Journal of Agricultural Big Data >
Research on Construction of Fisheries Science Data Center
Received date: 2019-07-05
Online published: 2019-11-28
Basic, 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. 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. It also provides important technical support to development strategy and scientific decisions, and improves the modernization of fishery. 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. The overall architecture of the data center supports data fusion, big data analysis and cloud computing services. 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 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. 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 .
Feng Lu,Lihua Wang,Shuo Xu . Research on Construction of Fisheries Science Data Center[J]. Journal of Agricultural Big Data, 2019 , 1(3) : 57 -70 . DOI: 10.19788/j.issn.2096-6369.190306
| 1 | 王立华, 孙璐, 孙英泽, 等. 渔业科学数据共享平台建设研究 [J]. 中国海洋大学学报, 2010, 40(S1): 201–206. |
| 1 | Wang L H, Sun L, Sun Y Z, et al. Construction of Fishery Scientific Data Sharing Platform [J]. Periodical of Ocean University of China, 2010, 40(S1): 201–206. |
| 2 | 董诚, 黄鼎成.科学数据资源的管理[J]. 中国基础科学, 2006, 8(6): 20–24. |
| 2 | Dong C, Huang D C. The Management of Scientific Data Resources. China Basic Science, 2006, 8(6): 20–24. |
| 3 | 李励年, 谢营梁. FAO网站水产信息资源利用与检索途径 [J]. 现代渔业信息, 2004, 19(7): 17–19. |
| 3 | Li L N, Xie Y L. Utilization of Fishery Information Resource and Search Approach at FAO Website [J]. Modern Fisheries Information, 2004, 19(7): 17–19. |
| 4 | Tacon A G J, Metian M, Turchini G M, et al. Responsible Aquaculture and Trophic Level Implications to Global Fish Supply [J]. Reviews in Fisheries Science, 2009, 18(1): 94–105. |
| 5 | Rittenschober D, Stadlmayr B, Nowak V, et al. Report on The Development of The FAO/INFOODS User Database for Fish and Shellfish (uFiSh) – Challenges and Possible Solutions [J]. Food Chemistry,2016, 193: 112–120. |
| 6 | Niwa, H S. Exploitation Dynamics of Fish Stocks [J]. Ecological Informatics, 2006, 1(1): 87–99. |
| 7 | Desiere S, Hung Y, Verbeke W, et al. Assessing Current and Future Meat and Fish Consumption in Sub-Sahara Africa: Learnings From FAO Food Balance Sheets and LSMS Household Survey Data [J]. Global Food Security,2018, 16: 116–126. |
| 8 | Yugui Z H U, Hongbing L V, Jiansong C H U. Prediction of Global Sea Cucumber Capture Production Based on The Exponential Smoothing and ARIMA Models [J]. Iranian Journal of Fisheries Sciences, 2016, 15(3): 1089–1107. |
| 9 | Russo T, Morello, E B, Parisi A, et al. A Model Combining Landings and VMS Data to Estimate Landings by Fishing Ground and Harbor [J]. Fisheries Research,2017, 199: 218–230. |
| 10 | Costa-Pierce B A. Sustainable Ecological Aquaculture Systems: The Need for a New Social Contract for Aquaculture Development [J]. Marine Technology Society Journal, 2010, 44(3): 88-112. |
| 11 | Grüss A, Palomares M L D, Poelen J H, et al. Building Bridges Between Global Information Systems on marine Organisms and Ecosystem Models [J]. Ecological Modelling,2019, 398: 1–19. |
| 12 | Halpin P N, Read A J, Fujioka E, et al. OBIS-SEAMAP: The World Data Center for Marine Mammal, Sea Bird, and Sea Turtle Distributions [J]. Oceanography, 2009, 22(2): 104–115. |
| 13 | Fujioka E, Soldevilla M S, Read A J, et al. Integration of Passive Acoustic Monitoring Data into OBIS-SEAMAP, A Global Biogeographic Database, to Advance Spatially-Explicit Ecological Assessments [J]. Ecological Informatics,2014, 21: 59–73. |
| 14 | Vandepitte L, Bosch S, Tyberghein L, et al. Fishing for Data and Sorting The Catch: Assessing The Data Quality, Completeness and Fitness for Use of Data in Marine Biogeographic Databases [J]. Database, 2015: 1–14. |
