In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors.
Here we investigated the patterns based on 52,803 papers published in Proceedings of the National Academy of Sciences (PNAS) over the years 1999 2013.1 The content of dataset spans three science categories: social sciences and two principal sub-sciences in natural sciences, viz.
In addition, we present a new dataset of the DBLP computer science bibliography network with richer meta-data and verifiable ground-truth knowledge, which can foster future research in community finding and interpretation of communities in large networks.
To address this question, the authors used a hand-curated dataset of 2659 tenure-track computer science faculty members at 205 Ph.D.-granting departments across North America, which they had previously developed for a 2015 study of faculty hiring networks.
To date, much of the discussion of data integration in ecology has focused on the importance and possible use of 'dark' data in the 'long tail' of science, i.e., the large number of small datasets that make up the majority of science, that are not well indexed or stored and typically are not publicly accessible [ 16].
In addition, the exclusion of less-known journals and conferences from the Web of Science and the Scopus index might have led to a different dataset.
Grants-in-Aid for Scientific Research (KAKENHI) 22000003 from the Japan Society for the Promotion of Science (JSPS) supported the acquisition and analysis for the NOMan datasets.
Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science.
However, in many areas of science and engineering, a scientist's ability to analyze information is increasingly becoming hindered by dataset sizes.
