We present, an online platform that centralizes information on AMR in animals from 1,285 surveys from LMICs. However, surveillance of AMR is at best nascent, and the current evidence base to inform policymakers is geographically heterogeneous. In low- and middle-income countries (LMICs), demand for meat is rising, and developing policies against AMR is urgent. In high-income countries, surveillance systems helped inform policies to curb AMR in animals. Its positive impact in the future is infinite and will keep growing as long as it is maintained by the society.Īntimicrobial resistance (AMR) is a growing threat to the health of humans and animals that requires global actions. The Iwaki Health Promotion Project aims not only to produce a pluripotent big data but also to improve the average life expectancy of Aomori by creating a large platform in the society. It also led to the acquisition of external funding, publications of numerous research papers, creation of new health examinations, and the establishment of the Health Promotion Center (an institution for cultivating health volunteers). Consequently, the amount of data collected from the project has gained attention and became more open to companies and researchers participating in the Iwaki Health Promotion Project, resulted in establishing a larger platform. It has been used to promote public health, which has also created a stronger partnership among companies and research organizations. Since the numbers of academia, industries, governments, and citizens involved in the Iwaki Health Promotion Project increased over the years, the big data produced during the project has become increasingly pluripotent and adaptable. The Iwaki Health Promotion Project has been supported financially by the Japanese government since it was selected as the Center of Innovation program in 2013. The industry, government, academia, and citizens have involvements in data collection, aiming to build a platform that encourages societal innovation and subsequently extends life expectancy in Aomori. Since 2005, health data (approximately 3000 items per person as of 2020) of approximately 1000 adults have been collected each year during the Iwaki Health Promotion Project. We are trying to create a platform for social innovation to extend life span. Our model will furthermore contribute to a more profound development process within organizations and create a common ground for communication. Using the model derived six research gaps that need further attention for establishing a practically-grounded engineering process. We successfully validated our model by matching it with established data engineering topics. For the creation of the model, we conducted a systematic literature review on data lifecycles to find commonalities between these models and derive an abstract meta-model. In this study, we developed a data engineering reference model (DERM), which outlines the important building-blocks for handling data along the data lifecycle. Therefore, resources and frameworks that bridge the gaps between theory and practice are required. Yet, the professional development of such applications is still in its infancy and a practical engineering approach is necessary to reach the next maturity level. To exploit these advantages, the engineering of data-intensive applications is becoming increasingly important. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.ĭata forms an essential organizational asset and is a potential source for competitive advantages. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. There is currently a lack of dedicated infrastructure focused on the ‘manageability’ of the data lifecycle in TM research between data collection and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies focusing primarily on the challenges in data integration and analysis.
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