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
Clicks: 254
ID: 154777
2018
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Steady Performance
30.0
/100
253 views
18 readers
Trending
AI Quality Assessment
Not analyzed
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.
| Reference Key |
kawasaki2018datadata
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Akiyuki Kawasaki;Petra Koudelova;Katsunori Tamakawa;Asanobu Kitamoto;Eiji Ikoma;Koji Ikeuchi;Ryosuke Shibasaki;Masaru Kitsuregawa;Toshio Koike |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Year | 2018 |
| DOI |
10.5334/dsj-2018-029
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.