FAIR EArth Sciences & Environment services (FAIR-EASE)

地球和环境科学需要来自卫星、现场观测、模型、组学实验等的大量数据。地球系统各领域是相互关联的，即使各领域之间的接口对于具有重大社会影响的若干研究（如气候变化、农业和粮食、人类安全和健康）来说似乎至关重要，但目前的数字架构基本上是基于分布式和依赖于领域的数据储存库，这给综合使用所有环境数据带来了真实的困难。为了超越这一最先进的技术水平，FAIR-EASE的总体目标是通过与用户社区、欧洲开放科学云和研究基础设施在设计和可持续可用性方面密切合作，改进其不同组成部分的TRL，定制和运营分布式和综合服务，用于地球系统、环境和生物多样性的观测和建模。该项目将：（1）依靠运作前的现有服务，改进公平和公正的数据发现和数据访问服务，以便向用户提供一个简单和公正的工具，用于发现和访问由一系列欧洲数据基础设施管理和提供的环境多学科和综合数据集;（2）建立一个FAIR-EASE地球分析实验室，通过基于网络的接口，预定义的处理工具和按需数据可视化服务，用于远程分析和处理异构数据，促进跨学科协作，缩短取得成果的时间，提高生产力;（3）开发一些多学科用例（UC），以提供FAIR-EASE系统组件的需求，并验证和演示FAIR-EASE服务支持开放科学的能力。

Earth and environmental sciences require a large panel and volume of data from satellite, in-situ observations, models, omics experiments... Earth system domains are interconnected and even if interfaces between domains appear of primary importance for several studies with large societal impacts, such as climate change, agriculture and food, human safety and health, the present digital architecture is based essentially on distributed and domain-dependent data repositories inducing real difficulties for integrated uses of all the environmental data. To go beyond this state-of-the-art, the overall objective of FAIR-EASE is to customize and operate distributed and integrated services for observation and modelling of the Earth system, environment and biodiversity by improving the TRL of their different components implemented in close cooperation with user-communities, the European Open Science Cloud and research infrastructures in their design and sustainable availability. The project will: (1) Improve a FAIR-EASE data discovery and data access service, relying on pre-operational existing services, in order to provide users with an easy and FAIR tool for discovery and access to environmental multidisciplinary and aggregated data-sets as managed and provided by a range of European data infrastructures; (2) Set up a FAIR-EASE Earth Analytical Lab, with EOSC connectivity supporting, through web-based interfaces, predefined processing tools and on-demand data visualization services for remote analysis and processing of heterogeneous data facilitating the cross-disciplinary collaboration, reducing the time to results and increasing productivity; and (3) Develop a number of multidisciplinary Use Cases (UCs) to contribute requirements for the FAIR-EASE system components and to validate and demonstrate the capabilities of the FAIR-EASE service for supporting open science.