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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">IJPDS</journal-id>
      <journal-title-group>
        <journal-title>International Journal of Population Data Science</journal-title>
        <abbrev-journal-title>IJPDS</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="epub">2399-4908</issn>
      <publisher>
        <publisher-name>Swansea University</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.23889/ijpds.v11i4.3810</article-id>
      <article-id pub-id-type="publisher-id">11:05:3810</article-id>
      <title-group>
        <article-title>Advancing Population-Scale Data Linkage: Shared Learning from Austria and Wales</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Gracey</surname>
            <given-names initials="A">Nadya</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
          <xref ref-type="aff" rid="affil-2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Herzog</surname>
            <given-names initials="J">Tobias</given-names>
          </name>
          <xref ref-type="aff" rid="affil-3">3</xref>
        </contrib>
      </contrib-group>
      <aff id="affil-1"><label>1</label><institution>Administrative Data Research Wales, Cardiff, United Kingdom</institution></aff>
      <aff id="affil-2"><label>2</label><institution>Welsh Government, Cardiff, United Kingdom</institution></aff>
      <aff id="affil-3"><label>3</label><institution>Statistics Austria, Vienna, Austria</institution></aff>
      <pub-date date-type="pub" publication-format="electronic">
        <day>06</day>
        <month>07</month>
        <year>2026</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <year>2026</year>
      </pub-date>
      <volume>11</volume>
      <issue>4</issue>
      <elocation-id>3810</elocation-id>
      <permissions>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licences/by/4.0/">
          <license-p>This work is licenced under a Creative Commons Attribution 4.0 International License.</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://ijpds.org/article/view/3810">This article is available from the IJPDS website at: https://ijpds.org/article/view/3810</self-uri>
    </article-meta>
  </front>
  <body>
    <p>Austria and Wales have achieved what many jurisdictions still aspire to: secure, privacy-preserving infrastructures that enable accredited researchers to access and link more than a hundred administrative datasets for approved projects at population scale. This capability represents a major step forward in unlocking the societal value of administrative data while maintaining public trust.</p>
    <p>Both countries have invested in governance, systems, and secure data environments that make this possible. The Austrian’s Micro Data Center and Wales’ SAIL Databank - are at the heart of these efforts. Each provides a controlled, researcher-friendly environment for cross-sectoral data linkage, supporting high-quality, policy-relevant research across health, social care, and wider administrative domains.</p>
    <p>Shared progress by Statistics Austria and the Welsh Government includes robust data quality frameworks, automation of linkage and disclosure controls, and streamlined researcher pathways. These measures reduce friction, improve efficiency, and ensure that data remains both secure and usable. By embedding strong governance and technical safeguards, Austria and Wales have overcome barriers that continue to limit similar initiatives elsewhere.</p>
    <p>This paper highlights how these two countries have successfully operationalized population-scale data linkage, offering practical lessons for international efforts to build sustainable, secure, and researcher-focused infrastructures that maximize the public value of administrative data.</p>
  </body>
</article>