  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.
