Flexible & Executable Provenance in Data-Intensive Biomedical Research: A Flexible Research Data Service

 描述(由申请人提供)：追溯科学数据和断言的谱系对于“曾经确保科学保真度的制衡”至关重要(Collins&Tabak，2014)。随着数据变得无处不在的数字化，自动和大规模地生成和跟踪谱系增加了结论的有用性和质量。数据密集型科学的一个重大挑战是产生科学信息的谱系--来源--同时促进检索和重新执行。我们假设这样的能力可以提高断言的再现性，并使数据对社会更有用。我们的目标是为来源、数据完整性、存储和可重现的工作流程构建应用程序编程接口，使研究人员能够记录、检索和重新运行科学谱系。这项拟议研究的理由是，通过能够追溯复制结果并了解其来源以供将来使用，科学数据的价值得到了提高。来源也有助于衡量数据的重要性--它的影响。在前期工作的指导下，我们将通过追求两个具体目标来验证我们的假设：(1)构建用于来源、数据管理、数据完整性和可重复执行工作流的API，(2)提供一个存储和部署集装箱化计算环境的平台，该平台也是可重复数据科学的学习实验室。这种方法在关注灵活性和适应生物医学科学中的无数用例方面具有创新性，同时提供了一个中心，用于培训可重复数据科学方面的研究人员。通过创建一个开源的“灵活检索数据服务”，拟议的研究将显著影响我们使我们在生物医学研究上的投资更有用的能力。

 DESCRIPTION (provided by applicant): Tracing the lineage of scientific data and assertions is critical for the "checks and balances that once ensured scientific fidelity" (Collins & Tabak, 2014). As data become pervasively digitized, generating and following lineages automatically and at scale increases the usefulness and quality of conclusions. A significant challenge in data-intensive science is generating that lineage-the provenance-of scientific information, while facilitating retrieval and re-execution. We hypothesize such capabilities improve the reproducibility of assertions and make data more useful to society. Our objective is to build application programming interfaces for provenance, data-integrity, storage, and reproducible workflows that empower researchers to record, retrieve, and re-run scientific lineages. The rationale for the proposed research is that the value of the scientific data is enhanced by being able to retrospectively reproduce a result and by understanding its origins for future use. Provenance also facilitates measurement of data's importance-its impact. Guided by strong preliminary work, we will test our hypothesis by pursuing two specific aims: (1) Building APIs for provenance, data management, data integrity, and re-executable workflows, (2) Providing a platform for storing and deploying containerized compute environments that also serves as a learning laboratory for reproducible data science. This approach is innovative in focusing on flexibility and accommodating the myriad use cases across biomedical science, while pro- viding a hub for training investigators in reproducible data science. By creating an open source "Flexible Re- search Data Service", the proposed research will significantly impact our ability to make our investments in biomedical research more useful.