Open science represents a transformative research approach essential for enhancing sustainability and impact. Data generation encompasses various methods, from automated processes to human-driven inputs, creating a rich and diverse landscape. Embracing the FAIR principles -- making data and, in general, artifacts (such as code, configurations, documentation, etc) findable, accessible, interoperable, and reusable -- ensures research integrity, transparency, and reproducibility, and researchers enhance the efficiency and efficacy of their endeavors, driving scientific innovation and the advancement of knowledge. Open Science Platforms OSP (i.e., technologies that publish data in a way that they are findable, accessible, interoperable, and reusable) are based on open science guidelines and encourage accessibility, cooperation, and transparency in scientific research. Evaluating OSP will yield sufficient data and artifacts to enable better sharing and arrangement, stimulating more investigation and the development of new platforms. In this paper, we propose an evaluation framework that results from evaluating twenty-two FAIR-a tools assessing the FAIR principles of OSP to identify differences, shortages, and possible efficiency improvements.

翻译：开放科学代表了一种变革性的研究方法，对提升可持续性和影响力至关重要。数据生成涵盖从自动化流程到人工驱动的多种方法，形成了丰富多样的格局。遵循FAIR原则——使数据及广义的研究产物（如代码、配置、文档等）具备可发现性、可访问性、互操作性和可重用性——能确保研究的完整性、透明度和可复现性，研究人员借此提升研究工作的效率与效能，推动科学创新与知识进步。开放科学平台（OSP，即以可发现、可访问、可互操作和可重用的方式发布数据的技术）基于开放科学准则，鼓励科学研究中的可访问性、协作性和透明度。对OSP进行评估将产生充足的数据和研究成果，从而实现更好的共享与组织，促进更多研究探索和新平台的开发。本文提出一个评估框架，该框架源于对22种FAIR评估工具（用于评估OSP的FAIR原则）的评估结果，旨在识别差异、不足及可能的效率改进方向。