地球環境デジタルライブラリの構築手法に関する研究

University of Tokyo (東京大学

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

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

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

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

AMY coordinated observations, reanalysis and data management

Proceedings of GRENE 3rd Workshop (17-19 March, 2014, Bali, Indonesia

Randomised controlled pilot study in Japan comparing a home visit program using a Functioning Improvement Tool with a home visit with conversation alone

Objective: This study aimed to determine the effect of a home visit program using a Functioning Improvement Tool (FIT) compared with a home visit using conversation alone. Methods: Twenty-eight participants (mean age, 78.6±7.5 years) were randomly assigned to an intervention (n = 13) or control (n = 15) group for 3 months. The intervention group received a 60-minute FIT home visit program; the control group received a 30-minute home visit using common conversational techniques. Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB), and Geriatric Depression Scale (GDS) scores were evaluated. Results: The FAB score was significantly improved in the intervention group compared with the control group (2.5 vs -0.5, P = 0.02). Conclusions: Our FIT home visit program may help prevent dementia. Further studies with larger samples and longer follow-up periods are needed to assess the long-term effectiveness of an FIT home visit program on dementia prevention (UMIN-CTR number, UMIN000004767.

A randomized controlled trial of a Functioning Improvement Tool home-visit program and its effect on cognitive function in older persons

Objective: The aim was to determine whether mini mental state examination (MMSE) scores improved in older participants of a Functioning Improvement Tool (FIT) home-visit program. Methods: Two hundred fifty-two participants aged 65 years or older living at home and receiving preventive services or a community long-term care prevention project according to the Japanese social long-term care insurance system were enrolled and randomly assigned to an intervention group (n = 128) or a control group (n = 124). Intervention group subjects received a 60-min FIT home-visit program for 3 months, which included guidance, assistance, and help in writing and teaching calculation in order to complete the FIT. Control subjects did not receive any home visits. Cognitive function was evaluated by MMSE. Analysis of covariance was used to examine the effects of the FIT adjusting for baseline MMSE scores, age, and sex. Results: Fifty-three subjects were excluded because of withdrawal, hospitalization, death, relocation, or missing data of MMSE; 199 subjects (60 men, 139 women; age 78.6 ± 7.4 years) were analyzed. The baseline MMSE scores did not differ between the intervention and control groups (24.2 ± 4.3 vs. 24.1 ± 4.7, p = 0.90). After the study period, the change in the MMSE score was significantly better in the intervention group than in the control group (0.8 ± 0.3 vs. −0.1 ± 0.2, p = 0.04). Stratified analyses showed that the intervention strategy was most effective in subjects with mild cognitive decline, with baseline MMSE scores from 18 to 23 points (1.9 ± 0.5 vs. −0.1 ± 2.8, p = 0.04). Conclusions: Our FIT home-visit program improved MMSE scores in older participants with mild cognitive decline

Compartmental analysis of washout effect in rat brain: in-beam OpenPET measurement using a (11)C beam.

In-beam positron emission tomography (PET) is expected to enable visualization of a dose verification using positron emitters (β+ decay). For accurate dose verification, correction of the washout of the positron emitters should be made. In addition, the quantitative washout rate has a potential usefulness as a diagnostic index, but modeling for this has not been studied yet. In this paper, therefore, we applied compartment analyses to in-beam PET data acquired by our small OpenPET prototype, which has a physically opened field-of-view (FOV) between two detector rings. A rat brain was located at the FOV and was irradiated by a (11)C beam. Time activity curves of the irradiated field were measured immediately after the irradiations, and the washout rate was obtained based on two models: the two-washout model (medium decay, k2m; slow decay, k2s) developed in a study of rabbit irradiation; and the two-compartment model used in nuclear medicine, where efflux from tissue to blood (k2), influx (k3) and efflux (k4) from the first to second compartments in tissue were evaluated. The observed k2m and k2s were 0.34 and 0.005 min(-1), respectively, which was consistent with the rabbit study. Also k2m was close to the washout rate in cerebral blood flow (CBF) measurements by dynamic PET with (15)O-water, while, k2, k3, and k4 were 0.16, 0.15 and 0.007 min(-1). Our present work suggested the dynamics of (11)C might be relevant to CBF or permeability of a molecule containing (11)C atoms might be regulated by a transporter because the k2 was relatively low compared with a simple diffusion tracer