Streamlining science with structured data archives: insights from stroke rehabilitation

Nasrin Mohabbati-Kalejahi, Mohammad Ali Alamdar Yazdi, Fadel M. Megahed, Sydney Y. Schaefer, Lara A. Boyd, Catherine E. Lang, Keith R. Lohse

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)969-983
Number of pages15
JournalScientometrics
Volume113
Issue number2
DOIs
StatePublished - Nov 1 2017

Keywords

  • Bibliometrics
  • Data visualization
  • Meta-science
  • Randomized controlled trials

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    Mohabbati-Kalejahi, N., Yazdi, M. A. A., Megahed, F. M., Schaefer, S. Y., Boyd, L. A., Lang, C. E., & Lohse, K. R. (2017). Streamlining science with structured data archives: insights from stroke rehabilitation. Scientometrics, 113(2), 969-983. https://doi.org/10.1007/s11192-017-2482-z