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} </style> <link rel="alternate" type="application/opml+xml" title="Outline" href="https://web.archive.org/web/20211031010229/https://www.openedition.org/opml.php?pubtype=carnet"/></head> <body data-cmplz="1" class="archive tag tag-doing-digital-history-with-python tag-106 custom-background wp-embed-responsive wp-featherlight-captions group-blog hfeed"> <div id="page" class="site"> <div class="site-inner"> <a class="skip-link screen-reader-text" href="#content">Zum Inhalt springen</a> <header id="masthead" class="site-header" role="banner"> <div class="site-header-main"> <div class="site-branding"> <p class="site-title"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/" rel="home">Digital Humanities Lab</a></p> <p class="site-description">Leibniz-Institut für Europäische Geschichte</p> </div> <button id="menu-toggle" class="menu-toggle">Menü</button> <div id="site-header-menu" class="site-header-menu"> <nav id="site-navigation" class="main-navigation" role="navigation" aria-label="Primäres Menü"> <div class="menu-menue-container"><ul id="menu-menue" class="primary-menu"><li id="menu-item-145" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-home menu-item-145"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/das-dh-lab-am-ieg">About</a></li> <li id="menu-item-1140" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-1140"><a href="https://web.archive.org/web/20211031010229/https://github.com/ieg-dhr">DH Lab @ GitHub</a></li> <li id="menu-item-1590" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-1590"><a href="https://web.archive.org/web/20211031010229/https://www.zotero.org/groups/2449547/ieg_dh_lab/library">DH Lab @ Zotero</a></li> <li id="menu-item-1866" class="menu-item menu-item-type-custom menu-item-object-custom menu-item-1866"><a href="https://web.archive.org/web/20211031010229/https://soundcloud.com/transitsofmars/dh-lab">DH Lab – The Anthem</a></li> </ul></div> </nav> </div> </div> </header> <div id="content" class="site-content"> <div id="primary" class="content-area"> <main id="main" class="site-main" role="main"> <header class="page-header"> <h1 class="page-title">Schlagwort: <span>Doing digital history with Python</span></h1> </header> <article id="post-1939" class="post-1939 post type-post status-publish format-standard has-post-thumbnail hentry category-lab-report tag-digital-history tag-doing-digital-history-with-python tag-python tag-selenium tag-web-automation tag-web-scraping"> <header class="entry-header"> <h2 class="entry-title"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1939" rel="bookmark">Doing Digital History with Python IV: web automation</a></h2> </header> <a class="post-thumbnail" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1939" aria-hidden="true"> <img width="825" height="510" src="https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2021/05/Python_bivittatus-825x510.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Doing Digital History with Python IV: web automation" loading="lazy" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 60vw, (max-width: 1362px) 62vw, 840px"/> </a> <div class="entry-content"> <p>Before digital humanists can do things with data, they first need to collect them, and <em>web automation</em> (or more specific methods of <em>web scraping</em>) can be a quick way of gathering a large amount of data. While web automation denotes every remotely controlled action performed on the web, web scraping, web mining or web harvesting are focussed on reading and processing information (found on websites). This blog post presents useful Python packages for these tasks and explains the advantages of working with browser profiles. <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1939#more-1939" class="more-link"><span class="screen-reader-text">„Doing Digital History with Python IV: web automation“ </span>weiterlesen</a></p> </div> <footer class="entry-footer"> <span class="byline"><span class="author vcard"><img alt="" src="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=49&d=blank&r=g" srcset="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=98&d=blank&r=g 2x" class="avatar avatar-49 photo" height="49" width="49" loading="lazy"/><span class="screen-reader-text">Autor </span> <a class="url fn n" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/author/barget">Monika Barget</a></span></span><span class="posted-on"><span class="screen-reader-text">Veröffentlicht am </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1939" rel="bookmark"><time class="entry-date published" datetime="2021-05-07T14:45:05+02:00">07/05/2021</time><time class="updated" datetime="2021-05-07T21:43:52+02:00">07/05/2021</time></a></span><span class="cat-links"><span class="screen-reader-text">Kategorien </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/category/lab-report" rel="category tag">Lab Report</a></span><span class="tags-links"><span class="screen-reader-text">Schlagwörter </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/digital-history" rel="tag">digital history</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/doing-digital-history-with-python" rel="tag">Doing digital history with Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/python" rel="tag">Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/selenium" rel="tag">selenium</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/web-automation" rel="tag">web