Six years after the seminal paper on FAIR was published, researchers still struggle to understand how to implement FAIR. For many researchers FAIR promises long-term benefits for near-term effort, requires skills not yet acquired, and is one more thing in a long list of unfunded mandates and onerous requirements on scientists. Even for those required to or who are convinced they must make time for FAIR research practices, the preference is for just-in-time advice properly sized to the scientific artifacts and process. Because of the generality of most FAIR implementation guidance, it is difficult for a researcher to adjust the advice to their situation. Technological advances, especially in the area of artificial intelligence (AI) and machine learning (ML), complicate FAIR adoption as researchers and data stewards ponder how to make software, workflows, and models FAIR and reproducible. The FAIR+ Implementation Survey Tool (FAIRIST) mitigates the problem by integrating research requirements with research proposals in a systematic way. FAIRIST factors in new scholarly outputs such as nanopublications and notebooks, and the various research artifacts related to AI research (data, models, workflows, and benchmarks). Researchers step through a self-serve survey process and receive a table ready for use in their DMP and/or work plan while gaining awareness of the FAIR Principles and Open Science concepts. FAIRIST is a model that uses part of the proposal process as a way to do outreach, raise awareness of FAIR dimensions and considerations, while providing just-in-time assistance for competitive proposals.
翻译:在发表关于FAIR的开创性文件六年后,研究人员仍然难以理解如何实施FAIR的深度。对于许多研究人员来说,FAIR承诺为近期努力提供长期利益,需要尚未获得的技能,而且是科学家大量任务和繁重要求中的另一个问题。即使那些需要或相信他们必须为FAIR研究实践留出时间的人,也倾向于及时提供咨询意见,适当调整科学文物和过程。由于FAIR执行指导非常笼统,因此研究人员很难根据自己的情况调整咨询意见。技术进步,特别是在人工竞争性情报和机器学习领域,需要尚未获得的技能,并且成为科学家和数据管理者考虑如何制造软件、工作流程、FAIR模型和模型并进行复制。FAIR+执行调查工具(FAIRIS)通过系统的方式将研究要求与研究提案结合起来来缓解问题。由于大多数FAIRIR执行指南的普遍性,因此研究人员很难根据自己的情况调整咨询意见。技术进步,特别是在人工智能智能智能智能智能智能(数据、模型、工作流程和机器学习(ML)领域,使FAIR的采用FAIR的采用复杂化方法,同时提高FA-R的认识,同时提高FA-A-R的进度和基准。