October 21, 2021 Dataset Open Access

Schoenig, Jan; von Eynatten, Hilmar; Tolosana-Delgado, Raimon; Meinhold, Guido

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        <foaf:name>Meinhold, Guido</foaf:name>
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    <dct:title>Garnet major-element composition as an indicator of host-rock type: a machine learning approach using the random forest classifier / supplementary data</dct:title>
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    <dcat:keyword>garnet major-element composition</dcat:keyword>
    <dcat:keyword>host-rock discrimination</dcat:keyword>
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    <dct:description>&lt;p&gt;The database includes 13615 garnet compositions of eight oxides commonly analysed in lab routines: SiO&lt;sub&gt;2&lt;/sub&gt;, TiO&lt;sub&gt;2&lt;/sub&gt;, Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;, Cr&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;, FeO&lt;sub&gt;total&lt;/sub&gt;, MnO, MgO, and CaO (in wt%). These are complemented by the following covariables:&lt;/p&gt; &lt;p&gt;&lt;strong&gt;setting and metamorphic facies class&lt;/strong&gt;: code indicating the geologic/tectonic setting of the host rock&lt;/p&gt; &lt;p&gt;&lt;strong&gt;composition class&lt;/strong&gt;: code indicating the compositional class of the host rock&lt;/p&gt; &lt;p&gt;&lt;strong&gt;author&lt;/strong&gt;: authors of the original paper providing the data&lt;/p&gt; &lt;p&gt;&lt;strong&gt;journal&lt;/strong&gt;: journal of the original paper&lt;/p&gt; &lt;p&gt;&lt;strong&gt;region&lt;/strong&gt;: origin of the data, in the format &amp;quot;region, country&amp;quot;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;sample name&lt;/strong&gt;: sample ID in the original paper&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Pavg(kbar)&lt;/strong&gt;: if available, indicated pressure&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Tavg(&amp;deg;C)&lt;/strong&gt;: if available, indicated temperature&lt;/p&gt; &lt;p&gt;&lt;strong&gt;host-rock type and/or metamorphic facies&lt;/strong&gt;: facies indication of host rock&lt;/p&gt; &lt;p&gt;&lt;strong&gt;lithology and/or protolith&lt;/strong&gt;: composition indication of host rock&lt;/p&gt; &lt;p&gt;&lt;strong&gt;SiO2&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;TiO2&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Al2O3&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Cr2O3&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;FeOtotal&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;MnO&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;MgO&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&lt;strong&gt;CaO&lt;/strong&gt;: wt%&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;This research was funded by DFG grant EY 23/27-1.&lt;/p&gt;</dct:description>
    <dct:description xml:lang="">{"references": ["Sch\u00f6nig, J.; von Eynatten, H.; Tolosana-Delgado, R.; Meinhold, G. (in press) Garnet major-element composition as an indicator of host-rock type: a machine learning approach using the random forest classifier. Contributions to Mineralogy and Petrology; .doi: 10.1007/s00410-021-01854-w"]}</dct:description>
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