automation</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/web-scraping" rel="tag">web scraping</a></span><span class="comments-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1939#comments">2 Kommentare<span class="screen-reader-text"> zu Doing Digital History with Python IV: web automation</span></a></span> </footer> </article> <article id="post-1693" class="post-1693 post type-post status-publish format-standard has-post-thumbnail hentry category-lab-report tag-digital-history tag-doing-digital-history-with-python tag-nlp tag-python tag-topic-model"> <header class="entry-header"> <h2 class="entry-title"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1693" rel="bookmark">Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn</a></h2> </header> <a class="post-thumbnail" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1693" aria-hidden="true"> <img width="825" height="510" src="https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2021/01/1280px-Python_natalensis_Smith_1840-e1610616078826-825x510.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn" loading="lazy" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 60vw, (max-width: 1362px) 62vw, 840px"/> </a> <div class="entry-content"> <p><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/author/barget">by Monika Barget</a></p> <p>In April 2020, we started a <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/doing-digital-history-with-python">series of case studies</a> to introduce researchers working with historical sources to data analysis and data visualisation with Python. Today’s blog post covers topic modelling with the Python packages Gensim, spaCy, NLTK and SciKit learn.</p> <p>Topic modelling is one of the central methods of Natural Language Processing (NLP), the “automatic manipulation of natural language, like speech and text, by software.” (Jason Brownlee: What Is Natural Language Processing?, in: <span class="categories"><a href="https://web.archive.org/web/20211031010229/https://machinelearningmastery.com/category/natural-language-processing/">Deep Learning for Natural Language Processing</a></span>, 22nd September 2017) In its most basic form, a “topic” modelled by software displays word co-occurrences in texts, assuming that the frequency of co-occurrences defines certain areas of meaning. <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1693#more-1693" class="more-link"><span class="screen-reader-text">„Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn“ </span>weiterlesen</a></p> </div> <footer class="entry-footer"> <span class="byline"><span class="author vcard"><img alt="" src="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=49&d=blank&r=g" srcset="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=98&d=blank&r=g 2x" class="avatar avatar-49 photo" height="49" width="49" loading="lazy"/><span class="screen-reader-text">Autor </span> <a class="url fn n" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/author/barget">Monika Barget</a></span></span><span class="posted-on"><span class="screen-reader-text">Veröffentlicht am </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1693" rel="bookmark"><time class="entry-date published" datetime="2021-01-15T11:00:26+02:00">15/01/2021</time><time class="updated" datetime="2021-01-20T17:42:24+02:00">20/01/2021</time></a></span><span class="cat-links"><span class="screen-reader-text">Kategorien </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/category/lab-report" rel="category tag">Lab Report</a></span><span class="tags-links"><span class="screen-reader-text">Schlagwörter </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/digital-history" rel="tag">digital history</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/doing-digital-history-with-python" rel="tag">Doing digital history with Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/nlp" rel="tag">NLP</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/python" rel="tag">Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/topic-model" rel="tag">topic model</a></span><span class="comments-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1693#comments">1 Kommentar<span class="screen-reader-text"> zu Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn</span></a></span> </footer> </article> <article id="post-1522" class="post-1522 post type-post status-publish format-standard has-post-thumbnail hentry category-lab-report tag-digital-history tag-doing-digital-history-with-python tag-python tag-wordcloud"> <header class="entry-header"> <h2 class="entry-title"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1522" rel="bookmark">Doing Digital History with Python II: creating custom Word Clouds</a></h2> </header> <a class="post-thumbnail" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1522" aria-hidden="true"> <img width="825" height="510" src="https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/06/Coluber_argus_-_1700-1880_-_Print_-_Iconographia_Zoologica_-_Special_Collections_University_of_Amsterdam-825x510.