A Literature Review on Methods for the Extraction of Usage Statements of Software and Data

Software and data have become major components of modern research, which is also reflected by an increased number of software usages. Knowledge about used software and data would provide researchers a better understanding of the results of a scientific investigation and thus foster its reproducibility. Software and data are, however, often not formally cited but their usage is mentioned in the main text. In order to assess the state of the art in extraction of such usage statements, we performed a literature review. We provide an overview of the existing methods for the identification of usage statements of software and data in scientific articles. This analysis mainly focuses on technical approaches, the employed corpora, and the purpose of the investigation itself. We found four different classes of approaches that are used in the literature: 1) term search, 2) manual extraction, 3) rule-based extraction, and 4) extraction based on supervised learning.