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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15228007 |
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| _version_ | 1866902024232108032 |
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| author | Zack Eriksen |
| author_facet | Zack Eriksen |
| contents | <h1>Alkaline OIB Principal Factor Analysis (PFA)</h1> <h2>Overview</h2> <p>This project applies compositional data analysis and multivariate statistics—including principal factor analysis (PFA) and bootstrapped partial least squares (PLS)—to fractional crystallization-corrected ocean island basalt (OIB) compositions, incorporating log-ratio transformations to ensure proper handling of compositional data for multivariate statistical methods. The aim is to isolate and quantify the relative effects of partial melting and source heterogeneity on OIB compositions, and to elucidate the origin of enriched mantle signatures (e.g., EM-1, EM-2, HIMU).</p> <p>The code can be used to reproduce the results from the article "Disentangling partial melting and crustal recycling signatures in ocean island basalts with multivariate statistics", published in G-cubed (<a href="http://dx.doi.org/10.1029/2025GC012390">http://dx.doi.org/10.1029/2025GC012390</a>).</p> <h2>Implementation</h2> <p>The associated <code>README.md</code> files describe the structure of the GitHub repository, how to implement self-written <code>.py</code> files for compositional data analysis (i.e., log-ratio transformations) and PFA, and which Jupyter Notebooks (<code>.ipynb</code> files) to run to successfully reproduce our results.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15228007 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Disentangling partial melting and crustal recycling signatures in ocean island basalts with multivariate statistics - project code Zack Eriksen <h1>Alkaline OIB Principal Factor Analysis (PFA)</h1> <h2>Overview</h2> <p>This project applies compositional data analysis and multivariate statistics—including principal factor analysis (PFA) and bootstrapped partial least squares (PLS)—to fractional crystallization-corrected ocean island basalt (OIB) compositions, incorporating log-ratio transformations to ensure proper handling of compositional data for multivariate statistical methods. The aim is to isolate and quantify the relative effects of partial melting and source heterogeneity on OIB compositions, and to elucidate the origin of enriched mantle signatures (e.g., EM-1, EM-2, HIMU).</p> <p>The code can be used to reproduce the results from the article "Disentangling partial melting and crustal recycling signatures in ocean island basalts with multivariate statistics", published in G-cubed (<a href="http://dx.doi.org/10.1029/2025GC012390">http://dx.doi.org/10.1029/2025GC012390</a>).</p> <h2>Implementation</h2> <p>The associated <code>README.md</code> files describe the structure of the GitHub repository, how to implement self-written <code>.py</code> files for compositional data analysis (i.e., log-ratio transformations) and PFA, and which Jupyter Notebooks (<code>.ipynb</code> files) to run to successfully reproduce our results.</p> |
| title | Disentangling partial melting and crustal recycling signatures in ocean island basalts with multivariate statistics - project code |
| url | https://doi.org/10.5281/zenodo.15228007 |