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Doing Digital History with Python II: creating custom Word Clouds" loading="lazy" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 60vw, (max-width: 1362px) 62vw, 840px"/> </a> <div class="entry-content"> <p>by Monika Barget</p> <p>In the second edition of <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/doing-digital-history-with-python"><em>Doing digital history with Python</em></a>, I would like to address word clouds as a visual method of finding patterns in texts (see critical reflection in <a href="https://web.archive.org/web/20211031010229/http://www.themacroscope.org/?page_id=362">Basic Text Mining: Word Clouds, their Limitations, and Moving Beyond Them</a>). Word clouds display the frequency or importance of individual keywords in individual texts or entire corpora. There are many ready-made tools in multiple languages that help you create word clouds in different designs, such as the in-built word cloud generator in <a href="https://web.archive.org/web/20211031010229/https://voyant-tools.org/">Voyant Tools</a> or browser-based tools such as <a href="https://web.archive.org/web/20211031010229/https://www.wortwolken.com/">Wortwolken.com</a>. However, not all of them may be suitable for your specific use case. <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1522#more-1522" class="more-link"><span class="screen-reader-text">„Doing Digital History with Python II: creating custom Word Clouds“ </span>weiterlesen</a></p> </div> <footer class="entry-footer"> <span class="byline"><span class="author vcard"><img alt="" src="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=49&d=blank&r=g" srcset="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=98&d=blank&r=g 2x" class="avatar avatar-49 photo" height="49" width="49" loading="lazy"/><span class="screen-reader-text">Autor </span> <a class="url fn n" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/author/barget">Monika Barget</a></span></span><span class="posted-on"><span class="screen-reader-text">Veröffentlicht am </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1522" rel="bookmark"><time class="entry-date published" datetime="2020-06-26T11:00:20+02:00">26/06/2020</time><time class="updated" datetime="2021-01-20T17:43:02+02:00">20/01/2021</time></a></span><span class="cat-links"><span class="screen-reader-text">Kategorien </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/category/lab-report" rel="category tag">Lab Report</a></span><span class="tags-links"><span class="screen-reader-text">Schlagwörter </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/digital-history" rel="tag">digital history</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/doing-digital-history-with-python" rel="tag">Doing digital history with Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/python" rel="tag">Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/wordcloud" rel="tag">wordcloud</a></span><span class="comments-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1522#respond">Schreibe einen Kommentar<span class="screen-reader-text"> zu Doing Digital History with Python II: creating custom Word Clouds</span></a></span> </footer> </article> <article id="post-1406" class="post-1406 post type-post status-publish format-standard has-post-thumbnail hentry category-lab-report tag-digital-history tag-doing-digital-history-with-python tag-json tag-python tag-xml"> <header class="entry-header"> <h2 class="entry-title"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1406" rel="bookmark">Doing Digital History with Python I: reading (messy) XML & JSON data</a></h2> </header> <a class="post-thumbnail" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1406" aria-hidden="true"> <img width="825" height="510" src="https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-825x510.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Doing Digital History with Python I: reading (messy) XML & JSON data" loading="lazy" srcset="https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-825x510.jpg 825w, https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-300x185.jpg 300w, https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-500x309.jpg 500w, https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-768x474.jpg 768w, https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-1536x949.jpg 1536w, https://web.archive.org/web/20211031010229im_/https://dhlab.hypotheses.org/files/2020/04/tigerpython-2048x1265.jpg 2048w" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 60vw, (max-width: 1362px) 62vw, 840px"/> </a> <div class="entry-content"> <p>by Monika Barget</p> <p>During our <a href="https://web.archive.org/web/20211031010229/https://github.com/ieg-dhr/brownbaglunch" target="_blank" rel="noopener noreferrer">DH brownbag lunches</a> at the IEG, colleagues have repeatedly asked us if we could recommend Python packages for digital history. We have therefore set up a list of packages we at the IEG DH Lab are using for the analysis of text (stored, for instance, in XML/TEI or JSON formats), the modelling of historical networks, or the creation of interactive maps.</p> <p>The list <a href="https://web.archive.org/web/20211031010229/https://github.com/MonikaBarget/DigitalHistory/blob/master/Python%20for%20digital%20history.csv" target="_blank" rel="noopener noreferrer">Python for digital history</a> is based on our personal experiences and, though by no means exhaustive, may serve as an appetizer for “Doing Digital History with Python”. In a series of blog posts, we will try and introduce you to some of the packages mentioned through case studies from current IEG research.