ and maintain the core open-sourced code base.