Earth System Data Cubes: Avenues for advancing Earth system research

David Montero, Guido Kraemer, Anca Anghelea, César Aybar, Gunnar Brandt, Gustau Camps-Valls, Felix Cremer, Ida Flik, Fabian Gans, Sarah Habershon, Chaonan Ji, Teja Kattenborn, Laura Martínez-Ferrer, Francesco Martinuzzi, Martin Reinhardt, Maximilian Söchting, Khalil Teber, Miguel D. Mahecha
{"title":"Earth System Data Cubes: Avenues for advancing Earth system research","authors":"David Montero, Guido Kraemer, Anca Anghelea, César Aybar, Gunnar Brandt, Gustau Camps-Valls, Felix Cremer, Ida Flik, Fabian Gans, Sarah Habershon, Chaonan Ji, Teja Kattenborn, Laura Martínez-Ferrer, Francesco Martinuzzi, Martin Reinhardt, Maximilian Söchting, Khalil Teber, Miguel D. Mahecha","doi":"arxiv-2408.02348","DOIUrl":null,"url":null,"abstract":"Recent advancements in Earth system science have been marked by the\nexponential increase in the availability of diverse, multivariate datasets\ncharacterised by moderate to high spatio-temporal resolutions. Earth System\nData Cubes (ESDCs) have emerged as one suitable solution for transforming this\nflood of data into a simple yet robust data structure. ESDCs achieve this by\norganising data into an analysis-ready format aligned with a spatio-temporal\ngrid, facilitating user-friendly analysis and diminishing the need for\nextensive technical data processing knowledge. Despite these significant\nbenefits, the completion of the entire ESDC life cycle remains a challenging\ntask. Obstacles are not only of a technical nature but also relate to\ndomain-specific problems in Earth system research. There exist barriers to\nrealising the full potential of data collections in light of novel cloud-based\ntechnologies, particularly in curating data tailored for specific application\ndomains. These include transforming data to conform to a spatio-temporal grid\nwith minimum distortions and managing complexities such as spatio-temporal\nautocorrelation issues. Addressing these challenges is pivotal for the\neffective application of Artificial Intelligence (AI) approaches. Furthermore,\nadhering to open science principles for data dissemination, reproducibility,\nvisualisation, and reuse is crucial for fostering sustainable research.\nOvercoming these challenges offers a substantial opportunity to advance\ndata-driven Earth system research, unlocking the full potential of an\nintegrated, multidimensional view of Earth system processes. This is\nparticularly true when such research is coupled with innovative research\nparadigms and technological progress.","PeriodicalId":501123,"journal":{"name":"arXiv - CS - Databases","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.02348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.
地球系统数据立方体:推进地球系统研究的途径
地球系统科学近来取得了长足进步,其标志是以中高时空分辨率为特征的多样化多变量数据集的可用性呈指数级增长。地球系统数据立方体(ESDC)是将大量数据转化为简单而强大的数据结构的合适解决方案。为了实现这一目标,ESDCs 将数据组织成与时空网格相匹配的分析就绪格式,方便用户进行分析,并减少了对大量数据处理技术知识的需求。尽管有这些重大优势,但完成整个 ESDC 生命周期仍然是一项具有挑战性的任务。这些障碍不仅是技术性的,也与地球系统研究的具体领域问题有关。鉴于基于云的新技术,在充分发挥数据收集的潜力方面存在障碍,特别是在为特定应用领域量身定制数据方面。这些障碍包括转换数据,使其符合时空网格,并尽量减少失真,以及管理时空自相关性等复杂问题。应对这些挑战对于有效应用人工智能(AI)方法至关重要。此外,在数据传播、可重现性、可视化和再利用方面坚持开放科学原则,对于促进可持续研究至关重要。克服这些挑战为推进数据驱动的地球系统研究提供了大量机会,释放了综合、多维地球系统过程视图的全部潜力。当这种研究与创新研究范式和技术进步相结合时,情况尤其如此。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信