Characteristics of Dataset Retrieval Sessions: Experiences from a Real-life Digital Library

Abstract

Secondary analysis or the reuse of existing data is a common practice among social scientists. The complexity of datasets, however, exceeds those known from traditional document retrieval. Dataset retrieval, especially in the social sciences, incorporates additional material such as codebooks, questionnaires, raw data files and more. Due to the diverse nature of datasets, document retrieval models often do not work as efficiently for retrieving datasets. One way of enhancing these types of searches is to incorporate the users' interaction context in order to personalise dataset retrieval sessions. As a first step towards this long term goal, we study characteristics of dataset retrieval sessions from a real-life Digital Library for the social sciences that incorporates both: research data and publications. Previous studies reported a way of discerning queries between document of dataset search by query length. In this paper, we argue the claim and report our findings of indistinguishability of queries, whether aiming for dataset or a document. Amongst others, we report our findings of dataset retrieval sessions with respect to query characteristics, interaction sequences and topical drift within 65,000 unique sessions.