Convergence Accelerator Phase I (RAISE): Knowledge Open Network Queries for Research (KONQUER)

融合加速器第一阶段 (RAISE)：研究知识开放网络查询 (KONQUER)

• 批准号: 1937136
• 负责人: Lucila Ohno-Machado
• 金额: $ 99.99万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937136&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Knowledge

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is developing new tools to allow researchers to conduct more accurate, informed research across multiple, disparate scientific domains. The initial efforts will provide research tools that address national concerns such as health; re-use and study of data that the public has already funded through federally awarded grants; and improving training in science, technology, engineering, mathematics, and medicine. The team includes partnerships with researchers from biomedical, social, geo-science, and climate science fields and integrates extensive expertise from data cyberinfrastructure efforts including: DataMed, a biomedical discovery index previously funded by the National Institutes of Health Big Data to Knowledge initiative; Data Discovery Studio, a geoscience discovery index funded by the National Science Foundation (NSF); and Pangeo, a climate science discovery and integration platform funded by NSF and NASA. During Phase I, the project will develop a prototype search engine, called KONQUER (Knowledge Open Network Queries for Research) that will connect these disparate data types and facilitate queries across the integrated datasets. The initial focus of the project is on biomedical, geological, and climate science fields; however, the technology can be extended to cover other scientific disciplines in the future. Technological advancements have generated a large volume of data, but finding and analyzing those data in meaningful ways is often challenging. Real-world scientific questions cross multiple fields. For example, "Did the precipitation levels in California's Central Valley in 2016 cause an increase in the number of Valley fever cases?" To answer this question, requires data from health care, geolocation, and climate science. However, researchers are traditionally trained in only one field and are not familiar with the data resources of other disciplines, and even when they are aware of other data sources, the data may be structured very differently, all of which slows the process of discovery. KONQUER is envisioned to be a data discovery index capable of integrating various scientific fields and will use natural language processing tools (like a Google search) to decompose questions and retrieve information from the relevant data sources. To achieve this goal the project team will extend the DATS (DAta Tag Suite) metadata format so that geo/climate and science/biomedical datasets can be indexed in compatible formats, develop a pipeline for automated indexing, and develop a search engine that can query and rank the indexed data. The resulting KONQUER tool will accelerate and transform research and information retrieval so that new hypotheses and discoveries are possible across disciplinary boundaries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛的影响和潜在的社会效益是开发新的工具，使研究人员能够在多个不同的科学领域进行更准确、更知情的研究。最初的努力将提供研究工具，以解决国家关注的问题，如健康；重新使用和研究公众已经通过联邦授予的拨款资助的数据；以及改善科学、技术、工程、数学和医学方面的培训。该团队与生物医学、社会、地球科学和气候科学领域的研究人员建立了合作伙伴关系，并整合了数据网络基础设施工作中的广泛专业知识，其中包括：DataMed，一个以前由美国国立卫生研究院资助的生物医学发现指数大数据到知识倡议；Data Discovery Studio，一个由国家科学基金会(NSF)资助的地球科学发现指数；以及Pangeo，一个由NSF和NASA资助的气候科学发现和整合平台。在第一阶段，该项目将开发一个原型搜索引擎，称为KONQUER(用于研究的知识开放网络查询)，它将连接这些不同的数据类型，并促进跨集成数据集的查询。该项目最初的重点是生物医学、地质和气候科学领域；然而，该技术未来可以扩展到其他科学学科。技术进步产生了大量数据，但以有意义的方式查找和分析这些数据往往是具有挑战性的。现实世界中的科学问题跨越多个领域。比如，2016年加州中央山谷的降雨量是否导致山谷热病例增加？要回答这个问题，需要来自医疗保健、地理位置和气候科学的数据。然而，研究人员传统上只接受过一个领域的培训，对其他学科的数据资源并不熟悉，即使他们知道其他数据源，数据的结构可能也会有很大的不同，所有这些都会减缓发现的进程。KONQUER被设想为一个能够整合各种科学领域的数据发现索引，并将使用自然语言处理工具(如谷歌搜索)来分解问题并从相关数据源检索信息。为实现这一目标，项目组将扩展DATS(Data Tag Suite)元数据格式，使地理/气候和科学/生物医学数据集能够以兼容的格式编制索引，开发一条自动编制索引的管道，并开发一个可以查询索引数据并对其进行排序的搜索引擎。由此产生的KONQUER工具将加速和转变研究和信息检索，使新的假设和发现成为可能，跨越学科边界。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Coronavirus: indexed data speed up solutions

冠状病毒：索引数据加速解决方案

• DOI: 10.1038/d41586-020-02331-3
• 发表时间: 2020
• 期刊: Nature
• 影响因子: 64.8
• 作者: Ohno-Machado, Lucila;Xu, Hua
• 通讯作者: Xu, Hua

Multi-Cloud workflows with Pangeo and Dask Gateway

使用 Pangeo 和 Dask Gateway 的多云工作流程

• 发表时间: 2020
• 期刊: 2020 EarthCube Annual Meeting
• 作者: Augspurger, T.;Durant, M.;Abernathey, R.;Hamman, J.
• 通讯作者: Hamman, J.

Intake / Pangeo Catalog: Making It Easier To Consume Earth’s Climate and Weather Data

Intake / Pangeo Catalog：让使用地球气候和天气数据变得更容易

• 发表时间: 2020
• 期刊: 2020 EarthCube Annual Meeting
• 作者: Banihirwe, A.;Blackmon-Luca, C.;Abernathey, R.;Hamman, J.
• 通讯作者: Hamman, J.

Scikit-downscale: an open source Python package for scalable climate downscaling

Scikit-downscale：用于可扩展气候降尺度的开源 Python 包

• DOI: 10.1002/essoar.10507604.1
• 发表时间: 2020
• 期刊: 2020 EarthCube Annual Meeting
• 作者: Hamman, Joseph;Kent, Julia
• 通讯作者: Kent, Julia