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.

该项目开发了一个专注于极地数据科学的NSF EarthCube积木。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在这一领域的现有投资的基础上。该系统将收集、分析迄今通过科学期刊、多媒体信息、科学数据、网页等深网收集的丰富的文本和科学极地数据，并使其具有互动性。该系统建立在文本分析、搜索和可视化基础研究的基础上。它的主要目标是解锁90多种数据格式的非结构化科学数据，并使用NSF XSEDE超级计算资源扩展到10s-100s的数百万条记录。该系统将对从极地深度和科学网络爬行的数据进行信息检索和机器学习。爬行将通过通过EarthCube和Polar社区众包的科学问题来进行。该项目是与NSIDC、罗宁研究所和更广泛的社区合作，包括由NCEAS领导的新资助的北极数据中心，以构建我们建议的系统。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，包含丰富精选的数据集摘要描述、与数据集相关的新闻报道、ASCII笔记文件和数据集描述的网页，以及极地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将提供给更广泛的地球立方体和极地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特征(地理空间、时间、人、地点、科学出版物和主题等)的提取数据储存库。这将是对极地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科学问题，如S主席提出的北极地区国家战略。到目前为止，该团队还从CRYOLIST https://goo.gl/4dDyIS上的极地社区众包了30多个问题，并将继续征求这些反馈，并使用收集的信息来帮助社区优先考虑的科学。他们还将动员社会人士协助验证我们的系统。这本身不是一个预测性工具吗？尽管它可以帮助实现这样的预测。它的重点是建立用于文本科学数据分析的操作和核心能力，包括回溯性和前瞻性。