Geohumanities III: analysing early modern mobility through birth and apprenticeship letters

By Monika Barget

In the winter term 2020/2021, Jaap Geraerts and I worked with students in the Mainz MA programme “Digitale Methoden in den Geistes- und Kulturwissenschaften” (“Digital Methods in the Humanities and Cultural Studies”) to create a digital edition of early modern birth and apprenticeship letters. The edition includes records in French and Latin as well as German and highlights people’s cross-border mobility in the seventeenth and eighteenth centuries. „Geohumanities III: analysing early modern mobility through birth and apprenticeship letters“ weiterlesen

Games for the humanities / religious studies – scarce or simply hard to find?

by Monika Barget

Recently, I have more extensively explored serious games for teaching and research communication in the humanities. In a workshop aimed primarily at religious studies scholars, I intended to address analogue board games as well as hybrid and exclusively digital games. And apart from games I myself had used or contributed to, I hoped to present other examples in different languages. Especially in the field of hybrid and digital humanities games, however, my search retrieved few results. This post summarises the vivid discussion with participants that followed my invited talk “Seriously, games?” in the #digitalDonnerstag workshop series hosted by Arbeitskreis Mittelbau und Nachwuchs der Deutschen Vereinigung für Religionswissenschaft e.V. (AKMN). „Games for the humanities / religious studies – scarce or simply hard to find?“ 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

Geohumanities II: Gestaltung und Druck einfacher Karten in QGIS

by Monika Barget

Als Ergänzung zur Einführung in die Erstellung von GeoJSON-Dateien beschäftigt sich der heutige Blogbeitrag mit der Visualisierung dieser Geodaten im Open Source Geoinformationssystem QGIS. Das Tutorial führt Schritt-für-Schritt durch die Erstellung einer einfachen Karte und gibt Hinweise auf weiterführende Lernressourcen. „Geohumanities II: Gestaltung und Druck einfacher Karten in QGIS“ weiterlesen

Geohumanities I: Ortsdaten mit erfassen und bearbeiten

by Monika Barget

Für viele Geisteswissenschaftler:innen und Mitarbeiter:innen in der Forschungskommunikation sind Karten methodische Werkzeuge, um räumliche Beziehungen und Entwicklungen zu analysieren und zu vermitteln. Allerdings fällt es oft schwer, geeignete Werkzeuge zur Kartenerstellung auszuwählen, da die Möglichkeiten klassischer Einsteigertools wie Palladio und DARIAH-DE Geobrowser besonders im Bereich des Exports hochwertiger Kartenbilder beschränkt sind, während viele andere Werkzeuge aufwendigere Softwareinstallationen, Kenntnisse in der Arbeit mit Terminal / Kommandozeile oder sogar Erfahrung mit Programmiersprachen erfordern. In diesem Tutorial möchte ich deshalb eine einfache Methode der Aufbereitung geographischer Informationen mit vorstellen. „Geohumanities I: Ortsdaten mit erfassen und bearbeiten“ 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 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

Suche in OpenEdition Search

Sie werden weitergeleitet zur OpenEdition Search