</p> <p>Today’s post covers the <strong>extraction of data from XML and JSON files with</strong> <em><strong>xml.etree.ElementTree</strong></em>, <em><strong>lxml</strong></em>, <em><strong>json(5)</strong></em> and <em><strong>beautifulsoup(4)</strong></em> as reading structured text is often a starting point of digital history projects. <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1406#more-1406" class="more-link"><span class="screen-reader-text">„Doing Digital History with Python I: reading (messy) XML & JSON data“ </span>weiterlesen</a></p> </div> <footer class="entry-footer"> <span class="byline"><span class="author vcard"><img alt="" src="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=49&d=blank&r=g" srcset="https://web.archive.org/web/20211031010229im_/https://secure.gravatar.com/avatar/50ebd937deab201db5c98c4c213397eb?s=98&d=blank&r=g 2x" class="avatar avatar-49 photo" height="49" width="49" loading="lazy"/><span class="screen-reader-text">Autor </span> <a class="url fn n" href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/author/barget">Monika Barget</a></span></span><span class="posted-on"><span class="screen-reader-text">Veröffentlicht am </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1406" rel="bookmark"><time class="entry-date published" datetime="2020-04-30T14:00:22+02:00">30/04/2020</time><time class="updated" datetime="2020-06-23T21:30:40+02:00">23/06/2020</time></a></span><span class="cat-links"><span class="screen-reader-text">Kategorien </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/category/lab-report" rel="category tag">Lab Report</a></span><span class="tags-links"><span class="screen-reader-text">Schlagwörter </span><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/digital-history" rel="tag">digital history</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/doing-digital-history-with-python" rel="tag">Doing digital history with Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/json" rel="tag">JSON</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/python" rel="tag">Python</a>, <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/tag/xml" rel="tag">XML</a></span><span class="comments-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/1406#respond">Schreibe einen Kommentar<span class="screen-reader-text"> zu Doing Digital History with Python I: reading (messy) XML & JSON data</span></a></span> </footer> </article> </main> </div> <aside id="secondary" class="sidebar widget-area" role="complementary"> <section id="search-1" class="widget widget_search"> <form role="search" method="get" class="search-form" action="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/"> <label> <span class="screen-reader-text">Suche nach:</span> <input type="search" class="search-field" placeholder="Suche …" value="" name="s"/> </label> <button type="submit" class="search-submit"><span class="screen-reader-text">Suchen</span></button> </form> </section> <section id="recent-posts-3" class="widget widget_recent_entries"> <h2 class="widget-title">Posts</h2><nav role="navigation" aria-label="Posts"> <ul> <li> <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2271">Uncovering censorship in the 16th century with Transkribus and Python. Episode V: How did the censors actually change the text?</a> <span class="post-date">29/10/2021</span> </li> <li> <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2251">„Experiment, Ergebnis, Erkenntnis“ – Dokumentation einer Interviewreihe zu Projektmanagement in den Digital Humanities</a> <span class="post-date">15/10/2021</span> </li> <li> <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2219">Geohumanities III: analysing early modern mobility through birth and apprenticeship letters</a> <span class="post-date">01/10/2021</span> </li> </ul> </nav></section><section id="recent-comments-3" class="widget widget_recent_comments"><h2 class="widget-title">Comments</h2><nav role="navigation" aria-label="Comments"><ul id="recentcomments"><li class="recentcomments"><span class="comment-author-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2114" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode II: Let Python speak to Transkribus – Digital Humanities Lab</a></span> bei <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2154#comment-271">Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text</a></li><li class="recentcomments"><span class="comment-author-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2022" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints – Digital Humanities Lab</a></span> bei <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2154#comment-270">Uncovering censorship in the 16th century with Transkribus and Python. Episode III: Normalizing 16th century raw text</a></li><li class="recentcomments"><span class="comment-author-link"><a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2191" rel="external nofollow ugc" class="url">Uncovering censorship in the 16th century with Transkribus and Python. Episode IV: Detecting OCR transcription errors – Digital Humanities Lab</a></span> bei <a href="https://web.archive.org/web/20211031010229/https://dhlab.hypotheses.org/2154#comment-269">Uncovering censorship in the 16th century with Transkribus and Python. 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