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 in | Scientific data Vol. 12; no. 1; pp. 465 - 24 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
20.03.2025
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2052-4463 2052-4463 |
| DOI | 10.1038/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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Anna orcidid: 0000-0003-1982-4440 surname: Simson fullname: Simson, Anna email: simson@mbd.rwth-aachen.de organization: Methods for Model-based Development in Computational Engineering, RWTH Aachen University – sequence: 2 givenname: Anil orcidid: 0000-0002-2257-7025 surname: Yildiz fullname: Yildiz, Anil organization: Methods for Model-based Development in Computational Engineering, RWTH Aachen University – sequence: 3 givenname: Julia orcidid: 0000-0003-4123-5896 surname: Kowalski fullname: Kowalski, Julia email: kowalski@mbd.rwth-aachen organization: Methods for Model-based Development in Computational Engineering, RWTH Aachen University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40113815$$D View this record in MEDLINE/PubMed |
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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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| SubjectTerms | 704/106/125 706/648/697/129 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 |
| URI | https://link.springer.com/article/10.1038/s41597-025-04665-x https://www.ncbi.nlm.nih.gov/pubmed/40113815 https://www.proquest.com/docview/3179575150 https://www.proquest.com/docview/3179853138 https://doi.org/10.1038/s41597-025-04665-x https://doaj.org/article/36dba5dd884f4a6ca476e8b060c759b8 |
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