In order to know the performance of evaluated nuclear data in reactor physics or radiation shielding calculations, its benchmark testing with integral data is mandatory. Nowadays we have a huge amount of integral data, but some of them are quite similar to each other. We need to know the independency of available integral data, and also to choose a proper set of a limited number of integral data for benchmark calculations. Furthermore, it is beneficial to know how effective a set of integral data is for independent validation of each nuclear data in performing the validation test of nuclear data. In order to quantify the effectiveness of integral data in nuclear data validation, we propose several methods based on a concept of the active subspace. With the proposed methods, we can quantify the independency of a set of integral data, choose a minimum set of proper integral data, and quantify the possibility of independent validation of nuclear data from a set of integral data. These methods are adopted to a set of fictitious integral data and a set of actual integral data including experimental data about $k_{\mathrm{e}\mathrm{f}\mathrm{f}}$ and reaction rate ratio. Through these applications, effectiveness of these integral data has been successfully quantified. Furthermore, the proposed concept is utilized to interpret the nuclear data compensation effect, which has been recently discussed in the community of nuclear data.

为了了解已评估核数据在反应堆物理或辐射屏蔽计算中的性能，必须使用带有完整数据的基准测试。如今，我们拥有大量的完整数据，但其中一些彼此非常相似。我们需要知道可用积分数据的独立性，还需要选择一组有限数量的积分数据以进行基准计算。此外，了解一组完整数据对于执行核数据的验证测试中的每个核数据的独立验证的有效性是有益的。为了量化积分数据在核数据验证中的有效性，我们提出了基于活动子空间概念的几种方法。利用提出的方法，我们可以量化一组积分数据的独立性，选择最小的一组适当的积分数据，并从一组积分数据中量化对核数据进行独立验证的可能性。这些方法被用于一组虚拟积分数据和一组实际积分数据，其中包括关于${ķ}_{\mathrm{Ë}\mathrm{F}\mathrm{F}}$和反应速率比。通过这些应用，已经成功地量化了这些整体数据的有效性。此外，所提出的概念被用于解释核数据补偿的效果，最近在核数据领域已经对此进行了讨论。