| 15 | De Pooter D, Appeltans W, Bailly N, et al. Toward A New Data Standard for Combined Marine Biological and Environmental Datasets – Expanding OBIS Beyond Species Occurrences [J]. Biodiversity Data Journal, 2017, 5: e10989. |
| 16 | Bunsick S M. Update On The NOAA Aquaculture Program [J]. Journal of Shellfish Research, 2009, 28(3): 645–646. |
| 17 | Apeti D A, Lauenstein G G, Evans D W, et al. Recent Status of Total Mercury and Methyl Mercury in The Coastal Waters of The Northern Gulf of Mexico Using Oysters and Sediments From NOAA's Mussel Watch Program [J]. Marine Pollution Bulletin, 2012, 64(11): 2399–2408. |
| 18 | Sigler M, DeMaster D, Boveng P, et al. Advances in Methods for Marine Mammal and Fish Stock Assessments: Thermal Imagery and CamTrawl [J]. Marine Technology Society Journal, 2015, 49(2): 99–106. |
| 19 | Hartwell S I, Apeti A D, Pait A S, et al. Benthic Habitat Contaminant Status and Sediment Toxicity in Bristol Bay, Alaska [J]. Regional Studies in Marine Science, 24: 343–354. |
| 20 | Geronimo R C, Franklin E C, Brainard R E, et al. Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling [J]. Remote Sensing, 2018, 10(10): 1604. |
| 21 | Mark Y A, Philip A, Nelson A W, et al. Safety Assessment on Microbial and Heavy Metal Concentration in Clarias Gariepinus (African Catfish) Cultured in Treated Wastewater Pond in Kumasi, Ghana [J]. Environmental Technology, 2019, 40(3): 302–311. |
| 22 | 周国民. 我国农业大数据应用进展综述 [J]. 农业大数据学报, 2019, 1(1): 16–23. |
| 22 | Zhou G M. Progress in the Application of Big Data in Agriculture in China. Journal of Agricultural Big Data, 2019, 1(1): 16–23. |
| 23 | 赵瑞雪, 赵华, 朱亮. 国内外农业科学大数据建设与共享进展 [J]. 农业大数据学报, 2019, 1(1): 24–37. |
| 23 | Zhao R X, Zhao H, Zhu L. Progress in the Development and Sharing of Big Data in Agricultural Science between China and Foreign Countries [J]. Journal of Agricultural Big Data, 2019, 1(1): 24–37. |
| 24 | 科技部发展计划司, 财政部教科文司. 国家科技基础条件平台中心. 国家科技基础条件平台科技资源开放共享目录. [M]. 北京: 科技文献出版社, 2013. |
| 24 | Department of Development and Planning for the Ministry of Science and Technology, Department of Education and Culture for the Ministry of Finance, National Science and Technology Infrastructure Center. Open and Shared Catalogue of Science and Technology Resources for National Science and Technology Infrastructure [M]. Beijing: Scientific and Technical Documentation Press, 2013. |
| 25 | 巩沐歌. 国内外渔业信息化发展现状对比分析 [J]. 现代渔业信息, 2011, 26(12): 20–24. |
| 25 | Gong M G. A Comparative Analysis of Development Status of Fisheries Informatization at Home and Abroad [J]. Modern Fisheries Information, 2011, 26(12): 20–24. |
| 26 | 王欣, 高倩. 海洋国家实验室: 探索中国特色国家实验室建设道路 [J]. 走向世界,2017, 33: 30–33. |
| 26 | Wang X, Gao Q. Qingdao National Laboratory For Marine Science and Technology Explore The Way to Build The National Laboratory With Chinese Characteristics [J]. Openings,2017, 33: 30–33. |
| 27 | Mills C M, Townsend S E, Jennings S, et al. Estimating high resolution trawl fishing effort from satellite-derived vessel monitoring system data[J]. ICES Journal of Marine Science, 2007, 64(2), 248–255. |
| 28 | Peel D, Good N M. A hidden Markov model approach for determining vessel activity from vessel monitoring system data[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2011, 68(7): 1252–1264. |
| 29 | Natale F, Gibin M, Alessandrini A, et al. Mapping Fishing Effort through AIS Data[J]. PLoS ONE, 2015, 10(6): e0130746. |
| 30 | Enever R, Lewin S, Reese A, et al. Mapping fishing effort: Combining fishermen’s knowledge with satellite monitoring data in English waters[J]. Fisheries Research,2017, 189: 67–76. |
