Data science for oceanography: from small data to big data

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-05-26 DOI:10.1080/20964471.2021.1902080
Chengcheng Qian, Baoxiang Huang, Xueqing Yang, Ge Chen
{"title":"Data science for oceanography: from small data to big data","authors":"Chengcheng Qian, Baoxiang Huang, Xueqing Yang, Ge Chen","doi":"10.1080/20964471.2021.1902080","DOIUrl":null,"url":null,"abstract":"ABSTRACT The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven. Based on the types and distribution of oceanographic data, this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science. The ocean science has not fully entered the era of big data. There are two ways to expand the amount of oceanographic data to better understanding and management of the ocean. On the data level, fully exploit the potential value of big and small ocean data, and transform the limited, small data into rich, big data, will help to achieve this. On the application level, oceanographic data are of great value if realize the federation of the core data owners and the consumers. The oceanographic data will provide not only a reliable scientific basis for climate, ecological, disaster and other scientific research, but also provide an unprecedented rich source of information that can be used to make predictions of the future.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"49 1","pages":"236 - 250"},"PeriodicalIF":4.2000,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Earth Data","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/20964471.2021.1902080","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 12

Abstract

ABSTRACT The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven. Based on the types and distribution of oceanographic data, this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science. The ocean science has not fully entered the era of big data. There are two ways to expand the amount of oceanographic data to better understanding and management of the ocean. On the data level, fully exploit the potential value of big and small ocean data, and transform the limited, small data into rich, big data, will help to achieve this. On the application level, oceanographic data are of great value if realize the federation of the core data owners and the consumers. The oceanographic data will provide not only a reliable scientific basis for climate, ecological, disaster and other scientific research, but also provide an unprecedented rich source of information that can be used to make predictions of the future.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
海洋学数据科学:从小数据到大数据
海洋观测技术的快速发展,积累了大量的数据,推动着海洋科学向着数据驱动的方向发展。根据海洋资料的种类和分布,分析了大数据和小数据在海洋科学中的应用现状,并对未来进行了展望。海洋科学还没有完全进入大数据时代。有两种方法可以扩大海洋学数据的数量,以更好地了解和管理海洋。在数据层面,充分挖掘大、小海洋数据的潜在价值,将有限的小数据转化为丰富的大数据,将有助于实现这一目标。在应用层面上,实现核心数据所有者和消费者的联合,海洋数据具有重要的应用价值。海洋学数据不仅将为气候、生态、灾害等科学研究提供可靠的科学依据,而且还将为预测未来提供前所未有的丰富信息来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
期刊最新文献
A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020 An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea A mediation system for continuous spatial queries on a unified schema using Apache Spark
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1