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Geoinformatics: Collaborative Research: Proposal to enhance the experimental petrology database LEPR and to develop interoperability between LEPR and EarthChem

Geoinformatics: Collaborative Research: Proposal to enhance the experimental petrology database LEPR and to develop interoperability between LEPR and EarthChem
地理信息学:协作研究:关于增强实验岩石学数据库 LEPR 并开发 LEPR 和 EarthChem 之间的互操作性的提案
批准号:
0743933
负责人:
Mark Ghiorso
金额:
$15.98万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-09-15 至 2010-08-31

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中文摘要
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英文摘要
0743933GhiorsoThis award provides funding to improve the Library of Experimental Phase Relations (LEPR) and to develop a mechanism for interoperability of LEPR with the EarthChem database. LEPR is a relational database of compositional and ancillary data from almost 7,000 magmatic phase equilibrium experiments. The EarthChem data base contains composition, geographic distribution and other data on over 230,000 naturally occurring igneous rocks. The goals of the project are: 1) to streamline and partially automate the process of data entry to LEPR in order to facilitate updates and to enable potential contributors in the community of experimentalists to archive their published and unpublished data, 2) to improve query capabilities, incorporate modeling tools for analysis of data subsets, and develop practical examples of manipulating data in LEPR in order to motivate use of the database for research and for pedagogical purposes, and 3) to develop a process that will allow users to cross correlate data between LEPR and the EarthChem igneous rock databse.The enhancements to LEPR and the establishment of LEPR-EarthChem interoperability will enable a wider community of scientists to make use of these databases for research and for teaching. Students will have access to real-world data for projects without the hassle of having to gather data from a number of disparate sources. Most importantly, an enhanced LEPR and the LEPR-EarthChem connection will create a data-mining environment that promotes synthesis and model development from diverse data sources.
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