Reusability-targeted enrichment of sea ice core data

The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such as the validation of physics-based models and the training of data-driven algorithms. RESICE is enriched in two ways. First, RESICE combines d...

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Published inScientific data Vol. 12; no. 1; pp. 465 - 24
Main Authors Simson, Anna, Yildiz, Anil, Kowalski, Julia
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 20.03.2025
Nature Publishing Group
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ISSN2052-4463
2052-4463
DOI10.1038/s41597-025-04665-x

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Abstract The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such as the validation of physics-based models and the training of data-driven algorithms. RESICE is enriched in two ways. First, RESICE combines data and metadata originating from 138 sources including 107 data sets from the repositories Zenodo, Australian Antarctic Data Center and Pangaea. Second, RESICE contains additional automatically generated metadata tailored to specific reuse scenarios. RESICE is checked for plausibility and consistency, and it allows transparent retracing of each data point to its source. RESICE is accessible via Zenodo and the MOSAiC webODV, and it is extendable through the pyresice Python package. In addition to describing RESICE, we formalize the reuse perspective of an agnostic reuser, uninvolved in data acquisition, and we discuss the process of the cross-source and -repository combination of the database. Despite sources adhering to FAIR, this process is challenging and time-intensive due to the heterogeneity of the sources and their mismatch with reuse requirements.
AbstractList The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such as the validation of physics-based models and the training of data-driven algorithms. RESICE is enriched in two ways. First, RESICE combines data and metadata originating from 138 sources including 107 data sets from the repositories Zenodo, Australian Antarctic Data Center and Pangaea. Second, RESICE contains additional automatically generated metadata tailored to specific reuse scenarios. RESICE is checked for plausibility and consistency, and it allows transparent retracing of each data point to its source. RESICE is accessible via Zenodo and the MOSAiC webODV, and it is extendable through the pyresice Python package. In addition to describing RESICE, we formalize the reuse perspective of an agnostic reuser, uninvolved in data acquisition, and we discuss the process of the cross-source and -repository combination of the database. Despite sources adhering to FAIR, this process is challenging and time-intensive due to the heterogeneity of the sources and their mismatch with reuse requirements.The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such as the validation of physics-based models and the training of data-driven algorithms. RESICE is enriched in two ways. First, RESICE combines data and metadata originating from 138 sources including 107 data sets from the repositories Zenodo, Australian Antarctic Data Center and Pangaea. Second, RESICE contains additional automatically generated metadata tailored to specific reuse scenarios. RESICE is checked for plausibility and consistency, and it allows transparent retracing of each data point to its source. RESICE is accessible via Zenodo and the MOSAiC webODV, and it is extendable through the pyresice Python package. In addition to describing RESICE, we formalize the reuse perspective of an agnostic reuser, uninvolved in data acquisition, and we discuss the process of the cross-source and -repository combination of the database. Despite sources adhering to FAIR, this process is challenging and time-intensive due to the heterogeneity of the sources and their mismatch with reuse requirements.
The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such as the validation of physics-based models and the training of data-driven algorithms. RESICE is enriched in two ways. First, RESICE combines data and metadata originating from 138 sources including 107 data sets from the repositories Zenodo, Australian Antarctic Data Center and Pangaea. Second, RESICE contains additional automatically generated metadata tailored to specific reuse scenarios. RESICE is checked for plausibility and consistency, and it allows transparent retracing of each data point to its source. RESICE is accessible via Zenodo and the MOSAiC webODV, and it is extendable through the pyresice Python package. In addition to describing RESICE, we formalize the reuse perspective of an agnostic reuser, uninvolved in data acquisition, and we discuss the process of the cross-source and -repository combination of the database. Despite sources adhering to FAIR, this process is challenging and time-intensive due to the heterogeneity of the sources and their mismatch with reuse requirements.
Abstract The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such as the validation of physics-based models and the training of data-driven algorithms. RESICE is enriched in two ways. First, RESICE combines data and metadata originating from 138 sources including 107 data sets from the repositories Zenodo, Australian Antarctic Data Center and Pangaea. Second, RESICE contains additional automatically generated metadata tailored to specific reuse scenarios. RESICE is checked for plausibility and consistency, and it allows transparent retracing of each data point to its source. RESICE is accessible via Zenodo and the MOSAiC webODV, and it is extendable through the pyresice Python package. In addition to describing RESICE, we formalize the reuse perspective of an agnostic reuser, uninvolved in data acquisition, and we discuss the process of the cross-source and -repository combination of the database. Despite sources adhering to FAIR, this process is challenging and time-intensive due to the heterogeneity of the sources and their mismatch with reuse requirements.
ArticleNumber 465
Author Yildiz, Anil
Kowalski, Julia
Simson, Anna
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Snippet The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse scenarios such...
Abstract The Reusability-targeted Enriched Sea Ice Core Database (RESICE) combines data and metadata from 287 sea ice cores. The database enables reuse...
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706/648/697/129
Algorithms
Cores
Data acquisition
Data Descriptor
Humanities and Social Sciences
Metadata
multidisciplinary
Science
Science (multidisciplinary)
Sea ice
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Title Reusability-targeted enrichment of sea ice core data
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