An essay on the differences and linkages between data science and information science

Abstract

When there are differences in research objects and methodology between data science and information science, there are also linkages between data science and information science, based on the DIKW hierarchy to the concept chain, namely data – information – knowledge – wisdom. While knowledge metrics provides a quantitative linkage of data – information – knowledge – wisdom, information is the logarithm of data and knowledge is the logarithm of information, on which the mechanism of Brookes’ basic equation of information science is revealed. We suggest to maintain similar principles of data science and information science, including the principle of order, the principle of correlation, the principle of reorganized transformation, the principle of scatter distribution, the principle of logarithmic perspective, and the principle of least effort. Also, we extend to discuss a few issues on knowledge science.