%0 Journal Article %1 montero2024earth %A Montero, David %A Kraemer, Guido %A Anghelea, Anca %A Aybar, César %A Brandt, Gunnar %A Camps-Valls, Gustau %A Cremer, Felix %A Flik, Ida %A Gans, Fabian %A Habershon, Sarah %A Ji, Chaonan %A Kattenborn, Teja %A Martínez-Ferrer, Laura %A Martinuzzi, Francesco %A Reinhardt, Martin %A Söchting, Maximilian %A Teber, Khalil %A Mahecha, Miguel D %D 2024 %J arXiv preprint arXiv:2408.02348 %K imported topic_earthenvironment %T Earth System Data Cubes: Avenues for advancing Earth system research %X 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 …

@article{montero2024earth, 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 …}, added-at = {2024-11-29T11:53:34.000+0100}, author = {Montero, David and Kraemer, Guido and Anghelea, Anca and Aybar, César and Brandt, Gunnar and Camps-Valls, Gustau and Cremer, Felix and Flik, Ida and Gans, Fabian and Habershon, Sarah and Ji, Chaonan and Kattenborn, Teja and Martínez-Ferrer, Laura and Martinuzzi, Francesco and Reinhardt, Martin and Söchting, Maximilian and Teber, Khalil and Mahecha, Miguel D}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2157d2d5a25cf365f94237e892511f87d/joum576e}, citation = {arXiv preprint arXiv:2408.02348, 2024}, interhash = {e48cac1af0b2ced1f22dfdcefd9c1153}, intrahash = {157d2d5a25cf365f94237e892511f87d}, journal = {arXiv preprint arXiv:2408.02348}, keywords = {imported topic_earthenvironment}, timestamp = {2024-11-29T11:53:34.000+0100}, title = {Earth System Data Cubes: Avenues for advancing Earth system research}, year = 2024 }