Building Bridges Between Global Information Systems on marine Organisms and Ecosystem Models. Grüss A, Palomares M L D, Poelen J H, et al. Sustainable Ecological Aquaculture Systems: The Need for a New Social Contract for Aquaculture Development. A Model Combining Landings and VMS Data to Estimate Landings by Fishing Ground and Harbor. Prediction of Global Sea Cucumber Capture Production Based on The Exponential Smoothing and ARIMA Models. Yugui Z H U, Hongbing L V, Jiansong C H U. Assessing Current and Future Meat and Fish Consumption in Sub-Sahara Africa: Learnings From FAO Food Balance Sheets and LSMS Household Survey Data. Ecological Informatics, 2006, 1(1): 87–99.ĭesiere S, Hung Y, Verbeke W, et al. Report on The Development of The FAO/INFOODS User Database for Fish and Shellfish (uFiSh) – Challenges and Possible Solutions. Rittenschober D, Stadlmayr B, Nowak V, et al. Responsible Aquaculture and Trophic Level Implications to Global Fish Supply. Tacon A G J, Metian M, Turchini G M, et al. Utilization of Fishery Information Resource and Search Approach at FAO Website. The Management of Scientific Data Resources.
Construction of Fishery Scientific Data Sharing Platform.
Key words: fisheries science data, data center, data fusion, data mining, cloud computing, scientific data, big data, data sharing The end goal is to ensure the continuous operation of the data center, maximize the value of fishery science data, point the direction for further building fishery science data. The study also considers factors in the sustainable development of the data center, including data collection, systems construction, standards setting, shared services mode, skills and training, and energy saving. Technical roadmaps identify a storage and fusion platform for multi-source heterogeneous fishery data, a big data analysis and application platform in fishery science, and a cloud service platform. The overall architecture of the data center supports data fusion, big data analysis and cloud computing services. This study analyzes the characteristics, sources, and possible applications of fishery science data in the context of needing for fishery science data application, with the goal of improving the comprehensive service and smart decision-making ability of the data center and effectively preserving, managing, sharing and mining fishery science data.In the context of needing for fishery science data application We analyze and describe the function and position of the data center in terms of the demands for scientific data in the fishery technological innovation process. It also provides important technical support to development strategy and scientific decisions, and improves the modernization of fishery. The Fishery Science Data Center, which manages fishery science data and applications of that data, is a crucial strategic resource for technological innovation and industrial development. 关键词: 渔业科学数据, 数据中心, 数据融合, 数据挖掘, 云计算, 科学数据, 大数据, 数据共享īasic, original fisheries science data is generated in the process of fishery technological activities, and has important scientific significance and practical value for agricultural, marine and economic fields.
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