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.

该项目开发了一个专注于极地数据科学的 NSF EarthCube Building Block。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、DARPA 和 NASA 在该领域的现有投资的基础上。该系统将收集、分析迄今为止通过科学信息深网（科学期刊、多媒体信息、科学数据、网页等）收集的大量文本和科学极地数据，并进行交互。该系统建立在文本分析、搜索和可视化方面的基础研究之上。其主要目标是使用 NSF XSEDE 超级计算资源从 90 多种数据格式中解锁非结构化科学数据，并扩展到数十至数百百万条记录。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。通过 EarthCube 和 Polar 社区众包的科学问题将为爬行提供信息。该项目是与 NSIDC、Ronin Institute 以及更广泛的社区（包括由 NCEAS 领导的新资助的北极数据中心）合作构建的系统。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的网页、与数据集相关的新闻报道、ASCII 注释文件和数据集描述， 以及 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）。 CDR 将可供更广泛的 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、人物、地点、科学出版物和主题等）的提取数据存储库（EDR），这将成为对 Polar 数据资源进行交互式可视化分析的基础。收集到的信息将有助于回答诸如源自总统北极地区国家战略的科学问题。迄今为止，该团队还从 CRYOLIST https://goo.gl/4dDyIS 上代表的 Polar 社区众包了 30 多个问题，并将继续征求这些反馈，并根据社区的优先顺序使用收集到的信息来帮助科学研究。他们还将让社区参与协助验证我们的系统。这本身不是一个预测工具吗？尽管它可以帮助实现这样的预测。其重点是建立回顾性和前瞻性文本科学数据分析的运营和核心能力。