Research Article: “Identifying and Improving Dataset References in Social Sciences Full Texts”

The following research paper (preprint) was recently made available via arXiv.
Title
Identifying and Improving Dataset References in Social Sciences Full Texts
Authors
Behnam Ghavimi
GESIS – Leibniz Institute for the Social Sciences
University of Bonn
Philipp Mayr
GESIS – Leibniz Institute for the Social Sciences
Sahar Vahdati
University of Bonn
Christoph Lange
University of Bonn
Fraunhofer IAIS
Source
via arXiv
Abstract

Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for example by using their titles and the year of publication. However, in most cases explicit links that would provide readers with direct access to referenced datasets are missing.
Manually detecting references to datasets in papers is time consuming and requires an expert in the domain of the paper. In order to make explicit all links to datasets in papers that have been published already, we suggest and evaluate a semi-automatic approach for finding references to datasets in social sciences papers.
Our approach does not need a corpus of papers (no cold start problem) and it performs well on a small test corpus (gold standard). Our approach achieved an F-measure of 0.84 for identifying references in full texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
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This work has been funded by the DFG project “Opening Schol- arly Communication in Social Sciences” (grant agreements SU 647/19-1 and AU 340/9- 1), and by the European Commission under grant agreement 643410.

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