March 30, 2026 Dataset Open Access

Thiele, Samuel Thomas; Kirsch, Moritz; Frenzel, Max; Tolosana Delgado, Raimon; Kamath, Akshay Vijay; Guy, Bradley Martin; Kim, Yongwhi; Laura, Tusa; Járóka, Tom; Gloaguen, Richard

DataCite XML Export

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  <identifier identifierType="DOI">10.14278/rodare.4582</identifier>
  <creators>
    <creator>
      <creatorName>Thiele, Samuel Thomas</creatorName>
      <givenName>Samuel Thomas</givenName>
      <familyName>Thiele</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4169-0207</nameIdentifier>
      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Kirsch, Moritz</creatorName>
      <givenName>Moritz</givenName>
      <familyName>Kirsch</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1512-5511</nameIdentifier>
      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Frenzel, Max</creatorName>
      <givenName>Max</givenName>
      <familyName>Frenzel</familyName>
      <affiliation>King Abdullah University of Science and Technology, Thuwal, Saudi Arabia</affiliation>
    </creator>
    <creator>
      <creatorName>Tolosana Delgado, Raimon</creatorName>
      <givenName>Raimon</givenName>
      <familyName>Tolosana Delgado</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9847-0462</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Kamath, Akshay Vijay</creatorName>
      <givenName>Akshay Vijay</givenName>
      <familyName>Kamath</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3407-5222</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Guy, Bradley Martin</creatorName>
      <givenName>Bradley Martin</givenName>
      <familyName>Guy</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0009-0000-3643-6349</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Kim, Yongwhi</creatorName>
      <givenName>Yongwhi</givenName>
      <familyName>Kim</familyName>
      <affiliation>GeoRessources Laboratory, Faculté des Sciences et Technologies, Université de Lorraine</affiliation>
    </creator>
    <creator>
      <creatorName>Laura, Tusa</creatorName>
      <givenName>Tusa</givenName>
      <familyName>Laura</familyName>
      <affiliation>TheiaX GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Járóka, Tom</creatorName>
      <givenName>Tom</givenName>
      <familyName>Járóka</familyName>
      <affiliation>Geological Survey of Saxony, Saxon State Office for Environment, Agriculture and Geology, Halsbrücker Straße 31a, 09599 Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Gloaguen, Richard</creatorName>
      <givenName>Richard</givenName>
      <familyName>Gloaguen</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4383-473X</nameIdentifier>
      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data for Upscaling mineralogy with hyperspectral data: a benchmark dataset and machine learning framework to enable hyperspectral geometallurgy</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject>hyperspectral</subject>
    <subject>mineralogy</subject>
    <subject>mineral liberation analysis</subject>
    <subject>machine learning</subject>
    <subject>benchmark</subject>
    <subject>geometallurgy</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2026-03-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/4582</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-43223</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.4581</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/energy</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/hzdr</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Mineral liberation analysis (MLA) dataset accompanying the paper:&amp;nbsp;Upscaling mineralogy with hyperspectral data: a benchmark dataset and machine learning framework to enable hyperspectral geometallurgy. This describes the mineralogy of 204 thick-sections prepared from 49 drillholes sampled across 7 different locations and coregistered with VNIR-SWIR-MWIR-LWIR hyperspectral data. It is intended to help develop, test and benchmark methods for predicting mineralogy from hyperspectral data.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The data are stored as hycore (https://github.com/samthiele/hycore) Shed directories for easy loading, although individual MLA sections and corresponding hyperspectral images are all in ENVI format (so can be loaded by any hyperspectral analysis code or software). MLA outputs are also stored in their original (high-resolution) form as indexed bitmaps. The AbundanceMapping.xlsx file can be used to translate these MLA class indices into modal mineral abundances.&lt;/p&gt;

&lt;p&gt;Finally, jupyter notebooks used to derive the benchmarks presented in the paper are also included, in the Code folder. These illustrate how the data can be loaded and manipulated using hycore and hklearn (https://github.com/samthiele/hklearn), and used to train machine learning models that predict modal mineralogy given hyperspectral data.&lt;/p&gt;</description>
  </descriptions>
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