Challenges in Matching Dataset Citation Strings to Datasets in Social Science

Mathiak, Brigitte, Boland, Katarina. Challenges in Matching Dataset Citation Strings to Datasets in Social Science. D-Lib Magazine, January/February 2015, Volume 21, Number 1/2. DOI: http://www.dlib.org/dlib/january15/mathiak/01mathiak.html

Finding dataset citations in scientific publications to gain information on the usage of research data is an important step to increase visibility of data and to give datasets more weight in the scientific community. Unlike publication impact, which is readily measured by citation counts, dataset citation remains a great unknown. In recent work, we introduced an algorithm to find dataset citations in full text documents automatically, but, in fact, this is just half the road to travel. Once the citation string has been found, it has to be matched to the correct DOI. This is more complicated than it sounds. In social science, survey datasets are typically recorded in a much more fine-granular way than they are cited, differentiating between years, versions, samples, modes of the interview, countries, even questionnaire variants. At the same time, the actual citation strings typically ignore these details. This poses a number of challenges to the matching of citations strings to datasets. In this paper, we discuss these challenges in more detail and present our ideas on how to solve them using an ontology for research datasets.

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