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A bibliometric analysis of NOAA’s Office of Ocean Exploration and Research

Overview of attention for article published in Scientometrics, September 2012
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Title
A bibliometric analysis of NOAA’s Office of Ocean Exploration and Research
Published in
Scientometrics, September 2012
DOI 10.1007/s11192-012-0836-0
Pubmed ID
Authors

Chris W. Belter

Abstract

Bibliometric analysis techniques are increasingly being used to analyze and evaluate scientific research produced by institutions and grant funding agencies. This article uses bibliometric methods to analyze journal articles funded by NOAA's Office of Ocean Exploration and Research (OER), an extramural grant-funding agency focused on the scientific exploration of the world's oceans. OER-supported articles in this analysis were identified through grant reports, personal communication, and acknowledgement of OER support or grant numbers. The articles identified were analyzed to determine the number of publications and citations received per year, subject, and institution. The productivity and citation impact of institutions in the US receiving OER grant funding were mapped geographically. Word co-occurrence and bibliographic coupling networks were created and visualized to identify the research topics of OER-supported articles. Finally, article citation counts were evaluated by means of percentile ranks. This article demonstrates that bibliometric analysis can be useful for summarizing and evaluating the research performance of a grant funding agency.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 6%
Malaysia 1 1%
Australia 1 1%
South Africa 1 1%
Brazil 1 1%
Greece 1 1%
Iran, Islamic Republic of 1 1%
Unknown 74 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 20%
Student > Master 11 13%
Student > Ph. D. Student 10 12%
Librarian 7 8%
Professor 7 8%
Other 19 22%
Unknown 14 16%
Readers by discipline Count As %
Social Sciences 18 21%
Computer Science 14 16%
Agricultural and Biological Sciences 7 8%
Engineering 4 5%
Business, Management and Accounting 4 5%
Other 18 21%
Unknown 20 24%