Medical Theses and Derivative Articles: Dissemination Of Contents and Publication Patterns

Authors : Mercedes Echeverria, David Stuart, Tobias Blanke

Doctoral theses are an important source of publication in universities, although little research has been carried out on the publications resulting from theses, on so-called derivative articles.

This study investigates how derivative articles can be identified through a text analysis based on the full-text of a set of medical theses and the full-text of articles, with which they shared authorship.

The text similarity analysis methodology applied consisted in exploiting the full-text articles according to organization of scientific discourse (IMRaD) using the TurnItIn plagiarism tool.

The study found that the text similarity rate in the Discussion section can be used to discriminate derivative articles from non-derivative articles.

Additional findings were: the first position of the thesis’s author dominated in 85% of derivative articles, the participation of supervisors as coauthors occurred in 100% of derivative articles, the authorship credit retained by the thesis’s author was 42% in derivative articles, the number of coauthors by article was 5 in derivative articles versus 6.4 coauthors, as average, in non-derivative articles and the time differential regarding the year of thesis completion showed that 87.5% of derivative articles were published before or in the same year of thesis completion.


Dealing with Big Data

Authors : Tobias Blanke, Andrew Prescott

This book chapter attempts to counter anxieties in the humanities and social science about the role of big data in research by focusing on approaches which, by being firmly grounded in the traditional values of disciplines, enhance existing methods to produce fruitful research.

Big data poses many methodological challenges, but these pressures should prompt scholars to pay much closer attention to methodological issues than they have in the past.