Finding datasets in publications: the Syracuse University approach

Datasets are critical for scientific research, playing a role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent efforts by data repositories and funding agencies. This greatly affects our ability to track their usage and importance. A potential solution to this problem is to automatically extract dataset mentions from scientific articles. In this work, we propose to achieve such extraction by using a neural network based on a BiLSTM-CRF architecture. Our method achieves F1=0.885 in social science articles released as part of the Rich Context Dataset. We discuss future improvements to the model and applications beyond social sciences.Tong Zeng was funded by the China Scholarship Council #201706190067. Daniel E. Acuna was funded by the National Science Foundation awards #1646763 and #180095