Retrievability measures the influence a retrieval system has on the access to information in a given collection of items. This measure can help in making an evaluation of the search system based on which insights can be drawn. In this paper, we investigate the retrievability in an integrated search system consisting of items from various categories, particularly focussing on datasets, publications \ijdl{and variables} in a real-life Digital Library (DL). The traditional metrics, that is, the Lorenz curve and Gini coefficient, are employed to visualize the diversity in retrievability scores of the \ijdl{three} retrievable document types (specifically datasets, publications, and variables). Our results show a significant popularity bias with certain items being retrieved more often than others. Particularly, it has been shown that certain datasets are more likely to be retrieved than other datasets in the same category. In contrast, the retrievability scores of items from the variable or publication category are more evenly distributed. We have observed that the distribution of document retrievability is more diverse for datasets as compared to publications and variables.

翻译：可检索性衡量检索系统对给定项目集合中信息访问的影响程度。这一指标有助于评估搜索系统，并从中获得重要见解。本文研究了由不同类别项目组成的集成搜索系统中的可检索性，重点关注真实数字图书馆（DL）中的数据集、出版物和变量。采用洛伦兹曲线和基尼系数等传统指标，可视化三类可检索文档类型（数据集、出版物和变量）在可检索性得分上的差异。研究结果表明，存在显著的流行度偏差，某些项目被检索的频率远高于其他项目。特别地，部分数据集比同类别其他数据集更易被检索到。相比之下，变量或出版物类别项目的可检索性得分分布更为均匀。我们观察到，数据集的文档可检索性分布比出版物和变量更具多样性。