In the article “Evaluating institutional open access performance: Methodology, challenges and assessment” we develop the first comprehensive and reproducible workflow that integrates multiple bibliographic data sources for evaluating institutional open access (OA) performance. The major data sources include Web of Science, Scopus, Microsoft Academic, and Unpaywall. However, each of these databases continues to update, both actively and retrospectively. This implies the results produced by the proposed process are potentially sensitive to both the choice of data source and the versions of them used. In addition, there remain the issue relating to selection bias in sample size and margin of error. The current work shows that the levels of sensitivity relating to the above issues can be significant at the institutional level. Hence, the transparency and clear documentation of the choices made on data sources (and their versions) and cut-off boundaries are vital for reproducibility and verifiability.

中文翻译：

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评估机构的开放访问性能：敏感性分析

在“评估机构的开放访问性能：方法，挑战和评估”一文中，我们开发了第一个全面且可重现的工作流，该工作流集成了多个书目数据源以评估机构的开放访问（OA）绩效。主要数据来源包括Web of Science，Scopus，Microsoft Academic和Unpaywall。但是，这些数据库中的每一个都继续进行主动和追溯更新。这意味着由提议的过程产生的结果可能对数据源的选择及其使用的版本敏感。另外，仍然存在与样本大小和误差范围的选择偏差有关的问题。当前的工作表明，与上述问题相关的敏感性水平在机构水平上可能很重要。因此，