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#BELTRANS

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Sven Lieber<p>Hey <a href="https://hcommons.social/tags/library" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>library</span></a> folks 👋 ,</p><p>do you want to cluster your book editions with the well-known Work-set algorithm from <a href="https://hcommons.social/tags/OCLC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OCLC</span></a>, but you don't find a suitable reusable tool?</p><p>I recently faced this issue while working on the <a href="https://hcommons.social/tags/BELTRANS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BELTRANS</span></a> project at KBR (Royal Library of Belgium). All I found were many research papers describing the clustering and a few implementations that required me to install 2010-style Java software stacks.</p><p>So I decided to write an easily reusable small <a href="https://hcommons.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> script that follows the ideas of the Work-set algorithm: clustering based on descriptive keys. Nothing more, nothing less.</p><p>Check my blog post for more information and have a look at the script.</p><p>➡️ blog post: <a href="https://doi.org/10.59350/4hd4r-1tk44" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.59350/4hd4r-1tk44</span><span class="invisible"></span></a></p><p>➡️ script: <a href="https://doi.org/10.5281/zenodo.10011416" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.5281/zenodo.1001141</span><span class="invisible">6</span></a></p><p><a href="https://hcommons.social/tags/FRBRization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FRBRization</span></a> <a href="https://hcommons.social/tags/FRBR" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FRBR</span></a> <a href="https://hcommons.social/tags/IFLA" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>IFLA</span></a> <a href="https://hcommons.social/tags/clustering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>clustering</span></a></p>
Sven Lieber<p>New newsletter edition is out</p><p>➡️<a href="https://fair-data-digest.org/archive/3" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="">fair-data-digest.org/archive/3</span><span class="invisible"></span></a> ! </p><p>Many <a href="https://fosstodon.org/tags/FAIR" class="mention hashtag" rel="tag">#<span>FAIR</span></a> topics: </p><p>💻 filling data gaps via <a href="https://fosstodon.org/tags/ISNI" class="mention hashtag" rel="tag">#<span>ISNI</span></a> in <a href="https://fosstodon.org/tags/BELTRANS" class="mention hashtag" rel="tag">#<span>BELTRANS</span></a>, </p><p>📅 Ethical, Legal and Social Aspects (<a href="https://fosstodon.org/tags/ELSA" class="mention hashtag" rel="tag">#<span>ELSA</span></a>) of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="tag">#<span>DataScience</span></a> at a workshop from the FAIR Data Spaces project <a href="https://www.nfdi.de/fair-data-spaces/?lang=en" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">nfdi.de/fair-data-spaces/?lang</span><span class="invisible">=en</span></a> and </p><p>📹 <a href="https://fosstodon.org/tags/LawAsCode" class="mention hashtag" rel="tag">#<span>LawAsCode</span></a> from Interoperable Europe</p>
Sven Lieber<p>The new FAIR Data Digest newsletter edition is out! <br />🖥️ Handling author pseudonyms in <a href="https://fosstodon.org/tags/BELTRANS" class="mention hashtag" rel="tag">#<span>BELTRANS</span></a>,<br />🎓 listening to fundamental <a href="https://fosstodon.org/tags/KnowledgeGraph" class="mention hashtag" rel="tag">#<span>KnowledgeGraph</span></a> research at the Data Science Institute of the University of Hasselt and <br />📚 learning about the history of Wikidata<br />➡️ <a href="https://fair-data-digest.org" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="">fair-data-digest.org</span><span class="invisible"></span></a></p>
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