  Automated classification of metadata of research data by their discipline(s)
of research can be used in scientometric research, by repository service
providers, and in the context of research data aggregation services. Openly
available metadata of the DataCite index for research data were used to compile
a large training and evaluation set comprised of 609,524 records, which is
published alongside this paper. These data allow to reproducibly assess
classification approaches, such as tree-based models and neural networks.
According to our experiments with 20 base classes (multi-label classification),
multi-layer perceptron models perform best with a f1-macro score of 0.760
closely followed by Long Short-Term Memory models (f1-macro score of 0.755). A
possible application of the trained classification models is the quantitative
analysis of trends towards interdisciplinarity of digital scholarly output or
the characterization of growth patterns of research data, stratified by
discipline of research. Both applications perform at scale with the proposed
models which are available for re-use.
