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

灵活的

基本信息

  • 批准号:
    9243182
  • 负责人:
  • 金额:
    $ 14.49万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-06-01 至 2017-05-31
  • 项目状态:
    已结题

项目摘要

 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.
 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。

项目成果

期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Erich S. Huang其他文献

Diversity, Equity, and Inclusion in Clinical Research: A Path Toward Precision Health for Everyone
临床研究的多样性、公平性和包容性:通往每个人精准健康的道路
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Vindell Washington;Joseph B. Franklin;Erich S. Huang;J. Mega;Amy Abernethy
  • 通讯作者:
    Amy Abernethy
The User Experience of AI.
人工智能的用户体验。
  • DOI:
    10.1016/j.medj.2022.03.005
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    17
  • 作者:
    Erich S. Huang
  • 通讯作者:
    Erich S. Huang
Effects of Racial Bias in Pulse Oximetry on Children and How to Address Algorithmic Bias in Clinical Medicine.
脉搏血氧测定中的种族偏见对儿童的影响以及如何解决临床医学中的算法偏见。
  • DOI:
    10.1001/jamapediatrics.2023.0077
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    26.1
  • 作者:
    Keyaria D. Gray;Hamsa L Subramaniam;Erich S. Huang
  • 通讯作者:
    Erich S. Huang
Rationale and Design for the Duke Connected Care Predictive Modeling Pilot with a Medicare Shared Savings Program Population
杜克大学互联医疗预测模型试点与医疗保险共享储蓄计划人群的基本原理和设计
The Project Baseline Health Study: a step towards a broader mission to map human health
项目基线健康研究:迈向绘制人类健康更广泛使命的一步
  • DOI:
    10.1038/s41746-020-0290-y
  • 发表时间:
    2020-06-05
  • 期刊:
  • 影响因子:
    15.100
  • 作者:
    Kristine Arges;Themistocles Assimes;Vikram Bajaj;Suresh Balu;Mustafa R. Bashir;Laura Beskow;Rosalia Blanco;Robert Califf;Paul Campbell;Larry Carin;Victoria Christian;Scott Cousins;Millie Das;Marie Dockery;Pamela S. Douglas;Ashley Dunham;Julie Eckstrand;Dominik Fleischmann;Emily Ford;Elizabeth Fraulo;John French;Sanjiv S. Gambhir;Geoffrey S. Ginsburg;Robert C. Green;Francois Haddad;Adrian Hernandez;John Hernandez;Erich S. Huang;Glenn Jaffe;Daniel King;Lynne H. Koweek;Curtis Langlotz;Yaping J. Liao;Kenneth W. Mahaffey;Kelly Marcom;William J. Marks;David Maron;Reid McCabe;Shannon McCall;Rebecca McCue;Jessica Mega;David Miller;Lawrence H. Muhlbaier;Rajan Munshi;L. Kristin Newby;Ezra Pak-Harvey;Bray Patrick-Lake;Michael Pencina;Eric D. Peterson;Fatima Rodriguez;Scarlet Shore;Svati Shah;Steven Shipes;George Sledge;Susie Spielman;Ryan Spitler;Terry Schaack;Geeta Swamy;Martin J. Willemink;Charlene A. Wong
  • 通讯作者:
    Charlene A. Wong

Erich S. Huang的其他文献

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{{ truncateString('Erich S. Huang', 18)}}的其他基金

Training in the Biology of Injury and Inflammation
损伤和炎症生物学培训
  • 批准号:
    7882497
  • 财政年份:
    2004
  • 资助金额:
    $ 14.49万
  • 项目类别:

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