Earthcube Building Blocks: Collaborative Proposal: Polar Data Insights and Search Analytics for the Deep and Scientific Web

Earthcube 构建模块：协作提案：深层科学网络的极地数据洞察和搜索分析

• 批准号: 1639652
• 负责人: Ruth Duerr
• 金额: $ 12.96万
• 依托单位: Ronin Institute for Independent Scholarship Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1639652&HistoricalAwards=false
• 关键词: Earthcube; Building; Blocks; Collaborative; Proposal

This project develops an NSF EarthCube Building Block focused on Polar Data Science. The system will build upon work in Information Retrieval and Data Science and upon existing investment from NSF Polar, EarthCube, and from DARPA and NASA in this area. The system will collect, analyze, and make interactive the wealth of textual and scientific Polar data collected to date across the Deep web of scientific information -- scientific journals, multimedia information, scientific data, web pages, etc. The system builds upon fundamental research in text analysis, search, and visualization. Its primary goal is to unlock unstructured scientific data from 90+ data formats and to scale to 10s-100s of millions of records using the NSF XSEDE supercomputing resources. The system will perform information retrieval and machine learning on data crawled from the Polar Deep and Scientific web. Crawling will be informed by science questions crowdsourced through the EarthCube and Polar communities. The project is a collaboration with NSIDC, Ronin Institute, and the broader community including the newly funded Arctic Data Center led by NCEAS, to build our proposed system.The result of periodic and regular crawling will be a Crawl Data Repository (CDR) of raw textual data e.g., web pages containing richly curated dataset abstract descriptions, news stories tied to datasets, ASCII note files and dataset descriptions, and other textual data available on or pointed to by Polar repositories as well as scientific data (HDF, Grib, NetCDF, Matlab, etc.). The CDR will be made available for historical and future analysis by the broader EarthCube and Polar communities. In addition, an extraction pipeline will generate an Extraction Data Repository (EDR) of machine learning features not previously present (geospatial, temporal, people, places, scientific publications and topics, etc.) that will be the basis of interactive, visual analytics over the Polar data resources. Information collected will assist in answering scientific questions such as these derived from the President?s National Strategy for the Arctic Region. To date, the team has also crowd sourced 30+ questions from the Polar community represented on CRYOLIST https://goo.gl/4dDyIS and will continue to solicit this feedback and use the information collected to aid science as prioritized by the community. They will also engage the community to assist in validating our system. This is not a predictive tool per-se ? though it can help to enable such predictions. Its focus is on building an operational and core capability for textual scientific data analysis, both retrospective, and prospective.

该项目开发了一个NSF EarthCube构件，该构件的重点是极地数据科学。该系统将基于信息检索和数据科学的工作以及NSF Polar，Earthcube以及该领域的DARPA和NASA的现有投资。该系统将收集，分析并使迄今为止在科学信息深度网络（科学期刊，多媒体信息，科学数据，网页等）中收集的文本和科学极地数据的丰富丰富的互动性。该系统基于文本分析，搜索和可视化的基本研究。它的主要目标是使用NSF XSEDE超级计算资源将非结构化的科学数据从90多个数据格式解锁，并扩展到10秒钟的记录。该系统将对从极性深层和科学的网络爬网的数据进行信息检索和机器学习。通过Earthcube和Polar Communities众包的科学问题将为爬行。 The project is a collaboration with NSIDC, Ronin Institute, and the broader community including the newly funded Arctic Data Center led by NCEAS, to build our proposed system.The result of periodic and regular crawling will be a Crawl Data Repository (CDR) of raw textual data e.g., web pages containing richly curated dataset abstract descriptions, news stories tied to datasets, ASCII note files and dataset descriptions,以及其他文本数据可在极地存储库以及科学数据（HDF，GRIB，NETCDF，MATLAB等）上获得或指向。 CDR将由更广泛的Earth Cube和Polar Communities提供历史和未来分析。此外，提取管道将生成一个提取数据存储库（EDR）的机器学习功能（地理空间，时间，人，地方，科学出版物和主题等），这将是对极地数据资源的交互式，视觉分析的基础。收集的信息将有助于回答诸如总统国家国家战略的科学问题。迄今为止，该团队还从Cryolist https://goo.gl/4ddyis上提出了30多个问题，并将继续征求此反馈，并使用收集的信息来帮助科学，以帮助社区优先。他们还将吸引社区以协助验证我们的系统。这不是一个预测工具？尽管它可以帮助实现此类预测。它的重点是建立用于文本科学数据分析的运营和核心能力，无论是回顾性还是潜在的。