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Today, full-texts of scientific articles are often stored in differentlocations than the used datasets. Dataset registries aim at a closerintegration by making datasets citable but authors typically refer to datasetsusing inconsistent abbreviations and heterogeneous metadata (e.g. title,publication year). It is thus hard to reproduce research results, to accessdatasets for further analysis, and to determine the impact of a dataset.Manually detecting references to datasets in scientific articles istime-consuming and requires expert knowledge in the underlying researchdomain.We propose and evaluate a semi-automatic three-step approach for findingexplicit references to datasets in social sciences articles.We first extractpre-defined special features from dataset titles in the da|ra registry, thendetect references to datasets using the extracted features, and finally matchthe references found with corresponding dataset titles. The approach does notrequire a corpus of articles (avoiding the cold start problem) and performswell on a test corpus. We achieved an F-measure of 0.84 for detectingreferences in full-texts and an F-measure of 0.83 for finding correct matchesof detected references in the da|ra dataset registry.

A Semi-Automatic Approach for Detecting Dataset References in Social Science Texts