Document Type
Book Chapter
Date
Spring 3-1-2020
Keywords
science of science, dataset discovery, deep learning
Language
English
Funder(s)
China Scholarship Council, National Science Foundation
Funding ID
201706190067 (CSC), 1646763 and 1800956 (NSF)
Acknowledgements
Tong Zeng was funded by the China Scholarship Council #201706190067. Daniel E. Acuna was funded by the National Science Foundation awards #1646763 and #1800956
Disciplines
Data Science | Library and Information Science
Description/Abstract
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.
ISBN
978-1-5297-0586-7
Recommended Citation
Zeng, Tong, & Acuna, Daniel E. (2020). Finding datasets in publications: the Syracuse University approach. In Rich Search and Discovery for Research Datasets (pp. 158–165). SAGE. http://doi.org/10.5281/zenodo.4402304
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Information
Published in: Rich Search and Discovery for Research Datasets, SAGE, pp. 158-165.