Recommendations for Services in a FAIR Data Ecosystem.
Recommendations for Services in a FAIR Data Ecosystem.
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DOI：
10.1016/j.patter.2020.100058
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发表时间：
2020-08-14
期刊：
影响因子：
--
通讯作者：
Mokrane M
中科院分区：
文献类型：
--
作者：
Koers H;Bangert D;Hermans E;van Horik R;de Jong M;Mokrane M
作为研究政策和实践的一部分，FAIR数据原则和相关标准的发展和日益广泛的采用对研究数据服务提出了新的要求。本文强调了共同的挑战和优先事项，并就数据基础设施如何发展和合作提供支持FAIR数据原则实施的服务提出了一系列建议，特别是在构建欧洲开放科学云（EOSC）的背景下。这些建议涵盖了广泛的主题领域，包括认证、基础设施组件、管理、成本、奖励、协作、培训、支持和数据管理。这些建议根据不同利益攸关方群体认为的紧迫性进行了优先排序，并与行动以及建议的行动负责人相关联。本文是由FAIRsFAIR，RDA Europe，OpenAIRE，EOSC-hub和FREYA项目组织的三个研讨会的成果，旨在探索，讨论和制定科学界利益相关者之间的建议。虽然这些结果是一项正在进行的工作，但概述的挑战和优先事项提供了对当前问题的详细和独特的概述，这些问题被社区视为至关重要的，可以加强和改进通往公平数据生态系统的路线图。本文为数据和基础设施服务提供商提出了建议，以支持学术生态系统中的可查找，可访问，可互操作和可重用（FAIR）研究数据。制定这些建议对于协调实现公平数据生态系统的进展非常重要，在这个生态系统中，研究数据可以轻松共享和最佳重用，目的是降低当前学术系统的效率低下，并实现新形式的数据驱动发现。从广泛的社区咨询过程中得出的关键建议（按其紧迫性排序）包括：（1）资助者和机构应将FAIR对齐和数据共享作为研究评估的一部分，以及其他标准;（2）服务应通过识别缺乏本体的学科和丰富现有的本体注册表来支持特定领域的本体;（3）存储库应通过开发工具（如API）、共享最佳实践和进行FAIR认证来支持FAIR数据;（4）机构应通过建立数据管理计划为研究人员提供简单直观的培训来支持FAIR意识和实施。本文中概述的建议旨在帮助指导如何将FAIR数据管理指导原则付诸实践。本文为数据和基础设施服务提供商提出了建议，以支持学术生态系统内的FAIR研究数据，这些数据是通过广泛的社区咨询过程收集的。这些建议对于协调实现FAIR数据生态系统的进展非常重要，在该生态系统中，研究数据可以轻松共享和最佳重用，目的是降低当前学术系统的效率低下，并实现新形式的数据驱动发现。
The development and growing adoption of the FAIR data principles and associated standards as a part of research policies and practices place novel demands on research data services. This article highlights common challenges and priorities and proposes a set of recommendations on how data infrastructures can evolve and collaborate to provide services that support the implementation of the FAIR data principles, in particular in the context of building the European Open Science Cloud (EOSC). The recommendations cover a broad area of topics, including certification, infrastructure components, stewardship, costs, rewards, collaboration, training, support, and data management. These recommendations were prioritized according to their perceived urgency by different stakeholder groups and associated with actions as well as suggested action owners. This article is the output of three workshops organized by the projects FAIRsFAIR, RDA Europe, OpenAIRE, EOSC-hub, and FREYA designed to explore, discuss, and formulate recommendations among stakeholders in the scientific community. While the results are a work-in-progress, the challenges and priorities outlined provide a detailed and unique overview of current issues seen as crucial by the community that can sharpen and improve the roadmap toward a FAIR data ecosystem. This article puts forward recommendations for data and infrastructure service providers to support findable, accessible, interoperable, and reusable (FAIR) research data within the scholarly ecosystem. Formulating such recommendations is important to coordinate progress in realizing a FAIR data ecosystem in which research data can be easily shared and optimally reused, with the aim of driving down inefficiencies in the current academic system and enabling new forms of data-driven discovery. Key recommendations—ranked by their perceived urgency—resulting from an extensive community consultation process include that (1) funders and institutions should consider FAIR alignment and data sharing as part of research assessment, among other criteria; (2) services should support domain-specific ontologies by identifying disciplines that lack ontologies and enriching existing registries of ontologies; (3) repositories should support FAIR data by developing tools, such as APIs, sharing best practices, and undergoing FAIR-aligned certification; and (4) institutions should support FAIR awareness and implementation by establishing data stewardship programs providing simple and intuitive training for researchers. The recommendations outlined in this article are meant to help guide the way forward to putting into practice the FAIR guiding principles for data management. This article puts forward recommendations for data and infrastructure service providers to support FAIR research data within the scholarly ecosystem, gathered through an extensive process of community consultation. Such recommendations are important to coordinate progress in realizing a FAIR data ecosystem in which research data can be easily shared and optimally reused, with the aim of driving down inefficiencies in the current academic system and enabling new forms of data-driven discovery.
影响因子：
82.9
作者：
Long, Quan-Xin;Liu, Bai-Zhong;Huang, Ai-Long
通讯作者：
Huang, Ai-Long
影响因子：
9.8
作者：
Wilkinson, Mark D.;Sansone, Susanna-Assunta;Dumontier, Michel
通讯作者：
Dumontier, Michel
影响因子：
9.8
作者：
Wilkinson MD;Dumontier M;Aalbersberg IJ;Appleton G;Axton M;Baak A;Blomberg N;Boiten JW;da Silva Santos LB;Bourne PE;Bouwman J;Brookes AJ;Clark T;Crosas M;Dillo I;Dumon O;Edmunds S;Evelo CT;Finkers R;Gonzalez-Beltran A;Gray AJ;Groth P;Goble C;Grethe JS;Heringa J;'t Hoen PA;Hooft R;Kuhn T;Kok R;Kok J;Lusher SJ;Martone ME;Mons A;Packer AL;Persson B;Rocca-Serra P;Roos M;van Schaik R;Sansone SA;Schultes E;Sengstag T;Slater T;Strawn G;Swertz MA;Thompson M;van der Lei J;van Mulligen E;Velterop J;Waagmeester A;Wittenburg P;Wolstencroft K;Zhao J;Mons B
通讯作者：
Mons B