Analysis of Structural Characteristics and Networks of Cross-disciplinary Data Using Data Jackets

Abstract

In recent years, the expectations for data exchange and use that cross multiple fields have been rising. However, creating a data-driven innovation by coordinating data across different fields first requires a correct understanding of existing data structures and relationships. It thus is important to investigate the structural characteristics of data ensembles rather than analyzing individual data. Data Jacket (DJ) is a framework for describing an overview of data while keeping data itself confidential. This paper utilizes DJs to quantitatively assess overall data trends and characteristics and to understand the structure and system of data, their variables, and sharing policy of data. Results of the analysis revealed the network of data is a network with local proximity and a loose global network. Moreover, public data and private data in the data market have different variables and characteristics in the network.