Nowadays scientific evaluation is becoming increasingly important and necessary in many cases, such as faculty hiring, funding and promotion. Among existing evaluation metrics for individual performance, h-index is the most famous indicator and achieves a prominent role since its publication. However, h-index is inapplicable to comparing individuals from different scientific disciplines, primarily because it cannot handle the huge difference in collaboration habits and citation practices across disciplines. Such a shortcoming of h-index is rooted in its arbitrary definition, comparing two quantities with quite different scales, i.e., citation count for scientific impact and publication count for productivity. To combat this problem, we propose a new evaluation measure, \(h_u\)-index, which unifies citation count and publication count into the same scale. We theoretically analyze the relationship between \(h_u\)-index, h-index and other variants of h-index. We also study the behavior of \(h_u\)-index in empirical cases and researcher ranking tasks. Experimental results demonstrate that \(h_u\)-index has superior performance than h-index and achieves a better comparison of individuals across disciplines.

如今，在许多情况下，例如在教师的聘用，资助和晋升等方面，科学评估变得越来越重要和必要。在现有的个人绩效评估指标中，h指数是最著名的指标，自发布以来就发挥了重要作用。但是，h索引不适用于比较不同科学学科的人员，主要是因为它无法处理跨学科的协作习惯和引用实践方面的巨大差异。h- index的这种缺陷源于其任意定义，它比较了两个比例截然不同的数量，即科学影响的引文数和生产力的出版物数。为解决此问题，我们提出了一种新的评估指标\（h_u \）- index，该指标将引用计数和发布计数统一为相同的比例。从理论上分析的关系\（h_u \） -index，^ h -index和其他变体^ h -index。我们还研究了经验案例和研究人员排名任务中\（h_u \）- index的行为。实验结果表明，\（h_u \）- index具有比h -index更好的性能，并且可以更好地比较各个学科的个体。