Data Integration and Analysis System (DIAS) as a Platform for Data and Model Integration: Cases in the Field of Water Resources Management and Disaster Risk Reduction. (23rd October 2018)

• Record Type: Journal Article
• Authors: Kawasaki, Akiyuki; Koudelova, Petra; Tamakawa, Katsunori; Kitamoto, Asanobu; Ikoma, Eiji; Ikeuchi, Koji; Shibasaki, Ryosuke; Kitsuregawa, Masaru; Koike, Tosho
• Is Part Of: Data science journal. Volume 17(2018)
• Publication Date: 2018-10-23
• Subjects: data and model integration -- platform -- dam -- hydroelectric power -- flood control; Science -- Data processing -- Periodicals; Database management -- Periodicals; 502.85
• DOI: 10.5334/dsj-2018-029
• Languages: English
• ISSNs: 1683-1470

Abstract

The development of data and model integration platforms has furthered scientific inquiry and helped to solve pressing social and environmental problems. While several e-infrastructure platforms have been developed, the concept of data and model integration remains obscure, and these platforms have produced few firm results. This article investigates data and model integration on the Data Integration and Analysis System (DIAS) platform, using three case projects from water-related fields. We provide concrete examples of data and model integration by analyzing the data transfer and analysis process, and demonstrate what platform functions are needed to promote the advantages of data and model integration. In addition, we introduce the Digital Object Identifier (DOI), a valuable tool for promoting data and model integration and open science. Our investigation reveals that DIAS advances data and model integration in five main ways: it is a "sophisticated and robust integration platform"; has "rich APIs, including a metadata management system, for high-quality data archive and utilization"; functions as a "core hydrological model"; and promotes a "collaborative R&D community" and "open science and data repositories". This article will appeal especially to researchers interested in new methods of analysis, and information technology experts responsible for developing e-infrastructure systems to support environmental and scientific research.