Abstract
Recent advances in bibliometrics have focused on text-mining to organize scientific disciplines based on author networks, keywords, and citations. These approaches provide insights, but fail to capture important experimental data that exist in many scientific disciplines. The objective of our paper is to show how such data can be used to organize the literature within a discipline, and identify knowledge gaps. Our approach is especially important for disciplines relying on randomized control trials. Using stroke rehabilitation as an informative example, we construct an interactive graphing platform to address domain general scientific questions relating to bias, common data elements, and relationships between key constructs in a field. Our platform allows researchers to ask their own questions and systematically search the literature from the data up.
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Acknowledgements
The authors would like to thank Adam Raikes, a Ph.D. student at Utah State University, for his assistance with data management/extraction. In addition, we are grateful to the insights and feedback provided by Yedurag Babu, a recent graduate of Auburn University and a current data scientist at The Home Depot, on some of the earlier versions of the interactive visualizations.
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Mohabbati-Kalejahi, N., Yazdi, M.A.A., Megahed, F.M. et al. Streamlining science with structured data archives: insights from stroke rehabilitation. Scientometrics 113, 969–983 (2017). https://doi.org/10.1007/s11192-017-2482-z
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DOI: https://doi.org/10.1007/s11192-017-2482-z