Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction

Kawasaki, Akiyuki; Yamamoto, Akio; Koudelova, Petra; Acierto, Ralph Allen; Nemoto, Toshihiro; Koike, Takao

doi:10.5334/dsj-2017-041

Cited by 36 publications

(23 citation statements)

References 23 publications

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“…DIAS users also have access to climate change simulation data from the Coupled Model Intercomparison Project (CMIP; Meehl et al 2000;Kawasaki et al 2017), and the Nonhydrostatic ICosahedral Atmospheric Model (NICAM; Satoh et al 2014). These datasets are stored in an extra large volume disk array system with API (Application Programming Interface) software used to import the data.…”

Section: Outline Of Diasmentioning

confidence: 99%

“…DIAS is part of the Global Earth Observation System of Systems (GEOSS), and is linked to GEOSS data centers internationally through the exchange of meta-data via GCI (GEOSS Common Interface). An overview of DIAS and its infrastructure system, application development, and Research and Development community, is given in Kawasaki et al (2017).…”

Section: Outline Of Diasmentioning

confidence: 99%

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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

Kawasaki

Koudelova

Tamakawa

et al. 2018

Data Science Journal

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Section: Outline Of Diasmentioning

confidence: 99%

Section: Outline Of Diasmentioning

confidence: 99%

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

Kawasaki

Koudelova

Tamakawa

et al. 2018

Data Science Journal

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“…We investigated metadata for earth science data that are managed by the metadata portal called the Global Change Master Directory (GCMD) 3 and the project called the Data Integration Analysis System (DIAS) 4 [10] as examples. Both the GCMD and DIAS datasets are annotated with keywords from GCMD Science Keywords [7], and the GCMD Science Keywords vocabulary includes about 3000 keywords.…”

Section: Introductionmentioning

confidence: 99%

An analysis and comparison of keyword recommendation methods for scientific data

2020

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To classify and search various kinds of scientific data, it is useful to annotate those data with keywords from a controlled vocabulary. Data providers, such as researchers, annotate their own data with keywords from the provided vocabulary. However, for the selection of suitable keywords, extensive knowledge of both the research domain and the controlled vocabulary is required. Therefore, the annotation of scientific data with keywords from a controlled vocabulary is a time-consuming task for data providers. In this paper, we discuss methods for recommending relevant keywords from a controlled vocabulary for the annotation of scientific data through their metadata. Many previous studies have proposed approaches based on keywords in similar existing metadata; we call this the indirect method. However, when the quality of the existing metadata set is insufficient, the indirect method tends to be ineffective. Because the controlled vocabularies for scientific data usually provide definition sentences for each keyword, it is also possible to recommend keywords based on the target metadata and the keyword definitions; we call this the direct method. The direct method does not utilize the existing metadata set and therefore is independent of its quality. Also, for the evaluation of keyword recommendation methods, we propose evaluation metrics based on a hierarchical vocabulary structure, which is a distinctive feature of most controlled vocabularies. Using our proposed evaluation metrics, we can evaluate keyword recommendation methods with an emphasis on keywords that are more difficult for data providers to select. In experiments using real earth science datasets, we compare the direct and indirect methods to verify their effectiveness, and observe how the indirect method depends on the quality of the existing metadata set. The results show the importance of metadata quality in recommending keywords.

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“…The Data Integration and Analysis System (DIAS) was launched in Japan in 2006 with the goals of collecting and storing Earth observation data; analyzing these data in combination with socio-economic data; and applying the results to guide decision-and policy-making (DIAS, 2017;Kawasaki et al, 2017). Since its planning phase, the DIAS was intended to be an advanced e-infrastructure system capable of efficiently handling Big Data.…”

Section: Outline Of Diasmentioning

confidence: 99%

“…The CMIP5 Data Analysis System (Kawasaki et al, 2017) is a publicly available tool that comprises a set of dedicated functions that allow users to access, browse, visualize, analyze, subset, and download data from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which is a standard experimental protocol for studying the output of coupled atmosphere-ocean general circulation models (coupled GCMs) (Meehl et al, 2000). The third and fifth phases of CMIP (CMIP3 and CMIP5, respectively) (Meehl et al, 2007;Taylor et al, 2012) delivered comprehensive and coordinated data sets including future climate projections, which were used to prepare the fourth and fifth Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC, 2007;IPCC, 2013).…”

Section: Outline Of Diasmentioning

confidence: 99%

Design and Implementation of a Training Course on Big Data Use in Water Management

Koudelova

Kawasaki

Koike³

et al. 2017

Data Science Journal

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Big Data has great potential to be applied to research in the field of geosciences. Motivated by the opportunity provided by the Data Integration and Analysis System (DIAS) of Japan, we organized an intensive two-week course that aims to educate participants on Big Data and its exploitation to solve water management problems. When developing and implementing the Program, we identified two main challenges: (1) assuring that the training has a lasting effect and (2) developing an interdisciplinary curriculum suitable for participants of diverse professional backgrounds. To address these challenges, we introduced several distinctive features. The Program was based on experiential learning -the participants were required to solve real problems and worked in international and multidisciplinary teams. The lectures were strictly relevant to the case-study problems. Significant time was devoted to hands-on exercises, and participants received immediate feedback on individual assignments to ensure skills development. Our evaluation of the two occasions of the Program in 2015 and 2016 indicates significant positive outcomes. The successful completion of the individual assignments confirmed that the participants gained key skills related to the usage of DIAS and other tools. The final solutions to the case-study problems showed that the participants were able to integrate and apply the obtained knowledge, indicating that the Program's format and curriculum were effective. We found that participants used DIAS in subsequent studies and work, thus suggesting that the Program had long-lasting effects. Our experience indicates that despite time constraints, short courses can effectively encourage researchers and practitioners to explore opportunities provided by Big Data.

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Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction

Cited by 36 publications

References 23 publications

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 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

An analysis and comparison of keyword recommendation methods for scientific data

Design and Implementation of a Training Course on Big Data Use in Water Management

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