  In this paper, we investigate the retrievability of datasets and publications
in a real-life Digital Library (DL). The measure of retrievability was
originally developed to quantify the influence that a retrieval system has on
the access to information. Retrievability can also enable DL engineers to
evaluate their search engine to determine the ease with which the content in
the collection can be accessed. Following this methodology, in our study, we
propose a system-oriented approach for studying dataset and publication
retrieval. A speciality of this paper is the focus on measuring the
accessibility biases of various types of DL items and including a metric of
usefulness. Among other metrics, we use Lorenz curves and Gini coefficients to
visualize the differences of the two retrievable document types (specifically
datasets and publications). Empirical results reported in the paper show a
distinguishable diversity in the retrievability scores among the documents of
different types.
