The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines, taking into account the flexibility of the FAIR Principles. As an example, we present DIGITAL.CSIC repository as the first target of the tool, gathering the particular needs of a multidisciplinary institution as well as its institutional repository.

翻译：FAIR原则是一组旨在提升开放科学背景下数据可重复性与质量的实践规范。已有多种指标集被提出用于评估数字对象的FAIR化程度，其中包括通常存储在知识库或数据门户中的数据集。然而，诸如研究数据联盟所提出的指标是从可被解读的高层次视角提供的，且对于多学科知识库等特定环境而言并不总是具有现实可行性。本文描述了FAIR EVA，一个在欧洲开放科学云框架内开发的新工具，该工具面向开放知识库等特定数据管理系统，可在可扩展的自动化环境中针对具体案例进行定制。它旨在具备足够的适应性，能够适配不同环境、知识库软件及学科领域，同时充分考虑FAIR原则的灵活性。以DIGITAL.CSIC知识库为例，我们将其作为该工具的首个应用目标，汇总了多学科机构及其机构知识库的特定需求。