EAGER Collaborative: Bringing Together Computational and Linguistic Methods to Extract 'Dark' Geosciences Data for the EarthCube Framework

很大比例的可拱顶地学数据是基于对离散样本的分析，并且是手动收集的(例如，古生物收集、构造/构造数据、岩相学/矿物学数据、经济数据、地球化学测量、岩石力学等)。通常，这些数据只在已发表的文献中的表格中或在个人调查人员网站上的.pdf或电子表格中报告。通常，这些数据不会登记在标准化的、可公开访问的数据库中，也不会输入到这些数据库中。因此，为了发现和使用/重复使用这些数据，研究人员或其他感兴趣的各方必须手动梳理个别研究人员的期刊文章或网站的文本、数字和附录，有时还必须筛选原始实验数据。这一过程非常耗时，会减慢做出科学发现或验证研究结果所需的时间。因此，目前无法访问海量的地表地球地球科学数据。这种不可访问的数据被称为“暗数据”。这种渴望结合了顶尖计算机科学家和地球科学家的专业知识，他们的目标是创建一种搜索算法，以一种使下一代综合地球科学研究成为可能的方式，将这些暗数据公之于众。这一方法将涉及开发一种创新的搜索引擎“爬虫”，它将梳理地球科学文献，并将该语料库中的文本和数字中的黑暗数据公之于众。正在开发的网络基础设施工具将能够解释英文文本的语义和地球科学的概念。该工具将通过检查Macrostrat数据库的条目进行试点，Macrostrat数据库是岩性和地质年代学信息的结构化空间数据库，然后通过哈兹框架采用地球科学本体论进行信息提取。要解决的问题将是找出目前在多大程度上可以访问暗数据，以及是否可以提取这些数据并将其放入可访问的格式和存储库，以便网络服务或其他搜索引擎可以发现这些数据。这项工作的更广泛影响包括培训研究生和通过开发一种新的急需的数据搜索工具来增加科学基础设施。

A large percentage of vaulable geoscience data is based on the analysis of discrete samples and is collected manually (e.g., paleontological collections, structural/tectonic data, petrographic/mineralogic data, economic data, geochemical measurements, rock mechanics, etc.) Often, these data are reported only in tables in the published literature or in .pdf or spreadsheets on individual investigator websites. Commonly these data are not registerd on or entered into standardized, publicly accessible databases. As a result, for this data to be discovered and used/reused, researchers or other interested parties must manually comb through the text, figures, and appendices of journal articles or websites of individual investigators, sometimes having to sift through raw experimental data. This process is extremely time intensive and slows down the time needed to make scientific discoveries or allow verification of research results. As a result the vast amount of surface earth geoscience data is currently inaccessible. This inaccessible data is termed "Dark Data". This EAGER combines the expertise of top-notch computer scientists and geoscientists whose goal is to create a search algorithm to bring this dark data to light in a way that will enable the next generation of integrative geoscience research. The approach will involved development of an innovative search engine "crawler" that will comb the geoscience literature and bring dark data to light from the text and figures in this corpus. The cyberinfrastructure tool being developed will be able to interpret the semantics of English text and the concepts of geoscience. The tool will be piloted by examining entries on the Macrostrat database, a structured spatial database of lithologic and geochronologic information, and then employing a geoscience ontology by means of the Hazy framework for information extraction. Questions to be addressed will be to find out to what extent dark data is presently accessible and if it can be extracted and placed into an accessible format and repository where it can be discovered by web services or other search engines. Broader impacts of the work include training of graduate students and increasing the infrastructure for science through the development of a new and much needed data search tool.