  Today, full-texts of scientific articles are often stored in different
locations than the used datasets. Dataset registries aim at a closer
integration by making datasets citable but authors typically refer to datasets
using inconsistent abbreviations and heterogeneous metadata (e.g. title,
publication year). It is thus hard to reproduce research results, to access
datasets for further analysis, and to determine the impact of a dataset.
Manually detecting references to datasets in scientific articles is
time-consuming and requires expert knowledge in the underlying research
domain.We propose and evaluate a semi-automatic three-step approach for finding
explicit references to datasets in social sciences articles.We first extract
pre-defined special features from dataset titles in the da|ra registry, then
detect references to datasets using the extracted features, and finally match
the references found with corresponding dataset titles. The approach does not
require a corpus of articles (avoiding the cold start problem) and performs
well on a test corpus. We achieved an F-measure of 0.84 for detecting
references in full-texts and an F-measure of 0.83 for finding correct matches
of detected references in the da|ra dataset registry.
