Masters Theses

Date of Award


Degree Type


Degree Name

Master of Science


Information Sciences

Major Professor

Carol Tenopir

Committee Members

Suzanne L. Allard, Awa Zhu


This research combined archives of grant awards with a five-year period of bibliographic data from Web of Science in order to conduct an input-output study of research supported by the National Science Foundation. Acknowledgement lag is proposed as a new bibliometric term, defined as the time elapsed between when a grant is awarded and when a document is published which acknowledges that award. Acknowledgement lag was computed for the dataset, and domain differences in lag times were analyzed. Some areas, such as Plant & Animal Science or Social Science, were found to be more likely than other categories to acknowledge a grant seven or more years later, while other categories, such as Physics, were most likely to publish a grant acknowledgement in two years or less. In addition, rank-normalized impact factors were computed for journals in which these articles were published, as a measure of journal impact that is comparable across categories of research. The overall distribution of ranknormalized journal impact factors for research articles acknowledging support by the National Science Foundation was analyzed. Category-level analysis was also performed, and it was found that there were differences in the journal impact factor trends for publications from different domains in the dataset. Research in Materials Science was substantially more likely than other categories to publish in the most elite journals of its respective domain. Social Sciences research was also found to be one of the strongest research areas in terms of impact factor, despite being one of the smaller categories in terms of publication counts. However, other categories were found to be disproportionately more likely to have been published in lower impact factor journals for their respective fields, such as Mathematics and Computer Science. The methodology developed in this project demonstrates a workflow that could be implemented by the NSF or other agencies. The findings demonstrate that systematically linking grants to publications can yield information of strategic value, allowing agencies to better understand field differences in outcomes and providing a means for tracking changes in publication-related metrics over time.

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