Priests’ libraries in the Dutch Republic

By Jaap Geraerts and Sarah Büttner

The scholarly importance of “Priesterbibliotheken” (priests’ libraries) is well-known: they provide insight into what Catholic priests serving in the Holland Mission read or at least thought useful to own. Moreover, such libraries can tell us more about early modern Dutch Catholic spirituality, but also about the wider interest in books outside the remit of theology and pastoral care. Lastly, in particular libraries from the eighteenth century will also help to enhance our understanding of the schism which occurred in the Dutch Catholic Church. “Priesterbibliotheken (priests’ libraries) in the Dutch Republic” is a small pilot project which started at the IEG DH Lab in early 2021. The project aims to capture the bibliographical information contained in manuscript inventories of the libraries of Catholic priests serving in the Republic and making this available in digital form.
„Priests’ libraries in the Dutch Republic“ weiterlesen

Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints

by Markus Müller with the collaboration of Melina Ramirez

Introduction

Since the 1980s, our knowledge on 16th century censorship has been growing constantly thanks to coordinated research projects and new sources. In over ten years of research, for instance, José Maria Bujanda and his team edited all printed catalogues of forbidden books issued by Catholic authorities in the 16th century (eleven volumes until 1999). This made it very easy to check whether a certain author or book was banned or not. With the opening of the Archive of the Congregation of Faith (the former Roman Inquisition) in 1998 a vast number of manuscripts became available that provided an insight into the internals of the Roman Congregation of the Index (founded in 1572) and the decision making processes of the censors. These processes are particularly interesting for books that were not totally banned but expurged, meaning that the censors deleted or modified passages considered “heretical” and then reprinted the book. In many cases, the only way to find out what they actually changed is to compare the original with the expurged edition line by line and word by word. Because Rome was by far not the only Catholic authority that expurged books, the same book was sometimes also expurged by the Spanish Inquisition, the Sorbonne in Paris and other local inquisitors. They all had their own censoring procedures resulting in different expurgations. If we want to uncover the differences between these expurgations, we have to repeat the tedious manual comparison over and over again. These repetitive comparisons are exactly the kind of “boring stuff” that could be automated with Python. „Uncovering censorship in the 16th century with Transkribus and Python. Episode I: OCR with Latin prints“ weiterlesen

Doing Digital History with Python IV: web automation

Before digital humanists can do things with data, they first need to collect them, and web automation (or more specific methods of web scraping) 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. „Doing Digital History with Python IV: web automation“ weiterlesen

Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn

by Monika Barget

In April 2020, we started a series of case studies 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.

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: Deep Learning for Natural Language Processing, 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. „Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn“ weiterlesen

Doing Digital History with Python II: creating custom Word Clouds

by Monika Barget

In the second edition of Doing digital history with Python, I would like to address word clouds as a visual method of finding patterns in texts (see critical reflection in Basic Text Mining: Word Clouds, their Limitations, and Moving Beyond Them). 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 Voyant Tools or browser-based tools such as Wortwolken.com. However, not all of them may be suitable for your specific use case. „Doing Digital History with Python II: creating custom Word Clouds“ weiterlesen

Doing Digital History with Python I: reading (messy) XML & JSON data

by Monika Barget

During our DH brownbag lunches 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.

The list Python for digital history 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.

Today’s post covers the extraction of data from XML and JSON files with xml.etree.ElementTree, lxml, json(5) and beautifulsoup(4) as reading structured text is often a starting point of digital history projects. „Doing Digital History with Python I: reading (messy) XML & JSON data“ weiterlesen