EarthCube Data Infrastructure: Intelligent Databases and Analysis Tools for Geospace Data

GSTDTAP

项目编号	1639683
	EarthCube Data Infrastructure: Intelligent Databases and Analysis Tools for Geospace Data
	Alexander Kosovichev
主持机构	New Jersey Institute of Technology
项目开始年	2017
	2017-08-15
项目结束日期	2019-07-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	500000(USD)
国家	美国
语种	英语
英文摘要	Solar activity and variability are among the key factors determining the state of the Earth?s atmosphere, global trends and climate changes. Explosive events in the form of high-energy radiation and mass ejections cause geomagnetic storms in the ionosphere and magnetosphere, affecting biological systems, disrupting power grids, and communications. For understanding and predicting the complex and evolving Earth system, it is critical to investigate its coupling to the space environment and to solar variability. To facilitate interdisciplinary research on solar influences, PI and the team will develop a unique data environment that will integrate new and archived satellite and ground-based observational data. The integrated data environment will allow researchers to efficiently access solar and geospace data and use them for studying fundamental problems of solar activity and variability and their impacts on Earth systems, as well as for developing new predictive capabilities. The innovative interdisciplinary approach for building an intelligent integrated database, developed in collaboration between heliophysicists and computer scientists, will contribute to knowledge discovery in the EarthCube and associated fields. The proposed activities will facilitate the transfer of innovative data analysis, data visualization, and data-driven modeling techniques to in-class teaching at the undergraduate and graduate levels and to other fields of research that may benefit from a similar framework. The primary goal is to develop tools for data access and analysis that can be easily used by the Geoscience community for studying and modeling various components of the coupled Earth system. The project will develop innovative tools to extract and analyze the available observational and modeling data in order to enable new physics-based and machine-learning approaches for understanding and predicting solar activity and its influence on the geospace and Earth systems. The geospace data are abundant: several terabytes of solar and space observations are obtained every day. Finding the relevant information from numerous spacecraft and ground-based data archives and using it is a paramount, and currently a difficult task. The scope of the project is to develop and evaluate data integration tools to meet common data access and discovery needs for two types of Heliophysics data: 1) long-term synoptic activity and variability, and 2) extreme geoeffective solar events. The project will integrate existing data resources, such as the Heliophysics Knowledge Database (HEK), Solar Dynamics Observatory Joint Science Operations Center (SDO JSOC), Virtual Solar Observatory (VSO), Heliophysics Integrated Observatory (HELIO), and others. The methodology consists in the development of a data integration infrastructure and access methods capable of 1) automatic search and identification of image patterns and event data records produced by space and ground-based observatories, 2) automatic association of parallel multi-wavelength/multi-instrument database entries with unique pattern or event identifiers, 3) automatic retrieval of such data records and pipeline processing for the purpose of annotating each pattern or event according to a predefined set of physical parameters inferable from complimentary data sources, and 4) generation of a pattern or catalog and associated user-friendly graphical interface tools that are capable to provide fast search, quick preview, and automatic data retrieval capabilities
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文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/71597
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Alexander Kosovichev.EarthCube Data Infrastructure: Intelligent Databases and Analysis Tools for Geospace Data.2017.