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: 1639753
• 负责人: Chris Mattmann
• 金额: $ 51.5万
• 依托单位: University of Southern California
• 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=1639753&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NSF EarthCube Building Block，专注于极地数据科学。该系统将建立在信息检索和数据科学方面的工作以及NSF Polar，EarthCube以及DARPA和NASA在该领域的现有投资基础上。该系统将收集，分析和互动的文本和科学极地数据的财富收集到目前为止在科学信息的深网-科学期刊，多媒体信息，科学数据，网页等系统建立在文本分析，搜索和可视化的基础研究。它的主要目标是从90多种数据格式中解锁非结构化科学数据，并使用NSF XSEDE超级计算资源扩展到10 - 100万条记录。该系统将对从Polar Deep和Scientific网络抓取的数据进行信息检索和机器学习。爬行将通过EarthCube和Polar社区众包的科学问题提供信息。该项目是与NSIDC，Ronin Institute和更广泛的社区（包括由NCEAS领导的新资助的北极数据中心）合作，以构建我们提出的系统。定期和定期爬行的结果将是原始文本数据的爬行数据存储库（CDR），例如，包含丰富的数据集摘要描述、与数据集相关的新闻故事、ASCII注释文件和数据集描述、Polar存储库上可用或指向的其他文本数据以及科学数据（HDF、Grib、NetCDF、Matlab等）的网页。CDR将提供给更广泛的EarthCube和Polar社区进行历史和未来分析。此外，提取管道将生成以前不存在的机器学习功能（地理空间，时间，人员，地点，科学出版物和主题等）的提取数据存储库（EDR）。这将是对Polar数据资源进行交互式可视化分析的基础。收集的信息将有助于回答科学问题，如这些来自总统？北极地区国家战略。到目前为止，该团队还从CRYOLIST https://goo.gl/4dDyIS上的Polar社区中收集了30多个问题，并将继续征求这些反馈，并利用收集到的信息来帮助社区优先考虑的科学。他们还将与社区合作，协助验证我们的系统。这不是一个预测工具本身？尽管它可以帮助实现这样的预测。它的重点是建立一个操作和核心能力的文本科学数据分析，回顾性和前瞻性。