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An algorithmic historiography of the Ebola research specialty: mapping the science behind Ebola

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Abstract

The objective of this paper was to identify the intellectual profile of the Ebola research specialty and its behavior from its inception to 2014. This objective was met by chronologically mapping the information flows within the specialty using bibliometric and citation data extracted from 1638 Ebola research documents in conjunction with Histcite to produce an algorithmic historiography representing a view of the Ebola specialty’s intellectual profile and evolution. The present study was guided by the following research questions. What is the bibliometric profile of the Ebola specialty in terms of publication output and the impact of its authors, journals, institutions, countries, and years? What influential Ebola research has been produced since its discovery, and how has the research evolved? The most significant results show the Ebola specialty citation network as a small-world and highly cohesive network. The Ebola specialty citation network was found to be symmetrical in structure and segmented into four distinct cliques representing specific research focuses (i.e., uncovering divergent strains, immune responses and vaccines, Ebola’s pathogenesis, Ebola’s molecular structure and physiology). Key authors and contributing journals were identified. The most substantial contributions to the specialty were from the government and academia. The Ebola specialty had a slow publication output and oscillating citation activity for the first few decades, coinciding with several outbreaks. The greatest production of Ebola research articles occurred after 2000, along with exponential citation behavior.

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Acknowledgments

The author acknowledges Dr. Heting Chu at Long Island University for providing an initial review and suggestions. No financial assistance was received for the present research.

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    Nicholas V. Olijnyk

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Olijnyk, N.V. An algorithmic historiography of the Ebola research specialty: mapping the science behind Ebola. Scientometrics 105, 623–643 (2015). https://doi.org/10.1007/s11192-015-1688-1

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