Ever-est: The platform allowing scientists to cross-fertilize and cross-validate data

被引：0

作者：

Albani M. ^{[1
]}

Leone R. ^{[1
]}

Foglini F. ^{[2
]}

De Leo F. ^{[2
]}

Marelli F. ^{[3
]}

Maggio I. ^{[4
]}

机构：

[1] European Space Agency, Italy

[2] CNR-ISMAR, Italy

[3] Terradue, Italy

[4] Rhea Group, Belgium

来源：

Data Science Journal | 2020年 / 19卷 / 01期

关键词：

Cross-fertilization; Data Analysis; Earth Science; Education; Remote Sensing; Research Object; Virtual Research Environment;

D O I：

10.5334/dsj-2020-021

中图分类号：

学科分类号：

摘要：

Over recent decades large amounts of data about our Planet have become available. If this information could be easily discoverable, accessible and properly exploited, preserved and shared, it would potentially represent a wealth of information for a whole spectrum of stakeholders: from scientists and researchers to the highest level of decision and policy makers. By creating a Virtual Research Environment (VRE) using a service oriented architecture (SOA) tailored to the needs of Earth Science (ES) communities, the EVER-EST (http://ever-est.eu) project provides a range of both generic and domain specific data analysis and management services to support a dynamic approach to collaborative research. EVER-EST provides the means to overcome existing barriers to sharing of Earth Science data and information allowing research teams to discover, access, share and process heterogeneous data, algorithms, results and experiences within and across their communities, including those domains beyond Earth Science. The main objective of this paper is to present the EVER-EST platform in all its components describing the most relevant use cases implemented by the Virtual Research Communities (VRCs) involved in the project. © 2020, Ubiquity Press. All rights reserved.

引用

共 7 条

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