FAIR EArth Sciences & Environment services (FAIR-EASE)

地球和环境科学需要大量来自卫星、现场观测、模型、组学实验的数据。地球系统领域是相互关联的，即使领域之间的接口对于气候变化、农业和粮食、人类安全与健康等具有重大社会影响的若干研究显得至关重要，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存储库，这给综合利用所有环境数据带来了真正的困难。为了超越这一最先进的技术，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其设计和可持续可用性方面密切合作，改进其不同组件的TRL，为地球系统、环境和生物多样性的观测和建模定制和运行分布式和集成服务。该项目将：(1)改进公平-轻松的数据发现和数据访问服务，依赖于运行前的现有服务，以便为用户提供一个简单和公平的工具，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汇总数据集；(2)建立FAIR-EASE地球分析实验室，支持EOSC连接，通过基于web的接口、预定义的处理工具和按需数据可视化服务，对异构数据进行远程分析和处理，促进跨学科协作，缩短获得结果的时间，提高生产力；(3)开发多个多学科用例（UCs），为FAIR-EASE系统组件提供需求，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.