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

Authors

Akiyuki Kawasaki, The University of Tokyo
Petra Koudelova, The Czech Technical University in Prague
Katsunori Tamakawa, National Institute of Informatics
Asanobu Kitamoto, National Institute of Informatics
Eiji Ikoma, The University of Tokyo
Koji Ikeuchi, The University of Tokyo
Ryosuke Shibasaki, The University of Tokyo
Masaru Kitsuregawa, The University of Tokyo, National Institute of Informatics
Tosho Koike, National Institute of Informatics

Document Type

Article

Date of this Version

10-23-2018

Citation

Kawasaki, A, et al. 2018. 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. Data Science Journal, 17: 29, pp. 1–14. DOI: https://doi.org/10.5334/dsj-2018-029

Comments

Open access.

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.