Does the Use of Unusual Combinations of Datasets Contribute to Greater Scientific Impact?
<|reference_start|>Does the Use of Unusual Combinations of Datasets Contribute to Greater Scientific Impact?: Scientific datasets play a crucial role in contemporary data-driven research, as they allow for the progress of science by facilitating the discovery of new patterns and phenomena. This mounting demand for empirical research raises important questions on how strategic data utilization in research projects can stimulate scientific advancement. In this study, we examine the hypothesis inspired by the recombination theory, which suggests that innovative combinations of existing knowledge, including the use of unusual combinations of datasets, can lead to high-impact discoveries. Focusing on social science, we investigate the scientific outcomes of such atypical data combinations in more than 30,000 publications that leverage over 5,000 datasets curated within one of the largest social science databases, ICPSR. This study offers four important insights. First, combining datasets, particularly those infrequently paired, significantly contributes to both scientific and broader impacts (e.g., dissemination to the general public). Second, infrequently paired datasets maintain a strong association with citation even after controlling for the atypicality of dataset topics. In contrast, the atypicality of dataset topics has a much smaller positive impact on citation counts. Third, smaller and less experienced research teams tend to use atypical combinations of datasets in research more frequently than their larger and more experienced counterparts. Lastly, despite the benefits of data combination, papers that amalgamate data remain infrequent. This finding suggests that the unconventional combination of datasets is an under-utilized but powerful strategy correlated with the scientific impact and broader dissemination of scientific discoveries<|reference_end|>
@article{yu2024does,
    title={Does the Use of Unusual Combinations of Datasets Contribute to Greater
  Scientific Impact?},
    author={Yulin Yu, Daniel M. Romero},
    journal={arXiv preprint arXiv:2402.05024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05024},
    primaryClass={cs.DL cs.SI econ.GN q-fin.EC}
}