Characteristics of Dataset Retrieval Sessions: Experiences from a Real-life Digital Library
Zeljko Carevic, Dwaipayan Roy, Philipp Mayr

TL;DR
This study analyzes 65,000 real-life dataset retrieval sessions in a social sciences digital library, revealing that queries are indistinguishable whether users seek datasets or documents, and exploring session characteristics and topical drift.
Contribution
It provides empirical insights into dataset retrieval behaviors and challenges assumptions about query differences between dataset and document searches in social sciences digital libraries.
Findings
Queries are indistinguishable between dataset and document searches.
Session characteristics include diverse interaction sequences.
Topical drift occurs within retrieval sessions.
Abstract
Secondary analysis or the reuse of existing survey data is a common practice among social scientists. Searching for relevant datasets in Digital Libraries is a somehow unfamiliar behaviour for this community. Dataset retrieval, especially in the social sciences, incorporates additional material such as codebooks, questionnaires, raw data files and more. Our assumption is that 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
