RAPID: Collecting Reliable COVID-19 Datasets in Crisis Conditions

RAPID:在危机情况下收集可靠的 COVID-19 数据集

基本信息

  • 批准号:
    2029457
  • 负责人:
  • 金额:
    $ 7万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-01 至 2020-10-31
  • 项目状态:
    已结题

项目摘要

This RAPID project enables approaches to mitigate the negative impacts of COVID-19 on public health, society, and the economy by deploying technologies to enable collecting reliable COVID-19-related data sets under crisis conditions. In the midst of a crisis, such as the COVID-19 pandemic, generators of critical new data, such as hospitals and critical health organizations, lack the time and resources to make this important data readily available for use by others. One cannot expect the already overburdened primary data providers to do the extra work needed to make the data more accessible for others to use. Even those who already publish data on their websites often do not have the time to edit/modify the data, for example to apply newly introduced tags, such as Schema.org’s new tags related to coronavirus. Yet, these data are critical in a crisis in order to inform the public; improve emergency response; and aid the scientific community in its efforts to find solutions. Currently, the teams that are engaged in dataset collection are employing slow, tedious, and painstaking manual techniques. The interactive dataset collection tools to be developed by this project will provide an alternative approach, empowering a community of volunteers to help with data collection efforts. The data collection tools developed can be used with only an internet connection, a web browser, and brief training, thereby putting the effort well within reach of a large population of potential volunteers. Existing automatic data extractors assume that (i) webpages in a single website are structured uniformly, because they were produced from the same template and (ii) relevant webpages originate from a single website. As a result, much of the prior work in the area of web data extraction and ingestion focuses on ‘syntactic’ extraction. Currently, dedicated data collection teams are collecting data with a combination of expertise and time-consuming and painstaking manual effort. Other teams are hiring call centers to call hospitals in each state to collect their capacities. Such high-cost, high-effort approaches do not scale well to all the datasets that one would like to be able to access and analyze. Many COVID-19-related datasets are scattered over thousands of websites with similar information but no structural similarities--e.g., each hospital’s website may look different but may contain very similar and related data. The technical challenge that this project will tackle will be to build a ‘semantic’ data extractor that locates the information of interest despite divergent website structures. The software tools that will be created for data ingestion can be used by the many individuals who are keen to contribute their time and effort to help combat COVID-19, without compromising their physical distancing efforts.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities and is associated with the Convergence Accelerator Track A: Open Knowledge Network.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.
该RAPID项目通过部署技术,在危机条件下收集可靠的COVID-19相关数据集,从而实现减轻COVID-19对公共卫生、社会和经济的负面影响的方法。在危机中,如COVID-19大流行,关键新数据的生成者,如医院和关键卫生组织,缺乏时间和资源使这些重要数据随时可供他人使用。我们不能指望已经负担过重的主要数据提供者做额外的工作,使其他人更容易获得数据。即使那些已经在其网站上发布数据的人也往往没有时间编辑/修改数据,例如应用新引入的标签,例如Schema.org与冠状病毒相关的新标签。然而,这些数据在危机中至关重要,以便向公众提供信息;改善应急反应;并帮助科学界努力寻找解决方案。目前,从事数据集收集的团队正在使用缓慢,繁琐和艰苦的手动技术。 该项目将开发的交互式数据集收集工具将提供一种替代办法,使志愿者社区能够帮助数据收集工作。所开发的数据收集工具仅需连接互联网、网络浏览器和简短的培训即可使用,从而使大量潜在志愿者能够很好地完成这项工作。现有的自动数据提取器假设(i)单个网站中的网页是统一结构的,因为它们是从相同的模板产生的,以及(ii)相关网页源自单个网站。 因此,Web数据提取和摄入领域的大部分先前工作都集中在“语法”提取上。 目前,专门的数据收集小组正在收集数据,同时利用专门知识和耗时费力的手工劳动。 其他团队正在雇佣呼叫中心来呼叫每个州的医院,以收集他们的能力。这种高成本、高工作量的方法不能很好地扩展到人们希望能够访问和分析的所有数据集。许多与COVID-19相关的数据集分散在数千个网站上,这些网站具有相似的信息,但没有结构相似性-例如,每个医院的网站可能看起来不同,但可能包含非常相似和相关的数据。该项目将解决的技术挑战将是建立一个“语义”数据提取器,定位感兴趣的信息,尽管不同的网站结构。将为数据摄取创建的软件工具可供许多热衷于贡献时间和精力帮助抗击COVID-19的个人使用,而不会影响他们的物理距离工作。此RAPID奖由综合活动办公室的融合加速器计划颁发,与融合加速器轨道A相关:开放知识网络。该奖项反映了NSF的法定使命,并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

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Rastislav Bodik其他文献

Rastislav Bodik的其他文献

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{{ truncateString('Rastislav Bodik', 18)}}的其他基金

Collaborative Research: FMitF: Track I: End-usser Programming for CAD Systems via Language Design and Synthesis
协作研究:FMitF:第一轨:通过语言设计和综合进行 CAD 系统的最终用户编程
  • 批准号:
    2219864
  • 财政年份:
    2022
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
FMitF: Track I: End-User Programming with Synthesis-Guided Interaction Models
FMITF:第一轨:使用综合引导交互模型的最终用户编程
  • 批准号:
    2122950
  • 财政年份:
    2021
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
FMitF: Track II: Programming by Demonstration for the Browser with Applications in Data Science
FMITF:轨道 II:通过数据科学应用程序对浏览器进行演示编程
  • 批准号:
    1918027
  • 财政年份:
    2019
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
Convergence Accelerator Phase I (RAISE): Linking the Open Knowledge Network to the Web with End-User Programming
融合加速器第一阶段 (RAISE):通过最终用户编程将开放知识网络链接到网络
  • 批准号:
    1936731
  • 财政年份:
    2019
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
CAPA: Collaborative Research: ARION: Taming Heterogeneity with DSLs, Approximation, and Synthesis
CAPA:合作研究:ARION:通过 DSL、近似和综合来驯服异质性
  • 批准号:
    1723352
  • 财政年份:
    2017
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
SI2-SSE: Algorithms and Tools for Data-Driven Executable Biology
SI2-SSE:数据驱动的可执行生物学的算法和工具
  • 批准号:
    1535191
  • 财政年份:
    2015
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
Student travel support for POPL 2016
POPL 2016 学生旅行支持
  • 批准号:
    1549324
  • 财政年份:
    2015
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
Student travel support for POPL 2016
POPL 2016 学生旅行支持
  • 批准号:
    1625220
  • 财政年份:
    2015
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
SHF: Small: Programming Abstractions for Algorithmic Software Synthesis
SHF:小型:算法软件综合的编程抽象
  • 批准号:
    0916351
  • 财政年份:
    2009
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant
SoD-TEAM: Programming by Sketching
SoD-TEAM:通过草图进行编程
  • 批准号:
    0613997
  • 财政年份:
    2006
  • 资助金额:
    $ 7万
  • 项目类别:
    Standard Grant

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