Scholarly research leads to scholarly publishing. The scholarly literature from all over the world, including both developed and developing nations, continues to explode. Journals, book series, and conference proceedings continue to be major forms of scholarly literature. In the past, the medical sciences dominated the literature, but disciplines like physical sciences, life sciences, and social sciences following in the footsteps of medical sciences.
This study highlights the growth in the scholarly literature from different points of view.
Data was collection from the SCOPUS database, which contained 25,482 publications were observed, as of January 2008. The data was analyzed to trace the growth and development of scholarly literature.
* Determine the source type of publications
* Monitor active and inactive publications * Determine publications with up-to-date coverage
* Determine continental output
* Trace subject development
Price (1963, 1975) studied the growth in the number of scientists, scientific journals, and papers over the past two centuries, finding that the numbers doubled every 15 years. Since then, literature growth studies have become very common in the field of bibliometrics and infometrics. Studying growth patterns in the NLM's serials collection and in Index Medicus journals between 1966 and 1985, Humphreys and McCutcheon (1994) concluded that the data appear to support Price's analysis, which was further developed by Goffman (1966, 1971) describing it as an initial period of exponential growth, followed by saturation and slowdown to a steady rate of increase. A similar conclusion was reached earlier by Orr and Leeds (1964) concerning the biomedical literature. The " Law of Exponential Growth " has been further dealt with by Tague and others (1981), Ravichandra Rao and Meera (1992), Egghe and Ravichandra Rao (1992), and many others. The exponential growth of the literature is described mathematically by the exponential function YT =a.ebt where YT represents the size at time t, a is the initial size, and b is the continuous growth rate which is related to the annual percentage growth rate r, as: r =100(eb-1). Egghe and Ravichandra Rao (1992) claim, however, that the power model (with exponent 1) is the best growth model for sciences and technology fields, while the Gompertz S-shaped distribution fits better databases of the social sciences and the humanities.