Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction. (4th September 2017)
- Record Type:
- Journal Article
- Title:
- Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction. (4th September 2017)
- Main Title:
- Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction
- Authors:
- Kawasaki, Akiyuki
Yamamoto, Akio
Koudelova, Petra
Acierto, Ralph
Nemoto, Toshihiro
Kitsuregawa, Masaru
Koike, Toshio - Abstract:
- In 2015, global attempts were made to reconcile the relationship between development and environmental issues. This led to the adoption of key agreements such as the Sustainable Development Goals. In this regard, it is important to identify and evaluate under-recognized disaster risks that hinder sustainable development: measures to mitigate climate change are the same as those that build resilience against climate-related disasters. To do this we need to advance scientific and technical knowledge, build data infrastructure that allows us to predict events with greater accuracy, and develop data archives. For this reason we have developed the Data Integration and Analysis System (DIAS). DIAS incorporates analysis, data and models from many fields and disciplines. It collects and stores data from satellites, ground observation stations and numerical weather prediction models; integrates this data with geographical and socio-economic information; then generates results for crisis management of global environmental issues. This article gives an overview of DIAS and summarizes its application to climate change analysis and disaster risk reduction. As the article shows, DIAS aims to initiate cooperation between different stakeholders, and contribute to the creation of scientific knowledge. DIAS provides a model for sharing transdisciplinary research data that is essential for achieving the goal of sustainable development.
- Is Part Of:
- Data science journal. Volume 16(2017)
- Journal:
- Data science journal
- Issue:
- Volume 16(2017)
- Issue Display:
- Volume 16, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 16
- Issue:
- 2017
- Issue Sort Value:
- 2017-0016-2017-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-09-04
- Subjects:
- Climate change -- Disaster risk reduction -- Integration -- DIAS -- CMIP -- Myanmar
Science -- Data processing -- Periodicals
Database management -- Periodicals
502.85 - Journal URLs:
- http://datascience.codata.org/ ↗
http://www.codata.org/dsj/index.html ↗ - DOI:
- 10.5334/dsj-2017-041 ↗
- Languages:
- English
- ISSNs:
- 1683-1470
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 14583.xml