August 25, 2026 Software Open Access

Starke, Sebastian; Šmíd, Michal

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  <identifier identifierType="DOI">10.14278/rodare.4976</identifier>
  <creators>
    <creator>
      <creatorName>Starke, Sebastian</creatorName>
      <givenName>Sebastian</givenName>
      <familyName>Starke</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5007-1868</nameIdentifier>
      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf</affiliation>
    </creator>
    <creator>
      <creatorName>Šmíd, Michal</creatorName>
      <givenName>Michal</givenName>
      <familyName>Šmíd</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7162-7500</nameIdentifier>
      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf</affiliation>
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  </creators>
  <titles>
    <title>Removal of bremsstrahlung background from SAXS signals with deep neural networks: sourcecode</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2026</publicationYear>
  <dates>
    <date dateType="Issued">2026-08-25</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
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    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/4976</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="HasPart">https://github.com/Helmholtz-AI-Matter/bremsstrahlung_denoising/tree/v0.1.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="Compiles">https://www.hzdr.de/publications/Publ-37977</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="Compiles">https://rodare.hzdr.de/record/4956</relatedIdentifier>
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  <version>v0.1.0</version>
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    <rights rightsURI="https://opensource.org/licenses/MIT">MIT License</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This repository contains the sourcecode used to train neural networks for the removal of bremsstrahlung background from SAXS data. Also, it offers a script to apply trained models to new noisy data.&lt;/p&gt;</description>
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