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Streamlining science with structured data archives: insights from stroke rehabilitation

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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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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, USA

    Nasrin Mohabbati-Kalejahi & Mohammad Ali Alamdar Yazdi

  2. Farmer School of Business, Miami University, Oxford, OH, USA

    Fadel M. Megahed

  3. Center for Analytics and Data Science (CADS), Miami University, Oxford, OH, USA

    Fadel M. Megahed

  4. School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA

    Sydney Y. Schaefer

  5. Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada

    Lara A. Boyd

  6. Program in Physical Therapy, Occupational Therapy, and Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Catherine E. Lang

  7. Department of Health, Kinesiology, and Recreation, University of Utah, Salt Lake City, UT, USA

    Keith R. Lohse

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  1. Nasrin Mohabbati-Kalejahi
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  2. Mohammad Ali Alamdar Yazdi
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  3. Fadel M. Megahed
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  4. Sydney Y. Schaefer
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  5. Lara A. Boyd
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  6. Catherine E. Lang
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  7. Keith R. Lohse
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Corresponding author

Correspondence to Keith R. Lohse.

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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

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