| 31 | Palmer M C, Wigley S E. Using positional data from vessel monitoring systems to validate the logbook-reported area fished and the stock allocation of commercial fisheries landings[J]. North American Journal of Fisheries Management, 2009, 29(4): 928–942. |
| 32 | Bastardie F, Nielsen J R, Ulrich C, et al. Detailed mapping of fishing effort and landings by coupling fishing logbooks with satellite-recorded vessel geo-location[J]. Fisheries Research, 2010, 106(1): 41–53. |
| 33 | Stelzenmuller V, Rogers S I, Mills C M. Spatio-temporal patterns of fishing pressure on UK marine landscapes and their implications for spatial planning and management[J]. ICES Journal of Marine Science, 2008, 65(6): 1081–1091. |
| 34 | Geronimo R C, Franklin E C, Brainard R E. Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling[J]. Remote Sensing, 2018, 10(10): 1604. |
| 35 | Kaiser M J, Hinz H, Hiddink J G. Quantification and prediction of the impact of fishing on epi-faunal communities[J]. Marine Ecology Progress Series,2011, 430: 71–86. |
| 36 | Bueno-Pardo J, Ramalho S P, García-Alegre A, et al. Deep-sea crustacean trawling fisheries in Portugal: quantification of effort and assessment of landings per unit effort using a Vessel Monitoring System (VMS)[J]. Scientific Reports, 2017, 7: 40795. |
| 37 | Marzuki M I, Gaspar P, Garello R, et al. Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories[J]. Journal of Oceanic Engineering, 2018, 43(3): 689–699. |
| 38 | Watson J T, Haynie A C. Paths to resilience: Alaska pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2018, 75(11): 1977–1989. |
| 39 | Xu L Q, Liu S Y, Li D L. Prediction of Water Temperature in Prawn Cultures Based on a Mechanism Model Optimized by an Improved Artificial Bee Colony[J]. Computers and Electronics in Agriculture,2017, 140: 397–408． |
| 40 | Ashoka Deepananda K H M, Amarasinghe U S, Jayasinghe-Mudalige U K, et al. Stilt Fisher Knowledge in Southern Sri Lanka as an Expert System: Astrategy towards Co-Management[J]. Fisheries Research,2016, 174: 288–297. |
| 41 | 王斌, 徐建瑜, 王春琳. 基于计算机视觉的梭子蟹蜕壳检测及不同背景对蜕壳的影响[J]. 渔业现代化, 2016, 43(2): 11–16. |
| 41 | Wang B, Xu J Y, Wang C L. Computer-Vision Based Molting Detection of Portunus Tritubercularus and Effects of Different Backgrounds on Molting[J]. Fishery Modernization, 2016, 43(2): 11–16. |
| 42 | 段青玲, 张磊, 魏芳芳, 等. 基于时间序列GA-SVＲ 的水产品价格预测模型及验证[J]. 农业工程学报, 2017, 33(1): 308–314. |
| 42 | Duan Q L, Zhang L, Wei F F, et al. Forecasting Model and Validation for Aquatic Product Price Based on Time Series GA-SVR[J]. Transactions of the CSAE, 2017, 33(1): 308–314. |
| 43 | 颜波, 石平, 黄广文. 基于RFID和EPC物联网的水产品供应链可追溯平台开发[J]. 农业工程学报, 2013, 29(15): 172–183. |
| 43 | Yan B, Shi P, Huang G W. Development of Traceability System of Aquatic Foods Supply Chain Based on RFID and EPC Internet of Things[J]. Transactions of the CSAE, 2013, 29(15): 172–183. |
| 44 | Tai H J, Liu S Y, Li D L, et al. A Multi-Environmental Factor Monitoring System for Aquiculture Based on Wireless Sensor Networks[J]. Sensor Letters, 2012, 10(1): 265–270. |
| 45 | 邢文明. 我国科研数据管理与共享政策保障研究 [D]. 武汉: 武汉大学, 2014. |
| 45 | Xing W M. Research on Policy Guarantee of Scientific Data Management and Sharing in China [D]. Wuhan: Wuhan University, 2014. |
| 46 | GB 50174-2017, 数据中心设计规范[S]. |
| 46 | GB 50174-2017, Code For Design Of Data Centers[S]. |
| 47 | 赵彦云. 对大数据统计设计的思考 [J]. 统计研究, 2015, 32(6): 3–10． |
| 47 | Zhao Y Y. Reflection on Statistical Design in Big Data [J]. Statistical Research, 2015, 32(6): 3–10． |
| 48 | Shehabi A, Smith S J, Masanet E, et al. Data Center Growth In The United States: Decoupling The Demand For Services From Electricity Use [J]. Environmental Research Letters, 2018, 13:124030. |
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