Abstract

This dissertation reports the results of a comprehensive quantitative analysis of the inter-relations among research funding, scientific output, and collaboration. The research employed various methods and methodologies (i.e. data and text mining, statistical analysis, social network analysis, bibliometrics, survey data analysis, and visualization techniques) to investigate the impact of influencing factors on researchers’ performance, their amount of funding, and collaboration patterns. Moreover, a machine learning framework was suggested and validated for scientific evaluation of the researchers based on their productivity and level of funding. The Natural Sciences and Engineering Research Council of Canada (NSERC) was selected as the source of funding in this research since it is the main federal funding organization in Canada and almost all the Canadian researchers in natural sciences and engineering receive at least a basic research grant from NSERC. The required data on the scientific publications (e.g. co-authors, their affiliations, year of publication) was collected from Elsevier’s Scopus. SCImago was selected for collecting the impact factor information of the journals in which the articles were published in as well as the annual citation counts of publications. The data was gathered and integrated for the time span of 1996 to 2010. The most significant contributions are: 1) the unique data extraction and gathering procedure that enhanced the accuracy of the target data, 2) the comprehensive triangulation technique which was employed in this research that included various methodologies and used new variables for assessing the inter-relations, 3) the proposed machine learning framework for classifying researchers and predicting their productivity and level of funding.